AI in BI: Impacts to Power BI, Cognos & Tableau

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Pat Powers
BI Trainer and Consultant
Senturus, Inc.

Pat is one of our most popular presenters, regularly receiving high marks from participants for their subject matter knowledge, clarity of communication and ability to infuse. Pat has over 20 years of experience in data science, business intelligence and data analytics and is fluent across multiple BI platforms. They are a Tableau Certified Associate and well versed in Power BI. An expert in Cognos, their product experience goes back to version 6. Pat has extensive experience in Actuate, Hyperion and Business Objects and certifications in Java, Python, C++, Microsoft SQL.

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0:11
Welcome to this next in the presentation series from Senturus.

0:16
Today, AI in BI, and we’re going to be looking today at the impact to all three, Power BI, Cognos and Tableau.

0:29
So we’re going to get everybody in here and we’re going to take a look at all three of them.

0:33
We got some great demos today.

0:35
We’ve got some great stuff going on.

0:38
So here’s what we’re doing today.

0:40
Good new introductions for those of you who do not know me.

0:43
Then we’re going to talk a little bit about reality versus fiction.

0:47
I’m going to jump into demos and we’re going to do an overview of additional resources.

0:51
And then if there’s any time left, which I don’t expect there to be any today, unfortunately, we’ll do Q&A.

0:58
But again, this is a pretty big one.

1:01
So I don’t expect to have any type of time left at the end unfortunately that we will put stuff out there.

1:07
Don’t worry, not going to leave you hanging.

1:11
Hey, that’s me.

1:14
Pat Powers things that you know about me.

1:17
I’ve been doing this for nearly 30 years of data analytics, data science, data warehousing data, data, data.

1:24
I am certified in multiple products, Cognos, Tableau and I am currently getting my certification and I’ve been working on the tests and stuff this week in Microsoft AI.

1:35
So I’ll add another one to it.

1:38
Getting my Power BI and my Power BI Fabric AI cert.

1:43
Now before we get into it, want to go ahead and do a poll.

1:47
Got to have a poll.

1:49
This can be a pretty scary topic for some folks.

1:52
So what we’d like to know from you is how do you see AI impacting your work?

1:58
I’m going to go ahead and launch that poll.

1:59
I’m going to let it run for about a minute.

2:01
I’m very happy to report that very, very few of you are seeing it as a negative.

2:09
That’s awesome.

2:10
Actually.

2:11
I think that’s great.

2:13
All right.

2:13
So let’s go ahead.

2:14
We’ll stop that and let’s share those results.

2:19
I’m really happy to see that 61% of you see this as a positive thing.

2:25
That’s awesome.

2:26
That’s really, really good.

2:27
Now for the 34%, I think that by the time you leave here today, you will have a better understanding and I think you’ll have a much better understanding of how and where this can be used.

2:43
And I hope that you’ll be in that 64%.

2:47
Thank you all for participating.

2:49
Thank you all for giving me your opinion on that.

2:52
We appreciate it.

2:54
So let’s do a little overview.

2:56
Let’s separate fact from fiction.

2:59
OK, First off, no, the robot uprising is not happening.

3:06
It’s not eliminating your job.

3:09
And mostly because last time I checked, most humans don’t have six fingers or extra limbs.

3:18
Any of you, any of you who have seen AI generated images, you know exactly what I mean by that.

3:26
For some reason, fingers are just that’s it’s like death now.

3:32
It can’t do fingers.

3:33
And oh wait, I think this was supposed to be a giraffe.

3:36
OK, I think it was supposed to be a giraffe, but yeah, maybe, maybe there was a 2 headed trap.

3:46
I don’t.

3:47
But usually we end up with extra limbs, we end up with extra appendages.

3:52
So hey, let’s just get that out of the way.

3:55
It’s not eliminating your job.

3:57
It’s not.

3:59
It really isn’t.

4:01
And anybody who thinks that they can replace all of their workers and humans with AI, No, it’s not happening.

4:14
What it is here to do is it’s here to help, especially in business analytics, OK, especially in the area of analytics.

4:31
What we’re going to see today in all three tools is that these, these tools and these implementations are going to help you.

4:43
They’re going to uncover new insights.

4:46
You’re going to be able to quickly see outliers, anomalies.

4:51
You’re going to be able to see the spiders, George.

4:54
You’re going to be able to find those things really quick with just a single click of a button in most cases.

5:01
And that is what I’m really excited about and excited to show you is that literally with one click of a button, I can learn new things about my data.

5:12
Now, each of the three tools has a slightly different approach.

5:17
At the end of the day, the goal for any of the tools is to help you find insights.

5:25
But some have different strengths, different weaknesses, different implementations, different ways of doing these things.

5:32
So that’s what we want to show you today is, hey, here’s how this best tool best does it.

5:38
Here’s how this tool best does it.

5:41
One thing that is in common to all three of them is that the data is going to need to be prepped and cleaned.

5:50
Hello, Indira.

5:51
I see you on there.

5:52
I’m talking to you next week, aren’t I?

5:54
So I keep an eye out on who’s here and who’s not.

5:58
I love you all, but the data needs to be cleaned and prepped, and I’m going to stress that a few times throughout this now before you ask him the questions, well, what does that mean?

6:11
I’m actually currently in the process of writing up another white paper.

6:15
I’ve been working on that today of AI and structured data.

6:20
What does clean data mean?

6:22
OK.

6:23
So you’re going to hear me say that today.

6:25
You’re going to need clean data.

6:27
Well, what does that really mean?

6:28
So, we’re taking care of that.

6:30
We’re getting you covered there too because that’s what we do here at Senturus.

6:35
So again, each tool is going to focus on a different aspect.

6:40
For some of the tools, it’s analytics.

6:44
For some of the tools, it’s helping you become more self independent, being able to give these kinds of things to a different a different audience.

6:59
Anon asked if we adopt AI tools, will it impact the current tool usability?

7:04
No.

7:06
You can continue doing things the way you’re doing them today.

7:09
OK?

7:10
Anything that when we say data needs to be clean and structured, that it’s really no different than what you would clean and structure your dude to have good reporting, OK.

7:21
These are enhancements to the products.

7:23
They’re not necessarily replacements.

7:25
They’re not going to take anything away.

7:26
And that’s a great question on.

7:28
So thank you.

7:29
They’re not going to take anything away.

7:31
They’re going to enhance.

7:32
And again, I want everybody to just keep that in mind, all right?

7:39
All of them in one shape or another will help you create reports and dashboards and this.

7:45
When you start combining these things, when you start combining self-service with creating reports, what that does, not only does it provide insight, but it opens you up.

7:57
And this is what I love about it.

7:59
The minute that we start getting new insights, people are going to start asking questions.

8:04
And the thing you’re going to see with all of these is the ability to ask things in natural questions.

8:11
Literally anybody who’s used chat GPT or any of those tools where you can just say, hey, tell me this, yeah, the same thing holds true here.

8:23
And you’re going to see me do that.

8:24
You’re going to see me type in natural language, click questions.

8:29
And those natural language questions are going to build me reports, right?

8:36
Everybody and every company would love for every new employee to come in the door and hit the ground running, right.

8:43
That’s the dream.

8:44
We hire somebody.

8:45
We don’t spend 3 weeks trying to get them up to speed.

8:48
We sit them down at a desk.

8:49
We say here’s our data set go, Yeah, with these kinds of enhancements and these kinds of tools.

8:58
Yeah, you can do that.

9:00
Hey, I’ve never worked with your data set before, but let me ask a couple questions of it.

9:05
Let me generate some reports.

9:07
Oh, look, I didn’t need to bother IT.

9:09
Oh, look, I didn’t need to take up anybody’s time.

9:13
I can now make business decisions.

9:15
So you’ve now got a broader, wider audience in your organization able to get to data themselves, make decisions faster and smarter.

9:25
My goal for the last five years doing this is to take the burden off of IT.

9:31
Anything that I can show you, anything that I can point you towards that helps make IT job easier.

9:39
Oh, I’m there.

9:41
And I also like putting power in the hands of the people.

9:44
I want Donna and HR to be able to build her own reports every single day.

9:50
And with this, we’re getting there.

9:54
We’re getting there Gerald.

10:00
I’m going to talk about.

10:02
So Gerald had a comment about the cost and the price tag.

10:06
I’m getting to that.

10:08
I think that price tag that you’re showing that you’re asking about is a little high.

10:15
But without knowing your entire infrastructure, I don’t want to say that it’s wrong.

10:20
But they’re yeah, some of these do have a cost factor.

10:25
I do.

10:27
I do have to say that.

10:30
But yeah, I don’t know about that price tag though that that seems a little sketch.

10:40
That’s why you should talk to us at centuries.

10:42
By the way, if anybody would like to get on to our calendars, if anybody would like to talk more about this, when we’re all done here, I’m going to post a link in the chat window you can get on our calendars.

10:55
OK, for a 30 minute meeting, Gerald, I would highly recommend you do that and get another look.

11:03
You know there is a cost, but that’s a little scary and I don’t blame you for being cautious.

11:14
So here’s the pros and cons.

11:17
Just like I said, one of the cons, the pros, we’re going to have new insights, we’re going to get self-service Anon as what you were asking, these things are already built into the tool.

11:31
They’re ready to use.

11:32
There’s not, it’s not going to take away functionality, It’s not going to get rid of functionality.

11:39
They’re there.

11:40
Is there a cost involved?

11:42
Possibly some tools are going to require additional set up.

11:46
There’s also some other things for like the Cognos stuff that you have to do to your packages.

11:51
There’s not necessarily a cost involved in that, but there are some set up steps.

11:56
The big issue for most people though is going to be data prep.

12:00
OK, a lot of people struggle with having data that’s ready to be used for reporting.

12:06
This is no better.

12:07
This is no different.

12:09
OK.

12:10
This is no different.

12:12
You’ve got to have good data.

12:14
You’ve got to have clean data.

12:16
And of course, not everybody’s going to see the positives.

12:19
You know, there’s going to be some folks who think the Earth is flat, AI is taking their job.

12:24
And you know, and yeah, we just have to deal with that, right?

12:31
We just have to deal with it.

12:35
But look, let’s talk about the data thing one more time.

12:40
Not all of your data sources are going to lend themselves well to the AI tools.

12:45
And you might have duplicate data, you might have null values, you might have name issues, naming issues, and you’re going to see that one of the data sources I’m going to use today.

12:55
So your model and your source data, this data cleanliness, it’s up there at the top of your list.

13:03
Get on our calendar, talk to us and see if we can help you get your data situated.

13:09
In the meantime, start with a small data set because there is a cost involved for some of these tools.

13:15
You might want to also roll this out to a small audience, Gerald, that may be part of what that cost, that price tag was for.

13:22
If they’re saying we’re going to roll this out to 1000 people and it’s going to cost you X dollars, well maybe you should roll it out to 10.

13:31
OK.

13:32
The other thing too this is software people.

13:38
At the end of the day it’s still software.

13:41
Don’t think you’re going to drive your business solely on this yet.

13:45
You’re not actually Jared saw some of these do offer multi languages.

13:52
Some of them do have the ability to do multi language.

13:57
A lot of that depends on your overall set up and what the rest of the tool is set for.

14:04
OK, so great question.

14:05
The question was are questions only allowed in English?

14:08
It’s going to be tool specific and environment specific.

14:12
But the short answer is no.

14:14
Some of them do multilingual.

14:16
Before we get into the demo, look, don’t expect good results from bad data.

14:23
I’m pretty confident that every single one of you out there knows what GIGO means, right?

14:30
Hey, and we’re talking GIGO in this.

14:34
It’s not a replacement, it’s an enhancement.

14:38
OK, and dig deep in your tool.

14:43
If you have Power BI, dig deep in Copilot.

14:48
If you are in Cognos, dig deep in explorations.

14:52
Learn your tool.

14:54
OK, here’s what you all came for, right?

14:57
You want to see this in action?

14:59
Now before I show you Roy, I’ll show you something.

15:01
I want you to understand just how much companies are investing in this Just five hours ago.

15:10
Six hours at this point.

15:11
Now, just six hours ago, Google announced that the Google Assistant Gemini now has an AI model built in.

15:24
This is this is literally breaking news from six hours ago.

15:28
OK?

15:29
They are unleashing their full version of their next Gen.

15:33
AI model.

15:35
All right, so this just shows you this is not this is not 3D television all over again.

15:43
This is the Internet people back and Internet thing.

15:48
You kids, it’s a fad.

15:51
This, this is around here because, you know, do you all remember 20 years ago when we all got Microsoft keyboards, or we got keyboards and they had that Windows key on them and we all looked at that Windows key and we said, who’s going to ever use that?

16:06
And now, now you use it every day.

16:08
Windows L, Windows M, Windows N you’re using it constantly.

16:12
Well, guess what?

16:13
At CES this year, Microsoft and a number of other vendors introduced keyboards with Copilot keys.

16:23
OK, Window 11 PCs, Copilot keys built into the keyboard.

16:32
Who is just like, in awe of that.

16:35
So you know what?

16:37
We all got used to that Windows key on our keyboard.

16:39
Who knows how we’re going to get used to this in 10 years.

16:41
But the point things I’m showing you are not fly by night.

16:45
The things I’m showing you and talking about today are not they’re not.

16:50
Smell O vision.

16:51
And if you’re not old enough to remember, smell O vision.

16:53
Be glad this is going to be here for a while.

16:59
You’re going to have a keyboard with a little doody on there, little Copilot key.

17:04
So that’s where we’re going to start.

17:06
We’re going to start with Copilot, right, Derald?

17:14
But the Windows key, the Windows key works.

17:17
The other thing that I want to show you all what you’re about to see me do in Microsoft, there are some requirements.

17:25
What I’m going to be using is copilot, OK, I’m going to be using copilot and I’m going to put that link in here for everybody.

17:32
Those are your copilot requirements.

17:34
All right.

17:36
So now I’m going to come on here, I’m going to come in here to Power BI.

17:42
I’ve got a package open and in this package I’ve got some sales numbers.

17:48
And you know what I want to know.

17:51
I want to quickly know what’s going on here.

17:55
One of the things that we can use copilot in the desktop app is being able to generate measures for us and generate DAX for us.

18:10
Look, we all know that to get the most out of Power BI you need to learn DAX.

18:16
You do, I stress it all the time in my classes.

18:20
You need to learn DAX.

18:21
But DAX.

18:22
DAX is a programming language and not everybody wants to be a programmer.

18:26
So guess what?

18:26
I’m going to click on quick Measure.

18:29
So I just clicked up here on Quick Measure and look at this when I do a Quick measure now suggestions with copilot, I know you’re all going, oh ah yeah, you are.

18:44
So I’m going to sign in.

18:45
It does require me to be signed in.

18:49
Come on.

18:50
Hello.

18:53
I did.

18:57
I am signed in.

18:59
Thank you.

19:00
Don’t you do this to me.

19:03
You do this to me as did you this morning trusting while I’m reopening this I don’t know what is or isn’t available for government cloud.

19:22
That is a question that I unfortunately there we go that I unfortunately don’t have an answer to.

19:27
Sorry.

19:32
Yeah, right, Mario, it’s not going to take our job some days.

19:37
It’s still software people.

19:40
But look at this when I click on suggestions with Copilot.

19:43
So you know what?

19:44
Hey, can you please compare 2019 sales to 2018 sales?

19:58
And what it’s going to do is it’s going to look at my data set and look at that.

20:03
Look at that gang.

20:05
Not only did it give me a preview, it told me what it did and it wrote the DAX for me.

20:15
There’s my, oh and the ah, OK.

20:17
It wrote my DAX for me.

20:20
And if we were to actually create a visualization, if we were to build something that that emulated this and we were to throw 2018 and 2019 sales on here and we were to turn on data labels, we would see that it is about 64% of my 2019 sales, it really is.

20:48
But the nice part, it did the DAX for me, right?

20:53
Thank you, Mario.

20:54
That is pretty cool.

20:55
One click and I used natural language, but this is just the start.

21:02
This is literally just the start.

21:05
OK.

21:06
We can also do new Q&A visualizations.

21:10
So if I add in AQ and a visualization, where are you?

21:18
There you are.

21:19
Thank you.

21:22
So First off, I can use.

21:25
I can do what’s known as AD synonyms.

21:28
The AD synonyms is nice, it takes a minute.

21:30
So I’m not going to necessarily do it today.

21:31
I don’t want to run out of time.

21:33
But what it does is it allows you to see things.

21:36
So over here, my dim customer for example, I have birth date.

21:39
When you generate synonyms, it allows someone to type in date of birth, birthday, et cetera, and it knows that that all equates to this field.

21:52
Now you can manually do your own synonyms or you can allow it to create synonyms, right?

21:59
So I can sit here and I can ask that same question.

22:05
I can say, compare 2019 sales to 2018 sales in a column chart.

22:19
Look at that literally as I’m typing, As I’m typing.

22:30
It’s building this out for me.

22:32
And that gives me suggestions.

22:33
I can do it in a map.

22:34
I can do it in a matrix.

22:36
I could show more it was typing it out.

22:43
So I got one question coming in that what am I missing on this?

22:48
What is?

22:54
Oh, the Report Server Desktop again, that’s the question I don’t necessarily have an answer to Andre.

23:03
I’m sorry.

23:03
If somebody could look this up in the background while I’m working and answer Andre on that.

23:08
If this is if copilot is available in the Power BI report Server Desktop, Andre, we’ll get you an answer, OK.

23:18
The last update on that was like November I believe, so I’m not sure or September, I’m not sure what’s available in there.

23:26
OK, gang, again I typed that, eh?

23:32
How cool was that?

23:33
And hey, can it help me build whole reports?

23:38
You bet it can.

23:41
Andrew, you smarty pants, don’t change that dial.

23:44
Andrew, There’s more coming, eh?

23:48
Let’s go into the service.

23:51
Bippity, boppity boop.

23:52
Here we are in our service, eh?

23:58
Now here’s where those prerequisites come in.

24:00
You do have to have an F64 capacity, Gerald.

24:04
That’s where the cost in Power BI comes in, is having to have an F64 capacity, right?

24:13
But again, I think that price tag was a little scary.

24:16
So here’s what I’m going to do.

24:18
I’ve got a copilot workspace set up and in that workspace I’ve got a semantic model and I’m going to use this semantic model and I’m going to go ahead and I’m going to create a report.

24:40
Notice that here, right here, up the top I got a copilot button.

24:47
And Andre, somebody is looking at that right now for you.

24:52
So when I click copilot, I get a new pane and I’m going to say, hey, you know what, I’m.

24:59
I’m not sure what I want to do with this data set.

25:02
I’m not sure.

25:03
Can you suggest some content for this report?

25:07
Help me out here and it’s going to look at my data set.

25:12
Hey, it’s looking at it, Banu.

25:17
Not just a premium.

25:20
Premium in an F64 capacity.

25:23
Take a look at that link I put into the chat window.

25:26
That’ll give the full details.

25:27
OK, Bonham, there are definitely some prerequisites here.

25:34
OK, look at this.

25:37
OK, here’s a suggested outline for your report.

25:40
It’s giving me one on customers, one on product inventory management and my forecast.

25:49
Well, you know what?

25:50
Hey, why don’t you show me this one on customer segmentation?

25:55
OK, Bippity boppity Boo.

26:06
Come on.

26:08
This is also why you want to start with a small data set.

26:13
You don’t necessarily want to go against the 10 million row data set.

26:26
So what it’s going to do is it’s going to give me a page that’s going to basically show me all this demographics, income, education, purchase patterns.

26:33
Come on, please go a little faster.

26:35
These people are waiting.

26:36
Thank you.

26:37
Look at that, ladies and gentlemen.

26:40
Look at that.

26:44
Come on, that is so cool.

26:47
It gave me 7 visualizations.

26:51
Slicing out my customers by gender, by marital status, education, occupation and look at this.

27:02
It even put in two slicers for me so I can look at this for a particular year.

27:08
OK, I can look at this for a particular product line.

27:12
Bam.

27:16
Tell me that’s not cool.

27:20
Bam.

27:23
How neat is that?

27:25
But here’s where it gets even neater.

27:28
All right, what?

27:28
What does all this gobbledygook mean?

27:30
What does any of this mean?

27:32
OK, check this out.

27:35
We can add in a narrative.

27:39
We can add a narrative on here and with copilot for narratives, I can say show me an executive summary.

27:52
Yeah, let’s just do show me an executive summary.

27:57
And when I create that, ah, the Mario, somebody had to type that in Jack that kind of fine tuning.

28:14
So Jack’s question is can you do follow up requests?

28:16
You can once you start getting in there and you start doing saying that’s kind of what I’m doing here.

28:22
So when I do the narrative and I say show me an executive summary, OK it’s going to build me this look at this, gang.

28:30
Look at that and it even references which it’s telling me which chart it referenced to give come up with these numbers.

28:37
So Jack, I could go even more fine tune this.

28:41
I could say OK this is pretty good focus on education for 2018.

28:51
So I can as I start to as I start to learn my own data as I OK, so there’s content.

28:58
All right fine.

28:59
Be picky like that.

29:00
Let’s just.

29:02
I got a little tricky there.

29:03
I added in the four 2018, but as I learn more what to ask it, there we go.

29:09
So look at this when I say show me executive summary, focus on education.

29:14
Now as far as adjusting the size and formatting, Jack, no, not necessarily.

29:21
I could say do it in a column, do it in a pie, but I’m going to have to do some manual tweaking and that that’s why, you know, Mario, they still need us around to be able to do that kind of stuff.

29:30
But I can get my data to a finer point and I can get my results to a finer point.

29:36
Tell me that’s not cool.

29:38
People look at that.

29:39
When I say show me an executive summary, focus on education.

29:43
It doesn’t, it doesn’t exactly what I asked it to do.

29:48
Now I can take this, I can distribute this, I can get this into the hands of my entire organization.

29:56
I can look at this and I can say, hey gang, what do we need to do as a company?

30:01
Because here’s what we’re seeing.

30:06
What do we think, gang?

30:07
Is this?

30:07
Pretty nifty.

30:11
So that’s Microsoft’s OK, let’s move on to Cognos.

30:17
Let’s take a look at Cognos, IBM, Cognos Analytics with Watson.

30:22
Now, Watson has been around technically since the 70s.

30:26
Watson has been out there back when it was Big Blue, when it was playing chess and when it was beating Ken Jennings on Jeopardy.

30:38
They’re integrating it more and more and more each day.

30:43
OK, so they’re integrating this tighter and tighter with each release.

30:47
In version 12, we see that their assistant, the Watson assistant, is now available anywhere.

30:55
It’s right here on the homepage.

30:58
But here’s the cool part, man.

30:59
Anon, I’m going to go back to your comment.

31:03
This is already available today.

31:05
This is in the tools you have today.

31:07
This is an 11.2.

31:10
OK, this is an 11.2 system.

31:14
So Lawrence, yes and no.

31:17
I think when you see what I’m about to do, you see that there’s a lot of similarities.

31:21
Where the difference is in copilot and Power BI.

31:24
I got to set up my capacity, I got to set all that up here, I got to enrich my packages.

31:31
But otherwise there’s a lot of similarities here.

31:34
One of the things that IBM has is that there are more analytics going on in the background.

31:41
It’s leveraging its 50 plus years of analytic experience and what I’m going to do and again I’m doing this in a in an 11.2, I’m going to create an exploration, all right.

31:54
And I’m just going to use good old everybody’s favorite, which we’ve all been using for 20,000 years go sales query.

32:04
OK Sundar asked if this is safe to work with proprietary information.

32:15
Yes, OK And a lot of these companies have policies available on their website that explain it.

32:24
I’m going to give you a link to Tableau in a minute with where their stuff is.

32:28
They are very open about their security on the LLMS and if you dig on Microsoft site and Cognos site you will also see their policies on the LLMS and everything else.

32:41
The security is going to be as secure as any other thing else you would be doing.

32:49
Varun again.

32:50
I’m currently writing another white paper on how the data should be structured, but your starting point is I would want my data structured the same way I would want it structured for a good reporting environment.

33:05
OK, I want good facts and I want good dimensions at a start.

33:12
But with AI there’s a little more nuance in terms of naming and cleanliness.

33:18
And again, I’m currently writing another white paper on that that goes more into what do you need to do and what do you need to look out for.

33:26
OK, Varun.

33:28
So when I do an exploration here in Cognos, I start out very similar.

33:32
I can start with something.

33:34
I can start with a suggestion or I can go ahead and I can ask a question.

33:41
And if I just say, hey, you know what, I’m going to skip that and I’m going to let you do it.

33:46
I’m going to let you go out there.

33:47
And Cognos, I’m going to let you apply your analysis and you’re going to tell me what has the most impact in my data.

33:56
What do you see is of the most statistical relevance?

34:00
OK, Indira, the one for this has not been put up on the on the website yet or it may be in time for today.

34:07
I’m not, I’m not 100% sure, but if it is up right now, it’s at Senturus.com/resources.

34:14
OK, you’ll find all of our stuff there.

34:20
Senturus.com/resources this is what I do.

34:26
I write, I publish and I do this.

34:29
So look at this gang, what it is says, it’s looked at my data and it has said the piece of data that has the most impact that impacts your day of the most is sales target.

34:39
And over here it’s showing me my sales target and saying, hey, here’s my sales target by record start date, here’s my sales target by this.

34:48
And if I say, OK, you know what, that really doesn’t tell me much.

34:52
You know, instead, I really want to see the order value, what impacts my order value and it’s going to go, OK, here’s what impacts order value.

35:02
Now here’s what we’re talking about, clean data.

35:05
Varun, take a look at this.

35:07
You guys should be able to see where the problem is.

35:11
You should be able to see very quickly what’s a potential issue.

35:15
The fact that I have tables with the same field names, these are coming from different tables but because everything is named the same and because of the way things are joined I get my duplicates.

35:31
Luckily in here I can also adjust this and I can say you know what, show me the things that have the highest impact.

35:40
So this has a 27% to a 75% impact on my order value, the product cost, which is thicker and the quantity.

35:50
So that is a 33, that is a 68.

35:53
OK, so let’s go take a look at this.

35:57
With Cognos.

35:59
I can get, again, I can get more statistically deep here.

36:03
I can see my exact things, my drivers.

36:07
What is actually driving these numbers?

36:10
What is my predictive strength on this?

36:13
So the combination of product cost and unit price is a predictor of order value.

36:18
So here I’m getting more of that insight.

36:22
Here I’m getting more of that in depth and depending on the type of visual visualization I’m looking at, I can generate more of those analytics.

36:32
I’ve got more details and the nice thing is that I can do my own stuff here.

36:37
I can create my own visualization, I can create my own comparisons.

36:43
And the coup de grace if you will, is the assistant.

36:53
The assistant is where the big strength comes in, again, showing us insights, showing us what’s going on.

37:02
Just like Copilot, I can use natural language.

37:05
And you know, I’m going to say, hey, I don’t know what I want to do here.

37:11
Why don’t you suggest some questions for me?

37:15
Suggest questions?

37:18
You tell me, tell me what I should be looking at in this data and look at that.

37:25
All right.

37:25
You know what?

37:25
Compare sales.

37:26
Target gross profit by product line.

37:28
Oh, that’s kind of cool.

37:32
And look at that.

37:33
It says, here you go, here’s your gross profit compared to your sales targets by your product lines.

37:38
Would you like to see a whole dashboard on this?

37:40
Come on, raise those hands if you want to see a whole dashboard on this.

37:45
I know you do, right?

37:49
Look at you all go, beautiful.

37:52
Here we go.

37:53
Boom.

38:00
How’s that?

38:04
This created an entire dashboard for me, which I just messed up because I’m an idiot.

38:13
Don’t use your mouse.

38:13
Wheel over that idiot.

38:16
It created an entire dashboard for me.

38:19
I’m going to reset that real quick.

38:26
Come on, come back here.

38:27
Create Dashboard.

38:34
There we go.

38:34
Now I’m going to scroll this time and look at this.

38:42
Look at this.

38:42
And it also created two additional tabs.

38:45
Here’s a tab that shows me this.

38:52
So Mario, there is a concept of synonyms in Cognos just like there is in Power BI.

39:00
More has to do with the modelling and data modules, and you’re set up in there.

39:05
More manual work, but you can’t have that same concept as synonyms.

39:14
Here’s the here is the data based on my product lines.

39:18
So again, generating reports automatically for us.

39:23
Literally one click and I’ve got this and I can now distribute this to the rest of my organization and I can say, hey gang, let’s sit down and talk about our profit versus our sales targets and let’s see how we did per product line, what’s going on in our different product lines.

39:43
Here’s our, here’s our breakout by day of how we did it all depends on what we want to ask it.

39:49
So Beth asked how does this work with higher Ed.

39:51
I’ll tell you what, I’ve been looking at this for retail health insurance, on healthcare and insurance and higher Ed.

40:00
And when you’ve got a good data set, I can say show me my FTE, show me my student students that that dropped, show me my three-year, five year retention.

40:15
If I’ve got that data, it works just as great.

40:20
Gerald question.

40:21
Most of our users use Power BI with recording tables, blah blah.

40:25
OK, perfect.

40:26
Yeah.

40:27
When alternating cognitive use data modules have table relationships.

40:30
Yeah, this will work just fine.

40:33
It’s a matter of having clean data.

40:37
So, Beth, it’s going to work fine with higher Ed data if my data is set up.

40:40
Well, Gerald.

40:42
Yeah, if you’ve got good data modules, even if they’re in an aggregate level and you’ve got good relationships, that’s a lot of it.

40:48
People is the relationships and everything else.

40:51
Lawrence, you should have come to my last presentation, which was on sharing these things.

40:57
And the answer is yes, you can publish these things out to SharePoint.

41:02
You can share them out to different things.

41:06
Yes, you can.

41:07
You can drop them into Teams here.

41:10
I can send out into Slack and I can send out into Teams and I can embed it into SharePoint.

41:14
So yes, the answer is it’s very easy.

41:18
And that’s one of the big things here gang, is being able to share this information, share this with a wider audience with a single click of the button.

41:28
OK, Beth, yes you this is using an FM package.

41:33
You can also use data modules for this, but just like in Copilot, where I can do Q and A narrative, I can do a narrative here and I can get my narrative insights.

41:45
So again, insights, Simon, that’s what I’m going to wrap up with on the Cognos stuff.

41:54
So thank you.

41:54
Cognos said what’s going on?

41:56
Simon asked what’s new in 12.

41:58
Basically with this, the big difference Simon, is the ability to do this from anywhere.

42:06
OK, to be able to do this with from anywhere.

42:09
Mario, no, you’re going to have issues doing this with old lab sources just because of the way they’re structured.

42:16
Sometimes it’ll work, but I would check your results.

42:20
So take a look at this Simon.

42:24
In 12.

42:25
I don’t have to, I don’t have to go in to an exploration.

42:35
I can do it straight from here.

42:37
I can select the data source straight from here.

42:47
Sales and Marketing one should work.

42:55
Show list of relevant fields you might find useful.

42:58
Look at that.

43:02
So I can do this right from the home page.

43:06
What do you think of that Simon?

43:08
You like that so I can show influencers and I don’t have to go into there dependra.

43:23
You can upload excel files into Cognos and do this.

43:26
You would have to turn them into a data module, but once they’re in a data module you absolutely can.

43:37
Terrance, there are plenty of places where you can download industry standard data.

43:41
Cagle is one of a good place.

43:44
Google Data has a good one where you can get those types of things where you could do a comparison the side by side.

43:50
Especially here in Cognos where I can do it as an exploration and I can’t actually do a compare visualization and I can build 1 visualization off of one data source and build the other off of a second data source.

44:03
So I can absolutely and explorations do a side by side comparison, Indira, no modules, basically anything I can bring in from here.

44:15
OK, so a data module package etcetera.

44:20
But Simon, as you can see, this is what’s new in 12 is that I can do this right from my homepage and I can share it.

44:29
I can add it to a dashboard.

44:32
I can tell where I want it to go.

44:34
I can look at this.

44:34
I can create a new dashboard.

44:39
Mario, no, what we’re seeing here is more on this, giving you insights.

44:45
Again, remember I said that each of these has their own strengths and weaknesses.

44:51
So with power BI get the ability to do measures as well.

44:57
Here I’m focused on the statistical analysis and the generation of reporting.

45:02
MO, hi, MO.

45:04
Yes, I’ve got to get.

45:05
I got 9 minutes to get to Tableau.

45:07
So Tableau, I’m going to wrap up with Tableau here again.

45:11
OK.

45:12
Tableau is handling this a little different.

45:15
Tableau is doing something a little bit different.

45:18
And what Tableau is doing is they have two implementations of this.

45:22
One is for Tableau Cloud, which used to be Tableau Online, so that they’ve changed the name, same product.

45:29
And then there’s Tableau accelerators in the desktop.

45:34
Tableau Accelerators are what I’m going to be able to show you here today.

45:38
There is a great, great demo on Tableau’s site.

45:42
And I’m sorry for all of you who came for Tableau stuff.

45:44
I’m sorry that it’s overcoming here at the end.

45:47
There’s just a lot here, but MO and anybody else who wants to come for more on Tableau, I am doing a chat with Pat on this March 20th, I believe OK, where we’ll have more time gang to ask more questions, one-on-one.

46:06
So MO, if you’ve got time on the 20th and March, we can come and we can do this one-on-one.

46:12
So if you go to there, there’s a great demo on Pulse there which gives what they’re doing is they’re creating this next Gen.

46:18
experience layer which sits on top of an Insights platform, which sits on top of a metrics layer.

46:25
Willie Ice, really impressive.

46:26
And they have a lot of good information about their LLM usage and how the LLMS are affected, including using third party LLMS.

46:36
If you use AWS, if you use COHERE, anything within Salesforce’s infrastructure, they are they take that trust layer very seriously.

46:46
So the earlier question about security, they’re on this, they absolutely know that it’s an issue.

46:53
So let’s take a look real quick before I let you all go at how the accelerators work here in Tableau.

46:58
So you can see I’ve got a a data set loaded.

47:00
This is a saved data source that I brought in.

47:03
Nothing exciting here, just a sample superstore with a little bit of some custom fields, some joints, things like that.

47:11
What I’m going to do is I’m going to say, hey tell me what you see in this.

47:15
So I’m going to go up here to the accelerators.

47:19
One click, let that sink in.

47:27
Bam.

47:29
With one click it generated multiple dashboards.

47:39
It looked at my entire data model.

47:42
It even gave me a what if a what if Commission page.

47:47
I can see how does my sales get affected if I change my Commission rate.

47:52
You know what if I give them only 9% Commission, how does that impact my profit?

47:58
What does that do to the compensation level?

48:00
So it gave me a what if it gave me my order details.

48:05
And you notice over here on the side, it’s over here doing stuff on the side.

48:09
What it’s doing is just like we saw on the other two tools.

48:14
It’s giving me insights.

48:17
It’s giving me narratives.

48:19
It’s saying, hey, take a look at this.

48:23
Alejandro Grove on 9/13/2022, they had an unusually high ship date.

48:31
What happened?

48:32
Let me tell you what happened.

48:35
Here’s what happened.

48:36
Oh, they had 10 records, so they placed ten orders on that day.

48:41
And that’s what caused the issue.

48:44
And all those were in the category of office supplies.

48:48
And those office supplies got stuck in the Suez Canal.

48:51
I don’t know.

48:53
And I say, oh, show me that and it’ll show me exactly what happened that day.

48:59
Here’s what happened to poor Alejandro Grove.

49:04
So again, being able to use this data guide, being able to see what’s going on, being able to see these outliers, being able to look at the different data that’s in here, this is pretty cool.

49:20
Just like we saw in Copilot, just like we saw in Watson, a single click generates me a full suite of information.

49:32
If you never have worked with this data set before, with one click you can uncover this.

49:40
Tell me you’re impressed.

49:42
Tell me how cool that is, gang.

49:47
Andrew, you are sitting on the edge of your seat, aren’t you?

49:51
I know you are, buddy.

49:54
Curtis.

49:54
You too.

49:55
You’re just sitting there on the edge.

49:56
And then MO MO’s always impressed.

50:03
All right, I love you all.

50:04
I hope you’ve enjoyed those demos.

50:06
Let’s wrap this up.

50:07
Let’s get back to this last part.

50:10
In summary, as you saw, those of you who came and saw this morning who didn’t know how you would use it, all right, it’s here to help.

50:22
It’s here to help.

50:23
Kamini, check out that link I put in there for the AI Pulse demo that has the information on the Tableau cloud and what version you can use.

50:32
Dave, you have a set of tables from Snowflake?

50:36
Have no idea.

50:38
I you and I’d have to look into that to see why Copilot can’t read from Snowflake MO.

50:44
If I can connect to the data source, it can connect to this.

50:48
Yes.

50:49
Indira, thank you very much.

50:51
Don’t leave yet, gang.

50:52
Don’t leave yet.

50:55
Don’t.

50:55
You can’t get out of here yet.

50:56
Look, don’t rely on this to do your job.

50:58
A lot of the questions you’ve been asking, hey, there’s still manual stuff that needs to be done.

51:03
It’s here to help you do your job faster and more efficiently, but it’s not going to take your job away.

51:08
They’re not coming for your germ, I promise you, OK?

51:12
You got to make sure your data is clean and ready to be used, Make sure that you understand what your tool can do.

51:20
You saw this.

51:21
You saw me do different things.

51:23
You saw the overarching was that they all can provide insights.

51:28
You saw that they can all get us to reports and to dashboards, but some have more an analysis, some can generate measures, some can generate what ifs.

51:39
Everything’s got a little bit different strength and weakness.

51:41
We’ve got our in person Fabric Workshop.

51:44
We’ve also got our comparison of Microsoft analytics tools.

51:46
We’ve got some upcoming things sign up on our website.

51:52
If you go to Senturus.com/resources, all sorts of good stuff.

51:56
When am I going to be talking again?

51:58
I don’t know.

51:58
Go to the website and find out.

52:01
Hey, I do know that in a few days we’re going to have one obtaining unit economics using Power BI.

52:10
But there, March 20th, you get me again.

52:13
That’s one of our Q&A sessions.

52:15
For those of you who never done the Q&A, they are totally open-ended questions.

52:19
We just sit and talk for however many hours you need.

52:22
If I got to sit there for two hours, we’ll sit there for two hours.

52:25
But do come on February 22nd.

52:27
Come, listen, that won’t be me, unfortunately.

52:29
But I’ll be there.

52:31
I’ll be there in the background.

52:32
I’m always there.

52:34
I don’t have a life.

52:35
I got nothing else to do.

52:37
Hey, MO, you and I can talk about that offline.

52:40
If you want to send me an e-mail, I’ll give you some information on that because I’m in the process of it right now.

52:46
Register for any of these at Senturus.com/events real quick.

52:50
About Senturus.

52:52
We have a full spectrum of analytic service.

52:54
You saw me do AI today.

52:56
We can help you get your data set up.

52:58
We shine in hybrid environments.

53:00
We have connectors that allow you to move from Cognos to Tableau to Power BI to all of these things.

53:06
OK, we’ve been doing this a long time.

53:09
No matter how big or small your project is, we got it.

53:12
We can help you out here.

53:13
We’ve got everything covered again.

53:16
We’ve got migration tools.

53:17
We’ve got connection tools.

53:20
Get on our calendars one last time before we leave.

53:23
I’m going to put the calendar link into the chat window for you.

53:27
OK.

53:27
So get on our calendars, have a 30 minute meeting.

53:30
Talk about us, talk to us.

53:34
Talk to Kay.

53:34
Kay can help you out.

53:36
We’ve been doing this a very long time, over 23 years, 1400.

53:41
Folks, a lot of you were here today and we thank you very much for being here.

53:46
If for some reason I missed a question, which I don’t believe that I did Anon, I know I ran out of time, I’m sorry, please come on the 20th of March and we can talk more about that in depth, OK?

53:58
I promise you that is the one that I feel the most guilty about.

54:03
But we will talk more about it on the 20th.

54:06
And again, if for some reason I missed a question [email protected], check out our website.

54:13
And with that three O clock on the dot, Thank you all very much for coming today.

54:19
You’ve been wonderful.

54:21
Wonderful as always.

54:22
Please thank you.

54:23
Thank you so much.

54:25
Have a great weekend.

54:26
Have a great tomorrow.

54:29
I hope you all enjoyed this.

54:31
I hope everybody learned something.

54:32
I hope you had a wonderful time.

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