3#15 - Valentina Niklasson - Data Governance and Data Stewardship - Inspired by Quality Management (Eng)

«Don’t make it hard to understand for the business. Make it simple and clear.»

Get new perspectives on Data Governance with Valentina Niklasson from Volvo Penta as she talks about certain patterns, stages in the acceptance of Quality Management or Lean, that Data has to go through. Her rich experience in making Data Governance business-centric emerges, showcasing how you can get an organization engaged in Data.

Gain insights on the synergy between lean methodology and effective Data Management. We explore the application of the PDCA Deming circle in Data and discuss how common languages and methodologies bridge the gap between Data, IT and business. This convergence is not just theoretical; it's a practical pathway to tapping into customer insights, translating needs into strategies, and fostering a culture where continuous improvement reigns.

Finally, we delve into the human aspect of Data and Data Stewardship, emphasizing the importance of people over technology in cultivating a data-driven culture. By engaging the curious early and involving them in the development of business information models, we build ambassadors within the business, ready to champion change. Valentina and I talk about the dynamic role of Data Stewards and the approach to involving business personnel, ensuring the smooth adoption of new processes and strategies.

Here are my key takeaways:
Quality management as inspiration

• Data is still treated as an IT problem, but should really be treated as a business problem.
• We need to find a better way to communicate across data, IT and business.
• Use the same methodology wherever possible and try to reduce complexity in processes.
• Try to adapt to the ways of working in the business. Not creating own ways on digital, data or IT.
• You need to understand customer relations, end customers and the entire value chain to define needs correctly.
• Standardized ways of working can help to do right from start.
• Deming Cycle, PDCA, can be directly adopted to data. Think of data as the product you are building, that should have a certain quality standard.
• Don’t make it hard to understand for the business:
  • Using the same forms and approaches.
  • Business data driven process.
  • Let the business take part in the entire process.
• Lean Methodology should take a bigger place in data.
• A product management mindset makes data quality work easier.

Data Stewardship

•  You need to ensure owning the problem as well as the solution.
• High data quality is vital for data-driven organization. Someone needs to ensure this.
• Stewardship can have a negative connotation. 
• The technical demands on Data Stewards are really big today.
• Data Stewardship works if the Data Steward is part of a broader team.
• The role of Steward needs to be adjusted to the fast-speed reality.
• Data Stewards need to be able to solve problems, not only report to a central organization.
• Data Stewards should be approached in the business. You need that domain knowledge, yet they cannot perform the entire stewardship role.
• Most important to empower Data Stewards to start working and analyzing the challenges ahead.
• Don’t force Data Stewards to be technical data experts. That should be a supportive role in the Digital / data organization.
• If you build something new, engage Data Stewards from the beginning. 
• You cannot take responsibility for something you don’t understand.
• If you want to be sustainable in Data, you need to help the people in your organization to be part of the journey.
• It’s not only about hiring new competency, but engaging with the knowledge you have in your organization.

Transcript

Data Governance and Quality Management Insights

Speaker 1 0:00

This is MetaDemo, a holistic view on data management in the Nordics. Welcome, my name is Winfried and thanks for joining me for this episode of MetaDemo. Our vision is to promote data management as a profession in the Nordics, show the competencies that we have, and that is the reason I invite Nordic experts in data and information management for a talk. Welcome to today's episode of Metadata. We have data governance as one of the core topics of Metadata. There's one thing that I'm always intrigued in is finding new ways to think of data governance, new ways to get that appeal of data governance spread in the organization and get that understanding of the importance of data governance spread. And today I have with me Valentina, and Valentina is working with data governance for Volvo Penta and she's going to talk about what Volvo Penta is as well and about her role in data governance. So, as a bit of a quick intro already said, data governance has always been viewed as a discipline without much appeal in the data world. You compare it to data science, for example, or data analysis that have been trending for a while. Data governance has always been there more on the backend side. I try to kind of understand data governance from different angles how to engage, not just to follow the rules, but to really understand the value of data governance and the value data governance can bring to organization. And one of the important things there and we're going to talk about this later as well is how to engage in data stewardship. How do you get those good advocates for data governance in the organization?

Speaker 1 1:57

We're going to talk about learnings from quality management, which I also think is really interesting, and a quick anecdote on that is I mean, I've been working with oil and gas companies for a lot of years and there has been a cultural shift in oil and gas since well, since we explored the first oil and gas fields in the 60s. It has a focus on health, safety, environment, but also quality. So HSEQ units have been established in every oil and gas company really, and what I think is really interesting is if you have been working in an oil and gas company, you probably noticed those. There are posters in every meeting room for every company I worked for that show their HSEQ focus points, and I always thought those were really interesting, and this is just one example where in every single meeting room of the organization there was a poster saying something about establishing strong quality metrics, managing change and drive innovation, and to be clear about how to follow actions and capture learnings on the quality side, and I always thought that, well, at one point, I would like to see something like that stated in a meeting room about data. That would be interesting. So maybe you at Volvo Penta are close to that.

Speaker 1 3:18

Valentina, welcome. Please introduce yourself.

Speaker 2 3:21

Thank you. I'm Valentina Niklason. I work at Rolpenta as a data quality manager for the moment, but actually you know how it is. You have a big room and that is just the description of the room. But I'm working with information management, data design, responsible for the governance and also working with the data quality. So I'm actually working with everything but not the IT landscape itself. But of course we are touching it all the time. I've been working at SKF before I come to Penta, but at all Penta I've been working actually for 35 years in the business, so it's a long time, a lot of business knowledge. I worked as a data quality manager, both in the factory, product development and purchasing everywhere and data engineering. I've been working as lately.

Speaker 2 4:16

Before I joined Digital Night, I worked as a product leader for our first Electrify driveline, so that was really interesting. You don't learn anything when you start and you have to invent everything from start. And there is actually my touchpoint, why I'm sitting here and working with data at Digitality because I was sitting in workshops making the data structure for the electromobility at Volvo Penta and how should we structure? Because that was a new animal coming into our company. We had the engines, we had all that structure. Every new project just followed the same. It's not so big difference, but here was something new. So after that I was joined the digital ID and I've been working with this for three and a half years. So you may think I'm a novice, but I have a lot of experience with the business and that is my approach. How do we do this business?

Speaker 1 5:17

friendly, oh, and I really enjoyed it and this was one of the things that was intriguing to me when we had our initial talk was we talk a lot about data as being a business function and not an IT function. But in many organizations we still treat data as an IT function and we treat it in the same cadence as technology projects. We treat it in the same way as we would treat our application portfolio, but really it's about solving business problems and being close to the business. So to have someone like you taking on a role like this, it's really important because you bring in that business perspective. Before we go on and talk a bit more about your hobbies and where your interest from data comes from, if you could explain to the listeners what Volvo Penta is as a company and how it's organized in the Volvo Group, just to get that understanding.

Speaker 2 6:05

Yeah, we are actually a lot of TDBAs. What is TDBAs? Business units? Of course Sorry for my Volvo language we are one of seven business units and in our group at Volvo Group, we have Renault, we have Mack, we have trucks Volvo Tracks, we have Mac, we have trucks, volvo tracks, volvo buses, vce, construction equipment, penta, volvo Penta I think I covered them all. Of course, when you are working as an own business unit, as a part of a bigger group, that means that you have common share data and you have your own data. That is the complexity we are facing a lot of.

Speaker 2 6:48

And so what are we doing at Lopenta? We are actually producing power solutions. Sustainable power solution. That is our goal with what we do. That means that we can make a driveline, but we don't have a truck, we don't own the chassis, we don't own anything, but we are producing everything that makes this move. And then, of course, we have generators and that part that we are also working. We are not only marine Marine engine is what everybody knows us about but we also have industrialization, mining and agricultural drive-lives, but the faucet can be owned for whomever wants to buy from us. So that is a lot of data when we talk about power solutions.

Speaker 1 7:36

A lot of data and a lot of complexity. Really interesting.

Speaker 2 7:39

Yes, and also pairing data between our customer and us. What is the other part? And it's really because we don't have the chassis. We have to fit everything together.

Speaker 1 7:49

Yeah, and this sounds kind of interesting because I always ask the same question to everyone on the podcast, and it's where does the initial interest for data come from? Where did it spark? And I think, with your background and your experience and how passionate you talk about the business, I think this is quite clear where that interest comes from. Right.

Speaker 2 8:10

I feel a really big need to have the work I'm doing connected to the business strategy. So that is where I think my passion is succeeding how can I improve, how can I help the business to reach the targets? And what is coming now Electromobilities, serviceability and sustainability everything of this demands a lot of data, and that is what my passion in life is to. How can I do this? How can I help the business reach new targets? How can I do this? How can I help the business reach new targets? So I think it's and I was sitting in another position on the other front in the workshops and thinking, yeah, I can do this, I can do this better, so it kind of drive me to start working with this.

Speaker 1 8:59

Fantastic. So what does Valentina doing when she's not working at Volvo Penta? What are your hobbies?

Speaker 2 9:04

I love working with jewelry. I like to be creative, to make silver jewelry, and we also have two horses show jumpers so that means that I'm out with my daughter I'm not on the horseback, but on the sidekick and helping her with the horses. That is actually what I have time with for the moment, but I love everything that is creative. It makes me happy, fantastic.

Speaker 1 9:32

It sounds like there's a good switch between work and hobbies, and I mean horses take a lot of energy and time.

Speaker 2 9:40

Yes, I think that creativity and curiosity is actually what is really important today in the data world.

Speaker 1 9:48

Very true. So let's dive a bit into quality management as your inspiration and maybe an inspiration for others working in the data field, because quality management is something that we can definitely learn a lot from, definitely learned a lot from when you look at your experience, when you look towards Deming or quality-centric approaches. What are the learnings you took from that with you along over into the data world?

Transforming Data Management Through Lean Methodology

Speaker 2 10:12

Yeah, if you look at all of that, I was actually building in the data quality system for a couple, maybe eight years ago, for the process-based management system, and I was thinking it's not so big difference because if you look into the ingredients and the approaches and the methodology it is the same. And if you think about it as making a product, you need to have the good quality and so on. What if the product in the future is our data data products? Services is only data. It's not so much about the product hardware, it's the software. A lot of the parts from the management system is applicable.

Speaker 2 10:57

You are following the same PDCA Deming circle. Whatever you have a current situation, you have to. You have a wanted situation. What is it? You identify the gap. You have to make it root cause. Analysis, because you're building capabilities. It's information, it systems, processes and organization. I know that in capability is a little bit wider than organization, but I put organization there too because they're the governance of it and you create a business case, implement solutions. You start small when you find quality of it. You measure it. Measure it, of course, and you can scale it up when it's okay. And that is the same for the data work. When it comes to the date, dates only the rules a little bit different, but not so much. If you look into the ISO standards, it's really the same ways of working. That is what I bring into this work. Don't make it hard to understand for the business. Make it simple and clear, and that is a problem, I think, between digital IT and when you talk to the business. It's how we talk to each other.

Speaker 1 12:08

We had a previous episode where we talked about data governance at the Lego Group, where we talk about the lingo. Some of the main issues that arise between translating between IT data and the Bith is the lingo, that we are using our own words, our own vocabulary. It's kind of a part of the identity, right? Have you looked into how you can change that, how you can adopt the language? Yes, actually I have, but it's not the language.

Speaker 2 12:32

Yes, actually I am, but it's not the language. I'm starting with using the same forms, the same ways of approaching. For example, if you have already a methodology for making business cases, use the same, it's not so big In the business case you have to put in what do you want to achieve? How do you, what is the problem, how big is it, what kind of regions, where is it impacting and what do you think it will cost in man, hours or whatever? And all of that we already have when we prioritize cases for the product and it's the same for the data. We prioritize cases for the product and it's the same for the data.

Speaker 2 13:22

So using the same ways of working takes away a lot about the language problem, because you use the business language from the beginning then, if you adapt to their ways of working but this is the tricky one, because digital editors are used to work with frameworks, playbooks I said no, you cannot use playbooks. They don't understand what is a playbook. They have the management system. They have actually already established ways where they find information and now we want to establish information in other places. That will create problems. I think we have to join this information for the product and for the data in the same place. That is how I approach the language thing for the moment.

Speaker 1 14:05

This is really interesting, and you talked about a lot of things that I would like to talk more about, but one thing that really stuck out to me was how you define problems in a quality system and how that can be matched to how you define problems in data. Do you want to elaborate a bit on that?

Speaker 2 14:23

It is from their need we have to start, not the starting point, what we want to achieve. And if we start from their need and use the user-friendly ways they are working in the business, I think that that is how we will approach and be data-driven you have to wake their curiosity for it.

Speaker 1 14:41

Let me ask you something that I always ask when we have those discussions in the business, and that is do we actually know what the customer needs, or do we only know what the customer wants? And these are two different things.

Speaker 2 14:53

What the customer needs. For that you have to understand their customers. If you understand their customers, you do understand what you can help your customer with. So that is one of the points that we are also looking a lot about, because we have and customers. We do have dealers, we have OEMs, boat builders, so we are working in different levels, but, of course, where we do fetch the information must be the data from the end customer and what is other doing innovative just to get some help in understanding the need outside there. We always need to be a step before everybody. So, yes, we are looking a lot about it. Customer relationship CRM we are building a huge CRM now and working a lot with customer relations and management for that, and that is to get the insights from our end customers into us.

Speaker 1 15:59

Another aspect that I thought was really interesting when we first had our initial chat, also about the inspiration on quality management. We did a previous episode on the podcast where we talked about inspiration from software development into data, which has been talked about a lot, but something about the methodology there is really interesting when we introduce agile methods into data. When we look at the quality side of it, there is an introduction of lean methodology into data as well. It has had an influence on structures or methods like data ops, for example, where you combine agile, lean methodology in the data field. Do you think that lean methodology and data works well together? Do you think that's a good approach on the quality side?

Speaker 2 16:46

Yeah, it is. I think that it sounds really good because the quality management and lean and data is all connected. I think, when it comes to the lean methodology, you focus on the customer value in the lean and if you look in the data, for example, you have the value creation in the process. So that is what we are aiming for the focus on the customer and it also promotes a culture of continuous improvement, and that is something we really need in the data field, for the data quality you have. One of the biggest thing in the lean, I think, is the waste reduction, waste variance that you want. You want to make a simplified common process and you also need to want to involve engaging people, and that is something we need really, really to do and get them going, because otherwise we will not do the data-driven switch we need to do, and data-driven decision-making encourages data metrics for decision-making, encourage use of data metrics for decision, improve and drive improvement.

Speaker 2 17:57

I think that Lean is actually really good ways of doing this. I've been there Also, when you have a problem, go to GEMP, don't go to the business. People Try to understand their situation. The solution is not always at our side on the digital idea, it is on the business side, so I think it sounds really good and we also have the knowledge in the business. If you can connect it to Lean, if you can connect it to our quality work. I had been actually 25 years ago pent up. We were three persons working with quality on the products at the whole company. So that means there has been a journey. It has taken time. Now everybody works with quality and lean. All the factory industry, 4.0, everybody works with lean. We call it world of production system, but it's the Lean methodology. So I think that implementing that and using that to leverage our data work would be fantastic.

Speaker 1 19:03

Very good, and there's so many aspects of that Lean methodology that fit, as you said, really well with how we do data quality work, how we do data governance work. And one thing that stood out was that you talked about waste and reducing waste, which is part of the lean methodology, but you didn't talk about it in reducing amount of data or how much data we are sourcing, but you talked about it on the process side, and I thought that was really interesting. How do we make processes simple, easy, reduce complexity, reduce waste and make them able for people to actually follow?

Speaker 2 19:40

Reduce the amount of data. Yeah, we will never reduce the amount of data, the wrong data. Yes, it is the process or data quality there. I think it's really, really helpful to have a standardized way of working so it helps the business to make right from start. That will reduce. So I think we have to back to the data design and start to engaging the people there to make the process work for us. And, yes, reducing the amount of data, the wrong data. I would say that is the way it's done and we have a lot of technology helping us with how you look at duplicates and whatever you want to do, the data lifecycle and so on, but a lot of the time, when you look at the processes, we don't have good implemented parts in the process for the lifecycle of the data. How do you maintain, how do you delete, how do you employ new customers, for example?

Speaker 2 20:42

That was one of the things we found when we started working with our business partner customer group. They have a lot of problems in the system and they said, yeah, it is the IT system problem. We need to make a business case and a CR here. It started there, but after it's a long story, so I will not tell all the story here, but when we started learning them what does this mean? And you have to build the capability for it to make this happen Okay, they said, so we get them information about the IT landscape, learn and teach them in the business the information and the process and everything. And they came back and said, oh, but we don't have a process for the life cycle of the customer. If you say like this, okay, I said, then it's easy, yeah, we will do that and start implementing the process. That is one of the ways we have been living with this for so long time. They didn't think that impacted. They thought it's so much easier to make a change in the system that would have been wrong in this case.

Speaker 1 21:49

But this is like a really great example for the television you are getting when working with data, and especially when you work with data closely related to IT and the technology side. So this is a really good example of taking a step back and actually looking at what is really the problem. What is the root cause we are facing? Another aspect that I wanted to talk with you about when it comes to quality management is data product thinking. We talked about data product thinking for the last what? Five years, Definitely, since we talked about or started talking about data mesh as a concept, but also before that, and when we think of quality quality for product, it should be easier to define quality criterias for a data product.

Speaker 1 22:31

It should be easier to define quality criterias for a data product. It should be easier to work in a product setting with quality, because it can limit your scope much better than without thinking of it as a product, thinking of data as an asset or as a whole. What are your thoughts on data product thinking and the influence it could have on quality management?

Engaging Data Stewards for Quality

Speaker 2 22:57

I think you're totally right that it will be even easier, but it is two different sides of the coin. You can say Not different. For example, when it comes to data quality work, quality management and that kind of that is the continuous improvement lot. How do you work with our heavy? I always divide slow data and fast data, and data products is more of a fast data approach, and when you talk about continuous improvement of that, but as it starts with the data products, actually we have one topic now with building a data product.

Speaker 2 23:37

It is the need-driven data quality process. It is the same, some kind of same way, but I think it will implement, because when you look at the processes for your data, for your quality work, you can implement it on a data product. But what we are doing is actually sitting down, looking at the business information models, making the information model together. What data do we need? Because sometimes they have this specific business question they want to build sustainability report. I really want to build that as a data.

Speaker 2 24:15

Okay, fine, what data do you need and what data do we have? What data do we need to create? Maybe Because it's not fit for purpose. So I think that is how we need to approach the data product part, because a lot of the times I feel that we put the data together as a product, we think it's a product, but how do we actually make the data quality in a good way in that data product? For that we have to make the specification, the business information. But it's the same processes I think that we can use. It's nothing different. So, yes, we can use it.

Speaker 1 24:57

I like your distinction between fast data and slow data.

Speaker 1 25:02

We always looked at data product and data processing from the analytical side, right, but not from the operational data side.

Speaker 1 25:10

There is such a huge difference in how we approach data, and I think that that divide between analytical data and operational data is something that we should probably whisk out going forward, because you want to be able to speed up the way you can use your data, maybe have more fast data, and then you have some data that is really dedicated for compliance issues that you really have to keep more slow data, as you call it.

Speaker 1 25:37

So I really enjoyed that. There's one aspect we wanted to talk about and this is something that is close to my heart and really interesting, I think, for many organizations and that is what is the value of data stewardship in today's data-driven organizations. And we talked about data product in the sense of data mesh, where it is more decentralizing. We see more responsibility for the domains. But the model of data stewardship it has some problems and some issues, right, it didn't, for all organizations, work as well as it was probably intended. So what do you think is? Is there still a value of having data stewards, or do we need to rethink the entire model.

Speaker 2 26:17

I think it's really a value of having them, because what is important in this new data-driven organization, it is actually high data quality. So, yes, we need them. Then, if you ask me I don't like data steward, it sounds like a cleaning data. Yeah, it just. It gives me the wrong thinking about what a data steward is for me. So, yes, we need a person that is working with our data in securing that we have an effective management of data with quality and security, and it's coming a lot more about this privacy, security and everything, cybersecurity and all of that part that will also recede in this data stewardship in the future. So, yes, they have a really important role, but the traditional role they have it's a little bit different. It was a lot about task forces. That is, at our part, it has been okay. Now we clean the data. Yes, we know after today it will be wrong, but we have to do it. It's a lot of different rules. We are changing the rules here.

Speaker 2 27:31

So I think that the technical part that is demanded from data steward is really big today and understanding it's a bold rule, but that is why, when I talk about data stewardship, stewardship is we have entity managers that are governing the structure. We have data stewards, the content part, and then information owner. For me it's the team. I see the stewardship and having a compliant data is a teamwork. Together we have what the data steward today may need to handle. But I think they are playing a crucial role. But the role needs to be adapted to the fast speed accelerating, evolving data-driven world we are in. So today it's one thing. In two or three years it will change that we know, with machine learning, ai and everything it will change. But still I think they're crucial for the high data quality.

Speaker 1 28:34

We need that Very good. I think the biggest issue in data stewardship as many of companies have done it so far is that it's kind of detached. There's like a central organization working with data, from governance to quality, to master data, metadata or whatever and then we have someone in a domain that has gotten a hat on to use 20% of his time to look at the data and it is just not maintainable. It's not something that you could actually do, because you don't have the tools to do it. You don't have the means to do it, not even the time to do it or the support from the central organization, because every time you meet an issue you have to go back to the central organization to fix it. So you kind of become more of a messenger. What would you say is and we talked about that setup that you have different people working together on those issues. But what would you say is a way to engage those domains better in the data and the data work and also get that engagement for data quality.

Speaker 2 29:39

Now you're talking about my topic I love to talk about because it's so important. In the middle of everything, we have the people they are and the business people, not digital IT, because a lot of the times when others talk about, yeah, but we have talked to your business, now you've talked to me and not the business. I'm a part of digital IT and that is a different role we have. We're playing different rules in this team. When it comes to the data steward, we are approaching them in the business, but that doesn't mean that they can fulfill the rule. The total rule of stewardship is so complicated today if you look at what they need to know, and that if you have a rule description, you cannot show it to them. So what I'm doing, I'm actually empowering the people to start working. I have, we have a problem. Okay, start working with the problem. What do you need? What can I help? So, dividing the data stewardship in one technical part, I think in the digitalitis, receding there because One technical part, I think in the digital ideas receding there because I cannot make them. They will be afraid if we start talking about that part and implementing it for them. They have the daily problem they have, so let them work with that. And coming to conclusion, what is the problem, for example, as I said, is the process wrong? They can fix it. Is it governance? Nobody's owning it. Coming to conclusion, what is the problem, for example, as I said, is the process wrong? They can fix it. Is it governance? Nobody's owning? Yeah, let them fix that, because they are good, they're talking to each other, but so letting them work with the part of the data stewardship that they feel comfortable with and learn the next steps is the best way to approach it, and that is how I have been working with them to engage them, because in that case they learn by doing. They start working with pain points and do what they can. I help them from my side and engage other people to help them to build the knowledge, and then they actually they have started working by themselves a lot in that way. So that is one way to engage them and another way is that is actually. This is how I engage for the data. We have to do it better. Then you have next topic. That is data we don't have, for example, where we built something new. Engage them from the beginning. Data by design, as I usually say, is a lot about having workshop, inviting the business people and engage them, and they are in the process learn by doing, creating it. Then while we start working in the business with it, I think it's really easy to get them engaged because they cannot say no to something they have been a part of creating.

Speaker 1 32:37

This is fantastic, Pure gold. I like that. It's really about them owning the problem and then they can also own the solution, right? Yes, so I really like that. It reminded me a bit of I've read Lauren Madsen's book Disrupting Data Governance, and she talks about that. The biggest mistake we have done in data is not being transparent about what it takes.

Speaker 1 32:59

We took the problem from the business and worked on it in our backyard and then we presented them the solution. Everything that we got was them complaining about either it's not the right solution or it took too much time to get the solution. It's not, the solution does not fit the problem. But the reason is that there is a distance between data and business and by doing what you are doing by making them own the problem, own the solution, get engaged you kind of solved that problem.

Speaker 2 33:27

Yes, actually we have. We have been working with this the business information models. It's a tool, but we use the tool to evolve the knowledge competence and we use the tool to engage people and also to make the decision process faster, More agile, as I say, because everybody has been involved. Everybody has said yes, Then it's a no-brainer to start doing it because, yeah, I can, and also leverage the governance, because if you have created something, you have no problem to own it because you know what you own.

Speaker 2 34:07

Today, we are trying to make people taking governance of some data that they don't know what the quality is, what is it for? They are afraid to take it. They're saying no, they don't know the data privacy rules, nothing. And we say, yeah, but you have to take the governance of it now. So approaching them in the right way, I think, is really, really important in this case. And what if we are making everybody a data worker that we are aiming for, because data will be what we are as a foundation for us to make our business grow Then we have to start with the people and we have to build them, not tell them with the rules and everything.

Speaker 2 34:53

This is what you need to adhere to. Nobody wants to adhere to, as you said, putting a top off as a hat. Actually, they already are 100% full, but they need to understand that we help them to shape them and change, take away a lot of the inefficiency in the daily work and make it efficient in the data work, Because we are in a huge transformation journey. When it comes to the people also and that is nothing that we talk about, I know a company that say okay, we need to have new competence here, get rid of thousand persons here, get thousands more, a new one with the right competence so we can build a new. For me, it's not sustainability in the person's side, people's side. We have to help them to build this transformation. Do this transformation and it will hurt in the beginning.

Speaker 1 35:45

Transformation do this transformation and it will work in the beginning. This is interesting because I talked with someone about this not that long ago. Competency is something different than knowledge. You can get people on board with the right competency but they still wouldn't have the right knowledge, because knowledge is something that you create in the business Through the work you are doing, through the experience you are making, through the context and the purpose. You know what you are doing and it's not that easy to capture that knowledge. I mean, we talk about that for knowledge management for quite some time. There's some explicit knowledge that you can capture in documents and work, and then there is some tested knowledge that people have in their head, but that knowledge is really shaping the organization and that knowledge is really shaping the organization and that knowledge is mainly domain knowledge. So what we want to do is not just getting the data competency in and relating out all that domain knowledge, but we want to have that intersection between domain knowledge on the one side and data knowledge on the other side.

Speaker 2 36:41

Yeah, you're totally right with that. The knowledge. I think that getting the knowledge and understanding and also implementing, train the trainer, you have your champion. You're actually getting them to get more and more competence and knowledge about things and what I'm doing. I'm sending them out speaking to others, because it's a huge difference to open people to get competence and knowledge. We have to get them to open their minds for something new.

Speaker 1 37:16

And then you are in a situation where the real value with data can be created once you apply that data knowledge in your domain, and for that you need domain knowledge.

Speaker 2 37:27

Exactly, you're totally right. That is why we work a lot with workshops, because we need to get in the knowledge from the business on what is needed, what is it we are aiming for, the business strategy and so on, but we also need to translate it in. What does that mean for the data? What data in this domain do we see as critical? What can we live with? What do we need to govern in 110% and what can we govern in easier? What do we use the data for? The data is so much more. And to get people to understand yeah, if you have done a product right, it's right, but of course the context, how you use your product, will change and then sometimes it will be right, sometimes not right. So that is the knowledge and the competence about the data is a lot, but looking at how the product is working for them and then implementing it on software thinking, that is how I think to make them understand that it's not so different.

Speaker 1 38:40

Wow, we went through so much and we're already at the end of it, but before we finish up, there's time for you to give us some key takeaways, or even a call to action.

Speaker 2 38:49

Yes, I can do so, because what is the takeaway from this? To get the data-driven journey to take new heights, I think we need to think a little bit more about the people and not so much about the technology Methodology. You can use a lot of the methodology we already have, that is a one door opener or eye opener, because that takes away fear from people. You want to get them from the fear zone to the pro zone, of course, in the easiest way, so take the easy way out of it, then Use the same. Then you can change and adapt. Engage the right people yeah, you can say, okay, how do I engage the right people, the people that are actually willing and curious and want to know. Start with that, and then the other will fall if they see that, oh, but he can. And then the other will fall if they see that, oh, but he can. And then I can Engage them from the beginning.

Speaker 2 39:50

When you start working with your data design, when you do break down the business strategy, for example, in business information models, that will be the requirement and you can look at the impact and so on. Engage them in that, because in that case they feel that, ah, I have been a part of it, so that is also really important. So use the processes you have. Adhere to the people and start working. Train the trainer. Let the people be the ambassadors out in the business so they feel that they own it, and then we can talk about the tools after that. That is the takeaway I would want to send out from today.

Speaker 1 40:32

Very good. Thank you so much, thank you.