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The Gradual Process of Building a Data Strategy

Vijay Yadav, Director of Quantitative Sciences & Head of Data Science at Center for Mathematical Sciences at Merck breaks down the different pillars of a scalable data strategy.

Mar 2022

Photo of Vijay Yadav
Vijay Yadav

Vijay Yadav is the Director of Quantitative Sciences and Head of Data Science at the Center for Mathematical Sciences at Merck. He is a seasoned data leader who drives the analytics strategy and roadmap for Merck’s Manufacturing teams and owns the development and deployment of advanced analytics capabilities throughout Merck. Vijay has over 20 years of experience working in pharmaceutical and chemical manufacturing and has deep insight into developing data strategies that scale. 

Photo of Adel Nehme
Adel Nehme

Adel is a Data Science educator, speaker, and Evangelist at DataCamp where he has released various courses and live training on data analysis, machine learning, and data engineering. He is passionate about spreading data skills and data literacy throughout organizations and the intersection of technology and society. He has an MSc in Data Science and Business Analytics. In his free time, you can find him hanging out with his cat Louis.

Key Takeaways


Becoming data-driven is a holistic effort: Data strategies requires examining a variety of levers that an organization can scale. This includes ownership, data asset management, governance, enablement, culture, skills, and much more.


The most important component is people: When trying to become data-driven, the cultural and skills component is arguably the most important. Organizations cannot accelerate technological investments without building up a culture of analytics and data.


Data transformation is iterative: As opposed to traditional technology projects, becoming data-driven is a long arduous process that requires investment, time, and patience. Companies looking for ROI within 6 months of their data transformation will most likely be disapointed.

Key Quotes

We know that ROI cannot be that easy derived for data transformation. It takes some time to build out the organization's capabilities. If somebody's looking at ROI in six months to a year, it's probably not practical.

When building data literacy and evangelizing the value of data—it's important to keep people engaged with events and tools like hackathons, community of practice, and upskilling. It's a gradual upskilling.


Adel Nehme: Hello everyone. This is Adel, data science educator and evangelist at DataCamp. One thing I love about hosting the DataFramed podcast is that I get to speak with really smart data leaders who can break down the different levers for becoming a datadriven organization. And you can argue once you put all these levers together, you have the key components of a data strategy.

Adel Nehme: This is why I'm so excited to speak with Vijay Yadav. Vijay Yadav is the director of quantitative sciences and head of data science at the Center for Mathematical Sciences at Merck. He's a seasoned data leader who drives the analytics strategy and roadmap for Merck's manufacturing teams and owns the development and deployment of advanced analytics capabilities throughout Merck.

Adel Nehme: Vijay has over 20 years of experience working in pharmaceutical and chemical manufacturing and has deep insight on developing data strategies that scale. Throughout the episode, we speak about his background, the key components of a data strategy, how data culture and people are arguably the most important aspect of a data strategy, how to structure upskilling programs for data science, and more. Now let's dive right in. Vijay, it's great to have you on the show.

Vijay Yadav: Thank you, Adel. So great to be here.

Adel Nehme: So I'm excited to discuss with you, all things data strategy, how to operationalize and build data culture, how to accelerate data science within an organization. But first, can you give us a brief background about yourself and how you got into th... See more

e data space?

Vijay Yadav: Thank you, Adel, it's great to be here, by trade I am a computer scientist and I started my career in IT space and data is always part of, I consider myself as a data practicer, even though in my college days, I was specialized in artificial intelligence and numerical methods statistics, but I got to apply those techniques later in the career.

Vijay Yadav: But I can tell you that throughout my career data has been part of past and majority of the solutions, I have been able to apply some of the AI and machine learning techniques to deliver some business value for the company. Currently I'm with the Merck, I'm heading their data science team and primarily focused on developing the AI and ML solutions to make manufacturing modern kind of capability enabled area.

Adel Nehme: There's definitely a lot to impact today. And what I'm really excited to talk to you about is really the different elements of a data strategy and how to get there. You're a very experienced leader in the data science space, and you've worked a lot on data transformation projects.

Adel Nehme: And I especially love this topic of data strategy because it really allows us to dig through basically all of the elements of what makes a organization data driven. And I think that's very exciting, so I'd love it if you can walk me through kind of from a high level, the different components or elements of a data strategy.

Vijay Yadav: No, that's great actually, and that's the way you want to think holistically, what the data and analytic strategies would look like. In my experience, there are six pillars of that strategy. The number one at the top is basically data analytics strategy itself, who owns that strategy, who develops that, right? Who are the players basically want to do that?

Vijay Yadav: So that's the first component that the very strategic point of view that as well, what are the business objective end of the day I think you want to have your strategy based on the actual business strategy where business wants to basically go, right? So that's the first component. The second component is operating model. This is the new area, right? What are the different players who plays the role? What is the rules, responsibility of data and analytics organization?

Vijay Yadav: What is the role of technology function? What is the role of business functions, different business function we have, what is the data product team, whether it's engineering team or design team. So there are multiple players how we come together, orchestrate in a way that is a cohesive in order to make this strategy in executing and drive the value one of that, right? So that's the second component.

Vijay Yadav: The third component, I will say the data assets itself. So how do we go about building the data assets? You have to really think about it's we all call it, data is oil and data is gold, it's an asset, right? So how do we take that and make that we can basically drive the value, preserve it, right, to do that. Now it's not, you take all the data and then you dump into a data lake, right? That is not a strategy you're putting in the data.

Vijay Yadav: What I mean by data is strategy is that what can we do that is accessible in a easy way. It is going that only people who need to access. They have to have access, it makes sense for someone to look at that it's a smaller democratize the data itself, right? So how do we do that as well? That's a component and the next component is the infrastructure, the platform itself.

Vijay Yadav: So we have a data, how do we apply the infrastructure, put to infrastructure that is scalable and it can take the speed the business wants to do that. So infrastructure, whether it's a data platform. And also when we talk about the data science field, if data scientists are working, what kind of tools and technology they're using, right? So we got to put together a place where it can be sellable and is scalable, right?

Vijay Yadav: And the next component I would say is the data product management. And this is fairly new concept. If you talk about the Google and Facebook and Meta now, the concept, the data product is it's really in Silicon valley is very prevalent, but what it is not prevalent is in other industries, whether it's pharma, whether it is other ones. So how do we bring the concept of product? When I say products, a data product, right?

Vijay Yadav: The concept is that it is not that you take an initiative, you start and finish, you have to develop that and you want to make that product accessible to the entire company. And that's how you have to treat as a product, so what kind of methodology we need to basically bring on that as well, right?

Vijay Yadav: And the last one, I would say the people and culture, even though it is the last one, but I'll keep at the last, right? So how do we organize this organization itself, data and organization, what is the culture and culture I can tell you is the biggest component in this strategy at all levels. And we'll talk more as we go, but at the high levels, these are the six area that I think are really required.

Ownership of the Data Strategy

Adel Nehme: That's awesome. So there's definitely so much to unpack here. I'd love to go through each element by each element and dig through and kind of pick your brain. And I said, I love this conversation around data strategies, just because how holistic it is. And because we get to tackle all of these different elements, lets start off kind of with the ownership of the data strategy, you mentioned, that's one of the first elements when you think about ownership.

Adel Nehme: Often time I see within an organization there's often a trade off or kind of a friction, especially as they go about their first data analytics, the beginning and within their data analytics journey around who owns the strategy. And it could be, it's often a debate about whether it's IT, or the data team, or the business functions. And in some sense, who do you think should own the data strategy in its execution?

Vijay Yadav: No, that is such a great and important question. I can tell you that the company who are doing well, those company, the business owns a data strategy, and the company who are not doing well, it's a very traditional way somebody else owns. So it could be technology as someone else, right? In my opinion experience, I can tell you is that high success you will get when the business owns a data strategy, so what I mean by business, owning that.

Vijay Yadav: So think about who is the user of the data. It is the business, business knows that what basically good looks like. Now, we may not be capable, but that's where we want to put together. Who can represent the business? What good looks like to them, right? To do it that drive the value, right? If the business is the one, this would be the one driving, what do I need really to do that?

Vijay Yadav: Not IT organization traditionally that's what it is. And also the ownership in terms of drive the value. So let's say we develop a solution, right? We deploy it end of the day business using it. Who makes the commitment that, hey, we develop this tool. Now, here the value we are driven, not IT, not any other function. Business is the one who's going to make the commitment that we define what success looks like, we are using it, and here is the value of delivering.

Vijay Yadav: So you can take that value and go to the next level and basically do that. So in my experience and opinion, and very strong opinion is the business is the one that should basically own that strategy. Now business is the business of doing technically pharma company making the medicines. You need people, data in kind of expert strategist, really to represent business. And they are the one basically on behalf of the business, basically want their strategy to drive it.

Adel Nehme: What you touched upon here is really that data science and data to a certain extent is not a traditional technology project, right? It's not something that you take from point A to point Z. It's a mindset shift, it's kind of a new methodology for solving business problems.

Vijay Yadav: If you talk about the business data, that business of data is feeding, enabling your main line of business, right? So if we think of vertical of the business of data, and when you talk about the business, then you have to have a customer. So your business is the customer of the business of data.

Adel Nehme: And this is why it should be owned by the business. Is that correct?

Vijay Yadav: A hundred percent. That's exactly right. You summed it that really well, to do that. So one comment I wanted to just make, so the way I think today, and those are the different factors. So any companies, at least they're in the business of two things. One is the primary business they are in. So if you're a pharma company, you are making the pharma product, that's your primary business.

Vijay Yadav: But you're also in the business of data. Business of data is a concept where and you're driving value out of that. So I think the mind shift here is that you are not only the business of what you're doing, but you're also business of data. If you want to take your organization to the next level of innovation.

Adel Nehme: And conversations with the C-suite, especially how do you convince them that, Hey, the data strategy should not be owned by IT because probably the C-suites are very comfortable in handing off these technology driven projects towards IT and not towards the business owners necessarily. So how is that conversation goes and how does that kind of mindset shift within the C-suite happen?

Vijay Yadav: That is such a great question, and I can tell you it's not easy one and folks at that level, right? They have been operating in certain way. What I would say basically makes the case for them is that the fact that the business understand what good looks like, right? IT organization is not the one it's basically they are expert in something, but they are not close to the, what the business basically looks like.

Vijay Yadav: What I have done in my experience is that showing that, so the success at, hey, listen, when we came in and we represented the business, we talked to the business, we defined the ownership of the business. Here's what we delivered, and here are the value. Now you compare that outcome with any other solution that you have basically done, and you can see the difference, just assigning that ownership, driving business is driving that you the difference, right?

Vijay Yadav: So there's no a straight answer. I think it takes time. So sometime you saw the success that this is strategy if you change it, then I can tell you that this has proven better value than traditional way of doing things.

Adel Nehme: Given that the business should own it. What is the role of IT then in executing and ideating the data strategy?

Vijay Yadav: If you're saying data is business, then what IT does, IT is a big role to play. So think about, we talked about one of the pillars is the enablement, right? It's the data and analytics enablement, right? So the infrastructure, right? IT, so for example, if you need architecture, if you need to architect a solution, that's a purely technical role that has to be played by that, right?

Vijay Yadav: Or you need a platform, the IT platform that we need for that IT, so I would say that the delivery, once we define what success looks like, the business says, here's good looks like, and here, my vision for this particular solution. I'm going to lay out the vision, this is what good looks to me.

Vijay Yadav: Let's define those pieces, and then IT, you come and enable us wherever that technology component basically comes into play. So I would say IT still plays a big role, but I think the vision is the one at the driver's seat. That's the piece that difference I see there.

How do you organize data teams?

Adel Nehme: That's awesome. So we covered kind of the ownership of the data strategy first component, let's move on to the second component, which is the operating model, right? How do you organize data teams?

Adel Nehme: How do you organize the players, as you mentioned them within this organization, within this new practice, within an organization to deliver value, right? So in terms of structuring the operating model for a newly formed data team, what are the different options that you can pursue?

Vijay Yadav: So one of the things really I have seen in my career as success is that one of the, in the operating model, the sponsors play a big role. So what I mean by that? So the sponsor are the one, basically you're a champion for the business case, now when you make the business case to develop a data product using [inaudible 00:13:39] and any analytics product to do that. It has to be based on the value that is being driven.

Vijay Yadav: Now, the business sponsor is really the key for you to take, represent you at the senior level. Not only that, if we involve the sponsor, then they are the one making the change management possible. So it's not that I go and develop a model here it is. And somebody just adopts it. It's a change management process that has to take place in order to deploy the new solution.

Vijay Yadav: So if you have a sponsor, then you can basically take it, so that's number one in any operating model, every time you're launching a data product solution, your sponsor are really playing the key role there as well, right? So that's one piece to it, right? Now, talk about the product management piece, right?

Vijay Yadav: So who sets the vision for the data product? What good looks like, right? So normally the product team is the one vertical that basically do, and I have done that successfully in multiple roles that I have basically played. So that is one role that is basically someone who's defining the vision, representing the business here, the roadmap basically looks like, and this is how we are basically going to do that.

Adel Nehme: Kind of the roadmap of the data team here.

Vijay Yadav: That's right, to do that. Now IT is a play, IT is, you can say is that's where they're coming to play as a team, playing the role in delivering the IT infrastructure, the platform architecture, anything compliance, for example, in these days, I think data compliance is really part of that, right? So we have to have that element into that as well.

Vijay Yadav: And so we define who is doing what, right? So clearly will define. The other element is that the business subject matter experts. Now, if we are taking the, if we are in saying that, hey, business is own status strategy, right? So the business has to allocate the resource as a subject matter experts. So as the project is going on, development is going on. We have someone who has the expertise to basically provide the insight, what good looks like and things of that nature, right?

Vijay Yadav: So there's another business plays as role. So we talked about the sponsorship, we talked about the, It, then we got product team. Then we got business side subject to matter experts. And I think somewhere in that, adjacent is a change of management comes into play. So who plays that role, right? That's another element of that. And on top of that, think about the priority.

Vijay Yadav: So who defines, which projects would we take that, is it a product team? Maybe someone from the business that, hey, we got 10 things here, here is number one. So someone has basically a priority person that you're basically running it. And also you want to drive the value out of that, so somebody should be responsible to track the value into the process.

Adel Nehme: Even kind of before thinking about how to define a team then, companies and organizations should really think about who's owning the process, who's setting the vision and the rope member of the data team? Who's sponsoring the change management, ensuring that the data team is supported even before thinking about who to get as a data scientist or how to build a data science team?

Vijay Yadav: Absolutely. That's really important. And I think that's a major problem right now we have is I have been in a situation where, when I engage as, so nobody knows who's doing what, and I think I have seen the success if you clearly define the roles and responsibility, and a very clear map, then I think you have a high level of success in that scenario.

Adel Nehme: And without kind of that base layer that you're discussing of setting the roadmap, setting the processes, you're setting up data scientists to fail because they don't know necessarily what they are building or who they are building for. Is that correct?

Vijay Yadav: A hundred percent. I think one of the element and we'll talk about that is that to me, I think customer experience, a user experience is the key. What I've seen is that we design traditionally people try to define a solution and try to find the user after the fact that is not the case. Your solution should be really tailored to the business and the user. You need to understand the user base and you design a solution of that as well to do that. Go ahead.

Centralized or a Decentralized Model

Adel Nehme: Awesome. Now that we've have this operating model, a relatively set in place. How do you organize the data team? Some organizations they opt for a center of excellence centralized data team, other organizations, they opt to, we take a data scientist, we put them in the business and they have skin in the game, and they're part of the business.

Adel Nehme: Which model works for you, that you found works for you most between a centralized and a decentralized model and a follow up on that is what are the trade offs between both?

Vijay Yadav: I would say the mixed model. So think about the analytics needs that are different in nature, the complexity is different, right? So if you embed a data science team or data analytics professional, as part of the business who are doing let's say dashboarding or any type of insights that we can basically create in a simple way, doesn't need advanced analytics up that, right?

Vijay Yadav: So I think you can embed those as part of supporting day to day and they understand the local needs. They're part of that. I think the major piece you need to centralize, and what I've seen basically working is that you take the advance, a complex piece because you need to bring the different skill set and together and if your fragmented, you may not be able to drive the values.

Vijay Yadav: I would say it's a mix of the two, you support the local where things can be supported by not too complex, very local, but when you take the major transformation initiative, I think having a central team with a different skill set, I think that's really important.

Adel Nehme: And especially early on the analytics journey, it's very important to kind of set the stage with the centralized model and then you can over time embed and become much more hybrid. Is that correct?

Vijay Yadav: That's a hundred percent. I think that's, the key point is that a lot of organizations are still maturing. You don't know what the central basically looks like, so I think there's a whole data culture piece. You want to make people aware that, hey, the data can drive the value, right?

Vijay Yadav: So that is part of the culture when you go and work, assign the people embed as part of the local. So people are getting aware, they're getting to know as the time goes by maturity level up. Now you can centralize because now the company is much more aware of what data can deliver for them.

Data Asset Management

Adel Nehme: The third component and fourth component that you mentioned, at beginning was kind of data asset management, as well as data analytics enablement. The thing this is where a lot of IT plays a massive role. So let's start off with data asset management, oftentimes it's the least exciting element of a data strategy, let's say, but it's probably one of the most important because it's really the source of truth and kind of determines the truthfulness levels of the insights one captures.

Adel Nehme: So moving on to the other components of data strategy, of course we can dedicate an entire episode towards asset management, data asset management, and data governance, but I wanted to gain your insight on how important should data asset management manifest itself within a data strategy and how so?

Vijay Yadav: It is the critical component of the data strategy. One thing that you'll see that we complain, oh my God, data is not clean. The data is scattered, it's desperate. If you think about the region behind why we have so much problem with the data, what's wrong with that, why did we not think about the data in a structured way? And we can reference it.

Vijay Yadav: One of the element that is really a success is that you want to give the context with the data. So what that basically means is the metadata. So think about, I have a piece of image, right? I'm just showing you the piece of image which has something to do with, let's say a vision inspection, a product is inspected, you take the image of the product and machine is telling whether the product is defective or not.

Vijay Yadav: Now I want to attach that image, one piece of information with everything else outside it, where it came from? Which side it was, which plant it came from, which machine, right? So that you could collect that data in a more hierarchical way, the organization, the site, the product, and any other metadata you have.

Vijay Yadav: Imagine if you can attach the metadata along with every piece of information that exist in the company, that's when you makes it power, ready to go and filter the information, extract it because now you have defined it. So giving a context to the data is really critical piece. Now that is not easy tasked by any means. It is the complex, it can take time, but I think if you think on those lines, it's really important.

Vijay Yadav: One biggest challenge that, we are having is that anytime, if I'm a user in the shopflow, and I enter the data through my own hands on the keyboard are the machine that I'm looking in front of me. I can see the data that data captured and send you the data league. Now, imagine what exactly happened in the process. The data was just so close to me, I just saw it. I just put on finger, all of a sudden it went to the leg.

Vijay Yadav: Now I have to go figure out where it is, so it went away from me. Now the data strategy has to be, want to bring back the data to the user where it belongs. And that is a huge undertaking itself. So rather than sending the data lake, how can we bring it? And that's where strategy belongs that can we break the data into domain is specific data, for example.

Vijay Yadav: So if I work in the shopflow, I'm not interested in supply chain, I'm not interested in something else, can you give a context to the data, just, I understand myself, bring it closer to me, if you just sold to me, I understand what exactly it is because I don't have to search for that. So you make a data more specific in a democratic way that I understand it really well don't give me the COCME to float and try to find out to do that.

Vijay Yadav: And also of course the data governance is another challenging piece, so you don't want to give access to everything to everyone, right? So if you have a give the context to the data, we talk the metadata, you should be able to control that because if data belongs to certain plant certain site, now I can say, hey, you are owner for this particular place and I can assign attribute, everybody else is basically out. So I think the metadata and giving context is a really important factor to make the data governance much easier to do that.

Vijay Yadav: So answer your question is not easy, but it's really critical piece. And that technology now is enabling in that space, actually, to do that. There are multiple technologies coming up, how to give the context to the data, things of that nature.

Adel Nehme: What are the first steps that you want to formulate this part of a data strategy to ensure that at least you're on the right path when it comes to data governance and data quality?

Vijay Yadav: So one of, if you think about how do we get the data? Data is nothing, but it is outcome of a process, as the process is happening. So think about we are manufacturing medicines, so as the process basically moves from left to right, underlying all the data is captured. So what is happening is that as the process moves, your data is generated in the process.

Vijay Yadav: Now what is happening is the region that we have a lot of gaps because your process is broken. So if your process is broken, that's when you, de-link your data set. And that's where you have the challenge. So the first thing is that you really need to look at your business process, a high level. You don't have to go in detail and you want to make sure that your process are linked together. So your data underlying data is linked together as well.

Vijay Yadav: Now you can say that, that's possible Vijay, we can basically do that, but we are talking multi-site, multi-country how do we do that, right? And that's another player that you, that's why we say the metadata is really important to do that, so when you define the process, you almost literally, you have to give more like a process ID, site ID, a product ID, whatever the other metadata is, give the unique definition to each data element.

Vijay Yadav: And you give that uniqueness to when you capture the data, each piece of that, and that is how you can segregate and put the data governance overall.

Data Analytics Enablement

Adel Nehme: So we talked about how do you make sure that your data is at least properly governed and you're on the right way to creating a high quality, standards of high quality around data. Now, the next step of this and the next piece of this is data analytics enablement. How do you make sure that people are able to fish for insights, an easy, straightforward in streamlined fashion, right?

Adel Nehme: So we've talked about setting this foundation, but ultimately you need to enable folks to work data with real time. I'd love it if you can walk me through what organizations can do as part of their strategy to enable people to work with data at scale, what are the different tools, analytics enablement that they need to think about to be able to operationalize data at scale?

Vijay Yadav: Think about there are two, maybe three buckets actually. So the people who are very technical, people data scientists, right? And then you can, so they are purely technical. We can talk about that in a moment. The second category of people is the, we call citizen data scientists. So these are the people who are not necessarily a trained data scientist, but if you can give them a tool, we have a concept of Auto ML, right?

Vijay Yadav: So Auto ML is really a great way to get it started, where you don't have to understand the underlying technical details of algorithm, but you can visually, you can really look into that and you can basically drive the value. And the third element in the bottom is that people are not really into that much of data science. So can we train them coming up, upskill them, a very basic tools, whether it's a Spotfire or Qlik Sense to do that.

Vijay Yadav: So I think in order to move the needle in upskilling people at almost you able to take these three categories of people to bring to the next level of maturity, right? So having a training, a mentorship program and training programs and ongoing basis, so we can talk more in detail is really key. Auto ML is really good, great capability, and in fact, in Merck, we are pursuing that avenue.

Vijay Yadav: Data IQ is one of the tools that we are basically using for that, and it serves the scientist, also citizen data scientist as well to do that. And then of course, a very technical side of it that how can we train the different languages and more upskilling, more technology. We train that those people as we, so I would say that we need to focus all three areas.

Adel Nehme: And what about kind of the descriptive analytics area that certain extent kind of empowering the insight layer, not necessarily just the machine learning layer, where is the room for business intelligence SOS within that empowerment?

Vijay Yadav: So I think to, I know data camp, right? We are using that tool really to, in a data literacy program. We also have some of the Minitab, for example, a jump, so some of the descriptive kind of tools that we are basically using to upskill people for the basic hands on kind of work to do that. So there's a group of some of the tools that we can bring to the bearing actually to upskill people for the basic fundamental as well.


Adel Nehme: Going to execution, we talked a lot about these different levers, data analytics enablement, data asset management, making sure that the infrastructure is there who owns the strategy, how to set the operating model? These are all big strategic decisions that require a lot of change management, stewardship over execution of the data strategy, right?

Adel Nehme: Given the amount of these layers, that organizations need to push and pull on. I have a couple of questions around execution. One, how should leaders go about prioritizing these different levers? Do you begin working on infrastructure first, then enablement, or is it all in tandem and then which parts of the organization owns these different parts of levers?

Adel Nehme: So we can start off kind of with the execution process and timeline, how do you prioritize these different levers over time? And where does the work start when you go for execution?

Vijay Yadav: The culture is big part of that, right? So one thing that we have to really see is, does everybody in the company sees data as an asset and how do they see it? So somebody hasn't worked in the shopflow all their life, right? And if they have not seen how the data can deliver the value for them. So I think the education and literacy program is definitely something that you want to get started all across the company, right?

Vijay Yadav: That doesn't need any, there's no predecessor for that. I think the more people are changing, the culture pieces is changing. I think you'll see a lot of things fall into place, slowly. One thing I noticed my experience is the people is really powerful creator. If the, we all talked about the refining structure, I think that if I have to keep everything at the top, I will keep people at the top.

Vijay Yadav: If people understand the value, they're motivated, they can get things done even the technology is not there. So if I want to deliver value for the business, ideally, it'll cool if I have a platform, a model deployment, I can manage the life cycle. I can version control all those things, but listen, if I'm motivated and if I know there's value in this one, even I don't have those technology components, I'll still able to do that.

Vijay Yadav: It's going to be slow, but I will still deliver the value. So I think what I would see is that putting the infrastructure, we don't have to really make, unless you put this, we are not going to deliver the value. So there's a long hanging fruit all across that we can deliver without having the heavy lifting infrastructure. And you want to drive the value, create the awareness, here are the value.

Vijay Yadav: As the people are maturing, their thinking are maturing, even the business senior leaders thinking is changing, now you want to scale it up, right? And that's when you can take your time really to put the infrastructure in place as the maturity basically goes, I would say that it's not that unless you put everything in place, then when you can drive the value, I think it's, you can start a low hanging fruit and just keep on building up on it until you get to the stage where you need to scale it up.

Adel Nehme: So in some sense, there's a lot of value that can be done in kind of the culture, the skills level, the enablement. And then over time you start scaling these different levers, like infrastructure governance, and be iterative in your approach, is that?

Vijay Yadav: That's what exactly I'm saying.

Adel Nehme: In terms of who owns these different parts, so we talked about the kind of who makes sure, to extent that all of these different pieces are being executed upon?

Vijay Yadav: So we talked about business of data. So what I was referring to that is that's a data and analytics organization within the business. So this organization basically is part of the business reporting somewhere, this structure to do that. We can call CDO or CDAO or call it any other names, but that's a basically self-standing vertical within that organization. So for most part, actually, we've talked about all the different components, majority of that is driven by at least owned by that organization.

Vijay Yadav: But the, IT comes in to place, we'll talk about that as well. So I would say it is the mixed responsibility. There's not one that everybody won't see this, IT has a role to play this data and analytics organization to play that the business is sponsored, that they have to play subject matter experts. So, and the business owners and business units coming to place. So I would say there are multiple owners, but overall I think the driving force is data and analytics organization, as you talked earlier.

Adel Nehme: Definitely. So we kind of highlighted in our conversation, kind of the iterative nature to a certain extent of executing on a data strategy. How do you kind of enable that mindset shift as well of, hey, data science is an iterative project. It's not necessarily something that you do, it's one and done data strategy is something that you continuously do, basically, as long as the technology exists and that you can take value out of it.

Adel Nehme: So how do you adjust the process when executing the strategy and these data projects to account for the iterative nature of the data game, to a certain extent.

Vijay Yadav: So here's what I've seen work really well. It is, there's no magic wand for that. How you do it, convince people, so let's say if I am and I have done it, almost every case very successfully. So let's say you got a concept, and I took the one, I joined Merck for example, I took one concept and I basically had the proof of concept for a solution that was really, it is solving the business problem.

Vijay Yadav: So I said, I presented to the senior management, this is what value is basically driven. They can clearly see that value that can be basically driven. Now, I said, if you want to scale it up, this is what you need to make the investment. So I think it is the time where if you can see the value, if the stakeholders can see the value, they're willing to come with you. So you have to show the small success, whatever the success it is. And when people see the success they're willing to come with you.

Vijay Yadav: The other element I would say is that I've seen is in the senior leaders. If you can see that you can help them in their success, whatever the objective. So if I'm ahead of business unit, if I go in front of them that, hey, listen, here is something that I want to come and solve your problem. And if they can see just helping them successful, they're not basically they're on board to do that.

Vijay Yadav: So you have to take that element, so there's whole showing and coming in front of them and showing in the value, if you can show a small value and that willing to scale, I think we have with them. So it's a very gradual process. People understanding the value, a more organic way, that's the one.

Vijay Yadav: But at the extreme side, if someone comes says, we are going big on data, here is the money, just take it on and show the value. That is not the scenario, most of the cases, right? Every time a company is investing in something, they're looking for ROI. And we know that ROI cannot be that easy for data projects, it takes some time.

Vijay Yadav: And if somebody looking ROI in six months and a year, probably that's not practical, it's a gradual. You can go that way. Or if somebody's willing to invest a big money, then you can go that way as well, but I would say it's a culture and mindset and sewing the success slowly and reliving the value. I think going to bring business on board.

Data Culture

Adel Nehme: Definitely culture and mindset is super important when it comes to enabling analytics across the board, enabling the scalability of data science and any data strategy, right? So I'm excited to really dig through that. Of course, the big elephant of the room that we're talking about here is data culture.

Adel Nehme: The majority kind of organizations have yet to crack the data culture problem, I'd love your perspective on not only the importance of data, culture, and how it enables data strategy, but how a lack of data culture stops data strategy?

Vijay Yadav: Oh my God. That is such a critical question, I cannot tell you. And I had to navigate this, and I think to a very high rate of success, I was able to get in this. So there are multiple components to it. So when you talk about the culture, culture is a big word, right? It may mean different things to different people. So let me break it down What I mean by that?

Vijay Yadav: So the one of the element is, if you start, the senior management we talked about earlier, is senior management thinking the old way that, hey, somebody else owns the data strategy not us. IT traditionally has been a kind of driver for IT solutions. Now, if some, if you can see that mindset, but yes, indeed data belongs to the business strategy, right? If you can see that's a huge change, that's a big undertaking itself to do that.

Vijay Yadav: So how do we make that happen at that level? Senior management do understand the value of the data, but I think understanding and doing are two different things, right? So can we convince them in that, and again, we talked about the sewing, the success is that, so that is at that level, middle management again, if we can sew the part of the success that somebody can see, wow, I can do this.

Vijay Yadav: I never imagined and have multiple examples where people never imagine that's possible using the data. But once we saw this small prototype, it said, well, how about this? How about that? I love to do this. Now they're part of the journey now, to do that. Now they're becoming part of that, right? So I think coming back to that piece, sewing the success and coming with that and going hand on hand, I think that is really big component of that.

Vijay Yadav: The other thing I touch upon that. So how do we upskill people? So we, one of the things that we did in work, we ran hackathons. Now hackathons is you take some business, most challenging business problem when bring people together and you solve the problem. One of the thing that I brought in that concept was I brought in participants, who are not going to contribute, they're just going to watch.

Vijay Yadav: So the, in each team, I just put together business folks who have some interest in data. I said, you just watch come to this hackathon, this meetings, just observe who's doing what I really and how they're thinking, how they're talking. And I cannot tell you is that after that hackathon was over, these folks wanted to be engaged part of the data science initiative going on, and they will volunteer their time to work on other projects to do that.

Vijay Yadav: So how do we go and create the literacy and the value of that element of that? So I would say it takes time, it's a gradual upskilling, we also have some of the tools like data camps, Minitab, or jump. How do we bring organized on a regular basis, the training and awareness program for that as well.

Vijay Yadav: And the last component, which is really what we call a digital mentorship. So one of the things that we implemented here at Merck, is that there are people who are willing to switch their career and they want to learn more about data. What can we do for them? So we establish a digital mentorship program, where we identified the people who are willing to mentor some people in the data field. And then we surveys the people who are willing to learn things.

Vijay Yadav: And we basically paired them up, and we had three months program, actually highly successful program. And we are able to scale it up 20, 30 people in one batch. Now, imagine if you have that kind of scale and solving the problem and bringing people on board, be more aware how they can solve the problems, it is a huge impact. So we can scale it up actually even more, have a digital mentorship program for that.

Adel Nehme: A hundred percent completely agree on your first element of showing a win to galvanize the data culture, and show people the value of data science. There's nothing like showcasing a prototype or a low hanging fruit of, hey, I just automated 10% of your workflow and saved you time to a certain extent to do something more meaningful with data science, to be able to showcase the value of data science and showcase the importance of data.


Adel Nehme: To kind of harp on your second point on upskilling, how large is the intersection to a certain extent between upskilling and data culture? In some sense, is data culture synonymous with upskilling and data skills? Or is there an additional cultural element, behavioral element that kind of can't be filled out with an upskilling program to start?

Vijay Yadav: So there are some mix up almost everything really, so there are definitely a not upskilling, but there's the mindset itself, right? So the mindset is really the other element of that. And that's a totally different upskilling probably will not change that, if somebody is working in certain way, then they're thinking certain way, they're making a decision certain way, how do we change them?

Vijay Yadav: So that's a totally different area that we want to handle, and hopefully I think if the company at the top level is pushing in that around, and you're forcing people to think different way. So I would say leadership by example. So if you want to change people's behavior, I think it's had to coming from the top. And you don't have to just talk about that, you have to demonstrate that.

Vijay Yadav: So if I'm a senior leader, not only that, I just talk about how useful data is basically leading by example. Showing this is what we have basically done making the investment, that's really key. We can all talk about all data and analytic strategy. There's certain level of investment that has to be basically made to do that. If you can show that example, coming from the top, other part of that organization basically coming with the fold.

Vijay Yadav: So in any change, this is what we're talking is the change management, right? So we are changing people how we are thinking, and their ways are working. So upskilling is a big component of that change, to do that. Now in any part of that organization, there are people who are motivated, they want to do things differently.

Vijay Yadav: What I have done, if you want to galvanize the team of people, you got to go after the people who are really excited, bring them together, create the energy actually. And one of the elements that I did was when I joined Merck, we created something called a data science knowledge network. And what the idea was was that you create a community of people, not only pure data scientists but also subject matter experts.

Vijay Yadav: These are the people highly motivated. They want to do things out of their regular job. They're not data scientists, right? So how do you bring that kind of culture, and you create, and when people see other people doing something different, there's wait a minute, let me see what other person is basically doing. So you can bring that way, creating the network of people, talking and showing success, coming together learning, and growing and giving them opportunities.

Vijay Yadav: I can tell you that I've given the opportunity for people who are part of some of the hackathons as some part of this one. I said, come, if you are, want to have experience, I'm willing to give you the project. So on top of a regular job, I've been able to basically mentor and coach these people, and that's a hands-on experience, that's a huge difference that can make in someone's career.

Adel Nehme: That's actually how I got into data science, a very similar dynamic, where I was also working in an organization on a data science job, but I was very excited about it. I was allowed to be able to contact the data team and then, that was my first experience with data science in a professional setting.

Adel Nehme: And kind of harping on that last point, that around community, I really want to expand into that, how important it's creating a community to galvanize data culture? And secondly, what are the most effective tactics that you found to kickstart the community of practice around data skills and data culture?

Vijay Yadav: It is highly important. If you think about the outside world, if you create a product, what the companies are doing, almost everyone, they want to create the community around their product. So if we are selling a pen or you're selling a phone, you got a software piece of software, almost everybody wants to organize a group of people, a community of people.

Vijay Yadav: What they're basically doing is that they're talking to making these folks, all the users at the system, talk to each other and learn from each other. That way, number one, your product is what is getting out. And you are getting a lot of feedback from that. So think about if I create the user community and I'm a lot of people are talking about a lot of other things. Can I get inside out of that? What are they exactly talking about?

Vijay Yadav: Now that becomes ground for me to go and mine the data, to develop my product to the next level, same thing here as well, we can apply the same. We create the community of people they're talking, they're developing. And other people are basically looking into that. And we can in this community, you can find out what is important to them. Who's facing what kind of challenge.

Vijay Yadav: So I think organically, you're capturing the thoughts of people when you bring the community, that's the best way to get that information. You will not get any other means really to do that. So I think that this community is the best way to build a data literacy program. And I'm happy to talk to anybody who wants to get some guidance, how to create community.

Vijay Yadav: I know we have a limited time here, but I'll touch upon that as well, but if the audience wants to really get some brainstorming on that, how to do it, I'm happy to share that, but primarily there are four components to any community. Number one is knowledge sharing, so when you bring there, everybody at the different level of a skill set, when you interact, the people are basically learning and sharing, right?

Vijay Yadav: The number two-component is that of problem-solving. So we are coming together, it is not just, we are just talking, why don't we come together, take a business problem on top of our day-to-day job and come together and solve the problem. That's the innovation hub actually, you can call it to do that, so you can create the community to solve the real problem. Hackathon is part of that community as well, I can talk about.

Vijay Yadav: So number three is we talked about is that upskilling, so when you have that community, you understand who needs, what kind of skill. You can capture the data, now based on what they're saying, where the feedback is, you can create your training program, a workshop for that kind of skill gap that you might have.

Vijay Yadav: And the last component I would say is the digital mindset. So when you people say coming, when you look at things, how people are functioning, how they're working, you get more, it's more a snowball effect basically to do that. So community around that as it get bigger and bigger. They do that, and now when you build the community, you want to make sure that community is represented by all facets and of the business unit.

Vijay Yadav: It's not some data scientists sitting in their room, you want to bring the sponsorship from the different business functions. They're promoting it, that people need to participate in that, and also they're participating in a hackathon or they're participating into learning programs. So there's a whole development piece that can be used by engaging the different business functions.

Call to Action

Adel Nehme: That is so awesome and so comprehensive. And I know that we're almost running out of time, but I want to end with one final question, Vijay, do you have any final call to action before we wrap up today's episode?

Vijay Yadav: No, thank you so much. I can tell you the folks data journey is not easy. I would say that all the components that we talked about that, those are the key critical component, there's no one fixed formula. I think you need to consider some way, and depending upon your situation, you can pick and choose where to basically start.

Vijay Yadav: Culture is the biggest element of that, I can tell you that, so if you can work on that is slow process will get there, but it's going to take a lot of effort and speaking up, right? So if I'm data scientist, if things are not working out, then you got to speak up and say, what do you need? Do you need to make it more successful, right?

Vijay Yadav: So I think we have to create the voice of change in the culture element of that. And upskilling, I would say that people are the highest element of that. So if you can empower people, I think you can get a lot more done that we can't imagine even what people can accomplish, if they're empowered to do things.

Adel Nehme: That is awesome, Vijay, thank you so much for coming on DataFramed.

Vijay Yadav: Thank you so much, Adel. So nice talking to you.



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