Successful Frameworks for Scaling Data Maturity

iscuss. But before, can you share a bit of a background about yourself and what you're most known for in the data space?

Ganes Kesari: I'm a co-founder and a chief decision scientist at Gramenar I have about 20 years of experience solving organizational challenges using technology. Co-founder Graham primarily to help organizations make better decisions using data analytics and storytelling. We serve hundred-plus clients, the likes of the World Bank, and Microsoft Ernst & Young, and in my role, I lead our client advisory and innovation practice.

I work with CIOs and chief data officers on their data strategy roadmap, helping them realize business ROI from their data investments. Work with several organizations in terms of analytics, data visualization, and tracking ROI and measuring this on a month-on-month basis and reporting it back to the executives to secure subsequent investments in helping the executives do that.

I also lead our innovation practice. We do app application research at our AI and Story. Done a lot of interesting stuff in terms of applying, say, geospatial analytics and novel forms of storytelling like data, comics, and so on. So some of those are some, some great interesting stuff created by the team. And apart from all of this, I am very passionate about teaching and writing, and to ask me what I'm known for.

I am quite visible. I, I'm active on LinkedIn. I share what I've learned and also the mistakes I've committed and what I've learned from them. So I write for Forbes and Entrepreneur. I have also spoken at several industry events and other forums. I also run guest lectures at institutions like Princeton and Rutgers University.

Overall, my world revolves around data.

Defining Data Literacy

Adel Nehme: That's really great. I think you're in the right place. So, you know, one of the things that I'm really excited to dive deep with you about is the topic of data culture and becoming data-driven. I think given your experience leading Gramenar, working with a lot of data leaders on their own data culture, and their own organization, I think we can all agree that building a data culture is absolutely mission-critical for any organization that wants to treat data as an asset. So what I wanna first start off with is maybe demystifying some of the terms, right? I think our industry is drowning with buzzwords, data transformation, digital transformation, data literacy, data fluency, data culture transformation, and the data camp is even like guilty of using a lot of these terms, right?

But maybe walk us through how you define a data-driven organization, maybe how it relates to some of these terms.

Ganes Kesari: Sure there's no positive of buzzwords in data science, which in itself is a , is buzzword answering a question, what is a data-driven organization in simple terms? An organization that is able to effectively and consistently utilize data for decision-making across all levels. That is what a data-driven organization is.

The organization is treating data as a strategic asset, not just as a support function or as an enabler, and they're able to. Utilize data not just for strategic decisions but also for everyday decision-making where data is a way of life for everyone within the organization that is a data-driven organization.

Adel Nehme: That's good and I completely understand. It's kind of, you know, an organization that has the habit always of looking at data when making decisions as well. And I think there are many dimensions to becoming data-driven. We've seen this with DataCamp for business customers, for example. There are cultural investments, people skills, investments, technological and infrastructure investments.

Right. I'm very excited to dive deep with you on these different dimensions. Right. From your experience working with data leaders and a lot of these transformation projects, what have you seen to be the most challenging aspect of building a data-driven organization?

Ganes Kesari: Firstly, completely agree that building a data-driven organization is very, very difficult. This is a recent survey by New Vantage partners that found that about 26% of the organizations only have managed to build a data-driven organization. And when they looked at data culture, which is a higher level where everyone really believes in data and then reads data day in, day out, that is sub 20%.

So why is it so tough? There are four shifts I see as critical to becoming a data-driven organization. You'll have to shift your skill set. You'll have to shift the toolset. You'll have to shift the process. And finally, you have to shift your mindset. So skill set is where you build for the team creating the solutions. They need to be upskilled on data analytics, and storytelling. And for the people using these solutions, also need a skill set, and upgrade. And second aspect, what we really mean by tool set, all the technology solutions, architectures, the tech stack. You need to invest and build that, and it has to be a technology strategy.

And third process set is where you need to integrate all of these back into your business workflow so that this fits squarely into the business process. It's not on the side where people have to make a deviation to go and use these insights and then come back into the workflow. Any organization that designs a.

Data solutions like that are bound to fail. So it has to be, it has to rewire the processes. That's where the third aspect comes in. Finally, the most important and difficult one is shifting the mindset. You can train the people, build tools, and rewire the processes, but how do you overcome, the cultural resistance and make people comfortable and naturally turn to data when faced with a question?

That's the mindset shift, which is also important.

Approaching Data Literacy Transformation

Adel Nehme: How should data leaders approach this transformational project? Should it be an investment in tandem, and all of these levers happening at the same time? Is it procedural, stepwise investments? How do you approach that as a data leader?

Ganes Kesari: This is best-done step by step. Again, Big Bang Investments. There are several organizations that I've seen firsthand. They take up a mega project, six months, one year, and pour millions of dollars into it. That the risk of failure is high. There are some organizations that have succeeded. The chances of success are much higher when you take this step by step.

The four levers we talked about in terms of skill set, tools that pro and process it and mindset, it's best to start with the data that you have, the analytical capability and tools that you have and with the people that you have. So start with small, simple initiatives and you'll have to build a roadmap which helps you use the current capability to solve some simple, descriptive, smaller problems.

And then you realize the benefits project, the benefits to the rest of the organization and say, this is what we could achieve in say, a few weeks or a month's time. And then build upon that. And that's how you build momentum for success. So it has to be done step by step, and you'll have to come up with a roadmap, which helps you do.

5 Dimensions of Data Maturity

Adel Nehme: I'm very excited to get like, expand on that notion, discuss how to build a roadmap, all of these things. But I think what's first more important is maybe to tie a lot of the conversations that we're happening they're having here with the concept of data maturity. This is something I've seen you speak about.

This is something also we've spoken about at DataCamp. You know, data maturity framework. I've seen you speak about this extensively. Walk us through how data framework helps organizations prioritize their data journeys.

Ganes Kesari: A lot of people ask me, Do we have to go through all of this? Can you just tell us some projects to start? We would like to get started tomorrow. So I see this as a self-assessment. You need to first find out where you are before you can chart the path to where you need to get to. So the self discovery is what data maturity helps you understand. And this concept is what is really data maturity. It is a reflection of the organization's readiness and capability to embrace data for decision making. And there are five dimensions. We have a data maturity, a framework at Gramenar, and there are five dimensions we've thought through. We've seen this work very well across our clients.

One aspect is vision. Second is plan, Third execution. Fourth dimension is value realization, and fifth is culture. So these are the five dimensions, and you'll have to assist the organization on how strong is the vision with data. Do you have a plan for data, short term, long term? And are you able to translate that into a roadmap, a set of projects and capabilities to build?

That's a planning aspect. Third one, how well you are able to execute it. What kinda tools process do you have? And. How are you able to improve adoption and measure roi? The value realization part is the fourth one, and fifth is culture. Is there resistance to data within your organization? How ready are the people to embrace a technology like this?

So when you assess the organization on these five dimensions, you can find out, for instance, there are some areas when we run these maturity assessments, we've seen, some organizations that are great on vision and. The executive management is completely aligned. However, they suffer from poor execution or poor value realization, whereas others have a very strong execution muscle, good tools, good people, great data science team, but they don't have a vision.

And hence they're not able to pick the right projects. So you need all these five and some proportion. Without that, making any investment could lead to failure. So a data maturity assessment tells you what level you are on each of these dimensions, where you need to focus on next, and it's overall the, I talked about data maturity and improvement, and capability.

That takes time. That's a journey. You can't directly move from a low to very high maturity. You'll have to do it step by step and work on all these five dimensions.

Adel Nehme: And what's really great about the framework, I think, is that it allows organizations to really be honest with themselves about what is important. Right now. I think an easy pitfall an organization may have, or like an executive team may have, for example, that may have poor vision, right? Would be okay. We don't necessarily have a great data infrastructure.

We don't necessarily have. A lot of data talent yet, but what we're gonna do is we're gonna hire like 10 PhDs in machine learning and build out really complex machine learning projects, right? And without necessarily having a good grasp of one, the tool stack, the cultural adoption of such tools within the organization.

And this leads to, you know, regrets of working with data science within the organization.

Ganes Kesari: Absolutely. Very true. Are several organizations we've seen that have this capability, but they're not able to get the ROI because they are missing some of these dimensions.

Levels of Data Maturity

Adel Nehme: Given that, walk us through the different levels of data maturity that you've seen organizations operate and, and what are the kind of investments that they can make to progress alongside the maturity levels.

Ganes Kesari: A good reference here is Gartner's Data Maturity, the five levels of data maturity, which Gartner's extensively published and talked about. I think that's, a good framework where organizations start at level one of these five levels. Organizations start at level one, where they're using data opportunistically.

When there is a big challenge, they're not able to use their gut feeling. That's when they turn to the data. It is a very opportunistic usage. And level two is where few teams have started believing in data, or maybe one business leader or one technology leader starts investing in it. So there are pockets within the organization that start using it.

Level three is where the adoption is slightly broader, but they don't use it strategically. Level four is where they have gone beyond that, and they are almost the entire organization is using it and they're using it and realizing business ROI. However, the only thing that differentiates these level four from level five is that they view data as a strategic asset, and they view it as central to business strategy data central to business strategy.

So that's a fifth level. You can see that across these five levels. For you to progress, you need to. Improve the four dimensions, the four aspects we talked about, the four shifts we talked about, right? You need to continuously build your skill set, train the data analytics team and the users you need to improve the data literacy of the data consumers within the organization.

So that's a skill set aspect. And when it comes to the tool set, First, you'll tap into the tools that you already have access to, and then you'll progressively bring in more sophisticated tools as you measure. And importantly, you'll also tap into not just internal data, but also external data and even variety of data, not just structured, but unstructured data.

So similarly we talk about skill set, tool set, and same thing reflects on these other two dimensions of the processes. We talked about coverage of the organization, and finally the. How, in fact, one characteristic of the higher levels, typically levels three and four is when there's a data leader, like a chief data officer or a chief analytics officer is hired at those levels.

So each of these levels are reflective of a certain kind of maturity and organization need to progress through these.

Adel Nehme: So maybe if I'm a data leader trying to start off in my journey and I don't necessarily have a good understanding of my organization's data maturity. How do I measure my organization's data maturity? What are the things that I need to be aware of that I need to be consistently looking at to understand where my organization sits on the data maturity spectrum?

Ganes Kesari: You should start with the self-assessment. You could do this yourself if you have the data leaders can run this internally if they are able to come up with these right frameworks and tool sets, or they could hire an external partner to do that. But what is important is looking at three aspects. One, you reflect on the organizational strategy and the role of data as part of that in the current state where the organization is and number.

Talk to people, talk to the technology team, talk to the business team and understand what are their priorities? Where do they see the gaps with the data practices, and how much data is able to support their business priorities. There's a second aspect talking to people and, and running surveys and figuring it out.

And a third aspect, which is often missed out as you'll have to inspect the assets. You can't go with what people say. Again, each person has their own bias and blind spots. So when we run data maturity service, what we've seen often, it's uh, at times it's funny, but when you look at how people view it, then you can understand this better.

Typically, the data maturity scores, when you do a self-assessment for the technology team, naturally the scores are much higher compared to the business teams. So the business teams enter reports that we don't have the data of the right quality availability is not great. Whereas technology teams say that we don't get to know about the business priorities or that the business priorities keep changing.

They're not able to. Keep pace with it. It changes too often, so the score, there's always a little bit of attention and different opinions you'll have. That's when, when you go and inspect the assets to find out, look at what project was run earlier, how much was it able to help, what were some gaps? So when you inspect the assets, you'll be able to find for yourself.

So when you combine these three aspects, the vision priorities, the survey and talking to people, and third inspecting assets, that'll give you a very good understanding of the organization's current data maturity level and what needs bridging immediately and the path you need to take to reach your end goal.

Steps to Become a Data Driven Organization

Adel Nehme: That's really great and I really love that the way you set it out. So we definitely discussed how to diagnose where you are in the maturity spectrum and how to, and necessarily how to approach setting the path. Right. But I think this is an. On opportunity to discuss how to move across that path. Right?

So walk us through maybe the different steps that you recommend to become a data driven organization.

Ganes Kesari: So there are five steps. One, begin with the business strategy. Reflect on what the organization wants to achieve, what are the business priorities for the current year, for the current quarter, and. For the next four to five years, what is the vision for the business? And based on that, come up with a vision for data and a data strategy.

That's the first step. Painting the vision for data. And number two, translating this vision into an execution roadmap to achieve this, if we have to achieve certain milestones in the coming quarter, the coming year, what initiatives, what strategic initiatives should we. For instance, it could be, let's say, let's take the example of improving top line, if that is a business priority for the current year, I wanna grow my revenues, so I need to look at certain aspects of customer relationship management or expanding into newer markets.

So based on these, the business initiatives you can come up with. The data strategically, data initiatives. For instance, how can I gain market intelligence through data to choose the markets I need to launch and expand into? And if you want to grow the top line by expanding share of wallet of my customers, can I use data to identify?

What are the current share of wallet? What, what are they happy about? What are they not happy about? Or how can I get them to spend more with with me and keep them happy? So that way the strategic initiatives will directly follow based on the business priorities. At the second part, you pick relevant initiatives into your roadmap.

And third one is based on the projects you want to execute, you build the capability. This is clearly a third step because we want to build only those capabilities based on the projects we want to execute. For instance, of the next six months, we are gonna be doing a lot of descriptive and diagnostic projects.

You may not have to onboard heavy talent in ai. That can come in little later, which also helps you reprioritize because people typically tend to hire five to 10 PhDs in data science, and then they try to figure out what project to execute, which is again, putting the cart before the horse building capability should only follow.

The prioritization of strategic initiatives. So that's step three. Step four is once you have, once you decide the capability you wanna build tools, people, then what are the processes? Which business areas, which business processes should you rewire and so that you can plan for that integration we talked about earlier?

And do I call it as a fifth step? Some of it has to happen slightly earlier, which is the people aspect, mindset and shifting. So these are the five steps coming up with the visions, data strategy, a roadmap for execution, building the capability, processes, and the people aspect. You'll have to do this iteratively in cycles.

It's not. It's not like a linear process, like I would visualize this in a way of doing multiple cycles of these five steps. You start with this one. One cycle goes on for say two to three months, and then you revisit the cycle, keep expanding it again and again.

Adel Nehme: It's like doing a circuit training where you work out multiple muscles, but you do it over like nine months or so. You keep adding more weights, making it more complex, and so it's like that iterative muscle of developing these different functionalities. I love that. So one thing that you mentioned here, especially at the end that is extremely important is, The data science roadmap, walk us through your best practices.

If we're building an effective data science roadmap, how long should roadmap be? What are the inputs the leaders need to consider, especially as they evaluate where they are on the data maturity spectrum?

Ganes Kesari: We talked about starting with the business priorities for the roadmap. That's the most important thing. It's not about what is urgent or the loudest voice on the floor, what , what they're asking for, but instead, start with what is strategic for the business, and it's important to. Balance the business impact and feasibility.

Imagine this like a two by two, right? Low impact, high impact and low feasibility. High feasibility. Anything which is high on impact and high on feasibility. Feasibility as you have the data, tools and people to execute it, that's something which you have to start immediately because it's going to impact the business in a big way, and you can.

Starting today. So high impact, high feasibility should be those initiatives you prioritize. Whereas there are projects which, which are low on impact, but very high feasibility. A lot of people get distracted by these. Yes, we have the data tools available. Even if people are not asking for this in a big way, we can finish this off in say, two to three weeks.

Can we go ahead and do that? That's a question I often get from technology teams. I would say, even if it is. Two to three weeks of effort. If it's not going to really solve anyone's problem in a big way, why bother? So balancing impact and feasibility is, is very helpful. And don't let urgency come in the way.

Yes, at times it is a really burning need. So then in that case, you'll have to look at it as an exception, but otherwise, impact and feasibility is what you should really go after. And you talked about how should you structure, how long should it be? I would recommend that you should have a balance of a short term and a long term roadmap.

For many of our clients, there are clients where we have done a five year roadmap. With a lot of projects for the next 12 to 18 months, horizon, which has most of the projects and the other aspirational initiatives go into year two, year three onwards. What is very common is typically coming up with one to two year roadmap is very common ask, which I think also makes a lot of sense if you're looking at limited resources and a short term to focus upon.

Let's say you're building a a 12 to 18 month roadmap, you should have a good mix of some short term initiative. Things that you can deliver quickly and demonstrate the quick business benefits, which I usually call as quick wins, and you have to balance that with some strategic initiatives, which need more investment, but at the same time will give much more longer term benefits.

So you'll have to balance both of these. So to summarize, start with the business priorities, balance, impact, and feasibility and balance, short and long term initiative.

Example of Wins: Short Term and Long Term

Adel Nehme: I love the balance between quick wins and long-term strategic priorities. I think quick wins are such a great way as well to impact that. Component of the steps, right? Which is the culture adoption, all of these things. Because if you can showcase the power of a quick win, you'll be able to get the people on your side to create more cases for an investment in data science.

It's a virtual cycle. Paint a picture for us on what those quick wins look like, and especially common quick wins that you can see in various industries, and how do you pair those with long-term projects as well? What do these strategic long-term projects look like as well?

Ganes Kesari: When we talk about projects, I usually ask business leaders, What do you think should be the relative spend on simpler analytical tools, capabilities like descriptive diagnostic? Analytics capabilities versus the predictive and AI capabilities, the real advanced analytics capabilities. And I also ask them, where do you think the biggest business benefits will come? Perhaps based on the market buzz, a lot of people get carried away and they say, We need to put more money into predictive analytics, because that is where the biggest business benefits would come. Agreed That predictive analytics needs more investment or needs more investment? Relatively, but often I've seen across industries.

Majority of the quick wins come from simpler data and analytics initiatives. You identify descriptive initiatives or descriptive diagnostic. For example, we were working with a manufacturing firm finding out what was driving the, the failures. Across the batches. So it's more of diagnostic analytics. What causes failure of batches that was very impactful.

Or for instance, what are the major drivers of manufacturing yield like what are those two, three factors I need to tweak and play around to improve my yield? Very important for a manufacturing organization, these kind of projects. Can give you a lot more benefits in the short term term as compared to, for instance, predicting the the machine parameter setting.

That's again, another initiative you could do that. If I have multiple parameters going in to come up with a golden batch, as in pharma industry, batch as a concept, what is the optimal yield and what parameters I need to tweak for the machine. That's a predictive analytics engagement. But even before that, looking at failures and the factors driving yield that could.

Much more impactful and quicker project. So look at descriptive and diagnostic analytics before you get into predictive analytics. Uh, the forward-looking questions, and there is a place for both. But start with these, and this is where a lot of industries can benefit immediately.

Adel Nehme: I completely agree. I think like the examples of disruptive analytics are so useful because it showcases, you know what, just uncovering data insights can lead to improved decisions. And even on the predictive analytics examples, I think there even within predictive analytics, there could be a category of predictive analytics use cases.

That could also be easier to pull off than any others that kinda showcase clear value. One thing that comes to mind is like a simple customer churn model. It doesn't need to be embedded in software. It doesn't need to be embedded in a business process. It just shows you who are the customers or category of customers that are more likely to churn.

And then you can make, maybe take a quick action on it for a marketing campaign and see if that works out or not. So these are also a category of predictive analytics that could.

Ganes Kesari: That's right. Yeah. Just to add onto that, and that's a great example. Even within predictive analytics, you could do a. With simpler statistical or machine learning techniques as opposed to doing what you can do with say, deep learning or the more advanced algorithms. So the trade off is always there, and you could start with the simpler ones, but I would make a slight correction there that whether it is simpler or a more advanced one, integration into the business process, I would say is, is critical.

I'll give you an example of a telecom firm we were working. We were working with, they were trying to identify which customers are likely to churn and be able to churn model. We saw the accuracy. Decision trees and some of the simpler models, it was able to give an improvement of about 35%, whereas deep learning and the more advanced techniques were able to give almost a 60% improvement.

So you're, you're seeing almost 25% difference. So we went for the more advanced techniques, but then it obviously has engineering cost. And the bigger challenge we faced was explaining. With the decision, you can say that these are the three factors why you need to act on these customers and why they're likely to leave as per the the way the model sees.

Whereas when it comes to say, deep learning, it just spits out a set of. Customer names and numbers saying, Go and act on them without giving much of an explanation that we saw The resistance from the marketing teams was a great test. They were not ready to act on it. Cause they said, without explanation, some of these customers are using our products very heavily.

Why would the model think that they're going to leave? So explainability also becomes a challenge when you go for advanced techniques. So it's always good to start with a simpler test.

Challenges of Executing the Roadmap

Adel Nehme: That's a great nuance and I really appreciate that. So I think it's also, given that we're talking about roadmaps, connecting. To the original question, how do you ensure that a roadmap is executed upon as a data leader? I think it's often easy to fall into analysis paralysis. It's often easy to fall into, you know, reprioritizing the roadmap, et cetera.

What are the main challenges in execution that you find, and how do you keep the teams motivated to execute on the roadmap?

Ganes Kesari: One common challenge I've seen when it comes to building roadmaps and executing on it. A lot of teams create a roadmap, but they face practical challenges in acting upon them, and the final projects they greenlight and execute are very different from the roadmap. Why does this happen? After the roadmap is created day to day, there are business priorities that keep changing, and see if there is some other major crisis in a business or there are the leaders perceptions have changed.

They asking for a different initiative. That's one reason why people end up picking. Different projects compared to what they already prioritized in a roadmap. It's always good to revisit the roadmap, but bring in the same factors in terms of business impact, feasibility, and we talked about short and long term, bring in all these factors.

Anytime you are revisiting the roadmap, bring these in and compare it holistically. Otherwise you will plan for something and you will end up executing without even revisiting what you planned. So that's one challenge. And another common challenge I've seen is people, Let us say there is capacity to run five projects and there are four or five functions chief data officer is in touch with, and if they're trying to pick five projects, there is a tendency.

Allocate one project to each of these functions so that we satisfy a little bit of everyone. But the challenge with this approach is that you need to cross a particular threshold for the business benefits to kick in. So it's often the case that you need to do two, three projects in adjacent areas within one business function, so that you're able to deliver the sizeable business benefit, which is.

Which the teams would, would notice and it demonstrate ROI for the business. So when you split it one per function, and if, if it's not able to cross that critical threshold, then that's, again, none of these functions would ultimately see an ROI from this effort. So by trying to please everyone, you'll not be able to demonstrate ROI for anyone.

So it's important to look at a portfolio. When I'm picking projects, am I building a minimum portfolio of two to three projects in adjacent areas which have high synergy so that I can deliver a strong ROI as a combination of all these projects? Don't try to average out and, and try to please all functions by distributing your projects.

Adel Nehme: That's really great and I love that. So kind of walk us through. How do you cross some of these challenges and some of the ways data leaders can pre-emp these challenges from the get-go and, and the roadmap execution.

Ganes Kesari: One biggest factors that you can leverage is executive involvement. So we are talking about business priority impact, and I think so far the, I've repeated this multiple times, is what is the benefit for the organization and who better to. Reflect and decide on that then and execute it. So you should have executive involvement throughout the engagement, not just for signing the check, but also reviewing the initiatives and green lighting saying, Yes, this initiative makes sense, or if you're going to change and swap it out with another initiative, this is a good alternative.

So executive involvement is critical. What is a way to operationalize that? I've seen that forming a steering committee, a data and analytics steering committee, is a good vehicle to. You bring together. Technology leaders, and business leaders, and ensure that there are at least one or two executives as part of the steering committee.

And the steering committee should be tasked with ensuring that the vision for, for the data is delivered upon and that there is a certain ROI which the organization would've projected. The steering committee should own and ensure that the, the projects they pick and how they're reviewing it, it leads to that roi.

So the steering committee should meet periodically to greenlight the initiatives review, whether it's being executed in spirit and whether the ROI is being delivered, and any changes in the roadmap. Like the, the question you earlier you were asking any changes in the roadmap or other difficulties, it has to be tabled to the steering committee.

That's one group that can reflect and support the initiatives along the.

Importance of Driving Data Culture

Adel Nehme: Okay. That's great and I, I really appreciate how you make that distinction as well. I think the last thing that we need to touch upon, which is obviously one of the most important aspects of building a data driven organization is the people and. Components that we discussed throughout. You know, one thing that you mentioned greatly here throughout the discussion so far is the cultural resistance.

It's the importance of creating that mindset. And I think a big part of that is that a lot of the adoption of data science projects ride on the fact that there needs to be this cultural adoption and this lack of resistance within the organization of data science projects. So maybe as an introduction or a prelude towards.

Conversation here. Walk us through why building or improving the mindset and shifting the culture is absolutely so critical for the adoption of data science.

Ganes Kesari: We talked about vision for data. When everyone within an organization understands that this is how data and analytics is going to help us, this is where the organization can go with data and the business benefits are obvious, then they'll be much lesser resistance. So I often say that the leaders have a important role to play here in explaining why data and analytics is.

How it'll enable the business calls, Who should get involved and what is expected of each stakeholders. So the why, how, who, and what all four aspects communicating. It clearly is leaders responsibility, and this is, and leaders do a great job of storytelling and, and communicating this. Then alignment is much.

Adel Nehme: I completely agree, and I think maybe touching upon here the more practical side of things. What are some of the ways and tactics by which you can shift mindsets within the organization and increase the adoption of data science and maybe what is the role of education and data literacy here at that heart of that shifting mindset.

Ganes Kesari: So when you talk about mindset and shifting, interestingly, the biggest hurdle for shifting the mindset is not a technical or a capability, but it's semantics. The. People use to communicate data is often the biggest inhibitor. When people talk about data, they usually, I've heard technology teams talking about data governance or we are talking about data science and we throw in a lot of jargons into this and we assume that the other person knows this because this is our world.

But the other. I've had this thing, like at times I've, when I post on LinkedIn, uh, couple of times there are some friends who reached out asking for abbreviations of some simple terms. For instance, even cdo, they asked, What does a CDO mean? And then I realized that depending on the function in industry, CDO could mean a number of different things.

I've come across this in my other engagements as well. Mean doctors are, for instance, in in marketing world, CDO means something else. So the language you used often turns into a roadblock. So if we are able to simplify. Reducer jargons and communicate in direct terms that this is what we mean. This is what is expected that actually can help win people over and align teams for a common purpose.

So what, what do we really mean by data literacy, right? The ability to read, write, and communicate with data. And insights identified using data. That's data literacy. In simple terms, if everyone within the organization is data literate, just like you literate with a particular language that the organization uses, a primary language of communication, then communicating with data will be seamless.

Adel Nehme: Completely agree. It creates that common data language, right? I think another side of the cultural resistance or the mindset shift, especially in frontline workers or like in within the organization, you see a. People that are distrustful of data. There's a fear of automation. There's a fear of data and redundancy within the role, which I think is super valid, right?

To have that fear. Walk us through, maybe as a data leader, how do you assuage these concerns?

Ganes Kesari: I've come across three types of fear. One is fear of unknown or the fear of new technology. People are worried there's this new thing, AI or data science, which we have no clue about. Suddenly people have started talking about it. That's a fear of the unknown. Second is fear of automation. Once they understand AI's capabilities for this data, The fear of automation that it will take away the jobs.

That's again, very common. And the third fear is fear of getting expos. Even when people get over these two, I've heard comments again off the boardroom. When you talk one on one to people, they say that I'm comfortable doing it this way because I too much of transparency and too much of insights from data will actually expose some inefficiencies within the system.

So we don't want that much of clarity with data. So we are comfortable the way we are doing business. So the fear of getting exposed to the third fear, how do you tackle these three as a leader? The first one, Fear of unknown or new. You'll have to tackle it through, say, data literacy and other communication mechanisms to explain what the technologies really is and how it can help the business.

So that's the first aspect. Education, the fear of automation. So when leaders talk about the purpose for data and the future state for the organization, That's where it's important to paint a picture of the future, that once we have these data analytics initiatives going live, this is where we expect efficiencies to kick in at the same time.

This is where we expect the people to still stay engaged. And move on to some higher level problems or a different set of challenges, which they're not tackling today. So when you paint that picture of the future and show the role that people will play, then you'll be able to tackle the fear of automation and for the, the fear of getting exposed.

It's the tougher thing compared to all the three. That's the, the toughest you'll have to. Talk about why the organization needs it, how some of these inefficiencies are hurting the business, and what kind of incremental gains you could get for the top line or for the bottom line. And also when you show some of the quick wins and build momentum as we talked about earlier, then people slowly get over that and then they see that, yes, this actually I will be able to change the ways of working for the team.

And after all, we'll be able to change the ways of working and we may not get exposure. We'll be able to adapt. Leaders should play and act on these three aspects, The fear of unknown, fear of automation, and fear of getting exposed through some of these techniques, which are very essential for ensuring adoption and a culture of data.

Adel Nehme: That's really great and I love how you categorize these three fears. I think the first two fears are more closely related to each other, and one thing that we've seen is really effective, at least with DataCamp for business customers. You know, that roll out like. Upskilling programs, transformational programs, et cetera, is that there's a very important need to create this messaging of like what's in it for you from becoming a data iterate professional from adopting these skills, from working with data.

I think there is massive cure benefits to be discussed at the organizational level for the individuals, but on the fear of exposure, I think there needs to be this psychological safety. For a lot of leaders, employees, and like workforces, data is not gonna be used to punish. It's gonna be used to improve, and that really needs to be embedded in the messaging, which I think leads to my last question here.

On the cultural side, there's a lot of internal evangelism, excitement, habit changing communication that needs to go into a data culture transformation program. What have you seen are the most impactful tactics that a chief data officer or data leader can have when communicating about the transformational?

Ganes Kesari: Well, talking about the business benefits and the impact of data, so we talked about painting a picture of the future and how you. , quantify some of the business benefits and talk about the new state where. It's not just the business benefiting or the people will be able to do their jobs better. So if you, when you talk about these benefits at different levels, that's one way that chief data officers and and analytics leaders will be able to communicate.

And another important aspect they need to keep in mind is that with the adoption, Rising adoption, rising investment in in data and analytics tools. I also see that there's a good focus on execution and adoption, but the experimentation with data and analytics is reducing. I think it's an important responsibility of data and analytics leaders to also channelize some.

Efforts and energy into experimentation with data. Cause still the eco, the ecosystem is evolving. There are a lot of tools and I would expect that this, for the next couple of years, there'll be a lot of flux and new technologies coming in. And there is great research, which is helping with advances in these technologies.

So how can you run experiments to find out what is the latest and greatest, which of these advances can help your business solve some challenges and do some quick pilots to test it. And if it is ready for productionization, then roll it out across the business and plan for adoption. So I think these are some points that data leaders need to keep in mind.

Adel Nehme:  That's really great. And given here that you mentioned the next few years are gonna be in flux, how do you see the data literacy, data culture conversation evolving? What are some of your predictions about how organizations are gonna become data driven in the next three to five years or so?

Ganes Kesari: I'm, uh, really happy with the way the adoption and progress in this space, uh, has happened over the last few years. About five years back, we were talking about the promise of. AI yet again and promise of deep learning and three years back, the top most challenge I've noticed was data culture, whereas, It is a challenge, but it is not the, the, the top most one anymore.

There are several organizations which have been able to achieve this in, at least in pockets. So the way I, I look at this in the future, I think data literacy will continue to stay important and a lot of organizations would have progressed quite a bit on these dimensions and with greater adoption, eventually I would consider data and analytics to have actually delivered on the promise.

No one talks about data analytics in the same excited way It is business as usual. It becomes invisible, and it delivers the business benefits silently. That is a future state where I would consider the promise to have been realized.

Adel Nehme: I completely agree. I cannot wait till we don't have any more relevant topics to talk about on the podcast anymore because everything has been done right.

Ganes Kesari: We'll always have new things to talk about. This is what, again, as as practitioners, we, we've talked about the, the fear of what comes next, the, the, the relevance threat. I, I'm pretty sure we'll be discussing some really advanced topics in the podcast and be happy to come back and discuss about those topics in a few years.

Call to Action

Adel Nehme: So again, as we wrap up, is there any final call to action that you have before we end today's?

Ganes Kesari: I would just emphasize the focus on benefits and ROI that needs a lot more attention. Can we call it out as leaders talk about it and have a focus on it throughout? That will make all the difference.

Adel Nehme: That's really great. Thank you so much, Ganess, for coming on data.

Ganes Kesari : Thanks for having Adel. It's a pleasure.