DataFramed
DataFramed

Episode 81 · 10 months ago

#81 The Gradual Process of Building a Data Strategy

ABOUT THIS EPISODE

The data journey is a slow painstaking process. But knowing where to start and the areas to focus on can help any organization reach its goals faster.

Today’s guest, Vijay Yadav, Director of Quantitative Sciences & Head of Data Science at the Center for Mathematical Sciences at Merck, explains the 6 key elements of data strategy, complete with advice on how to navigate each.

Join us as we discuss:

  • The different components of a data strategy
  • Shifting mindset within the C-Suite
  • Structuring the operating model
  • Enabling people to work with data at scale
  • Most effective tactics to kickstart a community around data science

Find every episode of DataFramed on Apple, Spotify, and more. Find us on our website and join the conversation on LinkedIn.

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You're listening to data framed, the podcast by data camp. In this show you'll hear all the latest trends and insights in data science. Whether you're just getting started in your data career or you're a data leader looking to scale data driven decisions in your organization, join us for indepth discussions with data and analytics leaders at the forefront of the data revolution. Let's dive right in. Hello everyone, this is a doll, data science educator indiventulist at data camp. One thing I love about hosting the data frame podcast is that I get to speak with really smart data leaders who can break down the different levers for becoming a data driven organization and you can argue once you put all these lovers together, you have the key components of a data strategy. This is why I'm so excited to speak with Vijaiyad of. Vijaiyat of is the director of Quantitative Sciences and head of Data Science at the Center for Mathematical Sciences at Mark. He is a season data leader who drives analytic strategy and Road Map for marks manufacturing teams and owns the development and deployment of advanced analytics capabilities throughout. Mark Vigi has over twenty 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 up skilling programs for data science and more. Now let's dive write it. The giant's great to have you on the show. Thank you, Adell. So great to be here. So I'm excited discuss with you. You know, all things that is strategy, how to operationalize and build data culture, how to accelerate their science within an organization. But first, can you give us a brief background about yourself and how you got into the data space? Yeah, thank your Dell. It great to be here. By trade, you know, I'm computer scientists and I thought of my career in it. Space and a data has always be part of I consider myself the data practitioner, even though in my college days I was specialized in artificial intelligence and no medical methods statistics, but I got to apply those techniques later in the career. But I can tell you that throughout my career data has been part of my passion and majority of the solutions I have been able to apply some of the AI in machine learning techniques to deliver some business value for the company. Currently I am with the work I'm heading their data science team and primarily focused on developing the AI andml solutions to make manufacturing modern kind of capability enabled area. There's definitely a lot to impact the day and when I'm really excited to talk to you about is really different elements of the data strategy and how to get there. Your very experienced leader in the data science space and you've worked a lot on data transformation projects and I especially love this topic of Dair strategy because is really allows us to dig through basically all of the elements of what makes an organization. They did driven and I think that's very exciting. So I love it if you can walk me through, kind of from a high level, the different components or elements of the data strategy. Yeah, now, I met that is that the great actually, and that's the way you want to think holistically. What the data and and I'll strategy so look like? In my experience, there are six pillars of that strategy. The number one at the top is basically data and and leg strategy itself. Who owns that strategy? Who Develops that right? Who are the players basically want to do that? So that's the first component. At the very strategic point of view, that as well. What are the business objective and of the day? I think you want to have your inn 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 and other sacnization? 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 DEGIGANT team? So there are multiple players. How we come together archestate in a way that it's a cohesive in order to make this strategy and execute in and drive the value out of that right. So that's the second component. The third component, I will say that the data sets itself. So how do we go about building the data sets? You you have to really think about it's we all call it data is oil and it is gold. Is An asset, right. So how do we take that and make that we can basically drive the value a preserve it right to do that. Now, it's not you take all the data and then you jump into into a data lake. Right. That is not the strategy of putting in the data. What I mean by data strategy is that what can we do that is accessible in a easy way, it is governed that only people who need to access they have to have access. It's it's makes sense for someone to look at that is a smaller democratrize. The data itself, right. So how do we do that as well? That's a component. The next component is the infrastructure, the platform itself. So we have a data how do we apply the infrastructure? Put together infrastructure that's is scalable and it can take their speed. The business wants to do that. So infrastructure, whether it's a platform, 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 searable and scalable. Right. 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 you know mat an hour, the concept the data product is it's really in silicon values very prevalent. But what it is not prevalent is, you know other dustries. You know that's a farmer whether it is other one. So how do we bring the concept of product, when I say products, a data product right? The concept is that it's not that you take any city, 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 one right, and the last one, I would say the people and culture, even though it is a last one, but I'll keep at the last right. So how do we organize this organization itself? They die and aganization. What is the culture? And Culture? I can tell you the biggest component in the strategy at all levels, and we'll talk more as we as we go, but at the high level these are the sixth area that I think are really required. That's awesome. So there's definitely so much to unpact here. I'd love to go through each element by each element in big through and kind of pick your brain and I said I love this conversation around data strategy just because how holi sic it is and because we get to tackle all of these different elements. Let's sort of kind of with the ownership of the data strategy. You mentioned. That's one of the first elements when you think about ownership. You know, often time I see with an organization there's often a tradeoff or kind of a friction, especially as they go about their first data. I don't think during at the beginning, within their data don't think this journey around who owns the strategy, and it could be as often at the bet 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 and its execution? Now that that is such a great and important question. There I can tell you that the company who are company who are doing well, those company, the business owns a strategy, dide strategy and the company who are not doing well. It's a very traditional way. Somebody else wants it could be technology, as someone else right in my opinion. Experience, I can tell you is that high success, you will get one. The business owns a daydie strategy. So what I mean by business owning that? So think about who is the Huger after 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, 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. What do I need really to do? That not, you know, it organizesn't it redactionally, that that's where it is. And also the ownership in terms of drive the value. So let's say we develop a solution. Right, we deployed end of the day, business using it. Who makes the commitment that, hey, we develop this too. Now here the value weight 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 a here is value or delivering. So you can take that value and go to the next level of basiculity. Do that. So in my experience and opinion, in very strong opinion, is the business is the one that should basically own that strategy. Now, business is the business of doing you know, technically, if farmer 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, physically own their strategy to drive it. Where you search up on here is really that data science and data destiny, say, is not a traditional technology project, right. It's not something that you take from point eight to points it's a mindset shift. It's kind of a new methodology for solving business problems. If you talk about the business data, that business of data is feeding enabling your main line of business. Right, so we think of vertical up 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, and this is why it should be owned by the business. Is that correct? A hundred percent. That's exactly right. You you some did it that really, really well to do that. So one comment. I wanted to just make it. So the way I think today, and those are the differensity factors. So any companies, at least they are in the business of two things. One is the primary business they are in. So if your farmer company, you're making the farm of product, that's your primary business, but you're also in the business of data, business of data, the concept where and you're driving value out of that. So I think that the mind shift here is that you're 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 you know ways. Yeah, and in conversation is with the C sweet especially, how do you convince them that Hey, the day a strategy should not be owned by a t because probably c suites are very comfortable and hanging off these technology driven projects towards it and not towards the business owners necessarily. So how is the conversation goes and how does that kind of mindset shift within the C suite happen? 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 a 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. I tagonizes San is the not the one. Is Basically they are exporting something, but they are not close to the what the business basically looks like. What I have done in my experience. Is that showing that? So the success that hey listen, when we came in and we repretended the business, we talked of the business, we defined the ownership for the business. Here's what we delivered and here 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 can see the difference. Right. So there's no state 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, given that the business should own it. What is the role of I t then, in executing and ideating the day a strategy. If you're saying data is business,...

...is than what it does, it t has a big role to play. So think about we talked about. One of the pillars is the enablement right of the data and analytics. enablement right. So the infrastructure right. It is 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. Are you need a platform? The ty platform that we need for that it. So I would say that the delivery, once we define what success looks like. The business says he has good looks like, and here's my vision for this particular solution. I'm going to lay out the vision. This is what good looks to me. Let's define those pieces and then ITU comme and enable us. Where about that technology component basically comes in the place? I would say it is still plays a big role, but I think the business is the one at the driver's seat. That's a piece. That difference. I see there. That's awesome. So we covered kind of the ownership of their strategies first component. Let's move on to the second component, which is the operating model. Right, how do you organize data teens? How do you organize the players, as you mentioned them, within this organization, with this in this new practice, within an organization to the liver 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? So one of the same real I have seen in my career as success is that one of the in the operating model they sponsors play a big role. So what I mean by that? So the sponsor are the one basically your champion for the business case. Now, when you make the business case to develop a data product using aim lan, any analytics product, sure do that. It has to be based on the value that is being driven. 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. There's a change managing process that has to take place in order to deploy the new solution. So if you ever 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 response 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 rights. 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 in multiple, you know, roles that I have basically played. So that is one role. That is basically someone who's defining the Vision, represent the business here at the road map basically looks like, and this is how we are basically going to do that now, and of the Road Map of the data team that side. That's right. Yeah, that's that's right. To do that. Now it is a play. It is you can say is that that's where they're coming to play as a team, playing the role in in delivering the IT infrastructure at the platform architecture, anything compliant, for example, in these days. I think data compliance really part of that. Right. So we have to have that element into that as well. And so we define who is doing what, right, so they clearly will define. The other element is that the the business subject matter experts. Now, if we are taking there, if you're any saying that, hey, business is owns that strategy. Right, so the business has to allocate the resource. I just subject matter expert. So as the project is going on, development is going on, we have someone who has the expertise to basically provide the inside what good looks like, a thing of that nature, right. So there's another business plays as role. So we talked about the sponsorship, we talked about the it, then we got a product team, then we got business side. Subject to my experts, and I think someone and IGANIZATION is a change management comes in the place. Who plays that role? Right? That's another...

...element of that. And on top of that, think about the priority. So who defines at which projects would we take that is IT product team? Might someone from the business that hey, we got ten things here, here is number one. So someone has a basically a priority. person that you basically running it, and also you want to drive the value out of that to somebody should be responsible to track the value into the process. Even kind of before thinking about how to define a team, then the court, companies and organizations should really think about who's owning the process, who's setting the vision in the road member the data team? Who Sponsoring the change manument management, ensuring that the data team is supported, even before thinking about who to get as a data scientist or how to build the data science. Absolutely, absolutely, that's that's really important and I think that's a major problem right now. We have is I have been in situation where when I engage at so nobody knows who's doing what and I think I have seen the success. If you clearly define the rules, that responsibility and a very clear map, then I think you have a high level of success in that scenario and without kind of the bass layer that you're discussing of setting the Road Bou setting the processes, you're setting up data scientist to fail because they don't know necessarily what they are building or who they are building for. Is that correct? Hundred percent? I think one of the element, and we'll talk about that, is that. To me, I think customer experience are huge. Experience is the key. What I've seen is that we design traditionally. People try to define a solution and try to find the US after the fact. That is not the case. Your solutions would be really tailor to the business and the Yuga. You need to understand the Huger base. Are you designing solution of that as well? To do that, YEP, go ahead. Awesome. Now that you've like we've have this operating model, or relatively set in place, how do you organize the data team? You know some organizations they oft for like a center of excellence centralized data team. Other organizations they opt to. We think a data scientists. We put them in the business and they have skin in the game and their part of the business. which model works for you, that you found works for you most, between centralized the centralized model, and I follow up on that is one of the tradeoffs between birth, I would say the mixed model. So think about their analytics needs. That are different in nature. The complexity is different, right. So if you embed data science team or data analytics professional as part of the business, who are doing, you know, less a dashboarding, are any type of insights that we can basically create in a simple way. It doesn't need advance analytics up to a right. So I think you can embed those as part of supporting day to day and they understand the local needs their 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 skills set and together and I fear fragmented you may not be able to dry the values. 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 that's really really important. Yeah, and especially early on the analysis journey, it's very important to can set the stage with the centralized more on then you can over time embedd and become much more hybrid. Is that correct? That's hundred percent. I think that's 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 that's a whole data culture piece. You want to make people aware that here the data can drive the value right. So that is part of the culture when you go and work assigned the people in bed as part of the local so people are getting aware, they getting to know. As the time goes by, maturity level up. Now you can centralize because now the companies is much more aware of what data can endible for them. You know, the third component and first component that you mentioned at beginning was kind of data asset management as well as the analytics enablement. They think this is where a lot of it plays a massive role. So let's sort 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. So, moving on to the other components of data strategy, of course we can dedicate an entire episode towards asset management, data as management, data governance, but I wanted to gain your insight on how important should day asset management manifest itself within the data strategy and how. So it's a critical it is the critical component of the data strategy. One thing that you see the we complain, Oh my God, data is not clean, the data is scattered, is a desperate if you think about the region behind why we have so much problem in the data. What's wrong with that? Why did we not think about the data in a structured way and we can deference it. One of the element that is really a success is that you want to give the context with the data. So what the basically means is the Meta data. 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 of vision, inspection. A product inspected you take the image of the product and you know machine is telling with their product is defective or not. Now I want to attach that image one piece of information with everything else outside it, where it came from, which side it it was, which planted came from which machine. Right. So the you pick collect that data in a more hilartical way, the ignudgation, the site, the product and any other Meta data you have. Imagine if you can attach the Meta data along every piece information that exist in the company, that's when you makes me power really to go and filter the information is extracted with. Now you have defined it. So giving a context to the data is really critical piece. Now, that is not easy task by any means. It is the complex. It can take time, but I think if you think on those lines, it's really important. One biggest challenge that we are having is that any time if I'm a Uger and the shop floor and I am 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 did a captured in send you the data leg 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 went to the lake. Now I have to go figure out where it is. So it went away from me. Now the data strategy has to be one 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 specific data, for jam? But so if I work in the shaft floor, I'm not interested in supply chain. I'm not indish something else. Can you give a context to the data? Just I understand myself. Bring it closure 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 really well. Don't give me the C ocean. Me To float and try to find out to do that. And also, of course, the data governance is another challenging piece. So you don't want to give you access to everything to everyone. Right. So if you have a can, if you have a give the context the data. We talk the METADATA. You should be able to control that, because if data belongs to certain plants, certain side, now I can say hey, you are owner for this particular place and I can assign you attribute. Everybody else is basically out. So I think the Meta data and giving context is a really important factor to make the data governance much easier to do that. So answer your question is not easy, but it's really critical piece and the technology now is enabling in that space actually to do that. There are there are multiple technologies coming up how to give the context the data, things of that nature.

What are the first steps that you want to formulate this part of a data strategy to ensure that the sure on the right path when it comes to their governance in their quality? 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 a man factoring medicines, so as the process basically moved from left to right, underlying all the data getting captured. So what is happening is that the as the process moves, your data is generated in the process. Now what is happening is the region that we have a lot of gaps because your process is broken. So if your process of 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 processes are linked together. So you data, underlying data is a link together as well. Now you can say that yeah, that's possible be yeah, 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 real literally. You have to give more like a process, ID, site, idea, product idea. What about the Metadata is give the unique definition to eat data element and you give that uniqueness to when you capture the data, each piece of that, and that is how you can segregate it and put the data governance overall. 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, you know, a high quality, standards of high quality around data. Now the next step of this, in the next piece of this is they ad analytics enablement. How do you make sure that people are able to fish for insights and easy straightforwards in stream client fashion? Right. So we've talked about setting this foundation, but ultimately you need to enable folks who work dat it with real time. I'd love it if you can walk me through what organizations can do. Is Part of their strategy to enable people to work with data at scale. What are the different tools and Celtics enablement that they need to think about to be able to operationalize date at skill? 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 caus cities and 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 outoml. Right. So autohamt is really great way to get you started where you don't have to understand underlying technical technique, technical details of the Algorithm, but you can visually, you can really look into that and you can basically drive the value and the third element, and in the bottom is that people are not really into that much of data science. So can we train them? Coming up up Scille them are very basic, you know, tools where they's a spotifire click sense to do that. So I think in order to move the needle in in upskilling people at almost you 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 basics so we can talk more in detail is really keep Auto Emma is really great capability and in fact in work we are we are pursuing that avenue. Data Iq is one of the tools that we're basically using for that and it's sort of the it serves the scientists also cities and data scientist as well to do that. And then, of course, a very technical side up with that. How can we train the different languages and more upscaling, more technology, we train that those people as well. So I would say that we need to focus all three areas. Yeah, and what about kind of the descriptive descriptive earlythics area? Tess certainlystand kind of empowering the insight layer nocessarily just the machine learning layer. Where is the room for business intelligence shows within the empowerment. So I think to like a data camp,...

...right. We are using that tool really too, in a data literacy program. We also have some of the mini tape, 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 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. Going to execution, we talked a lot about these different levels. They don't thinks, enablement, Dataset 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 towardship over execution of their state strategy. Right, given the amount of these lawyers the organizations need to push and pull on. I have a couple of questions around execution. One, how should leaders go about prioritizing these different lovers? Do you begin working on infrastructure first and enablement, or is it all in tandem? And then which parts of the organization owns different these different parts of lovers. So we can start off kind of with the execution process and timeline. How do you prioritize these different lovers over time and where does the work start when you go for execution? You know, the culture is big part of that, right. So one thing that we have really see is, does everybody in the company sees data as an asset and how do they see it? So somebody hadn't work in the shaft flow for 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 this started all across the company, right. That doesn't need any there's no pretty season for that. I think the more people are changing, the culture pieces is changing, I think you will you see a lot of things fall into place slowly. One thing I noticed my experience is the people are really powerful creature. If the and in thent we all talked about the definish structure. I think that if I have to keep everything at the top, I will keep people at the top. 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 the business for value for the business, yeah, ideally it will cool if I have a platform, you know, a model deployment, I can manage the life cycle. I can versn't control all those things. But listen, if I'm motivated and if I know there's a value in this one, even I don't have those technology components, I'll still able to do that. It's going to be slow, but I always still deliver the value. So I think what I would say 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 low hanging fruit all across that we can deliver without having the heavy lefting infrastructure. And you want to drive the value, create the awareness here, the value as the people are maturing, their thinking immaturing, even the business senior leaders. Thing 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 places as the maturity basically goes. I would say that it's not that. Unless you put everything in place, then only can drive the value. I think it's you can start for low hanging food and just keep on building up on it until you get to this stage where you need to scale it up. So in some sense there's a lot of value that can be done and 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? That's like that? That's what exactly I'm saying. Yeah, in terms of who owns these different parts. So we talked about the kind of who makes sure the turning scent, the all of these different pieces are being executed upon. So we talked about business of data. So what I was referring to that is that so data and a lytrics agnijastion within the business. So this agnization basically is part of the business, reporting somewhere. The structure to do that. We can call CDEO,...

...are CDAO are call it any other names, but that's a basically self standing vertical within that agnijation. So for most part actually talked about the all the different components. Majority of that is driven by, at least owned by that agnijation. But the it comes in the place. We'll talk about that as well. So I would say it is. It is the mixed responsibility. There's not one that everybody won't see that. So I t has role to play, this data and and ultricsagnization to play, that the business re sponsor, that they have to play, subject my experts. So and the business owners and business units coming to place. So I would say they are multiple owners. But overall I think the driving force is data and analytrics Agnization, as you talked to earlier. Definitely. So we kind of highlighted in our conversation can of the error of nature starring stand 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. Their strategy something that you continuously do, basically as long as the technology says and that you can take value out of it. So how you just the process when executing the strategy and these data projects to account for the air of nature of the data game? Resturn extem so here's what I've seen work really well. It is there's no there's no magic want for that. How you do it? Convince people. So let's say if I am, and I've done it almost every case very successfully. So you don't tell you so what I mean by that? You saw the value. So let's say you got a concept and I took the one. I joined work, for example, I took on concept and I basically had the proof of concept for a solution that was really it is solving the business problem. Saturday I presented to the senior management. This is what values basiclity even they can clearly see that value. That's can be basically to even 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 they, stakeholders, can see the value, they're willing to come with you. So you have to sew the small success. What about the success? It is and when people see the success, they're willing to come with you. The other element I would say is that I've seen it in a senior leaders. If you can see that, you can help them in their success. What about the objectives? So if I'M A head of business unit, if I go in front of them that hey, listen, here's something that I want to come and and solve your problem and if they concede its helping them successful, then that basically they are on board to do that. So you have to take that element. So there's whole sewing and coming in front of them and sewing the value. If you can sow a small value and they's a willing to scale, I think you have with them. So it's a very gradual process people understanding the value more organic way. That's the one. But at the extreme side, if someone comes as you know what we are going big on data here is the money, just take it on. So the value, that is not the scenario most of the cases. Right. Every time company is investing in something, they're looking for do I and we don't that. Roy cannot be that easy for data projects. It takes some time. I'm somebody looking at Oy 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 the 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 deliving the value. I think you when bring business on board. That's the culture in mindset is super important when it comes to enable analytics across the board, enabling the scalability if 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 there a culture. The majority kind of organizations have yet to correct the data culture problem. I'd love your perspective on not only the importance if data culture and how it enables it is strategy, but how a lack of data is culture stops their strategy. Oh my God, that is such a critical...

...question I cannot tell you and I had. I had to navigate this and I think, to 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, culturally the big world, right, it may mean different things to different people. So let me break it down what I mean by that. 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 you know it solutions. Now, if some if you can sift that mindset that yes, indeed, data belong to the business strategy. Right, if you can see that's a huge change. That's be undertaking itself to do that. 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 and that? And again, I we talked about the swing. The success is. Is that? So that is at that level middle management. Again, if we can so the part of the success that somebody can see, wow, yeah, I can do this. I never imagine, and I have multiple examples where people never imagine that's possible using the data. But once we saw the small prototype is a 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, showing the success and coming with that and going hand in hand, I think that is really big company of that. The other thing I touch upon that. So how do we upskill people? So we one to think that we did in work. We Ran Hackathan's now Hackathans is you take some business most challenge business problem and bring people together and you solve the problem. One of the things that I brought in in that concept was I brought in participants. We are not going to contribute, they're just going to watch. So they in each team. I just put together a business folks who have some interest in data I said you just watch, come to this Hackathan, this meetings. Just observe who's doing what really and how they're thinking, how they're talking. And I cannot tell you is that after that Hackathan was over, these folks wanted to be engaged part of the data science initiative going on and they will volunteer their time to work another projects to do that. So how do we go and create the literacy and value of that element of that? So I would say it takes time. It's a gradual upscaling. We also have some of the tools like data camps, many tab or jump. How do we bring organize on a regular basis the training and awareness program for that as well? And the last component, which is really what we call a digital mentors it. So one of the things that we implemented here at mark is that there are people who are willing to switch their career and they want to learn more about data. What kind of we do for them? So we stablish additional mentorship program where we identified the people who are willing to mentor some people in data field, and then we serveys the people are willing to learn things and we basically paired them up and we had three months program actually a highly, highly successful program, and we're able to scale it up. Twenty, thirty people in one badge. 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 the huge impact. So we can scale it up actually even more to have a digital mentorship program for that a hundred percent. Completely agree on your first element of showing you when to calvinize the data culture and show people the value of data science. There's nothing like showcasing a prototype or a low hanging fruit of like hey, I just automated like ten percent of your...

...workflow and saved you like time. To certain extend to do something more meaningful with data science, to be able to showcase the value of data science and showcase the importance of data to kind of Herp on your second point on upskilling. How large is the intersection, to a sturning extent, between upskilling and data culture? In some sense is data culture synonymous with upskilling and data skillsures? There in additional cultural element, behavioral element, that kind of can't be filled out with an upscaling program to starning. Yeah, so, so there are some mix up almost everything really. So there are definitely a not upscaling. But there's the mindset itself, right. So the mindset is really really the other element of that and that's a totally different upscaling is probably will not change that. If somebody is working in certain way, then they're thinking certain way, they're making decision certain way. How do we change them? 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 a different way. So I would say, you know, leadership by example. So if you want to change people behavior, I think it's hard to coming from the top and you don't have to just talk about that, you have to demonstrate that. So I if I'm a senior leader, not only that, I just thought about how useful DAYTA is. Basically leading by example, sewing. This is what we have basically done, making the investment. That's really key. We can all talk about all data and and strategy, there's certain level of investment that has to be basically made to do that. If you can sew that example, coming from the top, other part of the aganization basically come into the fold. So in any change, I'm this is what we're talking is the change in management. Right, so we're changing people how we're thinking and they're their ways of working. So upscaling is a big component of that change. To do that. Now, in any part of the nization there are people who are motivated. They want to do things different what I have done. If you want to galvanize the team of people, you are 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 join work, we created something called data science, notledge network, and what the idea was that you create the community of people, not only pure data scientists but also subject my expert these are the people who highly, 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, that's a wait a minute, let me let me see what other person need basically doing. So you can bring that way, creating the network of people talking and sewing the success coming to whether learn and grow and give them opportunity. I can tell you that I've given opportunity for people who are part of some of the hackathons, some part of this one. I said come, if you are want to have an experience, I'm willing to give you the project. So on top of a regular job and I've been able to basically mentor and coach these people, and that's for hands on experience. That's a huge difference that can make in someone's career. Yeah, that's actually how I got into their sciences learning a wow, look at less, very similar, very similar dynamic, where I was also working in organization and on their science job, but I was very excited about it. I was caught to be able to contact data team and then, yeah, that's how I was going. First, sistance with data science and professional setting and kind of harping on that last point, a round community. I really want to expand to the how important screating communit and see to calvinize their culture and, secondly, what are the most effective tactics that you found to kick start the community of practice around their skills and their culture? It is highly important. If you think about outside world. If you create a product, what the companies are doing? Almost everyone, they want to create the community around their product. So we are selling a pen, are you selling a phone? You know, you got a software, piece of software. Almost everybody wants to organize a group of people, Community of people. What they're basically doing is that they're talking to making these folks, all the users of the...

...system, talk to each other and learn from each other. That way, number one, your product is what is getting out and you're 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, a lot of other things. Can I get inside out of that? What are exactly they're talking? Now that becomes ground for me to go and mind 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 an 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. 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'll not get any other means really to do that. So I think that this community is is the best way to data literacy program and doing that, and I'm happy to talk to anybody who wants to get some guidance how to create community. I know we have a limited time here, but I'll touch upon that as well. But if you know audience wants to really get some rainished arming and 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 skill set, when you interact the people are basically learning and sharing right. Number two component is that problem solving. So we are coming together. It is not just we're 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 innovation. Have 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 so number three years we talked about is that upscaling. So if you, 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 our workshop, for that kind of skill gap that you might have. 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 is more like a snowball effect, basticulity to do that. So community around that as you get bigger and bigger to do that. And now, when you build the community, you want to make sure that that community be presented by all facts and of the business unit's not like some data scientist sitting in their room. You want to bring the sponsorship from the different business function. They're promoting it that people need to participate in the at that and also they're participating in Hagathon or they're participating into learning programs. So there's a whole development piece that can be used by engaging the different business functions. That is so awesome and so comprehensive and I know that we're almost reaching out of time, but I want to end with one final question. They dry. Do you have any final cull to action before we were up to this episode? No, thank you so much. I can tell you the folks data journey is not eagy. I would say that all the component 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. We can choose where to basically start. 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 is speaking up right. So if I'm data scientists, if things are not working out, then you are to skip, you know, speak up and say what do you need? Do you need to make make its more successful? Right? So I think you're to create the Voice of change in the culture element of that and upscaling. I would say the 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 with things. That...

...is awesome. Vigi, thank you so much for coming on data framed. Thank you so much, Adam, so nice talking to you. You've been listening to data framed, a podcast by data camp. Keep connected with us by subscribing to the show in your favorite podcast player. Please give us a rating, leave a comment and share episodes you love. That helps us keep delivering insights into all things data. Thanks for listening. Until next time,.

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