Session Information:

Data-driven approach -Uniting the processes around digital transformation, technology excellence and business priorities

The world's data has exploded, reaching 64.2 zettabytes in 2020. A plethora of technologies store, structure, disseminate, analyse and bend this information to the shapes, we humans value. The language of business is defined in it's data, true "master data management" requires a paradigm where "data entities, attributes and relationships" are "First Class Citizens". What the heck are "First Class Citizens", don't we have enough border control issues!? The What, Why and How, accompanied by light hearted humour are all revealed in my workshop...tune in.

Session Transcript:

I'm very excited by our next guests. He's coming from the UK. We just came from Basel switzerland, and now we are back at the UK, and the, we have a cache. But, Thal here with us, I'll do a brief introduction on Cash, who has a wonderful background. And, he will talk more about that during his presentation.

He is an avid technologists my mind, and though a people person, at heart, he's passionate about innovation, coding, and engineering geek stuff.

He is on the turnout students and knowledge sponge and a bad dad joke teller. So, I could not ask for more cash. We're so excited to have you here with us. Thank you so much for sharing your wisdom and expertise with our global audience today.

Thank you so much Josie 64.2 zettabytes.

Can you imagine how big a number that is?

Well, I can tell you, it's 64 with 21 zeros behind it, so large, that if you were to stack these USB sticks on top of each other, they'd go to Mars and back.

Well, actually, I kind of made up that data because I couldn't be bothered to 21 zeros in Excel, but I think it's pretty large, anyway.

So welcome to my talk data as a first class citizen in the enterprise.

So I'm going to share my screen, now, but hope you can all see that.

So I'm Rackers and it's customary, I believe, for us.

At least two, Introduce ourselves.

Sorry, I think something that I think I'm back getting right. So it's customary for us to introduce ourselves. So I'm Rakesh Patel.

And I'm an Enterprise architect by training. Although I do a number of other things, I'm an angel investor.

I've had a couple of entrepreneurial endeavors myself.

And I'm a board advisor for a number of companies and med tech company, finance company, and a couple of others. I also really enjoyed development, or c-sharp, Java, Python, and C plus plus sort of guy.

I do enjoy dabbling in databases.

I very much rebel in all of my badges, I hope you can see them all there.

There's so many of these qualifications of the mast.

So you would think with all those qualifications, that ought to be perhaps a little bit more diligent with my advice.

But my typical day as a technologist looks like this, not a cut and pasting from Google, a small amount of data science, and stacks, and all that kind of really intelligent stuff and some basic arithmetic.

I'm always on digital advisory boards, and this is how it looks.

Generally, you're kind of going with it, CD, and so, this is how we get to the clouds, joking aside.

I've had 26 years of privileged life in technology, so I've worked in many industries, aviation, A banking in commerce, e-commerce, that is also a couple of stints.

Dominos Pizza, for example, which is a little fun, and also, in a number of other retailers, like Tesco's, in, in, in England, which is a bit like Wal-Mart. So, we've had a large breadth of experience and somehow, that kept me on bullets. I'm not sure what's going on and say, What are we going to talk about today?

We're going to talk about data as a first class citizen.

I'm guessing some of you out there thinking, well, what is a first class citizen, What I can do with data? I'm going to hope to demystify some of that.

I'm also going to share with how we can implement some of the things that I'm going to hopefully influence you all and present that there could be good ideas for the enterprise.

Now I've tailored this presentation not for developers.

Even though I really enjoy if statements and loops and c-sharp and C plus plus, I'm going to make this a little bit more palatable for just the general population out there who understands enterprise.

And of course, it's not to bring this a little you really intelligent and sophisticated people, but just to make it more palatable for us. I'll tune it. So it's not just for developers.

Now, this is the biggest problem that's out there. I started the industry back in 19 94, or thereabouts. Although, not a patch over 25 Anyway. That's another story.

But back in those days, garbage in, garbage out.

What's the big deal?

We used to look after our data and really try to analyze what it was gathering.

And also, in that case, we knew that what was coming out would be reasonably useful. But, of course, is about guys Got so much data, as I mentioned, 64.2 zettabytes of the stuff. So we can't keep up. can wait. I'm not saying, of course, that it's all garbage coming in.

But certainly, machine learning and AI helps us to disseminate that information, add a little bit more value to it. But nonetheless, she would be thinking about, what comes in first place.

I think we should.

So, what are the challenges?

Well, I've talked about data, explosions, we know. Only too well, it's only going to get larger and larger.

But also, we have a whole bunch of competing conventions, methodologies, one of them being agile, of course, and the other big microservices, where the enterprise is, of course.

Thinking about moving very quickly, Because they have to, of course, this is the way that the world is going. And we can't always think about the bigger picture can wait.

And that causes a lot of problems with, garbage in, garbage out. Are we collecting the right data? Is it accurate?

What does that column mean?

Well, I came up with this, because, like many of you, I'm sure, just could be me.

I've sat in countless enterprise architect and meetings, and I've been given a spreadsheet, or perhaps some data, to look over.

I really don't know, will the columns me and then we'll have acronyms and the industry specific sometimes, or perhaps someone's just made them up. I'm not sure.

Occasionally, I must admit, I've just pretended to know what they mean.

I'm sure some of you will know that story.

So what is a first class citizen?

Well, I think we've got enough border control issues at the moment with code it and all these issues about getting across borders. And of course, you've had Brexit in the UK Don't get me started on that.

So here I am talking about first class citizens, and also probability that I will talk about developments. Are you going to have to a little bit what it is? It's an entity that supports all of the operations that are available to other entities. And what does that mean when in programming? It means that we have constructs like classes, perhaps, or methods? That doesn't really matter too much, but they're all of the same level.

In other words, we can do a lot with those, and we don't decipher between them.

So is data really like this in an enterprise?

Is it a first class citizen?

So one of the ways we can answer that is by looking at some simple screen here.

We've got an app here, very badly drawn, but anyway. I think you get the idea. And we have an Excel sheet. So this is taken from my days working in a very known, well-known pizza place, Domino's had a lot of fun over there.

When I first took on the contract, I think I weighed somebody like 74 kilos.

Successfully.

I managed to pull our weight of 184 kilos because my eating pizza is giving demos to all the suppliers that will come in. But anyway, there's a lot of acronyms. And what is net inflow, For example, what is a quality score?

What is the year to date excluding EPS versus marketing.

So there's a lot of jargon that's out there in the enterprise, that we don't always know the meaning of notice here, in this particular example, which is quite classical. It doesn't have any help either, tell you what that is.

Typically, we can go and ask our colleagues. We can ask perhaps some data people in the enterprise.

But really, how many of us do have that data dictionary That tells us what all this information means.

More still, we have these entity relationship diagrams in databases.

You don't need to understand all of this, but it's pretty simple. An order would have a customer, and the order will have many detailed items, and so on and so on.

If you look carefully, they're just all names in columns on entities.

Still doesn't really tell you exactly what it means.

That lives in our minds, that lives in the enterprise, and that lives in the expertise of the business itself, but it's certainly not kapton there.

It might be by data analysts.

It might be by developers who keep notes of these things.

But it's certainly not a first clause.

Type of element, free into products.

It's not defined anywhere.

Reality, we have thousands of entities and enterprise power, does, it sounds, but probably maybe tens of thousands, in some others.

So why data as a first class citizen?

Well, knowledge is power, and power, of course, comes from innovation, and it needs fuel, and that tends to be the knowledge in the enterprise.

Where does that come from? A lot of it comes from data these days, to understand data.

It's not just to understand the values that are in it, But to understand what exactly that color means in the first place, what that property means, how does it relate to other properties?

Where do we look this stuff up?

It gives us strategic advantage.

So if Theta is freely available throughout the organization and the metadata behind them, it can give us a strategic advantage, because we're all singing from the same hymn sheet.

We're able to look up what something means, not just the value that was given to us.

Of course, using all of those tools, we can get to that speed of market position that we would really enjoy to keep us in our share schemes that are very bountiful when we retire.

So how do we code?

Well, we need to reduce this border control around data citizenships.

We need to make it truly first class.

I know I haven't got caught onto that it might be a little bit looser but it will become clear soon.

Technology can be very basic or comprehensive around making data first class citizen, and we'll come on to that show that it can be as easy as just having an Excel sheet.

That doesn't really tell you what the values are classically, but it will tell you what the meaning of the columns are. I'll show you that in just a second.

I can go all the way over to really comprehensive custom made tools. which I've been involved with a lot. And I'll share some of that with you. Nick also be the tools that are out there on the market.

The tools are on the market.

Most data management tools, there's quite a few of those.

And they do ultimately solve some of the issues I'm talking about, but not all of them, and we will see why in a moment.

So, here is metadata describing data. Now, I'm sure some of you have already seen this type rooms set up in different flavors.

And guys, is that, I'm sure that you have in your organization.

So at the top here, we have entity, which just means some thing in the business of importance.

I chose Pizza ..., tend to all enjoy those, and we understand sort of the customer here.

We have store, we have pizza, which is pretty obvious stuff. But is it really, I mean, what actually is a customer, could be the franchisee who buys the pizza store.

What could it be the customer who actually eats those pieces from the franchisees, even then. There are some nuances.

Because we have a store.

Now if we look over here, we have has duplicates And some rules.

So there are some basic, fundamental business properties around pizza and around the store and around the customer, but where do they lead?

I've been in countless meetings in two wonderfully sophisticated enterprises, and it's hard to find this one master set of tricks.

Certainly, I could go out to my stakeholders and compile such a list.

Where do they go to find their list and other stakeholders?

It doesn't manifest itself into lots of problems, but nonetheless, something small like this as a spreadsheet could really help. Of course, each of those entities will have properties.

We will have on the second grid Name Code Discount level, and they have their own nuances.

Apart from what type of data it is. There's a number, how many letters, and so on.

They would also have rules behind them.

They're also terribly important, because our discount level here can only be from 1 to 16.

And if your store that Sandy traded, cool, two years or less, you can be graded with a discount level, right?

Now, of course, this information is incredibly fundamental with your organization, and it should be everywhere.

It should be treated as a first class citizen.

But in order to do that, it's not always easy.

Because these things live on spreadsheets, they live on people's desks, and usually in hand-written notes like myself.

Well, sometimes in Word documents, but usually they're in the culture and the ethos of the jargon in the industry itself. But certainly, they're not always apparent and transparent, because they own the space more spreadsheet.

Small time for joke.

Three-d. database admins walk into a nosy group.

A little while later, they walk out because they couldn't find a table.

I've been divested of the years and I still can't get the hang of no SQL.

But I certainly enjoy that's more ..., so we turned to Master Data Management.

So this does aim to solve some of these problems.

There's lots of tools out there that we can use to take that master data in the company and all those entities, all those properties, and stash them into an all encompassing database of sorts and provide that out to all of our levels in industry. Below us an enterprise.

Some of this can be quite difficult, too, populate, because it's hard, especially large organizations for everyone to agree on all the attributes of a store or customer.

People like Salesforce, of course, come along, and tell us that we can model it differently, in different departments, and that adds extra flexibility, but with that, comes the balance of what is the one area of truth.

And because of that notion, we have massive data management that allows us to take feeds from all of these different disparate places, and put them into one pot.
16:06
As you can see here, they go across the entire organization.

There are a number of tools out there that you can use, that enterprises do use. I'm kind of oracle expert meetings. I've been using Mountain, not myself.

In those advisory sessions, I alluded to earlier on in mine, in my presentation. But, of course, there's, there's lots of others Pimco IBM, for example, and Informatica there's a whole bunch of S&P.

You may have come across all of those, and they attempt to be able to capture this data.

Now, the issue that I have seen in the industry, is that a lot of these tools don't allow the culture of the company may change.

No doubt, they allow us to capture some of them last date.

No doubt, they allow us to perhaps model that in a more sophisticated fashion, but what about the culture of the company itself?

Do they embrace data in it's first class pool?

Not just values, as I said, but the actual attributes around that. Do they speak the language of data?

Can they understand ELD diagrams griggs?

Can they use those to provide relationships between entities, want new and invigorating ways to capture and use that data?

But the answer is, it's very difficult, because not everyone has that background and data.

And not everybody sees the same amount of metadata. So, my advice, and these are the things that I've done in industry.

S two, educate, of course, and to help, and to support all those stakeholders that you're delighted to be working with.

And ask them to contribute all of that knowledge, if they wish into formats that, allow us to capture the meeting dates. And then, as I said, that could be in spreadsheets, it could be in an MDM.

But recently, the best tools that I've used to being custom made, and they're pretty straightforward, It's just to have a set of entities and a set of properties and put into those two different groups, All the different roles and meanings and understandings of data.

But the real key comes to this is that people need access to it, especially in larger organizations.

You tend to find different silos of developers and projects working, let's say totally isolated, but quite rightfully in their own service that they're providing, especially with micro services, there isn't this overarching, governance, generally allows them to access this meaning of data.

one of the things that we've done in the police is provide, Hey.

Department, that really governance, but allows people to approach them and have the meaning of the data represented in some custom applications that we use.

That allows everyone to get into this app, using an intranet, and be able to understand the glossary of terms and actually contribute to the one more step we do as well.

As across many of the applications, we provide links across every field where they can click.

And it will go off to this one repository of truth, allow them to understand what that is, and who the owner of that is, in fact. So, the metadata itself becomes part of the application.

And because it became part of the application, it became quite nation the culture, they came from the nation. It went on to become more integrated, and that's actually part of the process.

So, when I go to stakeholder meetings now, it can be a lot easier, because people are able to access this one, the repository and be able to contribute and shape how that actually looks.

Now, if I move along too, citizenship and data and see what's out there in the market, we have some fundamentally, huge shifts in the companies that provide us tools with that.

We have the great people at Splunk, for example, who can take some of our organize data and then re-organizing and provide this extra value metrics and AI and machine learning and really demonstrate to us the true value of that data.

But I will say, if you work with these organizations, they can help you to shape what your data should be looking like in the first place.

And that's not something that's always been promoted, especially in enterprise architecture is to bring some of that knowledge that's being outsourced back in house and fuel that, and use that to invigorate and enrich the data that we already know and love in the organization.

So, I hope that's giving you a good insight into data citizenship, that's not always an easy concept to understand, because there's a little bit of a programming term, but, really, the metadata around data is so valuable.

It is essentially the knowledge of the organization.

The more that it can be integrated and treated equally, and rightfully as the values of the data itself.

The more the organization of Knowledge can prosper and really fuel innovation, that allows people, to go into meetings, and be confident that they understand the true meaning of that, not do When I did, which is, just to pretend, sometimes, back in the old days, when you could get away with doing new and do that. So, I've kept this presentation a little bit early, but it gives us an opportunity for questions.

So if you have any, please call them over josey.

If you want to reach out to me, you can find me my LinkedIn address, which I'll put on the screen just now.

And my e-mail addresses that too.

So, I hope you've enjoyed that presentation, and just giving you a small insight into data citizenship as being first class, but also just reiterating the fact how important metadata updates and can be.

Thank you.

Think you're a cache hit: Excellent. I will keep an eye on the questions here, so feel free to keep typing questions as as we carry on the conversation with a cashier with a time that we have. ..., I'll ask you to stop showing this the presentation screen so that the audience can see us big on the. Excellent. And let you know, it's interesting that you talk a lot about you want to, you. You talked about the old school, you know, garbage in, garbage out for data, and the in the, in the, in the world of big data. And sometimes, you know, you're not getting a whole lot more, or less, can be more in a lot of different contexts. And, there's something to be said about small data and the right quality sample size collected the right way.

How, how do you work that in, with the approach that you have with the, or the cross industry organizations you work with?

Meaning that, it's not the decision, is not really a question that big data is good or bad, or small data is good or bad. But it's really, depending on the context and the quality that you can have, you know, you, you know, either one can provide great insights.

Um, how do you approach that with organizations? Because, you know, sometimes more is, is really not adding to the quality of what you offer your analysis.

Yeah, that's a wonderful question. Thanks for that very intriguing question. A lot of it comes down to just how you connect with your fellow humans.

But a lot of big data obviously goes with the volume that's there.

But also big figures just emotionally attract larger audiences and you kinda think it will lead to large profits.

But usually the devil's in the detail in most of that.

So I tend to approach it pragmatically. I look at what the small nuances are in a particular vertical or a particular domain.

Usually you will find specialists, business specialists, in those areas that will champion. Some of the small things, it could be very similar. Like AlphaGo. That's the Dominos example.

They enjoy Making Dough who doesn't make don't and not enjoy it.

Well, I did, and they have a missing doe report.

And this is all to do with, well, how much data is missing. And we can kinda work out well, the wastage is, and we can work out with any theft and that sort of thing.

And it's just one column inside an entity, but it can really change everything. So there was sort of guesstimate this, for example and that sort of thing.

And it won't be so apparent, that if you have enterprise architects, who can really champion the small nuances in the meaning of that, that can manifest itself into larger, larger returns in the end, and it certainly did for that pizza company. So, I think it's a bit of both, you have to balance it with the human connection, and you really have to dig deep to work out, that modeling and see would actually fit into the wider context.

That is such great insight. And, by the way, that jokes they, they did well. I mean, the audience enjoy that jokes you see. I think we're in the right age group. Our kids not appreciate as much as folks that are, You know, I'll, I'll add. To that, share a comment that you know I have. I done a lot of work in my, in my career, and the engineering and science. And you know, kind of industry.

And often, big data doesn't help us very much there, because there were some specific sources that we need in specific ways. And I had one example that was work of a very large power company, where one day, they send me 18,000 rows of data on something on that piece of equipment on the large power producing facility as an example. And they had collected that over nine months. And I looked at it, and that, you know, I did some analysis on it, and was mostly garbage, because the way that there was a lot of automated data that was created by the facility, that was within the range that was very controlled on purpose, because you're running a facility, and you want to keep it controlled. Because the fact it was so tightly controlled, it basically said that that input did not affect the output. It doesn't mean that it didn't affect, just means that for nine months, you control that So well that I there's no impact.

And you can send me 50 million pieces of data. It's not going to show me anything more than that. So we need an instead of 18,000 data points. We actually needed eight data points where they vary the input quite a bit and then from those eight data points, we created this amazing model of the entire system.

But again, to your point there, that on the dominoes as well that this, you know, it's, it's about understanding that the business process that you're dealing with and the context of the application. On this now, you talk about data citizenship, which is, which is, I think, there is an intuitive appeal to why you discuss here. I think it's not known by maybe half of our audience, and maybe the other half is not something they're familiar with.

The question that I see as a theme related to data citizenship is the question of governance for data and how does governance matches with data citizenship. How does that work?

Yeah.

So, crucially linked together, so.

the enterprises, I've been fortunate enough to work will have embraced this on various levels as time goes on data has become so valuable.

That there's not many organizations that would discount the value of it.

But as you rightly pointed out, when you're on your new prevents reference, is that big data just sometimes just adds more, uh, obfuscation then clarity to it.

So really, it comes down.

down to this, is in order for data citizenship to work, you really need to have the right tools in your organization for it to be put out there, marketed.

So, lots of enterprises will have hundreds of internal apps you can't count on your fingers it more like how many you can put onto this, this kit.

But really, if you look at the number of entities that are out there, that aren't always that, many key. So for Dominoes that would be stores and pizzas. And customers. The British airways who worked for vehicle aircraft and tickets.

And, of course, all the equipment on our own, around that, there'll be a whole bunch of metadata in each department, will have their own flavor for it. So the key, I think, is the organization to embrace that, but not in a governance. I think that word sometimes just wreaks havoc with Pedro revised New Business School. And somebody checking your grammar and so on and not everybody, please, Enterprise Architect comes along and says, Look, on the governing. Your data is not quite right. Now, it's, it's a, it's a shared knowledge check. Or we have Wikipedia, we go out to Google.

And it's kinda one element of truth, but this crowd, so Australia, That's what makes it so cool and unique, so for organizations which embraced that type of thing and have some kind of glossary, how they want to term it.

And allow people to contribute to that, really be able to reference it, this is really key. It could be in your spreadsheet where you've got columns on top of the column. Sums are equal description of all that color means, right?

There, you can put a link to your corporate Wikipedia.

I think the beauty really comes from there is too use technology and make it so it's crowdsourced.

Not so much governed by one particular place, and allow that cultural shift and gradually manifests itself through the organization, and hopefully you got some cool speakers, Maybe some people that can come along and make it sound a bit more appealing, and governance.

Very good. Yes that that's an excellent point.

Rakesh, at different question that has emerge has to do with the, a bit of a paradox of digital transformations. They have accelerated and they, you know, they, they were accelerating before the pandemic and then the pandemic hit and then they went into overdrive, right?

They really accelerated. There was this big migration, traditional technologists. And then people started, honestly, people start talking about, every time you use a digital technology, somewhere, they start saying that there was a digital transformation going on, Which is, a topic on its own.

But that, but if we may, as it be, We have, a, we have, now, some track record of digital transformations that have been happened for five years to 10 years, even, or, or, even longer than that. But if we look at what has been happening in the last five years or so, while we also have seen, is not an acceleration of digital transformation, but also an acceleration of failure, of digital transformations to reach their desired outcomes.

And the, you can, you know, depending on the source, you look, you know, the numbers can be anywhere from 50 to 80% plus off failures, as a, as a leader and practitioner in the field working with multiple organizations, business models and industries.

What do you, What do you see as the maybe potential root causes or this failed digital transformation. So, what are some of the things that you think that organizations, even with good intentions, they, they implement, these things, have good intentions, but they're not really delivering on the outcomes that they expected.

What do you think's behind that?

Yeah, there's there's always a number of things, the circle behind that. I mean, one of the things I can think of this.

probably not enough: free pizza, prefix, fuels everything, and clearly you get better results with better job. I've been involved with my fair share of failures. I don't mind to admit that.

And a lot of it comes down, really to, again, the human element of it all.

To understand what the end goal of the digital transformation is, is to understand what the people won't have delivered, and what kind of goals they actually need.

Sometimes the technologist, I'm guilty of this. You just want to build something cool. You want to use the next new technology, and you just want to data the heck out of it. You don't use machine learning and AI and all that good stuff. But really, is that going to be a strategic advantage?

You know, what does it add, really, that you couldn't have found out, maybe with some basic calculations?

So I think it's a case of being honest, sitting with stakeholders, and really just understanding what motivates them, what motivates the team, what then, is the end result for the company you work backwards.

It's not always easy to look at it in that sense. But it's certainly the right thing to do. And then from there, you don't need as many people then to figure out the exact details of how that works.

I've often found that in large organizations, sometimes there's a tendency to overhaul.

And we have lots and lots of people are chief of things, or director of that course, when that happens, there's lots we have underneath, generally, and then it's really quite hard to get consensus.

But I think what's really worked well for me is proof of concepts, small ones that lead onto the larger ones, and they really can add a lot of dimension to lots and talking, and meetings.

If there's a focal point to be had on the screen here, and it's working, that is so much more influential, especially across large teams, to become successful afterwards. Because it's iterative at that point.

We've built something that people, hopefully, have championed slowly but surely the end result.

But nonetheless, I go back to pizza recommendation. that's an unfair advantage comparative advantage when you can do that. Right. Now you already are, in my opinion, bridging to the next question that came up, which is there are there are really bad enterprise architects out there.

There are a majority, which are pretty good, and there are very few who are great.

What does greatness looks like? Where, in terms of an enterprise architect, what does it look like when you when you work with enterprise architects that are at that grade level? What is their differentiators?

I think it's me, yes. What does it look like? Yeah. Well, I was joking, I'm glad you're far better looking enterprise architects that are locally ****** in scope but OK, what does it really look like?

Well, it's the conduit, isn't it?

Between technology and the human emotion and all of the, the good stuff around psychology that makes that technology useful for people will see tending to the purpose for that.

So it's really that conduit, that I think, enterprise architects, they're successful, managed to blend and be able to transform at that point, something that's an emotional requirement into something that's technologically. And they'll just viable, but something that actually adds value going backwards again.

So it's not always easy to do.

So I think if you've come from the background, that is development, no biased here, of course, because that's where I came from.

It really can add a lot because the people that you speak to that, eventually going to implement all of the crazy ideas that you have, would certainly appreciate a level of affiliation with what they do on a day-to-day basis.

And, of course, having an engineering mind allows you to leverage technologies that are currently there, but also in the future, in a different way than if you hadn't had the experience.

If there's any enterprise architects out there that are not developers, come from that background and encourage them to do Hello, World, get into that environment.

And the other part, not to be dismissed is that there is a huge impetus towards being technically gifted.

That's crucial, and remind me, and that's what I've just said earlier.

But you have to be really connected with people.

Essentially, you must influence people and be influenced by them in order to reach the goals that you're trying to reach out to yourself.

But of course, for the company, and it's sometimes overlooked in this world of the 20 releases of the version, Python, for example or an app and so on.

And this also centered around technology and what it can do, underlying that.

There's a human element and I think really if you've got a blend of both, then usually those people have seemed to be quite successful.

Terrific. Rakesh, what a pleasure to have you with us. Our final words of wisdom for our audience today.

So, as those who are accelerating there, culture, business, and digital transformation, what would you suggest that they keep in mind as they go on this journey, to increase the likelihood of success on their own transformations?

What I would suggest this is that you should really, really try to improve your presentation, skills.

I've certainly tried to improve mind, and that's added a lot, try and be better with the jokes.

I think that can be a little bit better, and fine, too, But, also, do this be a student?

Totally for the rest of your life. Keep learning new things. Keep looking at the future, embrace them, enjoy them.

And if there's one last dad joke, I can have, it will be less.

I'll be considering making a movie about data, and all of those good things that architects do.

But I just can't think of a sequel.

..., thank you so much for sharing your expertise, your insights, and your dad humor, with our global audience today. And thank you everyone that had the misfortune of listening to me for the last hour.

Thank you so much. Fantastic. Fantastic. Reveal from a real practitioner and leader of a transformation. So thank you. Cash, ladies and gentlemen, that was the cash. But they'll, with, with a wealth of knowledge, and the cross industry insights, sharing with us today.

So, this completes our, they chew off enterprise architecture life. So let's take a quick preview of what's happening tomorrow. Tomorrow. we're gonna wrap up starting at the same time, you know, 9 0 AM, US. Eastern Time, whatever time zone that is around the world for you. We're gonna kick it off with ..., who is Director of Enterprise Architecture for SNP Global Ratings, and he is going to talk about utilizing EA and business capability modeling to drive Strategy and strategic technology investments.

After sandip's presentation, we're going to welcome Lead bogner, who is the Global E-commerce, Enterprise, Architect, and Innovation Strategist for March, and it's, it's not the planet, OK?

It is the company, and he's going to talk about a, about enterprise architecture framework for value creation. Using what he calls Digital ventures for innovation.

So, that will be followed by another tremendous breadth practitioner, we're going to have Brandt ..., who is the Head of Learning Strategy and Innovation at Shell, and Brandt is going to be with us, talking about the Intelligent Enterprise and the next generation operating model in the era of automation building, Enterprise Architecture for Today and tomorrow. He Branch is a leader in an organization that has been transforming itself at a global level is any scale. And has tremendous insights from his journey at Shell to share with us. And we're going to finish the day and the conference tomorrow with Bobby's Sundar Side. And Bob assured our shop is there is a leader at ...

or ..., I can never remember how to pronounce the name of the company, But it is.

It is a british multinational beverage company that with its headquarters in London but operates in more than 180 countries produces in with up with manufacturing operations and 140 sites around the world and it's the world's largest distiller. And until like 2017, when a Chinese company, I think may have overtaken then that ear. But it's 28,000 employees And during this transformation on.

On the on the sculpture business and transfer, digital transformation journey. And Bobby is the enterprise architecture leader for the company at a global scale. And he is going to be sharing the journey and insights with us.

So, tomorrow, we're going to have it packed with real leaders, and practitioners, who are implementing digital transformation and enterprise architecture at scale in their organizations.

Great sessions with great leaders, sharing their practical insights and knowledge directly with our global audience. So, again, thank you for being with us today. Thank you for your terrific engagement throughout, For those of you who want to ask additional questions, engage with the speakers and other participants post conference, checkout the LinkedIn under my name. And I'm going to be posting a short update soon after we wrap up here today, and feel free to ask questions, comments, Thank the sponsors for allowing us to have access to all this wealth of information at no cost and a global on a global basis. So for now, I am saying goodbye, wishing every one of you a great rest of your day wherever you are in the world. Thank you, and I hope to see you back tomorrow.