Modern Data Governance 101 – The Elephant in the Room

There is much confusion in today’s marketplace as to what Data Governance truly is, and why all organizations need it. Moreover, how can companies implement it right the first time? Savvy companies know that they can no longer turn a blind eye on Governance, as penalties for governance breaches can be hefty. Join us as we bring some much-needed clarity to this elephant in the room.

Agenda:

• What is Data Governance: Not one format fits all. Where does Information Governance Play? Why should you care?

• What is Required to be Successful: Right Sponsor, Right Technology, Right Use Case

• Establishing a Center of Excellence: Cross Functional Team, Health Radar, Prioritizing Change

• Engaging Your Peers For Success: Communication Planning, Training & Education, Policy Creation

• Ongoing Execution: Integration with IT and D&A planning, Ongoing communication and training, Avoiding Exhaustion

Our Panelists:

Kuldeep Singh: Kuldeep Singh is an accomplished Data and Technology Executive with over 20 years of experience in data strategy, platform architecture, data engineering, and data governance. He is recognized for his strategic insight and technical expertise, consistently driving transformation in organizations through robust data governance frameworks, innovative data strategies, and advanced engineering solutions. His leadership has led to significant cost reductions and improvements in regulatory compliance, showcasing his ability to promote data-driven decision-making and operational excellence.

Michelle Moore: Michelle is a transformation specialist with extensive experience in the design, planning, and execution of digital and operational transformation, operational management, and data management & governance for large, multinational organizations. She has a passion for operational excellence, enabling business strategies through the effective use of automation, integrated applications, and optimized data. Michelle delivers measurable business outcomes through cultural transformation across people, processes, data, and technology.

Transcript:

Kalia Garrido: Thank you so much for joining us today. My name is Kalia Garrido and I head up marketing and events here at Great Data Minds, which is now a HIKE2 company. GDM is a collective of passionate data activists, and we are on a mission to modernize the world of data. We do this in different ways. The st is that we have our services arm. This is where we do strategic planning, education, the deployment of critical data projects. And you can find us hike2.com and I’ll send some links.

Kalia Garrido: In the chat as we go. And then, in addition to our data services, of course, I, too, is a fully functional best in class innovation consultancy. And we specialize in digital transformation strategy design technical implementations. So be sure to check out our brand new website. We’re loving how everything is looking on there, and then the second part of what we do is with our data and analytics community. This is where we have content conversation. You can find us at greatdataminds.com. I’ll also drop into the chat some links around our Youtube channel. Our Linkedin page. This is where we host our events, we run our videos and our podcasts. And we do it with transformational thought leaders, just like the ones that you’re gonna hear from today.

So a little bit of housekeeping as we get started. Of course this is a webinar, so your cameras and microphones are off, but we welcome your conversation. We would love to hear from you both in the chat where we have Miss Julie Burroughs already. Lighten it up. And then the QA. As well. You can put in your questions there through the session. If you’d prefer to hold off for a little bit more of a formal QA time. We have a couple of spots baked into the talk today to accept questions. We’re recording our session. I always get questions. Where can I find this later, and that would be out on our Youtube channel. So like, I said, I’ll drop you a link so that you can find us there and we’ll follow up with an email after the event as well.

So allow me to begin with some introductions. I have one familiar face and somebody who’s brand new but we are so excited to welcome Michelle Moore back to the show, Michelle. Thank you for joining us again. We always need. We have to talk to you when it comes to data governance. That’s what we’re gonna do today. So a little bit about Michelle, she is a data governance and process automation specialist. She has extensive experience in the design planning and execution of digital and operational transformation, operational management and data management and governance for large multinational organizations.

So the new face that I’m excited to introduce everybody to is my co-worker, Kuldeep Singh.  Kuldeep is an accomplished data and technology executive with over years of experience in data, strategy, platform, architecture, data, engineering and data governance. And as the new chief data officer at HIKE2, he is recognized for his strategic insight and technical expertise consistently driving digital transformation in organizations through robust data, governance frameworks, innovative data strategies and advanced engineering solutions. It’s a lot of a way to introduce you both and say that everybody is in great hands. Today I’m personally so excited for this conversation. Kuldeep, please take it away.

Kuldeep Singh: Yeah, sure. Well, that was that was a lot. I mean, you know, it’s it’s interesting to hear when people introduce you. But thank you so much. Kalia. Really, you know, excited to be here. Good morning, everyone welcome. It’s so awesome to see such a diverse crowd here from so many different industries, and from all over the United States. So thank you for joining us. So I am excited to be here. And yes, this is my st time with GDM, you know I I’ve done a tons of research on GDM, and you know I’m so happy to be here, because, you know, we try to solve some of the toughest, and we bring up some of the toughest challenges that Us. Data professional face, you know, on our day to day basis and in our industries. So like, Kalia said, I’m Kuldeep. I’m the Chief Data Officer at HIKE2. I’ve nearly decades of experience in analytics, data, strategy, and platforms. So you can. You know, I’ve seen pretty much everything. I have been associated with data governance specifically for most of my career, including headed data quality functions globally for a large global multinational investment and wealth managers.

I’ve also very recently started and matured a data governance office at one of the United States Largest credit unions. And also, I started and led the data governance to a level of maturity at the Bill and Melinda Gates Foundation. So, as you can see, there’s a lot of diverse, you know, from an industry perspective. So today, like Kevin mentioned, we’re gonna kick off with this topic that affects every organization. And I think we, it’s become even more urgent right now, because, all of us want to harness value out of our data. Right? I was recently at a conference, and I heard these executives talk, and one of them was saying that everyone is ready for AI except for your data.

So why is this topic so difficult? Why is data governance been so difficult? You know, in the past decades that I’ve been dealing with it. It’s not easy. So if you have ever felt that managing your company’s data is like trying to organize a chaotic library where you know, books are constantly coming in. You are not alone. So data governance involves. I mean, if I had to define it, you know, it’s really all about data needs to be accurate, it needs to be secure. It needs to be accessible. Sounds simple and straightforward, but it is incredibly hard. I mean, depending upon what kind of an organization you are. I mean, it doesn’t matter. You know, you might be mid tier, a large global organization, national regional level.

It’s a thing it’s difficult, right? But so that’s what we’re gonna talk about today. Michelle, who was introduced earlier. She’s a global executive has years and years of experience area. I’m super excited to talk to Michelle about this. So let’s just jump in the. So I’m gonna kind of take us through this journey over the next hour  min or so. I’m going to start off with the topic. Why is data governance so hard? So, Michelle, you know, what are the main challenges? Organization space in implementing data governance? Can you talk to us a little bit about that?

Michelle Moore: I can. Yes, sorry. Happen to have a slide here. Just that talks about this. We’ll just get to the one that we want. I think this is why it’s so hard, right? This is not one isolated topic that you go out and deploy, you know. Pick it off the shelf and just stick it into the organization. It really is a a cultural transformation, right to do it properly. It has to be enterprise wide because you’re talking about the whole organization. Data is sort of a living entity within a business. It flows from system to system. That’s what makes a transaction happen? Right? So you’ve got many people involved in it. it also is tied to processes where you have requirements that you have to enter certain data, or you have options around data that you may or may not include, and trying to figure out what is critical to your business is one of the key aspects of governance, and that is also quite hard to do, because you again go back to people, and people will think their piece of data is the most important piece of data. So you, it’s hard to kind of prioritize in in, get a feel for what is the right area of controls that you need to put in place. I mean, if we step back in and say, Well, what do we mean by governance? Right? I think that is different, depending on which organization you’re talking about. So it isn’t a Size fits all problem to solve. It is very unique to wherever you are at that time both within the organization and within your strategy for the organization. So you know, you may be big. And you wanna just start focused on one product set right? So maybe just customer data or just product data again, depends on the nature of your business and then underlying all that is, you can’t really do effective governance unless you are bringing in the right tools to help you do that right? So that would be a data catalog. As an example, you want a really solid foundation that you’re gonna do your the a data analytics through potentially AI through. So that you have a really good catalog and understanding of? Where is your data created? Where is it being modified throughout its lifetime because it will change over time? And where are you then extracting it for use to do your analysis or create your AI etc, right? So many businesses. Especially larger older companies don’t really have a handle on that, because they have grown over time added things in done acquisitions right? And so the clarity around what data is coming from, where gets pretty muddy, and governance is intended to help clear that mud, so that you do understand what data is coming from? Where? And you know, is it controlled.

Can you trust that data, you know, is the output, then actually gonna be useful to you? Or are you gonna spend a lot of time reverse engineering it, trying to figure out who touched which bit last to know whether or not it’s right.

Yeah. So I think the the real challenge of it is the people that you need to get aligned around. What the true problem is you’re trying to solve that should take an enterprise wide view, and then it requires a cultural transformation across the different parties that participate in that target area, whatever that might be. To actually come to agreement on standards and processes and the technology that you’re going to use to manage the the data, definitions and standards around it.

Kuldeep Singh: So, Michelle, thank you. I mean, you know, just to summarize. This is what I heard, right? I mean one. You know the the data. Landscape itself is complex, right? And then you mentioned you know, lack of standardized processes. And W. But one question that comes to my mind is, is there a resistance to change within organizations that also impact? you know, data governance.

Michelle Moore: Yeah, I think there’s there always is resistance to change, no matter what transformational program you apply. Right? It’s it’s very rare that people jump right to it and go with the new ways. A small contingent will. A lot of people won’t so I think there was a little bit about change management in here and identifying a strategic problem to solve that people can rally behind right a call to action so that people understand the importance, the why do we need to do this? So I do think there’s resistance to change. You also have the fact that people want what they want, you know. And so what you typically find in organizations is Department A would have created now their own small data mark because they have an urgent need for some analysis that they have the capability skill wise to create as long as they can get to the data. The problem is, now you get that happening in department BC and D, and the outputs from those independent efforts to analyze the information that’s coming through the systems typically ends up with a different perspective on it, different definition. And you may think you’re looking at the same data. But actually, it’s been manipulated differently, or it is called something that you also call another piece of data. But it’s not the same data, right? So we see this a lot. And now you’re reconciling. This is why you end up doing a lot of reverse engineering? Why does this report that, says Customer Count, is X not match this report that says customer account is X, because they pulled from in different ways from one or more sources with different filters. And now we have different customer account. Pretty fundamental, right, and pretty easy to fix. It sounds like. But now you get into well, what’s the definition of a customer? What are we? Including as customers? Right? So it gets more the more you get into the detail, I guess the more complex it becomes. Hence the need for really clear standards and processes to manage data changes and then that is supported obviously by a solid foundation of technology.

Kuldeep Singh: Yeah, that makes total sense. I’m also keeping an eye on all the really great comments coming in. You know, one example from the industry that I’m familiar with is investment banking and you know, if you think about an investment decision, right? It’s all about timely data, it’s about accuracy the fines on the other side by regulators are like, you know, very, very extensive. However, can when you think about the complexity right of a large global investment bank. You know, you’re thinking about multiple sources of data, this market external data, internal data.

If you’re a large bank, you have multiple instruments that you’re dealing with. So as a result, you have multiple trading platforms. Now, you know, tab on top of it. You know, all of the internal risk management functions that you have to deal with, and also the investment managers who are externally kind of representing you at the bank and the product, you know that they’re talking to clients. And this high risk conversations happening because you’re talking about, you know, transactions that are in the billion, a billion dollar range, right? And then you throw on top of it, you know, regulations and trying to keep up with the ever changing landscape of regulation. So it just keeps getting very complex. And you know, just trying to get a handle of it. You know, it’s it’s it’s like you mentioned. It’s you know. It’s a pretty difficult task. You know, I want to kind of take this conversation we’re talking about. Why is it so hard? Michelle? You know. What about the value side of it? Can you talk about, you know, in from your own experiences? Maybe starting with the value that the business can talk to. Roi, how you want to define your value. And and then kind of, you know, going backwards and saying, Hey, we need to do something about this right? And then data is a big piece in there. Can you share an example? Maybe from your past experiences.

Michelle Moore: Yeah, I think. One example I have is a multinational company. That, grew by acquisition was a private company as they acquired then went public right now they got regions operating off of different Erp systems in the back end there, you know, the same business models right? They they bought, basically a replica of themselves, but a different erp running the business. So anybody in it knows. Well, then, probably all the tables and field names and everything else are different, right? Even though they mean the same thing. And so, of course, for several years they continue to persist with the existing back office systems and reporting and manually reconciled everything to kind of understand what was happening globally until you know the the scale of the business got to a point where they decided to go public. Now everything was different, right? All the requirements for reporting became much more stringent. And so we had to be real clear on what was good data. Right? What was data that will be used in external reporting? And as a result, now, I, finance job became exponentially harder because they don’t have new systems to support that outcome. They have to now really dig through all the different outputs, to reconcile them, to do their financial close every month. So the value in this scenario was the Cfo. Was getting pressure from the board that now we are public. You cannot take a month to close the books. You have got to do it within a week, so that we can go to the street and announce.

And so he had a burning need right to reconcile what was going on with all of the data and reporting so that his team didn’t spend you know, a month, basically reconciling. And so the data governance in that scenario launched with that as a north star that we were gonna reduce the financial close by weeks, which is a significant amount of time in terms of efficiency right back into the finance team in particular, but also inaccuracy and reliability, so that it was repeatable, and every month it was the same. And you could trust that the right data was coming from the right systems and meant the right thing.

In that scenario. We did as we went through the process of reconciling and centralizing all of that, reporting for the close identified one region that even though their output was labeled exactly the same as another region, the underlying algorithms were completely different, and the Num, the resulting number did not mean the same thing as the other regions, and even though the number was the same, it meant something very different. And if you normalize the one region to match the calculation of the other region, the number was way off right? So that was a massive problem. Because now, actually, what have been reported to the street was not right.

Michelle Moore: Right? So now it’s a question of what do we restate it? What do we do about that number? But it it wasn’t revealed until we went through the reconciliation process as part of data governance and standardizing each of the regions, moving them off of a regional solution onto a central solution with all the right controls that that was discovered. Right? So the value add there was both efficiency and accuracy. For the health of the business essentially. And you know, there’s lots of other ones. Particularly where you have multiple systems, right? That the more disparate your systems are, the more urgently. Really, you need to be dealing with data governance unless you’re gonna go on a strategy. We’re gonna consolidate our systems right as quickly as possible in the scenario just gave before different regional erps consolidating on the one Erp would would have been such a massive effort.

It wasn’t going to solve the problem the CFO had. Right? So data governance was a way to address that problem.

Kuldeep Singh: Oh, thank you, Michelle, for that. And what a great person in the organization to work with. Right? I mean Ls leaders. Another thing that we have to kind of, you know, maneuver is building that executive sponsorship, and I’ve always kind of, you know, looked into Cfos one, because, you know, they have big data challenges as well as you know, they have a big, fat paycheck coming up checkbook right? That they can kind of, you know. Actually write those checks.

So you know, that’s a great example. You know, I’m gonna move the conversation forward. This is all about, you know. What can organizations do companies do upfront to prepare for data governance?

Right? But you know, at a large credit union, I mentioned it earlier. There were major programs running simultaneously. One was just we were bringing in a new customer Crm, and we were building a customer data platform just more for targeted marketing, understanding our customers. And and the bank had, I think, or different channels where information was coming in from right. The customer information that transactions and the second initiative that we were running at the same time was Cecil in for those who on the, you know, are familiar with the banking cesal. That was a big deal right it’s banks are kind of reinventing the way they calculated their expected losses from their lending instruments, like, you know, for example, auto loan and board gauges.

And when you get went into credit cards. We needed data from years back. Right? So we were looking at data that was years old. And obviously, you know, a lot of gaps, a lot of challenges. Data was not managed. Well, we had a nascent data governance program going. But our board on our executives, we’re very concerned about us. You know one about our crm, but also about, you know, being able to meet the what the Regulators were asking us to do in a very, very, very tight timeline, right? But however, you know, that was an opportunity for us to now start talking about why we are where we are. And while the data governance program hadn’t kicked off but we started having those critical conversations with our executives. So Michelle question for you is, what can organizations do to get started? You know, what is that? Pre stop? Even before a data governance office has been set up.

I think. You refer to it at the beginning. There, the most important thing is an executive sponsor who gets it right. Without that it won’t go anywhere, because people won’t have any sort of directive to follow. That will empower somebody at a lower level to do anything right. They just won’t be able to do it. So an executive sponsor who is is clear on a problem they’re trying to solve like the CFO example, right? Or in another organization. It was a chief marketing officer, because they couldn’t get the right customer information together to figure out where they were going to go next? Right? They. They understood that the data was fundamental in them, making proper decisions and being effective in their job. So you need somebody at the sea level.

Michelle Moore: Who gets it? I would say it’s not typically a CIO, but a CIO can do that when it  gets down to issues more around security right? And what we find is that is the initial driver of taking action with regard to data governance. There’s some sort of breach leakage or breakage right? That suddenly becomes critical to fix for the business. And that kicks off a data governance program. So that can be a good starting point. And typically that will come out of it, but would quickly move for the right reasons, right to somebody on the business side.

That cares about derisking the business. So I think number one is, find the executive sponsor and so then, that ex executive sponsor should rally his peers to to talk about the problem they are trying to solve. And then I think it is identifying clearly for the organization as a whole. What is that problem? And where does that fit in priority with other things. The organization is trying to do right? So it starts with the strategy. Basically, you know, it’s gotta be top down. It can’t just start in some department who wants to try and do it across the board, because, as we said before, it is enterprise wide, you’re not gonna just control and fix the data in one place. Right? Everybody in the organization needs to understand why it’s important, why keeping it accurate is important, why protecting. It is important. So it’s about access rights, usage rights, all that kind of stuff. So I would say, that’s number one, and that’s not necessarily that easy to fix.

But if you can find the sponsor and identify a problem that you want to solve, maybe it’s a quality issue, right where improving data quality is gonna result in more efficiency for the organization. Because you gotta get costs down another strategic driver. Right? If you do that, then now you have somewhere to start, and I think the the st tactical thing to do is then do a health check right now. We do those all the time at how to come and do an assessment across different vectors of the organization to help the organization basically understand where their weak points reli in relationship to governing data, and therefore what work needs to be done to sort of bring it up to a level where they can start to, then talk about implementing a data governance council, or a what we like to call center of excellence where we have somebody. Now that’s running it as a regular part of business. And this is the cultural transformation piece.

It isn’t a project, right? It is something new to introduce into your everyday running of the business. It’s part of the operation. It should be part of your it intake prioritization process. So you’re reviewing what changes are being made to the systems, what controls might need to be changed as a result of that right? What standards are we agreeing to for data definitions as we’re adding new things on and integrating new systems in.

So it’s gotta be a an ongoing part of the operation, which is why the change management part is hard, because now we’re talking about doing things a little bit differently, when people might have been used to just going off and integrating new systems and not having to worry about any of that stuff. Right? So I, I think, start with the executive sponsor. Figure out the general strategic problem that’s trying to be solved. Do an assessment to understand? Where are your weaknesses that relate to that problem, and then you can build a roadmap and and then introduce a center of excellence. So we’ll manage that roadmap and and change the operational piece, so that you have that as a regular part of the business cadence.

Kuldeep Singh: No, that’s these are all great points Michelle. I mean thing that helped me, you know, doing this project because we were kicking off, and I was executive sponsoring it. You know, one of the most difficult questions for us in the Credit Union example, was, Build the different blending products, right? different definitions of a very critical metric in financial services called delinquency. Right? And you know, when you think about delinquency, how delicate is coo on his loan. Right? I mean, as simple as it sounds. It’s a very complex undertaking. What we were able to do, though, is because all of the people who control the definition of delinquency have day jobs, right? And so we tried to kind of make it easy for them. We had certain tools already in place. And we felt like, you know, the only way we could kind of help this group. A very, very busy group is by doing some upfront work, which is you know, the data governance tool that we had. We could create visibility to these different definitions of the metric delinquency. Right? So we did this upfront work from the systems we’re dealing, you know, with these learning platforms that where the transactions were being created. And then another thing that I’m noticing in the tools that are available today is that once you have some definition of these def. You know, metrics, you can ask people to join and collaborate. Right? So collaboration has become easier versus just imagine you know somebody who controls the definition of delinquency. Sitting in London office is trying to kind of, you know, have a conversation because these conversations takes sometimes months, you know, to to resolve. So that collaboration piece I feel felt that was very, very encouraging to me. And I know that we’re gonna talk a little bit about our tools, you know, later on.

I’m not endorsing anybody. But you know there are specific capabilities that we have, right. So we’ll talk about that. So moving on from here, I know we have some great questions coming up. So I’m gonna pause here in about  min. But I want to kind of very quickly move into organizational readiness, right? Which is kind of a a topic. So now, what we’ve done is that we’ve defined. Why is data governance difficult?

What are the pain points? You know how people are now starting to see value and attaching data governance to the value that it can create. So it’s not only just a defensive or regulatory and enforcement thing, but it’s directly tied into your company’s revenue, and you give some really stellar examples of that Michelle and now we are at a point where, you know, we feel like we have identified that we need to start a data governance program. There’s executive sponsorship. And you know, there’s some tools, maybe, that we have and some wide processes. But what can as we head into that, what can organizations do to get ready? What is the organizational readiness? You know? What does that look like.

Michelle Moore: I think that really is a focus on that assessment that I talked about a health check right? That looks across technology. It looks across disciplines, it looks across skill sets, and that includes. Do you have any change management capability? Because and what I mean by that really is, do you have a team that is able to go out in a regular basis. Train people create communications because what you really need to actually achieve the transformation required is this ongoing drumbeat of this is what we’re doing. This is why we’re doing it. And I reminded people, because it’s very easy to slip back into old ways, right when you’re when you’re changing. So I think it’s about the structure of the center of excellence related back to what’s the problem you’re trying to solve. And what do you? What is the prioritization of activities? Then you know who to involve? Right? Maybe your data governance pro program is all about data quality.

That’s a very different thing than a data governance program around securing the assets right of data and and reporting output. And so you would have different people involved in that. And when you get into a wh, what data quality we talking about is this customer data product data, it can’t just be general data quality. That might be a theme. But you will need to have some focused areas. Right? So again, it’s about the identifying the problem and then understanding what are the implications of dealing with that problem? Right? Or there’s different systems involved. Who are the Smes in those systems? How do you engage them in the process of standardization? And policy definition, right? So big part of their governance is writing down. Here’s what we will do with data. Here’s what we won’t do with data, right? And specifically, this data. And here’s what we won’t do with these reports. Here’s where we can send them. Here’s what we can’t send them. So it’s having the right people and right mindset of those resources that you can pull on to participate in the center of excellence is gonna help drive this program right? And it’s an operation. You know. I did this in a formal life, and it was set up as an independent function that sat beside it was not in ice. It reported into the Cfo. And the whole job of that was exactly what I just described. Right? We had teams of people focused on customer data, quality product data quality and information security.

Those were the areas that the business prioritized as the most important things to their business. And so we then organized around that, pulling the right Smes from the business to participate in that center of excellence and drove different initiatives because you have different requirements in each of those areas to make sure that we close things off. So there were. There was a project plan in each of those areas, product customer and security and that was executed to basically bring the organization up to standard level, right? A good level of governance. And then it was an ongoing reminder, and as new changes occurred, or new acquisitions were made that same team would meet to review the implications of whatever that was right, and then come out with new policy, changes or direction, you know, working with it on. Well, here’s the best way to integrate we or we don’t want to integrate that because it is not secured enough. Right? We wanna move migrate people over. So I think the the readiness piece is clarity on what is it you’re trying to do? What are your priority areas? And do you have the right people that are going to participate in the center of excellence to help you drive the outcomes that you’re looking for.

Kuldeep Singh: Yeah, no, I mean great. Great call outs there, Michelle. One thing I noticed.

Michelle Moore: Are you? Kuldeep? Yeah.

Kuldeep Singh: York, Industry.

Michelle Moore: Does get.

Kuldeep Singh: Young.

Michelle Moore: I just wanted to get a bit more to the technology piece. What have you seen in technology? Now, the emerging new technologies that really help with data, governance it, whether it be quality driven or security driven. What are you seeing out there.

Kuldeep Singh: No, absolutely. I’ll cover that Michelle. Thank you for that question. But one just taking it back to what you just said, and wrapping that you know. That comment. I had a thought there. I think what I heard is that it is not just an id challenge right? I mean, for the longest period of time. You know, governance was embedded, and either it and in my experience while it enables right, I mean all of the tools, and that we’re going to talk about. And as organizations are heading towards dig digital transformation right and digitizing their business processes, so there are some opportunities there, but essentially for the longest period of time governance was seen as an it function or a risk function, and many a times, in my, in my opinion, that you know the the information security guys, right? They didn’t understand what governance did.

And similarly, the guys who were building data warehouses. You know, in the in the technology divisions or the data divisions. They were so overwhelmed. Right? Because you know, just imagine in a large organization you have tens of thousands of sources. It doesn’t need to be tens of thousands of sources. Right? I mean, you might have sources. You might have data engineers on that team, and they are servicing. You know, a large community of users like at the Credit union I spoke about. You know, we have close to users warning reports, data things like that right? And we’re very small teams sitting in the middle. So you know, people were not talking. We didn’t know what data go or they didn’t know what data governance was doing. What data did governance couldn’t, you know, really influence the this Chief Information Security Officer. And you know, there were all these disconnects that were happening right? So so I just wanted to call out that it’s not just an it problem.

And where we are heading, you know, from trends in data governance. You know, things are shaping differently, and I think I have a slide down there that I’ll talk about here slowly. So yeah, I mean, I think, talking about trends in the data governance. What I’m seeing Michelle, is that one is really, you know, the the the move from data governance being a defensive strategy in an organization to a value driving strategy in an organization, I think, with AI coming on board really quickly, right? I mean, AI has been around for a while, and you know, data scientists have their laptops, and there are a bunch of data in there. And they were trying to kind of, you know, do great things, and and they did great things right. But the majority of the organizations they couldn’t really make much progress, because the data wasn’t clean, and and then they would hit all of the roadblocks that you spoke about. Right.

However, you know, I was reading this article. About a phrase that is overly used. Right, for instance data is the new oil. And this article was actually written by an investment. You know, manager who deals with oil based funds. Right? So he actually walked through the economics of you know what actually means when people say, data is oil. And if you have terabytes of data just sitting there, that’s not value, right? When you make an enhancement to those that data, it becomes value, and then you add more enhancements. It continues to generate more and more value. Right? So so I think I’m seeing a lot of that. And the technology large tech companies, building products and technology and tools are starting to kind of, you know, focus on and delve towards the data product mindset. You know, where people are actually getting value out of the data that they are touching.

So that that’s aspect of it. Other one is. I’m noticing that with digital transformation coming to the fore, right? organizations are moving data governance from being a centralized function to more of a federated function. Where. And I think, Michelle, you nicely said it.

Kuldeep Singh: That you know, a small team of data governance analyz trying to solve the world’s problem right. That’s not going to happen. It’s not an it problem. It’s a it’s kind of, you know, a collaborative effort. So I’m starting to see more of an federated approach. Whether the central teams like you call center of excellence is they’re just enabling different domains within an organization. And you mentioned customer domain. I was talking about lending, maybe as a domain right? So data governance function is shifting now from being an enforcer to an enabler. Right? And so is ibis. Actually, function is changing, too.

Kuldeep Singh: They’re not just managing and maintaining and keeping the lights on. They’re becoming enablers for these domain teams. Right? So so that’s another thing. I think the human centric I think I love that. I mean, I’m my research interest. I read a lot about human center design. Because finally, I feel, you know that there’s empathy for the human in the middle. Right? I mean, we are saying, Hey, and I know you don’t like to use the word steward, but a lot of organizations are still using the word steward. They don’t call them subject matter. Experts and stewards were hired their their job description never said, you know, hey, go fix data, governance right? It was like all about business functions.

And oh, by the way, you are also a data steward. And except, you know, another  h job, right? So I think shifting from just thinking about data, infrastructure, technology processes, defensive strategy. But thinking about, you know from a human’s perspective, what are the questions that they have? Right? What are the questions that they are not being able to answer. So when I go into organizations, Mitchell, you know what I do is that I do workshops. I talk to a lot of analysts and I’ve been doing this for like almost years now, where I asked them what are their biggest issues? And they say, hey? I am pretty data, savvy? I just don’t know where the data is right. And if I know where the data is, it just seems like we have versions of it. Right? Where is the documentation? I mean, you mentioned catalog, the catalog right? And you know, if you give me the data, I’ll I’ll create like thousands of excel spreadsheets, and I’ll serve my business the excel mark and excel is not going anywhere. But you know. But that that is a problem that we have. Right? So how do we continue to take these things? Value, you know, centralized to federated and a human centered approach? And really start moving the data culture, the dial of data culture. Right? How do we make sure that we have models that scale models, as in. You know, just people. Right? I mean, if you have data engineers, how can this solve? For the entire organization something needs to ship there. So how do you scale what tools and technologies you need? And tools and technologies are thinking human centered now, right? They’re not just building something in the vacuum even till years back. years back, I was using tools, Michelle. You know, there was a tool for data quality. There was a tool for data lineage. There was a tool, for, you know, data profiling. And then all these tools that made up, you know, the gamut of data governance, I think what? Where industry is heading as a trend is. It’s consolidating everything behind the data value stream. Right? So they’re thinking about where the data is being produced right? And then what happens? Once data is acquired, all of the, you know, initial processing that you have to do the risk assessment and the security assessment, the metadata capture the definition of your data. And then data flows through its own life cycle where it’s enriched and it’s defined. And it’s ready for use. And it’s create data products are created right? So that’s the value stream. If you think about data in the business of data, and you do your own digital transformation, right? For the business of data, you can create these value streams. And now, tools such as elation. You know, when a tool that I’m unfamiliar with and a tool such as snowflake right? And then, if you’re looking at a workflow or transformation tools such as dB, they want to talk to each other with open standard Apis, right? So Snowflake is where you store your data. You acquire store. It’s like a data. Warehouse.

Dbt is a workflow. It’s a etl kind of alt kind of a tool with a lot of you know. functions that I just mentioned built into Dbt, and then, of course, elation is a product that it’s a data governance. It’s a cataloging product, right? They all talk to each other. And now it’s not necessary to be doing data. And then data governance comes later. You’re doing all of that together, right? Because your technology has evolved to a point where all of these processes are now automated. They’re interoperable. They don’t have. They don’t seem like they’re separate. So a a lot of shift has happened there. But I start with humans. And I end with humans. And that’s where the data product comes in. And I feel like when we talk about data product. Somebody’s producing somebody’s consuming value is being driven right. And then we have the center of excellence, which is kind of, you know, enabling data.

Kuldeep Singh: governance, data trust and all of those things. I think that’s what I’m seeing technology head. Hopefully, I answered your question. Michelle.

Michelle Moore: Yes, yes, yes, yes, I did. Wanna link back to What can you do to prepare and hook that back to how we started around the value of data governance right? I think it’s not a easy sell. I’ll just put another slide up here. Sorry. Excuse me and slip my slide this one, but there are many hidden benefits to doing data governance right? And I I think these often get lost in the discussion around what you’re doing and the ends of it, or what we already talked about. You’re either gonna increase efficiency, which will decrease cost or and you’re gonna protect the business, right? And typically, they’ll work together. You come at it from one of those angles. As we said before, you have a data breach data leak, something breaks. You go in to protect the business mode, or you’re just struggling to get things out the door because you’re doing so so spending so much time fixing data or reverse engineering data. You know, you just can’t get things done. And when you get to a point of scaling the business now, it’s impossible. Right? So now, you have a real need to fix things. So there’s lots of things you can identify if you are trying to make a case for data governance that you can put into a measurable quantifiable view of of doing right. So you can speed up your it. Analytics development. If you have standards defined. And you know where your data is coming from, you’re not going to be spending % of your time tracking down the data. And is it the right data? So you can quantify a lot of the things around implementing data governance to help get something moving here. But again, it requires somebody at the executive level who understands it and has skin in the game to make something happen about it. And you can use all this stuff right in your temps to try and get something off the ground, if that’s what you’re trying to do and it’s a good way to measure yourself right. All these things are measurable. So if you implement a center of excellence and get data governance moving for your organization, you can assess on an annual basis.

Well, did we speed up it? Analysis, development? Did we reduce data sprawl, you know? Do we have one place to go to get things? Or is it all over the place? And we don’t know who has access to what. So there’s some good tools you can use, and good methods of quantification that I think is frequently been missing in data governance programs. And and it just seems like an esoteric thing that people are talking about. But actually, it really does hit the bottom line of the business.

Kuldeep Singh: Well, thank you, Michelle, for that, because it nicely wraps the on. You know our arms around what I was trying to kind of talk about where the trends are heading. But you still need the discipline that you just spoke about. And you know, just to start heading towards the wrap here. And so I wanna get to the questions. you know, AI, we can’t leave any conversation without talking about AI anymore. Right?

The good news is that you know, I’ve been doing a lot of research across some of the industries top tools, right? Like elation is one of them. And I’ve been looking at, you know, work with Calibra for almost years, and prior to that, I mean, now there’s Microsoft Purview, and then snowflake data break. They have, like all these catalogs that are built in but the the interesting part about what where these tools are heading is that AI is now infused with it. Right? We don’t have to have a separate process where people are doing data profiling. There’s AI is already looking for anomalies. You know AI is looking for usage patterns by the engineers. You know how they use and how they transform the data and and kind of trying to understand some patterns that could, you know, automatically benefit the business? Right? For example. I used to think that, hey, I’m sitting on top of this petabyte class customer data analytics platform. Can I start my own business from within, like, you know, some kind of an internal monetization? If you want to think about it that way where I am coming up. Of course there are regulations I can’t kind of, you know, hit the customer data in a certain way. But if I had the boards permission, and if I had the executive permission, and the Regulators were okay with it, what if I was kind of producing all of these insights behavioral insights about our customers, or you know, it can be any, any human right who’s touching the data. It could be our engineers. So this AI is now pretty much infused, and if we want to have a separate session, and it can talk about it for a long time. But essentially, you know, recommendations, insights, right anomaly and patterns and automation. And you know, inferences. Right? You know this happened. What should I do next? That those kind of things are now moving towards AI and the and they’re very reliable. And the data observabilities which is really monitoring how AI is interacting with your data value streams. You know, it’s all available now, right? So I think for the listeners really, I mean taking the data governance that we knew. And that’s what Michelle and I are trying to kind of, you know. Wrap our arms around here is just turning it upside down. Some structures remain.

It’s hard to kind of change, you know, a globe global organization suddenly. And that’s so those challenges remain. However, there is technology, there is methodology, and it’s been proven across large banks, healthcare institutes right? And and many other industries. Well, and you know, happy to talk a little bit more about it in terms of tool technology, process, transformation domains, etc. So with that, Kalia, I’m gonna hand it over. I know there might be some questions.

Kalia Garrido: Yeah, this has been a great discussion. Guys, we do have a question coming in from Alba. They are asking, do you have data make? Or do you think data management is the same as data governance? And if not like? Or if so, what’s the difference? What’s the similarities.

Kuldeep Singh: I have my viewpoint. Michelle, you want to go first.st

Michelle Moore: I would say they’re suddenly different data management. I would say, you can do one or the other, or you can do both. Right? So data management is actually the practice of focusing on the data quality and defining the standards of things that you’re gonna have across your systems and maybe the usage of things. But data governance is a bit more broad in that. You would start to establish company wide policies around how data is gonna be used?

It would include things like, some monitoring tools for data leakage, you know, looking for patterns of Pii, that type of stuff. So it’s a bit broader than I would consider data management itself to be. Data management is more tactical in nature and is would fall under data. Governance is the way I would see it.

Kuldeep Singh: I mean, I have a very similar point of view as Michelle. What I’m I’m reading about and from my own experiences, right? so organizations do need data governance right? The policies, procedures. There are regulators, there are people who are kind of, you know, holding us accountable right? So we cannot not have that. However, you know, when you start talking about data governance, there’s this aspect of enforcement.

How many of us here in the room. Want, you know, do well, when somebody is enforcing us to do something right or forcing us together. And that’s where you kind of move to data management data management is all about the flexibility and the value right? And and and in Europe I I was I was doing. you know some trends for a CEO and one of my companies that I work for, and I was starting to look at the market in Europe and in Europe. Some of the banks are calling you know their data governance teams. The data trust, right? Because you know that kind of there’s so much emphasis on trust with open banking standards. You’re handing the power of data to the consumer versus bank owning the all of that data. So they’re going from data governance to data trust. So, however, you see it, you do need data governance. You didn’t. You need enabling technologies within and processes within data management, all of it, because we want to create that trust.

But hopefully, that makes sense.

Kalia Garrido: Absolutely.  I see we have some comments coming in. I don’t know if that’s an A specific question, more of a thought viewpoint. So if anybody has any last questions, this would absolutely be the time to drop them into the chat or in the question, function. But if not, thank you all so much for joining us for this amazing conversation Michelle called deep. Thank you for sharing your expertise with us.

I know one of the things that we keep hearing at high to. It’s getting more and more traction these days is the data governance workshop that you all have put together. I know that there’s a lot a lot of businesses or enterprise businesses are focusing on this now as they as they really should be. So it’s important to note that if anybody has a specific situation at their own organization that they’d like to discuss in a little more depth.

Please let us know. I will send a follow up to this session. I’ll share a lot of those different links that we’ve shared, including our next session, which is going to be next month on data visualizations, and we’d love you to join us for that as well. So if anybody has any questions at all, they can simply reply directly to that email. But again, Michelle, and call deep. Thank you for sharing your expertise to everybody who contributed their thoughts in the chat we appreciate you we love when these sessions become so interactive as this. So great time today. Thanks so much. Everybody look for the recording on Youtube. And we wish you all a wonderful day.