Sigma Computing Explained to a Tableau User | A conversation with Katrina Menne

0:00This week, Tableau is about to showcase their new vision for Tableau for the future.

0:06And I think when a company does this, it's important to set the context and compare that vision

0:12to what other people are doing in the space at the moment.

0:15Now you can obviously go ahead and do that with tools like Power BI, but actually I think it's also important to contextualize that with what new entrants to the space are doing.

0:24Sigma Analytics is one of those companies.

0:27You might have seen them at Tableau Conference doing a little bit of uh guerrilla marketing.

0:30But nonetheless, I I don't often spend time talking about these tools because I've not used them and I don't have enough experience to be able to talk to them from a point of authority.

0:39But in the case of Sigma, I happen to be able to call Katrina Menet, a colleague of mine at Aimpoint Digital.

0:46She's also the author of a book about Sigma.

0:48So she knows a lot about Sigma.

0:50But here's the unique thing

0:52Katrina herself started her analytics journey on Tableau.

0:56And so she actually found Sigma having come from the data firm as it were, having been part of user groups and having used Tableau and you know gotten frustrated with certain aspects.

1:06that she didn't quite connect with.

1:08And so in Sigma, she found something that she could call a little bit more familiar.

1:12But more importantly, she's able to talk about both products, I think, in a balanced way.

1:16And so I spent over an hour and a half with her

1:19Talking about Tableau and Sigma, but mostly about Sigma.

1:22She showed me all the way through the software, showed me some things that are really easy to do in Sigma, but also highlighted

1:28the things she misses about Tableau.

1:30We had a great discussion.

1:31I really encourage you to watch it.

1:33As ever, there's timestamps so you can jump to the bits that you like.

1:36And more importantly, if you're on LinkedIn, you'll find the link in the comments.

1:39And if you're on YouTube, you're ready here.

1:41As ever, let's get stuck in.

1:43Katrina, how you doing?

1:45I'm doing well, Tim.

1:46How are you?

1:47Good, good.

1:48Thank you.

1:48Thank you for joining me.

1:50I think we we set this up roughly a month ago because I started my journey with

1:57Sigmat, right?

1:58And so um we'll come to this obviously as we talk more about your experience, but uh you've heard this a hundred times before

2:05before people always say you wrote the book on Sigma.

2:08So when when we set this up, I was like, this is perfect.

2:12You know, Katrina can come on the channel and talk to the Tableau family, the data families I like to call it, about Sigma.

2:20But before we get into all of that, first let's maybe start with an introduction of yourself.

2:26I'll kind of let you do do your own introduction, tell people who you are and sort of um yeah what you do.

2:32Yeah, I am Katrina Menny.

2:35I live in Minneapolis, Minnesota with my husband and three dogs.

2:40It started with a really heavy Excel background in my career and then I like moved into data visualization after I had like um I'd automated my entire team's

2:51whole week job down to a 20-minute macro and I was like, oh okay, this is the cool stuff.

2:57This is the the power of of data and data automation.

3:00And then that led me to uh to a a role where I wanted to learn more about data visualization and I found Tableau and I realized that I did not know a whole lot about Tableau yet, so I started

3:12working or trying to connect with other other users like yourself in the data community and I became really active in the Tableau, the local Tableau community, and then that kind of led me to to the jobs that I have have now.

3:27Amazing, amazing.

3:28So, like two important things you mentioned there.

3:30Um, firstly, three dogs.

3:32What are the breeds of your dogs?

3:34Obviously.

3:34Like we're dogby players.

3:36Of course.

3:36First thing we ask is wild breeds, yeah.

3:39Uh yeah.

3:39I've got two labs and one German pincher.

3:42So he's like a 30-pound Doberman kind of.

3:45Okay.

3:45Yeah, yeah, yeah.

3:46Amazing.

3:46I've got a Hungarian Vesla, so only the one.

3:49Um I'm about to have three kids.

3:50So I think I think we balance out three dogs.

3:52Balance, yeah.

3:54Yeah.

3:54It's kind of equivalent, right?

3:55Um so yeah, amazing, amazing.

3:57And I I I think we should highlight that you also work at Aimpoint.

4:01So you are the

4:03first colleague I've had on the channel, which is a huge privilege.

4:06I'll save you the uh the the necessity to sort of flatter yourself here by saying everyone at aim point is exceptional

4:14uh at something.

4:15Um we have so many talented people just in literally every part of the company.

4:19And you joined Aimpoint very recently

4:22And you are like a a Sigma superhero if I can term it that.

4:26Do they have a name for like Sigma Jedi's yet?

4:30They do not yet.

4:32Alright, alright.

4:33Yeah

4:34I I'm I'm fine with Sigma Superhero.

4:36That works.

4:38I think it does need some sort of specific like context to

4:42the brand around Sigma.

4:44They don't, you know, Sigma didn't go with like, you know, Snowflake with uh snow and Databricks with building, right?

4:50So yeah, it needs something, but w we can we can maybe think about that over time.

4:54And if anyone in watching

4:56has any suggestions, uh it feel free to to to suggest some.

5:00So um yeah no you talked about sort of your journey to through Tableau and I actually want to talk a bit about that because for a lot of people who learn Tableau

5:10Like it's a very sort of, let's say, I'm gonna call it interesting experience.

5:14And I I use the term interesting because I don't think I've ever met two people who have the same journey when when discovering Tableau.

5:22Um many people tend to have a use case, Tableau becomes a tool, and then once they connect with the tool

5:30it becomes the thing they use for everything, right?

5:32And I think, you know, if we sort of fast forward to where this conversation might go, you've kind of completed that journey and found something else that does a lot of the things you like to do better.

5:42Right.

5:43So

5:43Before we get to that bit, we'll come to that definitely.

5:46What was your experience of learning Tableau like?

5:48And yeah, just maybe maybe talk a bit about that

5:51Yeah, um I would say that my experience of learning Tableau was sort of a trial by fire or you know kind of do what you can.

5:59Um I was working at a

6:01Uh so I was working at this company and they had Power BI and I was like, I want to learn more about Dataviz.

6:06Power BI is just didn't click with me at the time.

6:09Um and so I went

6:11online and I I think I just googled like what are good data visualization tools.

6:15I did maybe a Udemy or something course on on Tableau.

6:20So it was very

6:22Very simple.

6:22I honestly looking back on it, don't really remember any of it.

6:26I think it was just like too much at the time.

6:28Um also

6:30Yeah, I was trying to I was also teaching myself SQL, so there's just like so much stuff going on because I I realized that I wanted a new job in that space.

6:39And so then when I was job hunting, I found a job that that had Tableau and they ended up using Tableau, Snowflake, and Ultrix.

6:48And so it was a really fun mix to learn all of those tools together.

6:53But at the same time, it was a

6:56The company um invested in people who are early in their career, they gave them a lot of opportunities to say, hey, go go learn this.

7:03But

7:04You have to go learn it.

7:05Um and so I just started Googling and trying to go through the the um courses that were online, anything that was free was really the thing.

7:14It was

7:15I didn't have a budget at the time, so I had to go to go to free things.

7:19And then as I mentioned, um I started being really active in the local Tableau community.

7:24And I would say that the two main things about that were one, just having someone else to ask questions to.

7:30I was the only data person at that job.

7:33And so I was like

7:34I don't have I don't have anyone to bounce ideas off of or just any I remember a lot of uh times I would I would do something and I'd be like, I don't know if this is the best way to do it, but it works

7:46So I'm just gonna go with it, I guess.

7:48Yeah, yeah, yeah.

7:49Um so the going to the user groups really taught me um, you know, new ideas and and new ways to solve like the same problems.

7:56And then I also got plugged in with a good crew of people who were doing workout Wednesday.

8:01And so we would meet every Wednesday for like an hour or something and we would just do as much of the challenge as we could.

8:06And I would say

8:08those two things really um uh what's the the no one say escalated but uh accelerated yeah yes accelerated my my understanding and growth

8:19Yeah, I can't I can't emphasize enough um you know reps really matter with something like Tableau because um

8:28Weirdly, I I think Tableau is one of those tools where the muscle memory of doing something only stays with you if you do it and you you you kind of go through the exercise of doing it because

8:41You know, thable is funny because it's it's quite a focus tool, but it's also quite a broad tool at the same time, right?

8:51That's a very focused thing.

8:53But the way you can do things, there's so many ways you can do the same thing within the product.

8:57And so this question of like what's the best way to do something

9:01becomes super important.

9:02But something like workout Wednesdays, makeover Mondays are really good for is really focusing you on why this specific path is going to be successful.

9:12in this specific use case.

9:14And once you get that muscle memory, it just becomes like a, you know, like a walk you do every week down the park.

9:20You you stop

9:21paying attention to the signs and you just do the walk and sometimes you take a detour, but it's all intentional, right?

9:26And so yeah, reps really, really count.

9:29And um just going back to something else you said there, and I I sort of want to pull this out

9:34You were the only data person in your in your team and I think I always tell people look if you're looking for a data job, those are the best jobs because uh y you said it yourself, like

9:45You have to do everything.

9:46So you kind of get really good exposure to pretty much everything.

9:51And um I'd like to think that then reflects positively in your career because it exposes you to so much more

9:57uh sort of possibility than if you work as part of a bigger data team where everyone has their roles, everyone has their silos that they stick within, right?

10:05Yep.

10:06Yep.

10:06There's a lot of uh you get to just try things because there's no one necessarily like watching over your shoulder and saying

10:13No, you can't do that or you know we we don't want to give you access to to that or to that data or something.

10:18So it's a very um green field environment which can be very exciting.

10:22Yeah, exactly, exactly.

10:24So before we talk about Sigma and what it is

10:27How did you like you said you know Snowflake uh Altrics Tableau, that's known in some fields as the SALT stack, right?

10:35So uh those the the the trio of tools kind of come up again and again

10:39Um, what led you from that to um Sigma, if that makes sense?

10:44Sort of how did you get from what is a very typical actually architecture for, you know

10:50let's say analytics from maybe four or five years ago, right?

10:53To where you are today.

10:54Yeah.

10:55Yeah.

10:56I would say that all the the

10:58Journey of getting to Sigma started with my Excel experience.

11:02Right.

11:03I just that was my bread and butter.

11:05That's what I I thought of when I thought of how to interpret data.

11:09I thought of it as in a spreadsheet or as Excel.

11:12I remember so in between um the that job and aim point now I did uh work at another consulting shop and so I remember a a client that I built them this very beautiful tableau dashboard and they always just would ask

11:26Can you export that to Excel?

11:28Can you just show me the numbers?

11:29All of those things.

11:30And I remember being really frustrated by like, don't you love my bar charts?

11:36Those kinds of things.

11:37But

11:37Um I as part of of that experience wanna and as we we were talking about like growing my Tableau experience as well and just like technical expertise, I

11:47uh figured out a way to make a pivot table in tableau because again that's something that everyone kept asking me for and having that familiarity of that look

11:56I think um the look and feel of a pivot table really helped with the user adoption in some of my uh more Excel

12:04Heavy clients, I guess you could say.

12:08And so that experience of writing that blog, um, it's

12:12I think it's like 22 steps.

12:13Uh, it's a multi-select parameter.

12:15You know, there's lots of complicated, advanced things.

12:18It was very fun to do in pushing my career and my technical understanding.

12:22But then when I saw Sigma for the first time, it's built in.

12:25Like pivot tables are built in.

12:27Yeah.

12:27And just having the spreadsheet interface just clicked for me and it made like everything that I looked at.

12:33uh made sense to me and the way that Sigma thinks about data was the way that I thought about data.

12:39And that was the big light bulb.

12:41And then the other, maybe tiny, tinier light bulb was, as you had touched on earlier, I really felt like I had kind of reached the cap of what I was going to reach in the tableau

12:52community.

12:52Yeah, I knew I was never gonna be a Tableau Tim or a Z master, those sorts of things.

12:57Like there's so many um great voices already and I had kind of

13:02Felt like it wasn't my jam anymore and I was starting to look around for some something else.

13:06And so again, when I saw saw Sigma, it made sense and it was kind of a right place, right time for me

13:12Yeah, like gosh, you touched on so many like salient points there.

13:16I think um many people share your sentiment that in terms of like you you you get so far with Tableau and then at that point

13:24You feel like you've had your fill and that's not necessarily means you you stop using it, doesn't necessarily mean you lose passion for it, but um

13:34there is still what I would say this itch that there is a better way to do something.

13:38And I I think that's that's fair.

13:40Like my whole channel exists because

13:44I think there is a better way to do something and I'm just showing people how I think it should be done uh, you know, with the features they get

13:52But in every single video, at the end of every single video, I will always talk about I wish Tableau did this, I wish Tableau did that, which up

14:02And you kind of synthesize it into sort of some salient points.

14:06Um, there are probably a couple of themes that come out of that.

14:09And the thing you said about, you know, Excel, like people, people wanting

14:14I'm gonna say this now, like the spreadsheet was probably one of the greatest innovations that like came because even to this day

14:26There's just something about that sort of let's say interface.

14:30I'm gonna call it an interface.

14:32Um that is that it makes sense to people.

14:34There's a table of data, right?

14:36You can choose what you want on your headers and rows, you can

14:39choose sort of the aggregation.

14:41And there's just something about that interface that people get.

14:43There might not know Excel.

14:44There might not be ninjas at Excel, but everyone sort of knows, ah, this cell, this number

14:49A73, I wanted to, you know, do this with E55 on tab number three, right?

14:56Like you can compute that in a very simple way.

14:59And I think

15:00With tableau, sometimes those linkages are more abstract.

15:04You know, how do you explain an LOD?

15:06How do you explain a window calculation?

15:08Like if you see my video on the window calculation.

15:10I'm sat there drawing diagrams and arrows, like showing you how like the window is moving and the the average is being done.

15:17And it's yeah, it's very hard to abstract that when we look at um

15:21Let's say relationships, and I'm telling you how like to do a date scaffold with relationships, and there's this complicated formula going on that allows you to do like the number of patients in the hospital.

15:31Like

15:32That is a very abstract way of thinking that unless you've got very um I'll say like a extremely creative mind to be able to visualize that in your head

15:42It's actually quite hard for most people to pick up, especially if you assume that data literacy generally is average, not great, if that makes sense.

15:50And so you do need something that's quite accessible.

15:53So I've I've talked for a lot there, essentially backing backing backing your point.

15:58And I think just before we sort of get into to Sigma itself

16:03It's a super interesting time right now in analytics because um I think I think there is very there's a very close sentiment that the the field is changing, the game is changing, all the tools.

16:14seem to be going through what I would call like a new era, but I don't think anyone knows where that era is going to land.

16:23So there's so much opportunity.

16:24And I think I'd love to get your perspective on

16:27Like on on your just just any general thoughts around that and any sort of trends you're seeing there that you think are worth sort of pulling out.

16:34And then then then we'll get straight into Sigma, I promise you.

16:37Yeah, yeah.

16:39I think um

16:41The the thing that I'm most excited for in the future of data and analytics is the thing that you touched on, in the sense that you have to like there's there's some data and some understanding of data that's accessible to everyone because

16:54We all look at our bank sheets.

16:56We all like understand how calendars work, or we've all like read a table before, but then there's also those conceptual things where it's

17:04Um, you know, my my husband works in IT, but we're very like so we both do computer things, but they're very different computer things.

17:12And when he talks about his world, I'm like

17:14Yeah, I don't cool.

17:15And then when I talk about my world, it's just he's like, you made a bar chart.

17:19Like, you know, and so there's

17:22There's that that disconnect between um the accessible things and then the okay, you actually have to have some schooling or training or watch some videos on on these things in order to understand the concepts.

17:33And I'm excited for the lowering of the bar of that second one where when more people are able to look at data and understand how it works in a

17:46uh more accessible way that offers a lot more creativity in in the data world.

17:50And I think that there's a need for new perspectives.

17:55Really excited.

17:56One of the great things about the Tableau community is that even though I mentioned at the beginning, I knew that I was never gonna be a Tableau Tim.

18:02Like there's always new voices coming out and there's always new people

18:06who are are making a brand, they are um establishing their voice and saying, hey, this is how I think things should be, or challenging, you know

18:15whatever the the common perception is.

18:18And so the to kind of pull it back in, I think the the thing that I'm most excited for about the data industry is that it's a bunch of curious

18:26innovative people and we're gonna keep being curious we're gonna keep trying to innovate on making things either better faster less expensive more accessible easier to understand all of those great things and

18:40I think it's it's a really exciting precipice to be on.

18:44Yes.

18:44But then there's also, I will also caveat that statement with a lot of that I think is going to be um

18:50For better or worse, fed by AI, and I am curious to see where that goes.

18:56So I I could not agree more.

19:00In my mind

19:02I feel like this change was happening before AI.

19:06I feel like this change had just started like two years ago, and AI sort of just turned up and hijacked that a little bit.

19:15Like it's

19:16It's it's and I don't think it's even just AI AI itself.

19:22I think companies have decided to market

19:25what they previously weren't marketing as AI, as AI, because AI is the buzzword.

19:31AI is the thing that catches on and flies and is like no tablet used to do machine learning.

19:37Now that's called AI

19:38Um and I know it's machine learning, it's not AI, it's not artificial intelligence, but AI as a term has just swallowed up all these things like autocomplete is apparently AI now.

19:48You know what I mean?

19:49Like it's kind of ridiculous.

19:53Have you seen that meme of Scooby-Doo and there's like a bad guy who's tied up and and like there's a mask and it says AI and then you take it off and it's just a bunch of if statements on the side of the side?

20:02Yeah, it's exactly that's

20:05I'll try and find it and put it up on the video when I edit.

20:08It's it's absolutely the case.

20:09It's absolutely the case.

20:11And yeah, I I, you know, I actually said exactly the thing you said about lowering the bar when I got my golden hoodie at Salesforce.

20:18I said

20:19One of the great things that, you know, is is about to happen is that we're about to l uh sort of lower the what was I say?

20:27I said lower the ceiling.

20:29Oh, I can't remember exactly what I said, but it sounded good because it was in a keynote.

20:33And uh and I said exactly what you said.

20:36Things are gonna get easier so more people can do things, but I also said that things are gonna get

20:41Um really complex things are also going to get easier for the people who know how to do those complex things.

20:46So that gap is going to get smaller and I think lots of lots of great things are going to happen.

20:51So

20:52Right, enough about Tableau and all the other things.

20:55Um let's start with like let's start what is Sigma?

20:59I know this is like a such a

21:01Broad question, and I know if I was to really ask you this and give you half an hour, you could easily fill half an hour.

21:06But if we sort of just scale it down and maybe you can use the context of Tableau as as like a reference point, yeah.

21:12How would you describe Sigma to the Tableau community?

21:15Maybe let's start there.

21:16Yeah, yeah.

21:17So when I'm talking uh in particular to the Tableau community, I often describe Sigma as Tableau Desktop plus Tableau Prep.

21:25where you've got a combination of data manipulation.

21:28So you can do joins, unions, you know, all of those fun things.

21:33You can do it outside of a workbook, in a workbook, which is very fun to do, and it I think it expands a lot of functionality.

21:39But then you also have the workbooks and the data visualization side of things.

21:43And so Sigma

21:45Really is kind of like having Excel plus the visual side of Tableau plus Tableau Prep.

21:52I think that's the simplest way that I would describe it.

21:55Uh that is absolutely spot on it.

21:57It's a it's a really good um it's a really good way of contextualizing what's possible

22:03One of the biggest critiques I know I said we'd stop talking about Tableau, but just very briefly.

22:07One of the biggest critiques I have of Tableau is that why on earth is Tableau Desktop and Tableau Prep

22:12Separate.

22:13So separate to the fact that actually you have to have like two Chrome tabs open to get like serious work done between the two if you're doing web authoring.

22:22Or if you're not doing web authoring, you do the desktop experience

22:25You have two pieces of software, one of which chugs on RAM Tableau Prep, and the other of which is just not designed for the web at all.

22:33So like they they sit in such disparate parts of the ecosystem, yet

22:38i in genuine genuine terms they should just be intrinsically one experience i know that's happening in the future but yeah tableau please yes sort it out yes back to back to sigma sigma's doing it right amazing um yeah

22:52If we dig a bit deeper into that, what would you then say?

22:55You know, with Tableau desktop, the the the thing people pull out is the drag and drop, right?

23:00With Sigma, I think you alluded to earlier, it is is it that spreadsheet sort of interface?

23:06Am I simplifying it too much by saying that?

23:09Uh I don't think that you're simplifying it.

23:12It is spreadsheet based, or like I would say that the majority of it is spreadsheet, but there is that drag and drop um typically with the visualizations, those interfaces.

23:20So it's kind of it's like a little bit of both.

23:23A while back I heard an interview from one of the co-founders where he was talking about like, how did you come up with this idea?

23:29And he described it as

23:31What if you could write SQL from Excel?

23:34And so that's kind of the the idea behind it is putting a spreadsheet interface on top of SQL.

23:41so that you can make it more accessible and understandable to to business folks or folks that don't have that technical or that data visualization background.

23:49Yeah.

23:50And I think that's actually super powerful because SQL, I that is probably one of the greatest innovations of the last like I don't know how long that gets no recognition whatsoever because it

24:00It drives pretty much every single piece of technology in one way or another.

24:04And um being able to give everyday people an interface to that

24:11Does a couple of things.

24:12Firstly it brings a lot of really powerful technology to those people.

24:15Secondly, it also brings the power of doing things.

24:19that way uh to a lot more people.

24:22So when I talk about SQL, I'm thinking of databases, I'm thinking of um very specific

24:28I'm gonna say computations and operations that you know advanced data analysts do with their data.

24:33But if you try and do those things in Excel, you might be doing in a fairly crude way, in a in a fairly, let's say, simplistic way.

24:40But if you're doing them

24:42In something like SQL, they're much more powerful, they're more optimized, they're operating at scale.

24:48And I know that Sigma is really sort of fond of being able to connect to what I would call

24:52modern databases, really genuinely designed for scale, right?

24:57So if we sort of push into that a little bit, like how have you how have you found that a sort of experience of Sigma working alongside these modern databases, but still keeping that

25:07uh Excel interface, you know, nice and accessible to people.

25:11Yeah, I think you you touched on one of the main points about it it being uh specifically designed for cloud or modern architecture.

25:18It just

25:19It allows for so much more data to be used in a lot of um in your in your builds.

25:24And I think that that um again is what people are asking for.

25:28for like from the IT team, from the data teams, they're like, I want more, I want more detail, I want more information, I want more granularity

25:36But then I also want to be able to do it myself.

25:38And so there's those two um two combinations that really come together to create something really powerful

25:44One of the the things that Sigma always talks about is the ability to prototype really quickly.

25:48I think as we we talked about at the beginning, um

25:52You kind of need some intro lessons to to Tableau in order to build something.

25:58I would say in in any platform, anytime you make any changes to learn something new or to bring on something new, you have to

26:05learn something new like where is this menu option?

26:08Where do I click to do these things?

26:10But in general, I would say the learning curve of of Sigma is significantly lower than than Tableau

26:16As well as one of the other things that I think is really great about it is I can build something or you can build something and then we can give it to someone else and they can go poke around and play with it a lot.

26:27eas easier more easily um in in that UI.

26:31And they also um can make mistakes and it's not costly.

26:35There isn't a

26:36You know, hey Tim, can you spend three hours writing this new LOD to add this new level of granularity so that I can think and then maybe that's not actually the question I need answered?

26:46Um

26:47Someone can just go and uh kind of right-click and drill down, choose what they want to drill down into.

26:53They can, you know, pivot or unpivot different things and explore it.

26:57And it's

26:58Accessible and cheap.

27:00Cheap in a good way.

27:01Like cheap and an inexpensive.

27:03There's not a it doesn't take a lot of time.

27:06So there's not a lot of huge investment.

27:07And then you can say, okay

27:09you know, hey, I really like this analysis.

27:11This is exactly what I'm looking for.

27:13Can you go productionalize it?

27:15Can you make it optimal?

27:16Um can you expand it?

27:17All of those sorts of things.

27:18So

27:19Uh it provides that lower bar of entry to create something that's really meaningful and still impactful.

27:26Yeah, yeah.

27:27And I think there is also something, you know, I'll say it again.

27:30Um when you instantiate something in Sigma, uh I so don't know why I'm using complex words.

27:36When you build something in Sigma, what you see is what you get in a very sort of direct way.

27:40Like the the table you're looking at is the

27:43Is the is going to be the outcome.

27:45The chart you're looking at is going to be the thing you see.

27:48Often in Tableau, that's sometimes not the case.

27:52For lots of very good reasons, but it's it's hard to decipher what that is.

27:56And if I sort of be brutal on tableau a little bit

28:00Um one of the things that struck me with with my Sigma experience when I was trying it, I went through the trial experience, you know, I got a trial account, whatever, and I set it up.

28:09I was like, okay, let's just do things like I'm a completely newbie.

28:12And it took me fifteen minutes to go from uh signing up to the trial to building an outc uh output.

28:19And yes, it was a guided

28:20step by step guide it took me 15 minutes to do that now the same experience with tableau if you try and sort of just follow that path

28:28There are so many points where it's easy for someone to get lost, exactly as you said.

28:32So so easy for them to sort of even get distracted, even the visual analysis bit, like

28:38I know that is actually a good thing for data best practice, but I realize in some respects it's also a massive distraction because

28:48Once you get to the vision element, now suddenly you start to worry about design, you start to worry about all these other things, you start to worry about formatting, you start to worry about finesse.

28:58And you start to worry about layouts and you're on my channel looking at layout containers.

29:02Like, what does layout containers got to do with analytics?

29:05Nothing whatsoever, right?

29:07So

29:08You know, it was a really eye-opening experience.

29:10When I when I step back and look at it and thought actually, there are people every day learning analytics for the first time.

29:17Yes, people like me, we know Tableau.

29:20So for us, getting from you know that idea to a chart, we don't have the what I would call burden of having to go back and learn all this stuff.

29:28But if you're learning Tableau as a new analyst today and you you sort of draw a list of the things I think you have to get right to get, you know, good with Tableau.

29:38It takes me three hours to do that in the crash course YouTube channel video channel that I have, right?

29:44And I think it took Sigma 15 minutes to essentially reach the same equivalent point.

29:49Now, yes, there's different levels of

29:51Complexity and yes, there's different sort of depth you can go to, but I genuinely feel like I knew what Sigma did in those 15 minutes.

29:59But I do think a lot of people still have to walk through at least

30:04an hour long video to get the gist of Tableau.

30:07And that's before you've even downloaded it.

30:09So yes.

30:11I'll I'll leave I'll leave that there

30:12I'll say that on your behalf because I know I know everyone will will be like, Oh Contrina, you know, she she she wrote the book on Sigma, of course she's gonna dunk on Tableau.

30:20No.

30:20I'm dunking on tablet here, so um yeah, sorry, I'll let you comment on that, sorry.

30:27Uh that's okay.

30:28I mean there were plenty of things that I uh

30:31am still not a fan of in Sigmo or that I miss about Tableau.

30:35And I think you know it's it's a uh any any platform you use

30:40is gonna have a direction and sometimes that doesn't meet exactly what you need and that's okay and it's uh you know I think that really the the big thing that um again drew me to Sigma was the fact that

30:54I felt like Tableau was mostly for Tableau builders or like people like Tableau is designed for people who use Tableau.

31:02And then Sigma is designed for everyone else in that way.

31:06Like you can definitely build very complex and complex and very beautiful analysis.

31:13Yeah.

31:13But the primary user or the the things and I think you you touched on it as well of the what you see is what you get.

31:19Like that's very much O Sigma's approach.

31:22Like why complicate it if you don't have to

31:25Why do a five-layer mapping layer to get a KPI when you could just have a KPI element built in that automatically does calculations for you?

31:34So that that simple approach of

31:36What you see is what you get in a good way.

31:39Um was one of my favorite parts about Sigma.

31:41What you see is what you get is very is it was the original philosophy behind Tableau as well.

31:46So you know

31:47It it just shows you how I think starting afresh can give you a a much more focused take on principles that I think everyone already agrees with.

31:55Um and yeah, I think oh God, um it in the

32:00In the last two years, Tableau realized exactly what you just said there, which is everyone else.

32:07I think um they've even used that terminology in conferences.

32:10showing a chasm between what they call creators today and everyone else.

32:15Um and they have this new

32:19Initiative called the Fourth Wave.

32:21It's essentially a complete rebuild of Tableau.

32:24I think I could call it that.

32:25And they're going to announce it at Dream Force.

32:27But it it goes back to this, I think, fear, this concern.

32:31that uh they don't make a product that's geared to everyone in the enterprise.

32:40being enabled to build their own answers and solutions.

32:45Yes, they recognized dashboards and, you know, views were

32:48a necessity, a necessary step.

32:50Yes, they realize they are still going to be curated experiences done by advanced analysts, but fundamentally with things like Tableau Pulse

32:59It's very, very clear that they realize now they need to do a lot more for what is actually their larger customer base, everyone else.

33:06So yeah, absolutely bad.

33:08Yeah.

33:08Yeah.

33:08Yeah.

33:09And in all um

33:11A lot of people at Sigma always use the phrase of of Sigma standing on giants.

33:15You know, Sigma is not created in a it's not like they

33:18they woke up one day and thought of this idea to like do data visualization spreadsheets like they used spreadsheets before they used other BI tools and there's a lot of um I think you you uh again maybe touched on it if I remember correctly that like

33:32There's innovation and creativity in the data world.

33:36And when you see a an approach that works relatively well, like again, Tableau is a wonderful tool.

33:43It will always hold a special place in my heart

33:45It is a great platform for the right use case.

33:48Sigma is a great platform for the right use case.

33:50Those use cases don't have to be the same.

33:52And that's really um the point of like

33:55You can do you can use both.

33:57You can use one or the other.

33:59It's really about fitting like that path forward that you need with you, your data, your users, their data literacy, your data size.

34:08There's so many things that go into it.

34:10Yeah, exactly, exactly, exactly.

34:12So um, gosh, so many, so many, you know, deep conversations.

34:17I know tablet product managers are gonna watch this video with um

34:20Eagle eyes.

34:21I I I I have this sort of statistic that I think twenty percent, thirty percent of my audience are just Salesforce uh product managers rewatching sex segments of the video.

34:32So

34:32If that's you, hit the like button or leave me some sort of hint to let me know that that's you.

34:37Um I think it I think it might be useful next if we

34:40Um if we could see a little bit of Sigma if that makes sense.

34:43And if we could sort of s see it in action so um people can sort of conceptualize, you know, what is what is this thing we're talking about?

34:49Why is it so radically different?

34:51What is the spreadsheet mentality

34:53Um and I I I don't want to do like a build demo b uh, you know, obviously build something, yes, but what I mean to to the audience here is don't treat this as uh like a

35:04like a um like a speed and tableau you have these like you know fond of re these challenges like a a speed build or iron viz or you know um the like

35:15design contest.

35:16This this this is not that.

35:18This is more of like a I'm an analyst.

35:21I need to understand what Sigma is.

35:23And what is the fundamental essence of this product if that makes sense, right?

35:27Yeah.

35:28Yeah.

35:29Let me share.

35:31So this is Sigma.

35:32As you can see, one of the main things that's really dice about is that it's browser-based.

35:37You can access it anywhere.

35:39This is the home page.

35:41You can see some things I've been looking at recently.

35:43And then this left side navigation is just kind of like how you find things.

35:48This create new is as we talked about kind of the these two things are bottling.

35:52This is like the tableau prep side of things, but I'm gonna hop into a workbook, which is the same as a tableau workbook.

36:00I'm going to connect to a table.

36:03This is just pulling in my data source for the first time and saying, what do you want to look at?

36:08What data are you trying to use?

36:10I'm going to use this plugs electronic hands-on-lamb.

36:13It is the equivalent or sigma equivalent to superstore data.

36:17And

36:18Here you see the first example of the spreadsheet like interface.

36:21Uh it is a table, and and and that's it.

36:25Um we've got the columns on this side.

36:28And then I'm gonna show um I will I'll do we'll do like a this is retail data um if anyone's unfamiliar with superstore data it is um

36:39This is like a electronic company.

36:41So this is an order by uh the granularity is an order number at the SKU level.

36:48So pretty large, it's also about 4.

36:505 million rows, but we'll just show how to add a calculation.

36:54I can come here to add a new column or over on this side

36:59And then all I'm doing is I can either click on something or I can type in we'll do price and then I select it that way, I click enter, and I'm gonna format this as currency.

37:14And this is my sales column.

37:16So now I have my row level calculation of my sales.

37:21And if I want to do something that is more uh

37:25higher like a higher level of granularity than this.

37:27Let's say we want to look at our our sales by region.

37:30I'll pull region up in here.

37:33I can also um you know click on here and do uh group columns

37:38And then I'll pop over to this side, and we can see that Sigma has kind of changed the interface of this table.

37:45This is going to be a lot more similar to a pivot table.

37:48where I can expand and collapse things.

37:51All this is doing is creating um group bys or for each calculations on the back end.

37:57So then I can take that sales column that we did earlier, pull that up into calculations, or again I can add a column up here and do sum of sales.

38:08And now I have my sales for each region.

38:12And that's that's about it.

38:15Um that's the the first level of

38:18kind of creating a a row level as well as a group or an LOD uh in in Tableau terms uh calculation in Sigma.

38:26Yeah, I mean uh just I'll call out two things.

38:29It's a in this sort of very brief demo you've done, it's immediately in my head solved something that I think is really tough for people to let uh learn about Tableau, which is

38:40When you create an aggregation in Tableau, you out of the gate you have to tell people no write sum of sales.

38:46And you're like, but why sum?

38:48Like it's I just want to multiply quantity

38:51by price.

38:52Why do I need to do so like why?

38:54Like and it's like because it's a row level versus an aggregation, but because you can't see that

38:59It's really hard to understand that.

39:02Well here you didn't have to explain that because it was clear when you're in the row level you're doing row level calculation.

39:09And it's clear when you aggregated it up that you needed to aggregate it up.

39:13So in a weird way, the table solves the problem for you and actually makes it much, much easier to understand.

39:18So yeah, no, it's really really obvious what you see is what you get

39:21Yeah.

39:22Yes.

39:22Yeah.

39:23And I think it it also adds a lot of of flexibility in like let me try something.

39:28Um, as we were talking about earlier, let me try it.

39:30And if it's what I want, great.

39:31If it's not, that's okay too.

39:33So if I want to, for example, see my region sales by quarter, I can pull my date column up here

39:41I can uh truncate this two quarters.

39:43So again, uh one other thing I want to call out that um Sigma has a lot of things that are built in, so you can use menu options to do things or you can type things in.

39:53So it's

39:54whatever you're comfortable with, whatever's faster for you.

39:58And then we can pull that same sales calculation up into that quarter calculation so that we can see our

40:06sales by quarter and and by region.

40:10So this for example is gonna give me so we've created two different levels of detail, two different levels of aggregation.

40:18We've got our region and our quarter.

40:20So this is going to be what was sold in this date in this region.

40:25But let's say I want to look at uh sales by quarter by region, I can quickly just flip these two things around

40:34And then expand this out and now I get this set.

40:38So again, a lot of flexibility, a lot of like, let me try, try it out, see if it doesn't work.

40:44You can also have like multiple statements in here.

40:48If you want to do that, I would say not a best practice to leave a group by by itself, but that's okay.

40:54You can undo.

40:56And then again, we can always like use these calculations as if they were row level calculations as well.

41:04So someone um always ends up asking, you know, how do I do a like percent of total so I can do a part divided by a whole?

41:13And now this is my percentage for this.

41:17And then for all of my uh Tableau demos that I give or folks who are familiar with Tableau.

41:24I always talk about like a fixed calculation or like a high level um LOD because everyone always asks about that.

41:31So one thing you'll note is um there is no like

41:35I can't pull something to like this top level grouping to do a table level group by.

41:41So they have this um option down here for a table summary.

41:44So then I can do the same.

41:47Yeah, yeah

41:48Sales and then this would be my total sales for my my table.

41:52And again, I can use that calculation in there too.

41:58So yeah

42:00However, you want to do it, there is like as long as you can write a calculation for it in Sigma or in Excel, like you would you'd be able to do it in Sigma.

42:10And you you're kind of referencing things.

42:11What I one one bit I like there is you're you were just grinding the calculation, clicking on the thing you want to reference, and then that becomes the calculation.

42:20And again, that keeps it contextual, uh uh like on the screen, um, which is good because there's no sort of nasty calculation window to open to then go and find

42:31some sort of nested like cat you know, as you do in Tableau if you're doing fixed or LADs.

42:37And um when you're in the formula, does it highlight the things that made it?

42:41Like is that um I can't recall.

42:44Yeah, there you go.

42:45Yeah, exactly.

42:45Yeah.

42:46Yeah.

42:48So for this one, for example, you can see the two colors, the two fields that that you're using.

42:54Um

42:55I I think that troubleshooting in Sigma is a lot easier as you called out, like not needing to do this other complex window or um

43:04I can see the numbers and I can see how it's calculating versus kind of having to conceptualize how my data is formatted in the background as well as how does Tableau think about your data?

43:14um and and understand how Tableau uses LODs and and all those sorts of things.

43:18The biggest difference uh between Sigma and Tableau in my opinion is that

43:23uh tableau generally starts at the top with like a top level aggregation and you use LODs to work your way down.

43:29Sigma starts at the bottom at a row level and you use these group by statements to build your build your way up

43:34So yeah, in my opinion, it's a lot more flexible because I'm able to designate each puzzle piece along the way.

43:41But it is um there's plenty of of scenarios where like

43:46Sometimes like an LOD would would be easier.

43:49Sometimes I I still think to revert to my Tableau logic.

43:52So it's it's a flip.

43:54You just kind of gotta get get used to thinking about how Sigma thinks about your data versus Tableau.

44:00Yeah, and it's an interesting one because as you say, I'm like, yeah, we you know, when you're building in tab, you do tend to build from the top down.

44:07So you have to start with some of sales.

44:09Then you bring in your dimensions, then you bring in your thing, then you bring in your presentation.

44:13So as you say, top down.

44:15Um with this uh I kind of call that drill-down um altering, if that makes sense.

44:20This is like drill-up altering.

44:21The thing about drill up altering is it's less likely to make a mistake.

44:25You're less likely to make a mistake because uh well you start at the lowest level

44:29And if immediately something isn't right, I think it's more apparent.

44:33Um it's a bit like um you know building a Lego model.

44:36Like it's more apparent if you're working with the wrong pieces.

44:39But if you start with the big piece at the top and you start to take

44:43things apart.

44:43It's actually easier to lose track of what what's supposed to be where because you didn't have any context of the detail that went into it.

44:51So interesting.

44:52I never thought of it that way, but that's a really good way of actually kind of drilling into it.

44:56Um but I will I will say this now, like everyone who uses Tableau, um they they hate tables, right?

45:03So

45:04Um is there a way to bring visuals into this?

45:11Yes.

45:12Yeah, uh you could definitely bring in visualizations.

45:16Um so I'm gonna come up here to this top menu and create what's called a child element.

45:21Um I'm gonna choose visualization.

45:22Basically, child element just means that it's sourced

45:25from that element, which if we go down to the lineage, we can see where things are are built from or connected to.

45:32This is again one of my favorite parts about Sigma.

45:34It's that that

45:35Tableau prepper that Alterx workflow visual where I can see see my joins, my unions, my new table sources, my groupings, all those things in here that really helps with troubleshooting as well as just like

45:49understanding how a workbook is put together.

45:52Like I've inherited some workbooks and had to go in and make changes and it's really hard to do in Tableau if the person didn't write good documentation.

46:01Sigma

46:02You could obviously hide things and and if good documentation is valuable everywhere, but I think having a visual really helps with that.

46:08But um

46:10Yeah, so here is the uh other kind of interface.

46:13We we've looked at the table one for groupings, then we've got our bar chart here.

46:18As we talked about before, we can do that drag and drop.

46:22So let's say we want to look at that sales by quarter, we can pull that up.

46:27Again, I can just type in something as well

46:31And then I get my my sum of sales.

46:34Nice, nice.

46:35And it's um like I I like the way it's got the x-axis and y six sort of like um denoted.

46:41And I don't know if this is me

46:44um not understanding something in Sigma.

46:46This is where you kind of get to find out how far I am in my journey, which is not very far.

46:51Um

46:51What I'm thinking I was trying to do is I was trying to rotate this this chart ninety degrees.

46:57You know how in like Tableau that is like a

46:59like a uh a thing and you've used that icon but what I was trying to do is I was trying to come at it with the tableau way, which is I was trying to drag quarter of day into the y-axis and some cells into the x-axis.

47:13And that didn't work.

47:15Does that make sense?

47:16So and and I could and I c I couldn't understand why.

47:20And I think it's because in Tableau, it does it doesn't sort of um what's the word?

47:25It doesn't um

47:28It doesn't dictate the context.

47:30But I think Sigma does have a preference of putting dimensions in the x-axis and uh metrics in the y-axis.

47:37And therefore that flip is actually a

47:40It's an aesthetic flip rather than like a semantic flip of the x and y axis, right?

47:46Yeah, so what it sounds like what you did was was to do this.

47:52And then flip it this way?

47:54Yes, which is probably not what you were going for.

47:58No, it wasn't what I was going for.

48:00Yeah.

48:00Absolutely.

48:01Yeah.

48:01You were spot on.

48:02Um Sigma does want you to put your dimensions here first and then do this.

48:07If this was something where, you know, hey, I was trying this or I didn't um

48:11you know, build it correctly right away.

48:13Like we can flip this and then you just, you know, click and drag them in there again.

48:19Um it's really, really simple.

48:21You can see that Sigma is defaulting anything that is in the the x-axis to it or in the scenario to an aggregation, which if you want to remove an aggregation, you can always just delete

48:33Or like remove that text around it.

48:36Um and then it'll it'll give you that.

48:40Yes.

48:40Yep.

48:41Yeah.

48:41But it

48:42It highlighted to me I'm coming at this process wrong.

48:46Like when you're learning something new, like uh the cut the analogy I've been using the last month is about cars.

48:51I use BMWs and Porsches

48:53Both fantastic cars.

48:55I don't think anyone would argue with that.

48:56But you don't drive a BMW and a Porsche in the same way because they're very different cars.

49:02A Porsche is a, you know

49:04the engine is literally put in a very specific place to make the driving experience better.

49:09In a BMW that typically a rear wheel drive, very different type of car, typically a saloon.

49:14a completely different experience.

49:16If you're gonna drive them fast, you don't drive them the same way.

49:19You drive them entirely differently.

49:21And I think to m to everyone who's learning a new tool

49:24Try not to come with your what I would say preconceptions or not necessarily biases, because biases has like a negative connotation, but your preconceptions and assumptions from the altar.

49:35into the new one.

49:36Just try things out as you said and see what happens and try and learn what the the norms are in the new tool, right?

49:43Yes, yeah, and and I'll um call out that when I was first learning Sigma, I did exactly what you said not to do, where I came with my my tableau mind

49:55And I was like, how do I do an LOD?

49:57Like, how do I do the these things?

49:58Where are my mapping layers?

50:00Like all this stuff.

50:01So um it definitely is is a different approach.

50:05And but I think it is a simple

50:07Um or once you kind of understand how Sigma works, or same way as once you understand how Tableau works, it it makes sense and you can kind of keep going from there.

50:15But I think that

50:15that trial and error probably would have been a lot softer and easier on me if I had come with with fewer expectations.

50:22Um or fewer uh

50:24conception or ideas about it being similar to Tableau.

50:28Another thing I will say though um with that is I think when

50:32When other people are asking me about how to learn Sigma, whether they're coming from Tableau, Power BI, ThoughtSpot, Click, and really anything,

50:41I think the idea of starting with something you're familiar with is really powerful so that you don't you can kind of remove uh again, one of the first things I did when I uh was learning

50:51Sigma was to upload the superstore data into Sigma because I was like, I know this data, I'll be able to like understand it, you know?

50:58Yeah.

50:59And kind of remove that that barrier of like, oh, I have to

51:02I have to learn the product types or learn this granularity or like something like that you can just kind of like make it easier for yourself.

51:09Yeah.

51:10But

51:10I will also say trial and error is just like poking around playing with it is is a great way to learn

51:18And I I just tried to break it.

51:21I tried to recreate Tableau and break Sigma were the ways I taught myself Sigma.

51:27Exactly, exactly.

51:28Um yeah, no, and I I will say this now, which is I do think the data analyst of the future, I do think the you know consultant of the future

51:40won't be using it won't be like what I would call a mono um I had a I came up with a term for it like a mono um monopractition is what I called it.

51:51Like when when when when I was learning Tableau, like

51:54If I got good enough at Tableau, I didn't need to worry about Power BI.

51:59I didn't need to pick up anything else because there was enough appetite and demand for my skill set to just be super hyper focused on Tableau.

52:07I don't think we live in that world anymore.

52:10I think the uh practitioner of the future is a multi uh tool practitioner.

52:17And I will say that, you know, Tableau and Power B are often sort of hand in hand, and anyone who's becoming a data analyst today should fundamentally learn both of those out of the gate if you want to go out in the world.

52:29But

52:29I think something like Sigma and there'll be other tools as well.

52:33I think it's important to have what I would call like a a grounding knowledge in them.

52:39So if you have to use them and you're going for a job interview, if you're going for something else.

52:43you have some good context around them, right?

52:46Because I I also think with all of these tools, the barriers have come down.

52:50They are all fairly accessible.

52:52You can

52:53Get a trial all of them and and understand them.

52:55And if you give yourself like I always say if you can't learn it in a weekend, it's not worth learning, right?

53:00Like if if you can't if you can't open it up and try it out over a weekend

53:05And you know, big hint there, you need you need to be the one investing that time, not asking your company to give you that time, right?

53:11If you can't learn it every weekend

53:13then it's probably not worth your time and there's other tools better that could do that job just as well that are better to invest in your time.

53:22So um

53:23I know people watch my videos uh like watch whole parts of my content.

53:27If you've watched sort of, you know, more than ten of my videos, I kind of I'm kind of like, oh Tableau's failing you there, because it shouldn't be that hard to learn all these things.

53:35But at the same time, yeah, I think

53:37This is a really good tool to to get familiar with.

53:39Um where I was going with my next question is what does like a finished dashboard equivalent look like, right?

53:46Like what is the we've we've sort of done the views and sheets, as it were, of of of Tableau.

53:51What is that?

53:52What does like a dashboard look like?

53:54I don't know if you've got an example that or if Sigma have an example that you can kind of walk people through.

53:59Yeah, I can definitely show a final dashboard.

54:03I'm gonna go to

54:04to one that I created uh just for fun about the TV show alone.

54:09I really love love this show.

54:12It's if um anyone's unfamiliar with it, it's like a wilderness survival show where each contestant gets to bring

54:1810 kind of predetermined items and then they just survive for as long as they can.

54:23And as the sh as the title uh kind of hints at, they are alone.

54:27Um it is

54:28It's a crazy show.

54:30I love watching it uh with all of my blankets and my popcorn because I'm like if I would be a terrible contestant.

54:37I hate being cold.

54:39I hate being hungry.

54:40So it's not for me

54:41Not for me.

54:42I will watch is it on Netflix?

54:44Is it is it like uh which which channel Yeah, I think some of the older seasons are on on Netflix

54:51Um or maybe Hulu one of those.

54:53It is a history channel original, so wherever you you get that.

54:58Okay.

54:59But yeah, so this is also an example of something that like you can

55:04You can apply when when you were talking a little bit earlier about um how you should know multiple tools.

55:10I think the the other thing I want to kind of add to that context though is

55:15You should know multiple tools and data visualization best practices or like fundamental data concepts.

55:21Like if you know those

55:23Plugging, you know, again as we talked about, like how does Tableau do this thing?

55:27How does Sigma do this thing?

55:28They're the same thing on the back end.

55:30So if you know what those

55:31Things are if you know hey I I need to do a part of a whole analysis.

55:36I need to do a time series analysis.

55:39Like

55:39You'll be good.

55:40Yes, exactly.

55:41Amen to that.

55:43Like I cannot tell you how many times I I get a little bit frustrated because people

55:49will say, I know all these skills with Tableau, and now everyone's moving to Power BI and I'm like, whoa, whoa, whoa, whoa, whoa.

55:56Like these are not Tableau skills.

55:59These are like

56:00Data skills.

56:01Data skills.

56:02And just cut the tableau out of that.

56:05Like go back to your CV, rewrite it all, minus Tableau, and you'll be fine.

56:10And it's exactly the same thing.

56:14Yeah.

56:14Sorry, I cut you short.

56:15Yeah.

56:15No, that that's okay.

56:17Um I did I wanted so I wanted to make this uh workbook, one to to answer a question that I had about the show itself, and then two to kind of show that hey, yes, you like you can make

56:27more than just uh business dashboards with within Sigma.

56:31So this is an example of how like you know you've got a story to it.

56:36So one of the the questions that I really wanted

56:39To answer was as I had watched the show, there's I think twelve or thirteen seasons now.

56:46Um the first season, so we can look at the first season has an average lasted days of

56:5220 21 days and a max of 56 days.

56:55And then as we continue on, this most recent season, um, this is this is

57:00So there there are no spoilers if anyone is watching the new season that's out right now.

57:04Don't worry, only on season nine, but we can see that like the the average has gone up as well as the max day has gone gone up pretty significantly.

57:14And so

57:15One of the things that I noticed was that the contestants became more and more professional oriented, like they became outdoor, like folks who had careers or activities that were very outdoors focused.

57:29Or there um there ended up being a lot of folks who came from military backgrounds.

57:34And so I was like, is this due to the the like

57:39increase in professionalism or like professional backgrounds or is there something else that's kind of letting lending these folks to to last longer?

57:49So that was the question that I I wanted to answer here.

57:53But we can take a look at some of the some of the visuals.

57:58So some of the things I'll call out with Sigma, you know, you've got obviously the opportunity.

58:02To do text, lots of filters.

58:05We talked, we didn't, we didn't touch on that, but these are all like really easy to build.

58:09They're all uh like already multi-select in there.

58:13That's one of the things I really like about it.

58:16But we can start to create some some really pretty visuals.

58:20Again, I think taking some really fundamental data visualization concepts of like

58:26Does this color match with this color?

58:28Is there a theme?

58:29Um, how can I make something like look like pop on a different color background?

58:34It doesn't all have to be

58:36white or gray backgrounds with blue, blue bar charts or text or those sorts of things.

58:41Um got some um some more advanced things with a box plot.

58:45So

58:46Again, we can we can see how like the bar itself is kind of trending in this general direction to lasting longer

58:53Um and then season seven was the one where uh so someone lasted a hundred days, which was insane.

59:00I don't like again, crazy, crazy stuff.

59:05I guess they don't know

59:07They no one has any context of how long they're lasting because the way it's probably produced means you just keep going until

59:15You can't do it anymore.

59:16So like, yeah.

59:18Yeah.

59:18You you don't know like you could there could be twelve people left or two people left.

59:23You have no idea.

59:25And you just gotta keep going.

59:27Um, this season was so part of why this season was an outlier was because they they set a 100-day minimum.

59:34They're like, oh, we're gonna up

59:37the prize money, but you have to make it 100 days because up to that point the highest had been 87.

59:42So I'm sure they were like, maybe we can

59:45not spend our prize money.

59:47I don't I don't know if that was the motivation behind it.

59:49But um so I just I thought that that was an interesting and then looking at the next two seasons are nowhere near a hundred days

59:56So having that um that mental I can count down the days to a hundred versus I don't know how long I have to last, that uncertainty I think is just so uh scary and

60:10trying and taxing um for the mind, which is interesting, but um interesting.

60:16Yes.

60:16So here's an example of a

60:18pivot table um we we didn't really touch on those uh or we touched on on how I like yeah I did yeah exactly yeah yeah but you can get so you know a good sense of things um that way

60:29So, whoops, the other thing that I mentioned, um, so each uh contestant can have 10 items.

60:36It's from a predetermined list.

60:38And so

60:39One of the other reasons why I wanted to make this visualization was because I had a a very uh

60:47Someone who had a very strong opinion that the salt lick was the item to bring and that everyone should be bringing that.

60:53And I was like, I don't think that

60:56a salt lick, you know, to like attract prey uh excuse me, attract prey.

61:01Um was the idea, I guess.

61:02And I was like, I don't really think there's data to support that.

61:05So I was kind of showing that to ding that yeah.

61:09Yes.

61:09It's not a thing.

61:11Um but you can see, for example, that like everyone brings a pot, everyone brings fishing, almost everyone brings fishing gear.

61:18Stereotypical survival gear.

61:20Yes.

61:20Yes.

61:21And then you kinda you start to

61:23to see how um it's so far down to no one think they'd be cooking you know so they you start getting into well you can use pot as the frying pan and you can um

61:36Another one, for example, like gillnet, that's something where you can make a gill net from paracord or from other other um trapping, not trapping wire, but like

61:47fishing wire, stuff like that.

61:48So you you start to see some trends, um which I think is also um

61:56Trying to remember if there was one that I had in there.

61:59Uh I also wonder if like some of the contestants from like season four onwards

62:05Having had context of previous seasons behave and perform differently.

62:11And then on top of that, you have producers who pick contestants with with ratings in mind, right?

62:18So you you pick

62:19You pick what are maybe deliberately vulnerable people to make for better TV, while simultaneously those people thinking I'm gonna use all the skills and the, you know, not like so many dynamics to sort of

62:32Really drill into the Yeah, yeah, yeah, yeah.

62:35Yeah.

62:36And I I think you're uh like you're spot on.

62:39There's uh with that that comment about like looking at other or previous seasons.

62:43Um

62:44They in in the seasons or in the shows, you know, they're just talking to the to the camera and they talk about anything and everything.

62:52And the later seasons all talk about

62:55previous seasons and they previous like stars of the shows and and their strategies and what they did and it's like, oh, this person did like created this specific kind of of shelter or something and I'm gonna recreate that and

63:10Like tried and test all of these.

63:11Yeah, so they there definitely are.

63:14I think another example is like Tarp, for example.

63:17We can see that there was a lot of people in season four that brought it, but

63:22Not as much like the farther.

63:24Yeah.

63:25Yeah.

63:25Like some do, but you know, some aren't the same.

63:29There's our salt, for example.

63:31Um

63:33I'd bring salt.

63:34I'd bring salt.

63:34If I'm gonna cook anything and it's not great, gotta chuck a bit of salt in there.

63:38It'll be fine.

63:39Yeah.

63:39Yeah.

63:40Yeah.

63:42This is fantastic.

63:44Yeah, I think this is also just a cool cool visual in the sense that um there is no right choice uh in the sense as long as you don't choose a Scotch auger, a Scotch-eyed auger, which

63:55I don't really know what that is, but apparently it's bad like it's not a you're gonna end up in in a lone place because of that.

64:02So um but I do I do think

64:06Slingshot is a really interesting story.

64:08There's um I think it was the contestant who lasted a hundred days had a slingshot.

64:13And so it was this like, oh, well he's the outlier, so that's

64:17Pushing that one out that way.

64:20Yeah.

64:20Or for example, like whoever brought soap um what placed third.

64:25So like

64:26That's not a typical item, but they lasted a long time with that one.

64:32But um I think the so again one of the questions I wanted to answer was

64:37Is there like a a professional lean?

64:40Does that give you more um kind of benefit?

64:44Yeah, or something like a head start into something.

64:47So I I looked so the the data behind this, which we can

64:52pop into real quick and I'll I'll also show you one of the other cool things about Sigma.

64:58So this

65:00For example, or this visual we can see is splitting out their profession by whether it leans outdoors, which is

65:08is the the ones that we talked about where it's like a they're like a hunting guide in Alaska like that's a their profession is something that's outdoors

65:15Something that's resilience or leans resilience is like the military or like a doctor or like things that are like mentally taxing, maybe, or mentally or physically taxing.

65:25And then you've got this other category, which was like

65:29There's one one of the first seasons where I think a guy was like a tax accountant and like just did this on the weekend or you know something like that.

65:36But um I'm gonna quick go to go to this this source um so we can see

65:43This will also show you how uh how great the the lineage can be.

65:49I love a good lineage art, Mike.

65:51I know this is like crazy and spidery towards the end, and it I think it can look intimidating, but

65:57The thing is when you're debugging you're quite focused.

65:59You're not looking at this whole thing.

66:00You're just looking for like where one thing tracks back so you can go fix the problem.

66:05And yeah, I think here you're starting with the data set, right?

66:08And you you're yeah, you you guess you you have the all the tables that you built along the way.

66:14Um I can see a join in the middle there.

66:17And then

66:18Uh yeah, wow.

66:19Like okay, so that's you bringing together two other bits of information with what you've got.

66:24Yeah, exactly.

66:25Okay, yeah.

66:26And see this makes total sense to me.

66:28You can you can just

66:29Follow it all the way through.

66:30Yeah.

66:30Yes.

66:31Perfect.

66:31Yeah.

66:32What you see is what you get.

66:33What you see is what you you understand for it and you can see how like you don't have to to read any documentation to to understand how this

66:42is like how the approach it is or how the person is approaching approaching this.

66:46But um does it get to calculation sorry before we does it leave the cal does it does it go down to calculation level or is it

66:53'Cause these all look like base tables.

66:54And I know um one of the things DBT added is like column level lineage, right?

66:59I don't know if 'cause the thing about Tableau I'm just asking from a tableau selfish perspective.

67:04It's so painful to go down to column level lineage in Tableau without paying for mortals or scraping a ton of data.

67:13Yeah.

67:14Yeah.

67:15So it does not right now.

67:17That is one of the things that I've talked to them about.

67:19Uh adding.

67:20I I hope to see it one day.

67:24I I wasn't excited when they added the the show controls, which are are like filters and parameters.

67:31This is really helpful if you're trying to like troubleshoot maybe this filter is not working the way you thought it was.

67:37You can see where it's sourced from

67:40from and and the targets.

67:41Right now I only have one target on here, but uh like that helps you at least craft some of those things off the list.

67:48So hold on, hold on.

67:49Could that filter have targets

67:52in multiple places.

67:54So this this kind of thing in Tableau where you have like a a crot like a a data source filter.

68:00But the problem with Tableau is that that like a data source filter means it's it's it's it's at the very beginning.

68:06What this suggests is that filter could act part of the way through the cycle, but not the like yeah, that's really cool.

68:13And being able to see in the lineage means, yeah, you yeah, okay, that's really cool.

68:18So you could connect it there, but you could also connect it.

68:21I guess anything downstream, uh if that makes sense.

68:25Yeah.

68:26Um Sigma's built on parent-child relationships, so anything you do in the parent element is gonna cascade down to that child element or you know anything that's sourced from it.

68:35So here I've got all of my filters targeted to my base data just because then like I have one source.

68:42This is a very, since this is a very simple analysis, I really only have the one data source.

68:47It just was like clean and easy.

68:50But you could also, as you said, you could do a quote unquote data source filter in the, this is my CSV that I uploaded, so I could

68:58filter this one and then all of my my join would have been um on those those filtered results.

69:06What's um one of the cool things that

69:09having this um ability to do data modeling and filtering or have your end user interact with it is that all all in the same place is that it can be incredibly flexible.

69:19So I can

69:21Which basically change my join criteria or how my join works live, like based on what the end user wants.

69:29And so that can lead to a lot of performance implications.

69:32Um or

69:33Perform his benefits and it can also provide a lot of flexibility in saying, you know, hey, I want to pull this or I want to compare this or build these things, things together.

69:43So very exciting.

69:45Wow.

69:46This is so fresh.

69:47And like my one of the uh earlier on last week, so for everyone's context, like I've been working with Sigma for I think

69:55probably two months at at this at this point like in in detail and one of the things I realized is that um I see why Tableau are doing the fourth wave if that makes

70:05sense because if you carry on with the approach they've had with with a tool that was really fundamentally designed you know like 20 years ago and you run with that concept while the concept I think still holds true and holds well with Tableau

70:19There is definitely a greater opportunity to reimagine things like this um in a new way rather than shoehorning them into the current experience.

70:28And I also imagine that behind the scenes

70:30shoehorning it into the current experience creates tons of technical debt because you have to go with you you have to go with the incumbent sort of methodology.

70:39Whereas if you start from scratch, and I think Sigma probably benefits massively from this, which is

70:44Yep.

70:44They can think of how to do things right, the right like right from the beginning, and they've it's probably they've probably left themselves lots of great opportunities to build on like the stuff they've built, right?

70:55So like they can come back to something and know that they've not

70:57you know, innovated themselves into a corner as it were, which I think is definitely the case in in some parts of Tableau.

71:03Yeah.

71:04Yes.

71:05Yeah.

71:05tech debt is a real thing in BI as well as BI platforms.

71:10And and as we talked about earlier, also like um you know, standing on the shoulder of giants, being inspired by or, you know, there's a lot of good solutions out there and Sigma's just kind of

71:20taking the best of of what they've seen and putting it putting it all together.

71:24Yeah.

71:25Um but yeah so the the other thing I wanted to to highlight with this one um that we haven't gotten a chance to talk about yet is input tables

71:33So this is the ability to write data to your warehouse.

71:39And before anyone asks

71:40You can do it in a very controlled environment.

71:42You can set permissions and roll level security to all that stuff.

71:44So don't worry about it.

71:45Who wants control?

71:46Who wants control, Katrina?

71:47We just want to we just want to create a dumpster fire of a database here.

71:50Come on, let us just do it

71:53Yeah.

71:55Of course.

71:56Like I um can I just push back on something, which is like why does everyone think that the minute you write back to something

72:02It's just immediately gonna cause chaos.

72:05Yeah, immediately terrible.

72:06And it's not immediately terrible.

72:08Like we live in a world where things like Notion, Monday.

72:12com

72:12All these things that just require inputs left, right, and center.

72:16And I always say to businesses, would you rather they put it in those kinds of places or would you rather they put it in the place where that's supposed to live?

72:23your database.

72:24And I was like, yes, pick one.

72:26Yep.

72:26Because it will happen anyway.

72:28Like if you don't let them write it back to database, they'll just put it in Excel.

72:32And

72:33I think this is a better place.

72:34At least here you get to see the mess and handle the mess and tame the mess, however much it might be.

72:39Elsewhere you just don't know.

72:41Yeah.

72:42Yeah, and and that's why uh Sigma created input tables was to have that flexibility but governed.

72:49Um

72:49In in the the book I I wrote I talk about like creating playgrounds for people instead of uh you know like you don't want anyone like running around in the streets like doing crazy stuff where you can't govern it

73:02But you also like can't give your kid a seesaw and expect them to be happy with that for the rest of their life.

73:08There has to be like some flexibility and so

73:11This this um this workbook with all of the the levers that you can pull with uh security and flex the flexibility

73:19Um it provides a playground for people to explore and work with data, but in a safe and secure environment.

73:25So it's a great great mix.

73:28That's really cool.

73:29That's really cool.

73:30And I think

73:31This feels like one of the things I think I've said to you before is I feel like Sigma is about workflows.

73:39Like you build something

73:41You you bring the insight, but then something happens after that.

73:45And the thing that happens after that to allow it to reach its natural conclusion

73:49It needs to be a workflow that closes the loop, as it were.

73:53And I think from what I've seen of Sigma, Sigma enables teams to build and

73:59Let those workflows happen in an analytical context.

74:02That's fundamentally to me what Sigma is.

74:04I know Sigma sort of uses a slightly different thing.

74:06I think I've I've messaged you before being like, why can't Sigma just just

74:11Treat itself like a workflow tool.

74:12It would be so much better than whatever marketing they've got on the homepage at the moment.

74:16But yeah, no, I I just fundamentally believe like this is the thing that stands out to me that's different from the Tableau experience.

74:22Because the Tableau experience is curated, governed, all of this lovely stuff.

74:26But this is what we've said you're going to look at.

74:28So you look at it, right?

74:29Sigma is very much different.

74:32Sigma

74:34empowers everyone to drill into what they need to.

74:37But critically, input tables lets you to let people complete those cycles, whatever those need to be.

74:44Update some information.

74:46uh change some stock numbers, uh send an instruction to someone, uh, complete an order.

74:51All of these things can be done here and they stay in context.

74:54Yeah.

74:55Yeah, and I can give you uh a preview of one of those applications that I that I've made.

75:04I think it's this one.

75:06Okay.

75:07So yeah, I've got this uh demo kind of workflow set up.

75:11Um this is a pet supply company because again I've

75:14I love my pets.

75:15Um and so this is an example of how Sigma is using the ability to combine the right back functionality.

75:23With the actions and the interactivity from their workbooks or um, you know, kind of that end user contribution to create something that is

75:31meaningful and and cur and not only solves a data question, but it produces data, it updates data, it

75:40fits within the normal workflow of of a business or of an organization.

75:45So just real quick, these are some example sales.

75:49I will also say that

75:52This demo is intended to be a something that you would update every day.

75:56And since it is a demo, I have not been updating it every day.

75:59So some of the numbers might

76:01look a little funky, but uh we'll we'll get there.

76:04Get we can get the idea of these things, but again, just just showing how um you know let's say we've got some some sales and some average profits, we can see how those things are kind of trending.

76:16as well as seeing like our our top ranked items and then we can look at um yes paginating uh through different things

76:26And then again, just some like production metrics.

76:28So we were not doing so great when I wasn't updating the demo, but now August is a good time for us.

76:35I wonder why.

76:36Yeah.

76:38So there's two different examples of of kind of there's two examples of how input tables work in this workbook.

76:45So this first one is going to be in order form.

76:48So this is a list of all of the items that we have available for sale in our in our warehouse or whatever and some information about it.

76:57I can type in a number that I want.

77:00So I can have 65 here.

77:02I could say I wanted to, oops, I'm this might be

77:09This is, as we were talking about earlier, creating a uh a secure environment or secure playground.

77:17So that was one of the settings

77:19Which I had not set to be in published mode.

77:22Good.

77:22So there's a demo of that.

77:27So if I have the right permissions and if it is set up in this way, I can click edit and then I could type in and say, you know, hey, I want to order

77:3750 bird leashes.

77:39I don't yeah.

77:39I don't know what that is exactly, but um I can put that that in there.

77:45And then I can also click this button and have it add to my order.

77:50So we'll

77:51Put all of these and then we'll give it a second to load.

77:56I wonder if this is gonna be added in draft mode.

78:02Is this another one?

78:06Alright, I gotta edit this one too.

78:08There you go.

78:10Boom.

78:11Let me make sure that this other one is set.

78:14Yeah, yeah, no worries.

78:19But it's interesting, like this is it is there's sort of the guardrails doing their job if in many ways.

78:24Yes.

78:25Um

78:27Just cause you want to update Katrina doesn't mean you can.

78:30Yes.

78:32Someone needs to give you the permission

78:35There we go.

78:38Straight away, yeah.

78:38Yeah.

78:39Yes, that they they are added to our our order.

78:42Right.

78:43Um

78:44And then we would get a shopping car, isn't it?

78:47A total.

78:48Yep.

78:49And then the idea would be you'd uh you'd kind of click place order and then it would clear that order form for you to be able to look at your previous orders.

78:57So that's the the one persona where it's um you know hey I'm trying to place new orders and then the second persona or the second use case is the the warehouse manager who's trying to understand like

79:08Well, what do we need to make?

79:10So part of this scenario is understanding that like, hey, if if Tim orders 50 bird leashes, we can't just go out and create

79:1950 bird leashes, maybe for whatever reason we have to create them at 100 at a time.

79:24So if Tim orders 50, now I have 50 in stock.

79:28So if next week Tim comes in and orders 25

79:31I don't actually need to produce anything because I have some on hand.

79:35So all the stuff on the back end in Sigma is uh calculating

79:40How much do you have on hand of every product?

79:43And then if you need to produce something, then it it pops onto this row and it says, hey, based on these orders.

79:51So we can scroll the way down to the bottom.

79:53We can see all of the items that we would need to produce, how many batches, what's that standard size, what is that gonna equate to

80:03And then I can say, okay, we are are working on whatever product ID this is.

80:12And then as a manager, I can come in and I can click this box and say, yep, these these ones are completed.

80:19And now I can move this one and this one into process.

80:23So note that we've got some uh data validation in here versus a checkbox versus on the order form.

80:30I could just type in free free numbers on things.

80:33So multiple types of input, not just uh like records and yeah.

80:38Yeah, and and again it's creating that that safe and secure playground.

80:42It's not it can be a free-for-all, but it doesn't have to be a free-for-all.

80:45You can create whatever bounds or limits you want based on your use case or what makes sense for your data.

80:52And then um and then those items would pop up in here in our production.

80:57So we can scroll.

81:01All the way down to the bottom.

81:02And I think this was the one that we were yeah, that we were looking at.

81:06So now this goes all the way back up to the top of the circle.

81:10And the next time that someone orders this product will

81:13Probably have a bunch on hand.

81:14Uh because we made a lot today.

81:15Yeah, you made made a ton more.

81:17Yeah.

81:18Yes.

81:18Wow.

81:19So it's a proper workflow, like as I as I hinted in the and I think there's a lot of customization in that.

81:24This this workflow is built in a specific way, but

81:27You know, every every company, even in the same industry or vertical, will be slightly different.

81:31So they'll tailor this to suit their own things.

81:33And I think there's also a nice thing here, which is because this is I think this was connected to something like Snowflake, right?

81:40There is a I think there's a theme in modern analytics where you have not just um, you know, one tool accessing the data.

81:49In your analytics stack, you tend to have many other things.

81:51So you could have um I think I've previously talked to you about retool, right?

81:55Like a it's a very similar kind of tool, but more developer code focused.

82:00You could have a retool workflow that picks up on these orders and these actions and pings something else to someone elsewhere who looks after

82:09like a slightly more intricate part.

82:11So because the commonality is the database, in this case Snowflake, you know, more modern databases

82:17You're not precluded from what like building those kinds of workflows separate to this, but because everyone's working on the same view, um, the context remains the same for everyone, which is fantastic.

82:27So yeah.

82:28Yeah.

82:29Yeah.

82:29And and you can, as you you called out, like, you can keep this data only accessible within this workbook.

82:36You can make it accessible.

82:38is somewhere else in Sigma or outside of Sigma, all of those things.

82:41And again, it goes goes back to what we've talked about several times around how Sigma is um

82:47creating a new approach of how things like should work or how we would actually want them to work.

82:53So instead of having to go

82:55Okay, well let me go to system A to order things, and let me go to system B to figure out, calculate what needs to be produced, and then

83:04system C to have my production records and track all those things and now now I have to to pay someone or or hire a team to take all my data and put it together and make sure that it's all lined up and the math works out correctly.

83:16It's like

83:17You could just do it here.

83:18Just uh in one spot and that's it.

83:20Exactly.

83:20No no need for altrix or any of that mess.

83:23Yes.

83:25That's what I call altrics at the moment.

83:27It is a bit of a mess.

83:28But anyway, um I digress.

83:29We're not here to dunk an altar.

83:31Um this has been a really eye-opening experience.

83:34I've I've really enjoyed this.

83:36Um we've probably got uh time for a couple of questions.

83:39So um

83:40Um firstly to close off this sort of Sigma uh thing, if people want to try Sigma, um, yeah, how do they do that?

83:48How do they I think we you and I have alluded to this off recording, but

83:52Uh let's touch on it for the benefit of everyone like during the recording.

83:56If I want to try Sigma, what do I do?

83:58Yeah.

83:59So there's two different ways you can try Sigma.

84:01The first one is to just go to the website and sign up for a demo.

84:04It's a two-week trial.

84:05You get

84:06access to anything and everything in there.

84:09Sometimes that's enough time for people, sometimes it's not.

84:13The other thing that I do want to call out is

84:16workout Wednesday, which some Tableau folks might be be familiar with.

84:20We do have a Sigma version of that.

84:22And if you um so we'll click on on this one and then you can

84:26sign up to be added to like the Sigma Workout Wednesday environment and then you you can have like a persisting account.

84:34It's not a full access, so like you don't

84:36Get access to all the admin things, uh, because we need some of that stuff on the back end, but you will be able to to continue to try uh new stuff, to do new challenges.

84:46and explore basically everything that we saw in the workbooks today.

84:50Oh that's a good thing.

84:51And then as well as um some of the data modeling stuff you'll you'll have access to.

84:55So this is is the the primary spot for like

85:00poking around and playing and uh learning Sigma.

85:04And then I do also just want to call out we have a uh user meetup as well.

85:09As I mentioned before, uh the Tableau community was very influential in my career.

85:13That's how I learned uh Tableau.

85:14So I wanted to kind of bring that to

85:16to the Sigma space as well.

85:18I also just wanted to find other Sigma nerds and and share like again, like look at my pretty bar chart, like look at the things that that I've made or the cool things that I've done.

85:27So we meet virtually, monthly.

85:29Um the next one

85:31So it's it's typically every uh second Wednesday, kind of around it's 11 a.

85:36m.

85:36Central, 9 a.

85:37m.

85:37Pacific.

85:38Don't know what time that is in London or for YouTube, but

85:42Oh, there's calculators out there.

85:46Yeah.

85:47Yeah.

85:47And I think uh you you've been very humble about this, but you wrote a book, right?

85:51So um I did.

85:52That that is also quite a good

85:54Um I I I will say this.

85:57There are lots of people in the tablet community who do prefer the written form, specifically a textbook or something like a book.

86:04So I will call out your book here because I think it's a

86:07It's a really good um there's there's something about books in where the author does actually think about the chronology of what they're putting down.

86:18in a way that a blog and videos and all of that stuff you find on the internet, even user groups and you don't get chronology with all of those.

86:26And so for some people, chronology is is sort of a a big

86:30uh thing that opens up the experience of a piece of software to them and a book tends to have that you know purely in the the way the chapters are structured so I would highly encourage your book as a starting point.

86:42Yeah, I think I'll I'll sort of let you pitch your book to the audience a little bit.

86:46Yeah.

86:47Yeah.

86:48So I will say that my book is free for download, so definitely rec recommend it.

86:53If you go to

86:55Sigma's website and resources and ebooks.

86:59Um it is called Spreadsheets for Dummies

87:03So as the the title indicates, it is all about how Sigma is taking the spreadsheet into the modern uh data stack.

87:12So we talk about the history

87:14of why spreadsheets are still a fundamental and useful tool and product and should continue to be a part of everyone's analytic journey.

87:24But then

87:25There also needs to be more to it.

87:26You can't just have tables.

87:28You need those visuals, you need those workflows, you need data manipulations, all those things.

87:32And how Sigma is um again taking taking the spreadsheet into the modern environment

87:37I will say it is the the audience or or uh just so that the folks have a good understanding of it, it is not intended to be a technical how-to or

87:47you know, learn Sigma.

87:49It really is what is Sigma?

87:51What are they trying to do?

87:52Exactly.

87:53But it's it's a I think it's a good read.

87:56I hope people like it.

87:58Well I'll I'll give this point of context.

88:01My my first

88:02um let's say commercial activity is Tableau Tim as with LinkedIn Learning.

88:07And it was a course on LinkedIn Learning, which is still available now.

88:10I've actually updated it just recently.

88:12Called Everybody's Introduction to Tableau.

88:14And one of the biggest fears I had was when I was pitching this to LinkedIn, I said, listen, the problem with all your stuff is it's too technical out of the gate.

88:23Like if you think about it just for one second, somewhere like LinkedIn, everyone is aspirational.

88:29Like people people are coming across new tools for the first time.

88:32So if you just go in hard on the technical thing

88:34you kind of vilify this perception that this is too hard, this is this is not for me, because the technical stuff is dry, can be difficult.

88:43and start so my actual like you know course I said number one you don't need to open the software once to complete this course

88:51Uh number two, uh if you only get past like the first five lessons in this course, perfect.

88:58That is all you need to get through.

89:00And if you finish this and watch to the end

89:02then you, my friend, should definitely be looking at some of the more technical courses because you've you've you sort of crossed the gauntlet, you're still interested.

89:10And you've gone there.

89:11So you know what?

89:12Like what you said, not technical.

89:13I think that is a perfect sales pitch to everyone.

89:16Yes.

89:16Especially my audience who's zero to 60, right?

89:19Yep.

89:19More often than not, not looking to go too technical into things to start to start off with.

89:24Theory, concept, um yeah, all that stuff leads and then the other stuff can follow.

89:30Yeah.

89:30Um I'm gonna stop sharing my screen and yeah, go for it, yeah.

89:34Yeah, yeah.

89:36On that note, I I will tell you a story about writing writing the book.

89:41So you know I I wrote it uh on my nights and weekends and one of the the weekends I had my husband read it just as like another set of eyes.

89:49As I mentioned, he's technical, but not in any way a challenge sheet-based person or whatever.

89:54And then uh the next weekend, we were talking about uh, you know, oh what do you what do you want to do this weekend?

90:00I think it was like a Friday

90:01And I was like, oh, I want to rewrite this chapter and then and then have you read it again.

90:05And he looks at me and he's like, do I have to?

90:07And I was like, what?

90:08You don't want to read my book about spreadsheets?

90:11Again?

90:15Yeah, we all have those moments, right?

90:17Yeah.

90:17He did a very good job of supporting me because he loves me, but I understand like I get it.

90:22It's about spreadsheets.

90:24Yeah, exactly.

90:25Yeah.

90:25Yeah, I have those moments with my wife.

90:27I was like

90:28She tries to explain to people what um what I do.

90:31She's like, You work with computers?

90:32And I'm like, Bree, don't kill me right now

90:36I do more than work with computers.

90:37Like everyone works with computers.

90:40But anyway.

90:40Yes.

90:41Um yes.

90:42So um check out the book.

90:44I think it's it's super valuable resource.

90:47Um, I think you've given people a great sort of jumping off point to go start learning, work out Wednesdays, get your reps in, get familiar with the tool, bring a fresh mind, don't bring your philosophies from before.

90:58I think if you are, you know, currently a Tableau user, Sigmar is a great sort of adjacent tool, definitely.

91:05It's it's going to be a much softer landing than someone like Power BI, definitely, for 100%.

91:10Um so if you're gonna choose to learn something new, give it a chance.

91:14I think it's it's really worthwhile your time.

91:16And I think in the current context with the fourth wave coming, it's also super important because I do think, I do think

91:23Tableau is going to go somewhere with the fourth wave.

91:25Does that does overlap some of the ideas around um what Sigma is doing?

91:30Difference is Tableau has got to bring its entire platform with it.

91:34So it's kind of it's it's kind of got a bit more of a burden to

91:38bring a completely different paradigm to it, but it it can't be as what I would say light um not light heart lighthearted uh sort of an unfair statement for Sigma

91:52Sigma is powerful because it's not burdened by its breadth and its depth and all of the other stuff.

91:59It's working towards that, but the the benefit of that is that out of the gate, it has all that power and it's fresh.

92:06When Tableau does its fourth wave, it will likely not achieve the breadth and depth of the Tableau platform because of this very problem.

92:13It has to work towards those things.

92:15Whereas Sigma's got a more open canvas, more open road, so it can actually take a more organic path to get there.

92:21And I think it actually might end up being a m much better route.

92:26Uh it and I'll just add that there there are plenty of things that I I wish Sigma did better.

92:31I'm excited for iteration.

92:37I think that that is also um you know one of the things that you you as you mentioned, like having a fresh perspective, like keep in mind that Sigma hasn't been around for as long as Tableau.

92:47Like they're

92:48There are things that are coming out.

92:50Um, I think about like the input tables that we saw um in there.

92:54The f the first time that I saw Sigma, I was like, oh, they should like

92:58Yes, input tables make sense there.

93:00And then um they've been slowly releasing new updates on the on those things or slowly, fastly, kind of depends on your perspective, but like

93:08They're consistently making updates to the platform and making changes, iterations.

93:12They're really receptive to feedback and understanding like

93:16What is your specific use case and how can we make it better, faster, easier?

93:20The what you see is what you get.

93:22So definitely if

93:24Sigma doesn't fit your use case right now, still keep an eye on it.

93:27Like there's a lot of things that are coming.

93:29Um and it's gonna be great

93:32So we have we have a closing tradition.

93:40Dire over VCO where previous guests asked and ask us a question.

93:44The previous guest I had on was showcasing her portfolio, it was Judith Becker, and she asked you, uh, as you're the next guest

93:54If you had unlimited resources and access to any analytics tool available to you on the pl in the world, what would be the project that you would do with all of that sort of access?

94:06Yeah.

94:08So we haven't touched on this, but I trained Brazilian jiu-jitsu and it's a um so it if you're unfamiliar with it, it's a martial art.

94:15It's kind of like wrestling and judo combined.

94:18And one of it it has a belt system, so you go white, blue, purple, brown, black.

94:23And there's this saying or this like

94:26You know, people like to make up statistics and I I've heard anywhere from uh 1% to 0.

94:321% of folks that start jujitsu get their black belt.

94:35And I wanna I want to know that.

94:37That would be my my question of like

94:40How many people actually make it through from white belt to black belt?

94:44Um how long does it typically take?

94:46There's uh an adage or kind of these the standard is two years in between each belt.

94:52So anywhere from

94:5410 to 12, 15 years is normal.

94:57And I just was like, is it actually normal?

95:00And then with that, the other question that I I really want to know is the the 10,000 hours.

95:05Kind of concept where is it actually 10,000 hours?

95:09Like there's plenty of folks that are in my gym who it's like they come in and they things just click for them.

95:16And then other times it's like

95:18You have to like you're like take your right hand and grab their left hand and you're like they're uh your other right hand.

95:24Nope, nope, they're yeah, it's still your other right hand clicking at all.

95:29You know, yeah, it's an interesting one

95:32Yeah, so that's what I I would just basically do an analysis of of jujitsu and and trends and stuff like that.

95:38And there's some really interesting technology around that as well.

95:41Like, you know, if if you've got unlimited resources during the gymnastics, the world Olympic gymnastics

95:46this year.

95:47They have a four point camera system that simultaneously judges the gymnastics.

95:53Uh I think it's like four of the apparatus.

95:56And what it does is it's not used for scoring.

95:59The judges do the scoring.

96:01But if there's a dispute or there is not an agreement amongst the judges, they call on this AI system.

96:06And the AI system

96:07reduces the movements of the gymnast to a linear model.

96:11So you basically see like a line model of the athlete doing the moves.

96:16And then when you do that, it's actually able to measure

96:19difficulty, complexity, rotations and movements.

96:23And so it you can all confer on some actual data points that say

96:28That looked exceptional, but what is exceptional in terms of numbers?

96:32Oh, it's that they got a seven score here versus an eight score there, right?

96:37Like

96:38And the the benefit of doing this in parallel with judging is that as the judges judge and they use the system, there's a bit of a feedback loop because uh they can now take all the data from the Olympics this year, uh, you know, where everyone's agreed the judging was good.

96:52and then apply it to the AI model.

96:54So now the AI model will go, hmm, there's a little bit of discrepancy between the judges.

96:58What is it this what what what was it that was actually good?

97:01Oh okay.

97:02It's this small factor here that made this move more aesthetic.

97:05So therefore, you know, you can actually measure it.

97:08So that would also be a really kind of good supporting data to to to your journey because I think

97:14It's combat is very um I I I don't know.

97:18Is it fair to say jiu-jitsu is a f is like a it's obviously a sport, but is it a form of combat or is it

97:24Is martial I don't know, it's yeah, like uh we're going deep here, but like what is yeah, what is jiu-jitsu?

97:29Is it a martial arts?

97:30Is it like what yeah, how would you classify it in in the sporting parameters?

97:35Um, so there's no striking or there's no kicking, no punching in Jiu Jitsu.

97:40So I don't think it it like there's typically like folks think of like karate or taekwondo.

97:45That's kind of on one side.

97:47Jiu-jitsu is all grappling.

97:49Um so it is the ability, so it's that's why I was saying like it's judo plus wrestling, so it's the ability to

97:56take your opponent down and then pin them and create some sort of submission where either you're trying to like push a joint in a direction it doesn't want to go or um

98:07Restricting bug flow so they will pass out.

98:09You know, like those are the options um so you so you get a tap out.

98:14Yeah, fair enough, fair enough.

98:15So there is a little bit of um

98:19Ah, there's there is a subjective element to that because the thresholds at which someone taps out can vary from individual to individual, like all of those things.

98:29And so

98:31Could you become really good at this because you've had an easier run-up to like getting to where you need to, if that makes sense?

98:37Like because obviously over thousands and thousands of thousands of battles, that that probably gets normalized, right?

98:44There's probably a pain.

98:45pain level that is average for everyone um and you're gonna meet your match one day.

98:49But anyway, I digress.

98:51Lots of um lots of interesting forms of analysis you could do.

98:55I I guess that also leaves it to you to pose a question to the next guest.

99:00So um I typically answer this question now, but then the next guest would answer it next.

99:05So it's kind of a question to me.

99:06But it's also a question to the next guest as well.

99:09So over to you.

99:10I don't know if you have one.

99:12Uh so my question would be what's the most fun you

99:15project you've ever been a part of, whether it's like a really cool impact or a great team or you learn something really cool?

99:22Yeah, it's it's a tough one.

99:24Um

99:26I'd love to say the most fun projects I've been on was because of the technology or the type of analysis I was doing or whatever.

99:33Like in reality, I just don't think that's the case.

99:36I I think

99:38I think you can have fun in projects, even though the use case you're working on isn't necessarily the most exciting.

99:46And so if if I drill into that, the most fun project I work with was um

99:52It was in a I have to be careful because I can't name the client, but it was in a um a company that looks after the UK electric infrastructure.

100:00There's only one, so you can figure out who it is, but that's how I have to describe them

100:04Um and it was actually the team.

100:07It was a team that made it super fun.

100:09Um I was the most experienced person on the project from a tableau perspective.

100:12And the the fun thing was everyone else

100:15was picking up Tableau for the first time.

100:16And it's kind of the classic consulting thing where um I think I was working at Accenture at this time and uh you know everyone staffed in the project was just thrown into it.

100:26And I was just like, how how am I the most experienced person at Tableau?

100:31And everyone was really good and passionate, but I would have thought like you need at least two or three people who with experience.

100:37But anyway, this team, like, we gelled, we we just bonded and it was just fun, even though like we weren't necessarily like, you know, kicking ass necessarily.

100:46We were building something that was pretty much well done.

100:48It wasn't like that innovative.

100:50We could have followed like a manual on how to do it.

100:52Yes, there were some challenges with the with the client.

100:55Yes, there was some sort of unique context, but there wasn't anything that

100:58you know, you couldn't have seen it a user group in a different context.

101:01So it was just the people.

101:03And I think I always go back to people because in the projects where I work solo and I'm really proud of my

101:09technical achievements, there's no one there to see it.

101:11There's no one there to appreciate it, annoying as that may be.

101:14And like and and I'm also not the person to sort of blow my own trump and hey guys did you s can I just say

101:21That project I just did, I just kicked ass.

101:24Like no one says that.

101:25No one does that.

101:26At least at least I don't.

101:27I'm I tried I tried to be at least uh if the client says that, great.

101:30Happy days.

101:32You know, from a from a team perspective, I think when you're in a team, you know when someone's knocking out the park and it's it's easy to celebrate and that comradery can actually help you all reach another level because it's kind of like a good dopamine

101:45Uh especially if you're not feeding yourself the the endorphins as it were.

101:49So I think yeah, I think it was that project.

101:53Um it's definitely not the

101:57It's definitely not the most impactful um thing I've done.

102:00The most impactful thing I've done, I think, would have actually been more recently at Aimpoint, um, working in the insurance sector.

102:06That, you know, I can't go into details again, but nonetheless

102:09That to me was the most impactful thing.

102:11So you could kind of cut this into different verticals, but I think anything either related to working in a great team

102:18or delivering a great outcome would be the sort of two stratospheres that I sort of head to.

102:23But I look I look forward to seeing what the next person uh that I interview answers that question because I think it is an interesting one.

102:31Lots of data analysts learning analytics probably don't think of fun as being what I've said.

102:37They think of it as being kind of

102:39Something more more visibly fun, more like idealistic like oh we went to a cool location or I was being busy in nomad doing work in I don't know some amazing country whilst, you know

102:49Doing my nine to five, but no.

102:51No, I'm I'm a boring old man.

102:53Um uh I enjoy I enjoy great colleagues and good outcomes.

102:57Yeah

103:01So listen, uh Katrina, thank you so much.

103:04Um it's been it's been fantastic.

103:05We've been going for two hours.

103:06Obviously, there's there's been a bit of back and forth and a little bit of editing I'm gonna have to do, but yeah, no

103:11Um it's been really good.

103:12I think lots of people will have learned a ton of great things, not just about Sigma, but also about Tableau as we talk about the context of where Tableau is going and

103:20Yeah, like uh I encourage people to go find you on your platforms.

103:24You didn't mention it much again.

103:25You're very humble.

103:26Data Katrina, this is your sort of YouTube

103:29uh brand as it were and uh you've got the book as well.

103:32Um you've shown people how to access that for free.

103:34Um you're also at aim point so if you're looking to kick ass with Sigma, come to Aim Point.

103:39Obviously I'd be mad not to say that myself.

103:42Um yeah like

103:44I think there's so much great opportunity in analytics and one of the reasons I had you on is because yeah at any point we do have lots of people just talent, super talented everything they do and they are the best in their field.

103:54You with Sigma, I know when I've talked to Luke, the two Luke's at Sigma and various other places at Sigma and I've mentioned you, they're like, oh yeah, love it.

104:02So you are you are the uh you know, you are the

104:06visionary equivalent over at Sigma.

104:08And I I think Sigma's going to be doing great things.

104:10If people are following you, then I'm sure they'll they'll stay in touch with that.

104:12So um yeah, keep in touch with Katrina

104:15Yeah.

104:16Well I appreciate you having me on.

104:18It was a really fun conversation.

104:20Uh I will say it's it's sort of I was telling my husband about how this is a surreal moment for me as I started my tableau or like

104:29When I as I mentioned, I knew that I was never gonna be a televisionary, those sort of things.

104:33And when I started working in Sigma, I saw the opportunity for those things.

104:37And as we've talked about before, I I had this like moment where I was

104:41I'm gonna be the the this person, the this person, the tableau Tim of Sigma.

104:47And so here I am.

104:48Like You're here on the channel.

104:50You are, you've done it.

104:51You've you've closed the loop on the channel.

104:53So that's that's fantastic.

104:56I will say as as you called out, like Sigma's new, there's lots of opportunities.

105:01So if anyone else wants to be a Tableau Tim of Sigma

105:04Please come join.

105:05We would love to hear your voice in the and the community is is growing and I'm and we're trying to to get things moving and growing, but there's always space for for you to come contribute whatever you're interested in.

105:16Yeah, exactly.

105:17Exactly.

105:18Sigma SAM, Sigma whatever's like get involved.

105:22Your name does not have to start with S.

105:24Yeah, exactly.

105:25Yeah.

105:25Like I uh mine was just a lucky piece of alliteration, so

105:29Yes.

105:32Katrina, thank you so much.

105:34And yeah, hopefully we'll be back again soon and we'll talk more about Sigma in the near future.

105:41Sounds good.

105:41Looking forward to it.