#125 Building Trust in Data with Data Governance

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Hello everyone, this is Adele, Data Science educator and Evangelist at datacamp. Arguably one of the biggest obstacles for organizations in building a data culture today is trust in data and trust ultimately boils down to data quality and an organization's ability to govern its data well. This is where data governance comes in and this is why it's so crucial. So how do you make gains on data governance? How do you bridge the gap between functional teams and technical teams to create a common data governance vision? These are all questions that are going to be answered by today's guest, Laurent Dress. Laurent is a data Governance Evangelist and Director of Professional Services at Data Galaxy. He is a seasoned professional expert with a proven track record of successful implementation of data governance related projects across the world. Laurent is skilled in data management, data governance, data quality, master data management and a lot more. He has a strong consulting background with PRINCE 2 practitioner certification and has been focused on pragmatic project management around data management throughout his career. He acted as the perfect middleman between IT and business regarding federating efforts and developing a unified view of an organization's data landscape. Throughout the episode we spoke about the state of data governance today, how data leaders and organizations can start their data governance journey, how to evangelize the importance of data governance and gain buy in from everyone within the organization, the state of data governance tooling today, and a lot more. If you enjoyed this episode, make sure to let us know. And now onto today's episode. Laurent, it's great to have you on the show.

Hello Adele. Thank you very much for the invitation and the opportunity to share around data topics.

I am very excited to discuss with you all things data governance and quality, how a culture of data trust promotes higher use of data, how data leaders can succeed at data governance and more. But before, can you please give us a bit of a background about yourself?

Sure. I started in the data governance and data world about 20 years ago. Prior to that I was a project manager and doing the liaison between IT and business, which helped me a lot in my data career. Then what I did is in fact I was working for a company where they implemented master data management. It was my first experience with data where I represented my business unit and then at the end took over the overall program management. Then I worked for major consulting companies and implemented data governance frameworks, organizations and tools internationally. So I traveled a lot around the world, which is also nice because you're confronted to different cultures and opens your mind. And then three years back I joined Data Galaxy to set up professional services and help our customers with the implementation and the return on investment on the data platform. Recently I moved to the evangelist position, so I'm spreading the good words around data governance. I'm also participating in podcasts like yours, but also in exhibitions and events, in keynotes. Bottom line, making sure that our product fits the market needs, that we also voice the market in terms of features and expectations, and also making sure that Data Galaxy is a known brand emea and now also across the us. On the personal side, I live in Belgium. I'm passionate about food. It's probably also the reason why I called my daily LinkedIn post the data Governance Kitchen. So do not hesitate to subscribe to my profile and you will get daily hints on data governance, Data Management, Data Catalog.

That's really great and we definitely have a lot of common. I also live in Belgium and I also am passionate about food. But I'm really excited to chat with you about all things data governance. You know, I'd love to set the stage for today's chat by trying to understand where the data governance and quality space are at today. I think we can all agree that the past few years have been, you know, marked by the march for organizations to become data driven. Organizations have been making massive investments in data infrastructure, talent, data collection, data culture and more. Naturally, the data quality space has evolved quite a lot to accommodate the growing needs of organizations. So maybe starting off, I'd love to first understand how have you seen the importance of data governance evolve over the past decade and what makes it such an important area of investment for data leaders Today?

That's a great question, Adele. It's the genesis of data culture in many organizations. What I've seen is that historically anything related to data was managed by the IT department in a very siloed way. It was a kind of kingdom approach around the data and the same was applicable to business departments. Very little information was documented, they had no common language and they were working in a very closed way and not sharing information across a line of businesses. Now things have changed, the mindset have progressed or evolved. Data is recognized as an important asset in many organizations. So it's, let's say three to five years back, we saw the rise of data self service in the API world. So we were giving the power back to the business. They were provided with data sets and able to run their personal queries and reports. But to be able to understand your data set, you also need to have this common language definition of KPI definition of business terms. So the rise of data catalog really started there and then it really helped building this common language. I mean we are both European based. If you put a German, a French, a Belgian, a Dutch, a Danish, an Italian and a Spanish guy around the table, they will have no common language. It's very difficult to understand each other. Same goes for the data. And the data catalog brought this kind of Esperanto type of language around the table and people were able to communicate and understand and look at the same thing with the same definition. So data governance helped a lot. It created the need for data catalogs and it was the rise of some of our competitors back in these days. I mean Collibra started probably 15 years back really and Data Galaxy, we really had the same. We were facing many feedback from our customers that they were lacking business and global language. And then last but not least is also the understanding from many organizations that data is a valuable asset. It's not just information on a server, but it can really make a huge difference in the competitive landscape, I would say. Or it's a competitive advantage. And the ones leveraging this information to the maximum are quite leaders on the market. If we simply take examples such as Amazon or Uber. I mean these guys, they know you in and out when you book a ride and they come with the right offer at the right moment. And this is because they are leveraging the available data.

That's really great. And there's definitely a lot to unpack here. You know, one thing I want to kind of deep dive in is how is data governance evolving? Given the rise and complexity of the different data landscapes, organizations Deal with right now. You know, we mentioned that organizations are making more and more investments in data. There is a big bigger recognition that data is recognized as an asset. Data is given back to the people, it's given back to the business. And this in turn creates an increasingly complex data value chain. So given how complex the data value chain is becoming across an organization today, how have the challenges with data quality and governance evolved? What do you think are the top obstacles in terms of data governance for organizations today?

That's right. Many companies are evolving in terms of technical stack, big organization. They have this legacy of I would say old fashioned systems. It's not old fashioned, but the old ways of doing things with relational on prem databases like an Oracle and AS400. But also they start understanding the importance of modern data stack in terms of data lakes and modern technologies. So they really have this struggle of where do we go, do we keep the legacy? Do we move to the full SaaS for instance and full cloud? Or do we have this hybrid approach? And what we see is many companies for now are at the stage of having this hybrid approach because they will never decommission old stack. Which is a total different story for startups because they are cloud native. They started with cloud tools, so it's a bit easier. In terms of data landscape. However, they all face the same challenges and I would say if we need to outline some of them is traceability is where the data is coming from, what are the system it goes through and what are in fact as well the transformation applies. And the more complex is your IT landscape or data landscape, the more complex it is to understand the journey of your data and the transformation because it goes through so many systems with replication, with aggregation, with transformation, that people are totally lost. They don't know which transformation and when they have a data quality issue at the end, they don't know where it happened. So it's very important to have this traceability and transformation understanding. And now also another type of challenge is indeed identifying the real source of error with let's say your transactional system. Your name ADL is on an invoice, but where is the issue with your let's say address? Is it coming from the initial transaction, is it coming from accounting, is it coming from marketing? So it's really important to be able to really find the source of error. It means that if you want to find this source of error and take some remediation action, you need to be able to contact the right person or the right people, which is a big challenge around Data stewardship. What I hear from many organizations when we start discussing about data catalog is of course we know where the error happened, but we never know who to contact. Is it Adele at accounting, Is it Laurent at Marketing? Or is it someone else in another department? That's where data cataloging is playing a very important role. And the last part is something which is not always understood by organization is your data context. Because data quality issues can happen, of course, but depending on the context, it has more or less impact. I mean, for instance, a birth date of someone. When it comes to generating the invoice, it has very few importance. I mean, it's not important that your birth date is correct when you receive the invoice. However, if you're from marketing and initiate a reward plan for your birthday, then it's very important because you need to receive this offer right on time on your birthday. So to summarize, I would say traceability, transformation, source of error, data stewardship and ownership and data context are very, very big challenges for organizations today.

That's really great. And let's dive deep, maybe into the stewardship component and the overall ownership of the data governance program. This increasingly complex value chain also puts stress on the ownership when it comes to data quality and data governance. Data is produced upstream by various functional teams, collected from various sources, then transformed downstream and is used by a variety of stakeholders within the organization. Who do you think is the owner of a data governance program or the data governance agenda in general? And who should be the main stakeholders when it comes to the data stewardship program?

Well, Adele, this is $1 million question because this is quite a difficult topic and it's a complex question. I think there are many, many stakeholders in the data journey which make things complex. Either they all think the responsibility resides with someone else, or they all think they are the owner of the data and the others can't touch it. So we really need to set the boundaries there. What I would say, and I tend to tell my customer, is first of all, there can only be one data owner in the entire value chain. I mean, it's like cooking. If you have 10 chefs in the kitchen, things will run very, very bad. So you only have one chef in the kitchen. You only have one data owner in your data value chain. It means that the exercise is quite complex to identify this ultimate owner. One approach is to say, department, the line of business who created this data the first time is the owner because he or she has the ability to modify the data if there is a data quality issue. So that's one approach, what I also think is important is to in fact draw your data value chain, because you have all the steps, all the systems, and it will definitely help your data governance organization to find out the ultimate ownership. Again, it's a value chain. So value chain means you have many links and each link needs to understand that he or she is receiving a data item, altering the data item, doing something with this data and passing it on to another person. So it's everyone's responsibility to make sure that when you pass the data to someone else, the quality of the data is very good. And then I would say that you need, once you've drawn this value chain, you need to define the chain of commands. So knowing exactly if you have a data issue, if you need to update the data item, who are the person in charge and who has the ultimate power to alter this definition? And this is where data governance and data catalogs are playing a big role, because the data catalog is the centralized information around your data items in terms of ownership, stewardship, traceability and definition and so on.

And if we want to kind of dive deeper there in an organization, let's say an enterprise organization that's still identifying its data governance strategy, who are the type of profiles that you think are suitable owners of the data governance agenda and what are the type of stakeholders that support them there, I would say.

Based on the implementation I made, you have various profiles. But where companies are successful is where you have a cdo, because the CDO is really the person at senior leadership level with the authority and the mandate to make sure that data is valued and data governance is implemented. And this makes a big difference because otherwise if it's just you and I, for instance, in a day to day life, your marketing and accounting, trying to do things around data governance, it will be very complex because you don't have the resource, you don't have the budget, you don't have the mandate. So you really need to have sponsorship at a leadership level with the cdo. Quite often you also see that you have head of data governance, for instance, who are playing a big role, or you have a CD office with a bunch of people managing the data governance, but it's really a separate function in the organization.

That's really great. And I think this really marks a great segue into what actually makes the data governance strategy and approach successful. As data becomes even more critical for decision making and product development, data governance can have massive implications for the organization, as we've discussed so far. So you've had quite a lot of experience in implementing successful data governance programs. Can you outline what you think are key components of a successful data governance strategy?

Yes, that's as well an important aspect because the word strategy is very important. I've implemented Data galaxy with around 80 customers in the past three years. So what I've seen with successful customer are having data vision and strategy at executive board level. That's very important because these guys will have to define what they want to do with data. Can be monetization, it can be data driven, it can be whatever they want, but at least they have the vision on what they want to do. And this is the very start. And then you have the CDO role which is in fact empowered and in charge of making this strategy live and also having this frequent communication between the rest of the data organization and the management board. So he's the kind of person relaying this information. And then they also have to define their why is. Why do we want to be data driven? Why do we want to value data? Because at the end there should be a business outcome. And then once this part is defined and clarified, they pass it on to the business. And I really say to the business because data governance and data cataloging and data valuation in general is the responsibility of the business. It's the end of the IT kingdom, doing everything around data. Business takes the lead and then it comes there to support, in fact to access the data, to trace the data. But it's really a business matter. And then once you have this strategy, business understands the importance. Now it's also to identify what are the daily pains, challenges with the data. Once you have these challenges, you will know how your data governance initiative and data cataloging will relieve the pain and help the business getting better. I also strongly suggest to have a data governance by design. So don't try to make it a big bang in the organization by building complex structure. Stay simple, be agile, be iterative. So try with one line of business with one use case and then two use cases and gradually expand in the organization. It will be way more efficient than trying to help this big bank. And then another success factor is how you identify the indicators around your data governance initiatives. Not that you necessarily have to identify the return on investment in terms of dollars, but the indicators which will make sure that you're on the right track. And remember, we identified pains. If you identify pains, you identify the benefits and you monitor these benefits. And once you have these benefits reported, you need to communicate across the organization. It's a key component of your data governance initiative. The way you will communicate every time, I mean every touch point in the organization, town halls, quarterly business reviews, CEO speech of the week or newsletter, whatever data should be embedded in there because this is the way management shows the importance of data in the organization. And the last part is really the human part. Data Governance initiative is based on human. It's a change management process and it's the same for many other projects. You can buy any tool, but the tool will only do what you tell him to do. And if you don't focus on human that, you understand that people are reluctant to change. They need to understand the value, they need to be informed, they need to communicate. Once you have this under control, then you have more chances to be successful in your data governance initiative.

That's really great. And there's a few things I want to really focus on here. So you mentioned data governance having macro benefits that are made clear. There is a why that is attached to it, that is communicated to the entire organization. CDO needs to act as an evangelist, for example, in the organization outside when discussing the data governance program. And finally, it's a change management program, right? It's also a cultural program. It's a way of work that needs to change. And I think one thing that we've seen across, you know, data framed episodes, regardless of analytics investments, whether it's a data literacy or data culture investment, a new machine learning project, the importance of evangelism and buy in right by the executive team and also by the wider organization, is extremely important for these types of investments. I think one thing standing in the way of investment and data governance and data quality is that it's not necessarily a shiny use case you can show in a quarterly earnings report. If you're a leader or something you can add to your cv like a machine learning project or you know, a dashboard if you're a data practitioner. How do you convince stakeholders within the organization to invest in data governance, especially if they don't yet view it as a strategically important project?

Adel, you're asking tricky questions, Inspector. It's complex and I don't have the magic sauce to make things happen from one day to another. But if senior leadership does not understand the importance of data and the value of data and data governance, my first reaction would be to tell them do nothing until you hit the wall and understand the importance of data governance. That's a total waste of time trying convincing someone who's totally reluctant to data information. Of course, it's a bit on the joke side that I'm telling this sentence but if they don't understand, I mean it's a long effort and most likely you need to have this external view or external people or external. I would say for instance a consultancy firm coming and explaining why it is important, how it works within other companies to show the importance. What I've seen is that most of the leaders and stakeholders, they have a vague feeling that the data is important, but in fact they don't know where to start. That's more this aspect of being aware of data governance. It's not that they don't understand, the importance is they don't know where to start. So that's where again you need to seek for help. There are many consultancy companies, expert companies, I'm not talking about the big four, I don't give any names because there are also partner of us, but I work for some. But you have really small companies with high level of expertise around data management, data governance framework, data governance strategy, which will educate the stakeholders on where to start and how to make it a success in their organization.

That's really great, but maybe kind of diving a bit deeper here. If I am a data governance stakeholder manager, I'm owning the data governance program within my organization. How do I evangelize the importance? What are kind of key tactics I can have to evangelize the importance of data governance within the organization to the wider organization? As it is indeed a change management program that requires a cultural adoption of data governance methodologies and techniques.

Indeed, change management and the educational part is really important. So you have to equip your people, your teams with not only the right tools, but most importantly with the right skills and knowledge. It's very important that not only people will understand the importance of data, but they will know what to do with the data they have. And also reinforce soft skills development around collective mindset. Because data governance is really a collective and organizational program. Also making sure that your people are able to listen. It's a lot of listening to the others and the challenges of the others having also certain dose of pragmatism and agility. It's the end of two years program with a waterfall project plan which will never be respected. In fact, just be iterative. Try to understand your long term goals, but be iterative on the day to day or weekly basis. And then also make sure that people can communicate easily with their peers. Communication is very important, explaining why you are doing things, trying to communicate the benefits of the organization. So it's really around the human skills and not technical skills, but soft skills which you have to invest. And then of course, once you decide to equip yourself with a specific platform, then you train the people on how to to use this platform from a system standpoint. But I would say that focus on soft skills, focus on understanding the world of data. They don't have to be data scientists or become data scientists or data experts, but they need to understand the overall picture of the data world.

That's really great. I couldn't agree more, especially on the importance of skills here because the evangelism of why you need to acquire these skills, but also clarifying, hey, once the data is of great quality, you'll be able to leverage your skills. To be able to derive value for the organization is extremely important. Now of course, one thing that's also really interesting given your role at Data Galaxy is as an outsider. It's been really interesting to watch the data governance space, you know, especially the rise of many different categories of tools and new startups in this space. Last few years we saw for example the data observability space grow in importance quite a lot. We're seeing more and more data catalog system grow into the mainstream. Walk us through the technical ecosystem for data governance governance tool, how it has evolved and what are now the must have tools for any organization that is mindful of data governance.

Data governance, I would say application landscape has evolved tremendously in the past years. What we saw a couple of years back was big players having one platform doing everything, ETL quality, storage, data warehouse and everything. So now we see more and more specialized players but still focusing a lot on the technical side. So data sources, data transformation, ETL data visualization, code parsing. There are plenty of players there, but very little business focus. Very few are really focusing on how I can serve the business part of the organization, what is the value I bring to them. And I truly think that this approach is, I would say over to only have the technical side, we really need to focus on the business value. And this is where we will from Data Galaxy standpoint, we are making the difference and the new players will also make the difference. That's one context of introduction. Now what we see is that many organizations are moving towards the best of breed applications to be integrated in their ecosystem through APIs for instance. Because each application, each platform is expert in what it's doing and not doing everything in average, it's really being specialized, it's really providing quality on a very narrow problem or challenge. Now, in terms of what should be your application landscape around the data in your organization, I would say first of all focusing on the technical part, you need to have your modern data stack which makes things way easier. So data lakes, data warehouse, but really on the cloud, which helps with the integration. And once you have this modern data stack, having data observability, so piloting from a finops standpoint, your data stack, understanding the cost of storage, the cost of computation, the cost of operation is very important. Then you have of course your ETL ELT stack to converge the data into one central location within your modern data stack, accessible to your organization. Then what we tend to see now is the data mesh or data product architecture and related applications. So we in fact have there the gateway between technical and functional, between IT and business, where you bring back the ownership at the business domain level, which makes things I think from a data governance way easier. Then you have your data quality, which is also important because if you want to be data driven, you need to base your decision on qualitative data. Then you have data visualization and bi plenty of players there, but they tend now to aggregate. So probably in couple of years we'll only have four or five big players in that area. And then also your conceptual data model platform which will help architects and business defining what should be the data model, the enterprise data model and how to roll it out in your organization. And the last one, which in fact will seal everything together is your data catalog. Because your data catalog should in fact within the data governance world we tend to use the house of data governance. Data catalog is the foundation of your house. So if you don't have your solid foundation with the information that can be shared across your organization, it will be very difficult to enable the rest of your data stack or data model stack in your company.

That's really great. And one thing that you mentioned here, I'm really excited to deep dive into the data catalog side of things. But before that you mentioned as well the rise of the data mesh architecture system. Where are we today in data mesh adoption? There is quite a lot of discussion in the data space about the transition to data mesh. How far along are we in the adoption of data mesh? And do you see that that is something that is common across organizations or is that something that is only adopted by relatively high data mature organizations today?

What I see around data Mesh, first of all, it has been a hype in 2022 with many books being written, but in terms of implementation, we are still at the stage where many organizations are still investigating the feasibility of data mesh on a narrowed scope or perimeter. It's not that they are Fully rolled out with Data Mesh. It's still a conceptual architectural approach because it gives guidance, but it doesn't really give instructions on how to implement it. It's really based on each company and each organization's to trying. So we've seen companies where they implemented partially data mesh on some parameters and we have companies where they are still trying to understand the architecture, understand the data mesh principles before investigating because it means that you will have to rethink your entire architecture and identify different type of profiles and responsibilities. So we are not there with companies fully data meshed, it will probably come in 20, 23, 24.

That's exciting to see. Now of course, given your work at Data Galaxy, I'd also be remiss not to talk about data catalogs. Walk us through some of the most important aspects of the modern data catalog. And what should data leaders think about when they consider a data catalog solution?

Data Galaxy in fact, yeah, we are indeed a data catalog solution. And our vision is that Data Galaxy solutions should act as data knowledge workplace with one goal is bringing data to the people, meaning that we are business driven. In fact, you need to have this information available within your organization and act like a Google or Wikipedia around the data for the enterprise. And there are some key aspects to consider when opting for a modern data catalog. First the user experience very important because in terms of adoption, this is one big enabler. So for me this is the end of the technical platforms finding information about your data. It should be as easy as googling something. I mean very simple interface, ergonomic, user friendly, few keywords and then you get the results. That's really important. Then you have integration API is given modern data catalog should have APIs for integration. And also it will ease the connection with your other modern data stack layers in a very simple way. You don't have to rebuild complex mechanism. I mean API will really free the data world. And then we have artificial intelligence back in let's say five or six years stewardship was a very intense manual activity and now AI should support so a lot of artificial intelligence running in your data catalog to in fact bring augmented stewardship. Helping data stewards with classification, with organization, with relationship and not having to discover everything by yourself, but having the tools supporting this and suggesting you relations, classifications, tagging and so on. And then a very important last aspect is the business value at the tip of your fingers. It means that in fact the data catalog should be part of the user digital environment. It's one of the key enabler in many organization and We've seen since we rolled out your browser, Chrome and Edge extension that you can browse the data catalog straight from your working environment. That adoption has increased. You don't have to connect to a third party platform. You really just have to query something and it's displayed in your working environment. And this is really how we will ensure from a holistic view that business and common people in the organization are leveraging the data catalog content.

That's really great and maybe I'd love to learn some examples of Data Galaxy in the real world, how it has helped real world data teams address some of the considerations you just mentioned.

Like I told you in the introduction, I've implemented Data galaxy with roughly 80 different customers. So each industry has its own priorities, each company has its own structure. But if I could pick one, I would pick the example of a major French insurance insurance company who implemented Data Galaxy. They were having a lot of challenges with data ownership and traceability and understanding of data transformation, data indicators and algorithm. So it was clear scope at the beginning. We need to have a solution which will help us identifying ownership and understanding our indicators and algorithms for a specific scope or perimeter. So what they did is they implemented Data Galaxy to provide the organization with a unique functional and technical desk. It's really the central point of information common language of their different use cases. So the definition of an indicator was the same across the entire organization, of course, clear identification of ownership and stewardship. What they also brought is the end to end data lineage. So traceability of all the systems and transformation applied across the system. They also built this platform not only to spread information, but also to gather information and help subject matter experts. Sharing their knowledge, documenting the information, valuing their knowledge. And last but not least is how the data catalog is able to support regulatory and compliance requirements. Here is a solvency tool for insurance sector as the evidence showing that yeah, we are in control of our data. We know where it's coming from, we know how it's transformed, we know who's the owner. So it supports really their initiative. And that's what we see in many organizations is building a common language, having ownership and stewardship identified, having traceability and building this common language around the data assets.

It's really awesome to hear and I highly recommend to the audience to check out the site now, of course as we wrap up Laurent, I'd be remiss not to ask you what do you think are the main trends data leaders need to be mindful of in 2023 regarding data governance and data catalogs?

There are many trends out there on the data governance or data management market and depending on who's publishing, you have different trends. But I would say one of the common theme in 2023 would be data democracy is in fact how data governance, data management, data cataloging will improve the decision making. So if you want to move to a data driven mindset, you need to make sure that your data is accurate. Then when you make the decision based on data, you're not fooling yourself. Then it's increased collaboration that you share the data. You in fact have more and more, I would say decision process around your data. I mean not only that you're data driven, but data is part of your day to day discussion. And whenever you think you need to mitigate something, you based your mitigation decision on data. And then is also in terms of trends, greater transparency. I mean it's giving people access to the data they need. And we are back to the beginning of this discussion where many organizations it's still siloed either it versus business or even business lines against business lines. So here it's really having transparency and building enterprise wide data sets and sharing it with the people. What we can see also is improved customer service through data. In today's competitive world, we roughly offer the same service and same pricing in terms of business. But the customer relationship will play a big role. And if as a customer facing employee, you don't have the relevant data to bring the right offer to bring the right service, it will become a competitive disadvantage. So really having this in mind will help many organization. And then the last one, which is a trend, but which is also a trend since many years, is reducing the data silos. I mean breaking barriers across department. Having this overall entire company wide data strategy and data language is a very important aspect.

That's really great, very exciting to see the year ahead in data governance. Now Laurent, as we close out today's show, do you have any final words for the audience?

I would give the market one simple advice. It's never too early to start your data governance and data catalog initiative. I mean even if it's in Excel, just do it. It will be the foundation of your future data catalog. Then make sure that you have a data vision and strategy drafted. Not necessarily ready to be rolled out, but at least drafted. And then also talk to the business, make communication key in your organization and then jump. I mean go ahead, harness the power of data governance and change. You will value your data and build your competitive advantage. So do it now. And then the last word is. Thank you Adele for the opportunity I really like to share around data governance, data management, data cataloging topics. And for the one listening to this podcast, do not hesitate to subscribe to my LinkedIn profile. I'm sharing day to day tips on data management and data governance.

Thank you so much Laurent for coming on the podcast. Really appreciate it.

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