In this episode of the Zinnov Podcast - Business Resilience series, Sanaz Namdar, Vice President, Research, Analytics and Perspectives at Dell Technologies talks to Rajat Kohli, Partner, Zinnov about the role of data, and how it can shape business strategies for companies in different industries. Data is a crucial component in directing how a company can run, and leaders can take informed decisions with the best insights. Oftentimes, companies tend to have goldmines of data accessible to them that can be very useful. Analytics have been able to shift needles for companies, and looking at the right data correctly, is imperative. Across industries, data has the power not only to understand what could have gone wrong, but to make predictions and forecast strategies going forward.
Which comes first – data strategy or business strategy? How can a data scientist unlock the best foresight into markets to create the best blueprint? Find out in this engaging conversation.
Rajat: Data is a powerful asset of any organization, but the analysis and insights derived from that data are invaluable for making data driven decisions. In fact, data and analytics are a core part of any business today that will enable and empower leaders to outline robust strategies that align with company objectives while also future proofing them against disruptions.
Hence a strong data strategy is not only critical, but an imperative. I'm Rajat Kohli, partner at Zinnov and I'll be a host for this episode. Today we have with us Sanaz Namdar, Vice President of Research, Analytics and Perspectives within Dell's Corporate Strategy organization.
Sanaz: Hello. Thank you, Rajat. Good to see you.
Rajat: Great. Likewise. Perfect. Let's get started with this episode and hear more from Sanaz. So Sanaz, I'll start... in your storied career, you have held many global roles in Strategy and Planning. Can you walk us through some of your career highlights from a technology lens?
Sanaz: I started my career at Accenture in consulting, through that I got exposed to a wide range of tech companies, kind of learning ropes on the job, growing up at Accenture. And then, after 12 years I moved on to Microsoft where my first role at Microsoft was leading the Strategic Planning for Global Alliances. I would say that's where I kind of got out of the consulting mode and really into the running the business mode in terms of what does a year long planning look like and how do you think about bringing the aspects of strategy into your planning, what you really want to accomplish? What KPIs you want to set up for your success and also how do you enable the team to get there?
So I would say that was my first real, let's say, non-consulting job where I owned strategic planning. From then on, I switched a bit over more into the data analytics world which was a very nice, I would say, merger of strategy and analytics.
Being in strategy roles, both in my consulting career, as well as the first role that I had at Microsoft, I realized that there's so much room for getting deeper insights into what is happening if we invest in building an advanced analytics function. So that's where the role that I took on after that was mostly focused on bringing advanced analytics into strategy.
Once I kind of got introduced into the world of analytics that really became my core passion and I knew that any other job or role that I had, I would want Data Science and Analytics to be a big piece of that. So at Dell technologies I run, as you mentioned, Research Analytics and Perspectives and Corporate Strategy where we're really trying to bring in Data Science into our corporate strategy.
It's very exciting, but it's also a newer thing that we're trying out and we're excited about all the work that we've done so far and we'll see where it goes.
Rajat: Interesting. So from the consulting, then the technology and then the Data and Strategy. That sounds very interesting. And that's a quite a journey you have had, Sanaz.
Now getting to the core of your work, core of your interesting work, as we veer towards a slightly different world today than a few years ago, how are organizations developing data related capabilities?
Are they reskilling? Upscaling their existing talent or outsourcing analytics? What is your viewpoint around that?
Sanaz: I would say all of the above depending on the function that you're in. So if you think about it, I would say Advanced Analytics has and AI... but let's say advanced analytics has been pretty mainstream in a marketing function or in a supply chain operations, services, anywhere where there's room for optimizing a customer experience or improving product capabilities.
So, it's pretty embedded in those functions. And I would say different companies handle it differently, either outsource a piece of part to a vendor who would be experts in producing predictive insights or maintaining all the different models or they may upscale and reskill internal teams to build those functions.
So I've seen both when it comes to, you know, the mainstream functions where they use vast analytics as core part of what they do. I would say strategy is a newer thing and strategy is a bit different because you're kind of the center of the vision for the company and where the company needs to go.
So you really need people who not only are data scientists and have amazing Data Science skills and can build robust models and predict what's going to happen. But also that they have knowledge of the business that they really understand what we're trying to solve, why we're doing what we're doing.
And that's why I would say the role that I had at Microsoft was important because actually it was my first role in the world of Data Analytics. And although I have an engineering background I never really practiced engineering. So I really came at it from a business side. I was not coming at it from a Data Science side, but that was exactly what that team needed.
They needed someone that comes from the business, understands the use cases, understands the, So What. We're going to build the fanciest models and we're going to have the most amazing predictions, but how is that really going to help us change our strategy? And are we convinced enough? Can we really... do we really buy into this model to then say, because of what we're seeing we're actually going to change our strategy in the way we're managing our Sales organization or the way we're investing in a certain area or in the way we're going to market in this area.
So when it comes to strategy, because you're making bigger broader decisions about the business, I would say it's important to upscale and reskill internal teams for two different reasons. One, like I said, understanding the business, but also familiarity with all sorts of data that the company sits on, because you really want to take advantage of the wealth of information that the company has in shaping your strategy.
Rajat: That's interesting. The two points that you highlighted, Sanaz. But if I look at how the future looks like around that, where do you see maximum investments going to happen? Is it around the skilling? Is it around the tools? Is it around the technology that is required to get some interesting aspects where the future lies?
Sanaz: That's a good question. I wish I knew where the future lies, but all that you mentioned is equally important. The tools and platforms, the technology, the talent, I mean, I'm sure you know there's this big trend around AI talent wars. So talent is huge and also talent at all levels. I would say when we look at the market the more advanced analytics type talent, I think is be going to become more and more desirable and more needed. I think we do have talent that have kind of come up through this different level of maturity on and data and using data and experimenting and analyzing data. But I think that true Data Science married with the business is the kind of skill that we need to have more of.
Rajat: Great. I think so everyone would be interested to know, as you mentioned the future, especially at the current times what's going to happen in the next three to six months. Looking at economic scenario, I'm sure that a lot of investments are on that what's going to happen in the next three to six months.
That makes a lot of sense. And now that you mentioned and some great points there, but if you look at it from strategy perspective and this is my personal favorite, which will come first, is it a business strategy or is it a data strategy or are they both outlined at the same time? Or should they function independently?
Sanaz: You know, growing up in technology, I would say we started with data strategy. I remember all sorts of Business Intelligence tools and it was all about bringing data together and connecting the dots across all the siloed data sources that the companies have and having to make sense out of the data, building data warehouses where you can house everything in the same place. We're now transitioning more to business strategy needs to dictate what kind of data we actually need to go get. And there's all sorts of very useful different levels of depth that you can collect data for the purpose of answering the business question that you have. And I think in that sense you want to think outside the box, you want to be thinking about where you go get that data. And if you only start with data strategy, you may just not necessarily think about all the other things you could get. If you start with data strategy, you say, here's my data. What can I do with it? Whereas, if you start with business strategy, you say, here's what I'm trying to solve. Here's the data I have. What other data do I need?
Rajat: Understood. Now I think that that's a great way to look at it. And a lot of our listeners are from different industries or the verticals. How do you think any business can decide the data that it needs? What is your viewpoint on that?
Sanaz: It comes down to the strategy, I would say. I think starting with strategy is important and then, what we've also learned is that Data Science and Analytics in the business and strategy world is very use case driven.
I would say, it's important to really understand what is your use case? What are you trying to predict? And then for each use case, the data set that you need will be different.
And that's what something we've learned. Yeah, maybe you will have a master data warehouse where you have all sorts of data and you connect the dots. But when when it comes to the analytics portion of it, when it comes to prediction, you really need to know what's going to help you get there.
So that's, I would say... knowing your use case is very important. What are you trying to predict?
Rajat: Interesting. And a lot of our listeners are very keen to learn from you. What are the interesting use cases that come from different functions? I'm sure that every business function in an organization is keen to predict something of something. Can you talk about any interesting use case, Sanaz, that the business functions are...?
Sanaz: Use cases like in marketing... it's all about how do you get people to buy stuff, right? Like how do you predict what Sanaz wants to buy tomorrow and put it in front of her face so she buys it. And supply chain is how do you do predictive maintenance? How do you make sure things don't break? How do you make sure you count for the shortages and raw materials and how do you see it coming, so you can manage your supply chain. In sales, it's all about how do you make sure you know what customers want?
The customer already has these technologies, given all that, what do we think they will need in the next year or two or three years? And how do we make sure we put it in front of them.
And services is all about understanding what services, issues customers will have, getting ahead of it before things break. So I would say any function will have their own specific use cases. So, strategy is a bit trickier because the nature of the prediction is longer term and it's more vague. It's not as specific.
I think what we've learned is that you can start with building blocks, so you can start with smaller. You don't have to solve the entire problem in one round. You can break it down into chunks. You can have smaller problems and you can do smaller things. And then build on top of what you have and then eventually make it a bigger use case.
That's usually the best way to go after the ones that are harder to answer right up front, you say, well, this is too big for me to answer, but what can I answer that can help contribute to this use case that I'm looking at and just building it out over time.
Rajat: Understood. Since some trends change pretty drastically, what are some of the effective ways in which businesses or organizations are sourcing and collecting data, especially in the recent times?
Sanaz: Yeah. So I would say sometimes organizations underestimate how much they are sitting on a gold mine of data themselves. So taking advantage of the data that you own is important and I think many organizations can still continue to build that out. Because I think we forget that the same data that's used for the day to day marketing can be used for other aspects of the business as well. So sometimes, it's the same data, but it's really just thinking outside the box on, Ooh, I could be using this data for other purposes as well. So I would say definitely taking advantage of the wealth of data that organizations have.
Rajat: Interesting. And this is my last question, Sanaz, to you. Any organization would like to take advice from you that, Hey, we are setting up a data strategy team. What are two ideas or two advice points that you'd like to share with them?
Sanaz: I would say, Believe in the power of data and become an advocate of analytics in your organization and be patient, because it takes time.
So, you know, I think what's important for leaders to understand is there is so much power and so much insight you can get from data and building and investing in an analytics function, but you also have to be patient because you can't just turn things around in a week and it's not like a PowerPoint.
It's really more about being patient about building and investing because ultimately you will benefit from it, you will see insights, you will have insights that you'll just scratch your head and say, I would never know this if I didn't have this data. And it just gives you so much competitive advantage and so much insights into your business that you wouldn't be getting otherwise.
Rajat: This is great. This has been a wonderful conversation, Sanaz. Believe in data and be patient. That's an important aspect that I'll also take as advice in case anyone is thinking to set up the data strategy team.
Thank you so much for sharing your perspectives with us, Sanaz. They have given us a lot of food for thought, as I'm sure our listeners will find them insightful as well. Thank you once again for taking out the time to be here with us today.
Sanaz: Great. Thank you so much.
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