Vaidya JRWe are living now in a volatile, uncertain, complex, and ambiguous (VUCA) world. And, to sustain and succeed as a business in this environment, a winning data and analytics strategy is key. With the enterprises becoming data companies globally, there is a clear shift in the analytics paradigms today. We want to infuse AI across the value chain, but can we truly ignore the fundamentals of data management? Garbage in is garbage out after all. How can organizations create a data and analytics strategy to future proof their data management landscape?
Contributor
-
- Vaidya JR, SVP and Global Head, BI, Big Data, and Analytics, Hexaware
Transcript
Sanjog Aul [00:00:22]:
Welcome listeners. This is Sanjog Aul your host and the topic for conversation is Creating A Winning Data and Analytics Strategy. So we are living now in a VUCA world that is a world that is volatile, uncertain, complex and ambiguous and to sustain and succeed as a business in this environment, a winning data and analytics strategy is key. With the enterprises becoming data companies globally, we see a clear shift in the analytics paradigms today. Yes, you want to infuse AI across the value chain, but can you truly ignore the fundamentals of data management? Garbage in is garbage out after all. So how can you create a data and analytics strategy to future proof your organization’s data management landscape? To discuss this I have with me Vaidya JR, Vaidya is the Senior Vice President and Global Head of Business Intelligence and Analytics Business at Hexaware, a consulting firm focused on transforming IT solutions and solving complex business problems using a combination of human creativity and intellect. Their three pronged strategy of automate everything, cloudify everything and transform customer experiences enables enterprises fast track into the digital era.
Sanjog Aul [00:01:44]:
Hello Vaidya, thank you for joining us.
Vaidya JR [00:01:47]:
Hi Sanjog, thank you. Thank you for having me over and it’s indeed my pleasure to be talking to you today.
Sanjog Aul [00:01:55]:
Great. So Vaidya, what is your view on the biggest imperatives for an enterprise for getting their data and analytics strategy right and who are the key stakeholders involved in this?
Vaidya JR [00:02:09]:
Yeah, so if you look at the biggest imperatives, as you rightly said, most enterprises are data driven enterprises. So today to be a data driven enterprise you need to be able to harness data, manage risk and create revenue generating opportunities continuously. So what you need is a solid business driven data architecture that can enable customer centric views and give you insights into customer behavior, their buying patterns, their spending patterns. So that is a business driven architecture perspective. The other part is the compliance driven data architecture and the compliance driven data architecture part needs high quality of data and governance around it to avoid costly missteps. Your data is what enables this and pulls together as to how data is sourced, integrated and consumed across the enterprise. When there is explosive data growth, compliance needs privacy and security challenges on one side, let’s not forget that we do have the other side to it.
Vaidya JR [00:03:27]:
Time to market pressures for the business. Business wanting to capitalize on a fleeting opportunity so it is imperative to get a winning data strategy in place, be able to accommodate both these conflicting interests, isn’t it? So if I have to answer the second part of your question, which is around who are the key stakeholders? Let me start by saying CXOs or the business sponsors would be the number one stakeholders. Their expectations are very simple. They expect the data and analytics ecosystem to provide them with a Lamborghini kind of experience, meaning a great user experience, state of the art features, and all of these features operating at great speeds, immaculately to glean insights. The second set of important stakeholders would be the technical folks themselves, as in the data architects, the data analysts, stewards, data scientists but they are working on building a finished Lamborghini for the end users. They need the right skills, tools and technologies to take the various kinds of data through the analytics value chain by, I would say, bringing in extreme automation and make the process seamless, like the way Lamborghini would flow through the assembly line and third, and the most important stakeholders are the customers.
Vaidya JR [00:05:05]:
Customers, they have in fact very high expectations. They want a hassle free, frictionless experience and expect that the requests are not delayed by the limitations of the technical systems in place. And also their personal and transactional data remain safe and secure. So it is imperative that all of the stakeholders come together like in a Formula one circuit crew, right? Operating to win the race in the respective industry and the respective businesses. Hope that clarifies the second part of your question too.
Sanjog Aul [00:05:47]:
Sure, Vaidya. So yes, I agree that enterprises are indeed racing to gather insights. But the question is, how should an enterprise go about winning this race?
Vaidya JR [00:05:59]:
So that’s the most important question in the minds of all business owners and IT leaders. From my experience of engaging with valuable clients across industries globally, I would pick three key dimensions that play a very important part in winning the race. First dimension is getting the data fabric ready and right. So let me just step back a little here and explain what the data fabric means. That’s the foundational piece in the whole game. So if you look at what it entails, it starts with data discovery, data acquisition, data consolidation. Now why do I say data consolidation too is because it’s not just about the data within the enterprise happily sitting inside the firewall anymore.
Vaidya JR [00:07:00]:
It’s more about external data in all forms and shape, like your IOT data, text, image and video data. So to be able to win the race, you got to be addressing some very key questions, like how do you ingest such disparate data sets from multiple sources into your data lake? So how do you deal with proliferation of point to point data feeds? Obviously, everyone knows they can be complicated and expensive to deal with. How do you deal with disjointed data sets? When I say disjointed data sets, I mean the siloed view of business data coming from various applications. Because that’s the way applications and application databases have been designed that are in existence and that’s the way we’ve seen it evolve, right? So we need to look at data from all angles and see how to address the challenges that I just mentioned. If you ask me, how do you do it, I would say that metadata management is of paramount importance here. Creating that semantic layer, enabling the data sharing mechanism to publishing and subscribing to data catalogs, getting those data APIs for easy consumption of relevant data across lines of business. These are some things which are very, key in getting your data fabric right. The second dimension, after getting your data fabric right, let’s remember here one thing.
Vaidya JR [00:08:52]:
It’s one thing to be able to get all the data under the sun, but quite another to be able to put it effective use. So 80% of the effort typically goes into preparing data for any analytics workload and analytics cycle enablement. So gleaning insights at a speed and to be able to enable the enterprise to be able to glean insights at a speed is the second key dimension. Bringing in high level of automation in the data preparation phase is a very key element because 80% of the effort typically goes into preparing data in any analytics cycle, right? To be able to profile the data coming in, identify outliers, identify the anomalies, identifying the data patterns intelligently and automatically, infer what data sets are similar enough to be blended together. And last but not the least, how do you handle missing data? But interestingly today all of this can be achieved by big time automating and leveraging ML. So how effectively you infuse ML is another key dimension. And apart from automation, the other important factor is how do you kind of enable data democratization? In fact what I mean by data democratization is how do you enable self service capabilities within an enterprise if you want to bring speed into your data to insight value chain enabling self service leveraging technologies like you know, nlq, just natural language querying, voice based querying, auto insights, gathering, you know, auto ML or automatic model selection, making ML more accessible and usable for business analysts. These are, you know, the key aspects of how you enable an enterprise to glean insights at the speed.
Vaidya JR [00:11:08]:
And the third and very important dimension is Data monetization. Once you enable enterprise to get the data fabric right, like I said before, and also enable the organization to glean insights at a speed, next level of maturity is enabling the enterprise for data monetization. So data monetization involves weighing up data as an assertion by estimating the economic value of data for various stakeholders, as in the suppliers, the partners and other consumers. So what it really involves is creating new business models around data as an asset. Hope that answers your question, Sanjog.
Sanjog Aul [00:11:54]:
Yes, it did. Thank you. So can you elaborate a bit on what you mean by newer business models in context of data monetization?
Vaidya JR [00:12:04]:
For sure. So let me give some examples out here. We recently we worked with one of our clients in the pharmaceutical world that collects de identified health information, I would say from a vast array of sources and then crunching that data, transforming that data, gaining insights. What they do is they sell that information in the secondary market. Buyers could be former firms that may be intent on gaining insights to refine their marketing strategies or attempting to figure out where do they invest next. So it’s like this, right? Our client has agreements with more than 120,000 sources of data around the world. Get anonymous patient data it collects from providers, payers and even pharmacies. So we work with them to enable them monetize the data through the three dimensions I just described.
Vaidya JR [00:13:13]:
Another example that I can think of is from the shipping industry. This client of ours is involved in providing port call services for ships across the globe. When I say port call services, I mean the entire range of those services under pilotage, towage, when it would involve anchorage and also maintenance of the ships. This client over the years have accumulated rich repository of some very unique data sets in the industry. Quite an interesting problem to solve in terms of being able to monetize the data. So we are working with them to enable them build a data platform and hence take them to the maturity level of monetizing the data that they have. I can also think of another very recent example here, and this is from the insurance industry. We are working with these clients, again you know, to be able to help them, you know, monetize their data around claims and loss ratios in the context of water flooding.
Vaidya JR [00:14:33]:
It involves crunching a lot of data and ultimately the outcome would be to give out location safety scores for property and you know what, real estate industry is a big time buyer of this data and insights. So to be able to win the race that is based on data, I would think these are the three dimensions that an enterprise needs to kind of master and get to the level of being able to monetize their data. All the stakeholders hope that answers your question on monetizing data.
Sanjog Aul [00:15:11]:
Yes, it did. Thank you. Let’s take a quick break, listeners. We’ll be right back after these messages and when we come back, Vidya, you mentioned that the devil lies in details. So what we would like to talk about are the execution considerations we must look at for setting up a winning data and analytics strategy. Please stay tuned listeners. We’ll be right back.
Sanjog Aul [00:16:25]:
Welcome back. So Vaidya, you did mention that the devil lies in the details. So what are some of the execution considerations for setting a winning data and analytics strategy?
Vaidya JR [00:16:39]:
Well, another great question, Sanjog, it starts at the very top. I would say executive sponsorship that understands and supports the strategic vision and more importantly understands that there will be pain at first and that you have to continue to drive through it. Since we are talking of execution here, we all understand any strategy is only as good as execution. Let’s see some of the key, you know, factors, success factors. First thing I would say is preparing the organization for the people aspects of this change. I would rate it as highly crucial as one embarks on the transformation journey. The existing roles will change, some roles may go away and then some new roles that will be needed and of course with a new set of skills.
Vaidya JR [00:17:38]:
So let me give you a recent example here. We were working with a CIO and his team in the E commerce space and they wanted to move their complete data warehouse ecosystem to cloud. Surprisingly, when we kind of got started with the engagement, lot of internal resistance from the team and we figured out that the team was only akin to handle a set of technologies required to support the legacy data warehouse. So their team had not been exposed to the emerging technologies but this move would warrant a compelling new set of skills. So we were early to recognize that and we worked with them to reskill their entire team at the customer’s end. These are some of the people aspects that we need to be really, addressing as we kind of embark on the journey and on the process front, it’s how well you adapt to agile methodologies, DevOps and continuous delivery methodologies. How we engage with a business right from the start and show them value continuously every sprint.
Vaidya JR [00:18:54]:
And that’s going to instill the world of confidence in them. And that will also do a lot of good for the other stakeholders involved in the execution. We saw the people friend, the process friend. Last but not the least is the technology front. There’s this problem of plenty for our customers. Every single customer that I work with, they say every day someone or the other technology vendor meets them to showcase their technology as a new coolest thing in the world. And that creates a lot of confusion in the minds of our customers. Sometimes we find our customers do not have the wherewithal and the required understanding of the emerging technology skills to be able to evaluate those myriad opportunities and technologies out there in the market.
Vaidya JR [00:19:53]:
And that’s where we come in and say leave it to us as that’s what we do for a living. So these are some of the most important execution considerations that I would suggest that any enterprise embarking on this data journey should take care of.
Sanjog Aul [00:20:13]:
So Vaidya, how would you recommend an enterprise go about selecting the consulting partner for such an effort? Because this is a monumental effort and you may need partners and if they chose your firm, how your playbook would read.
Vaidya JR [00:20:31]:
Oh, this is a very important question. I’m glad you brought it up. The right partner can put enterprises in the fast lane of the transformation journey. The first thing that you know you should look for in your partner is the business and data expertise. The partner should bring in strong expertise in the complete data value chain. They should be able to drive the data strategy, data architecture. When I say data architecture, I mean from data discovery to all the way through data monetization efforts.
Vaidya JR [00:21:11]:
Identifying the right use cases for the stakeholders help them monetize the data. So automation enabled frameworks and accelerators which would give our clients jump start to the whole process of gauging the current level of data maturity and complexity and recommending the way forward would be a very, key enabler and they should look for this capability in their partners. Partners should also bring in subject matter expertise, customer, industry segment and area of business. The second thing that I can think of is speed and reliability from the partners. They should be able to select partners and that partner of choice that will have automation throughout the data life cycle as a core value proposition. Recently, one of our large clients in the mortgage space wanted to move out of an appliance based legacy data warehouse ecosystem cloud. And the appliance came with a drop dead, you know, support stop date. That deadline was about a few days.
Vaidya JR [00:22:28]:
So when, when the appliance support has reached the end and was also very expensive to kind of, you know, continue operating, that they were looking for a trusted partner who could quickly help them move to cloud at a speed and in a highly reliable manner. That’s the key here, without disrupting the business and we were there for them. So speed and reliability is another very, key aspect in selecting the right partner. And I would say partner that can engage with the enterprise and kind of co create value to drive business outcomes would be of paramount importance. So what I’m trying to say here is it’s just not about the technology wherewithal but the ability to put the right technologies in place to drive the desired business outcomes as part of the digital transformation. That will be a key differentiator and that will be the essence of our playbook. You asked about what would be our playbook.
Vaidya JR [00:23:35]:
You would say right technologies to drive the desired business outcome and how do we engage with the various stakeholders within and without the enterprise to co create value working very closely and transparently with our customers? That is what I would recommend.
Sanjog Aul [00:23:56]:
Finally, how about this question which all enterprises are having today, that is with COVID 19, the coronavirus pandemic. How does my data and analytics strategy change?
Vaidya JR [00:24:11]:
Oh yes, very relevant question for the now and the way forward. So this question of you reminds me of a recent meme I saw in the social media. So this question was put to the CEO as to who designed your digital strategy. And it had multiple options, you, the CEO, CFO, the CIO or the CTO and the last answer was Covid and invariably everyone ticked Covid. So that’s how important the digital transformation is going to be. The post Covid era and digital transformation in my mind will be job number one in everybody’s agenda across industries.
Vaidya JR [00:25:00]:
Like how we started our conversation discussing on how do we succeed in a hookah world. Business plans are always going to be in a flex. Enterprises will move from multi year technology planning and executing large programs to really depending and relying on test and learn as you go kind of models, meaning very, very continuous test and learn approach. You’ll create MVPs, MVPs as a minimum viable products, work closely with the businesses to see the value and that’s how the world is going to move forward. As for digital initiatives, I would think business and IT strategies are becoming synonymous and given the VUCA factor, organizations will have to change directions as demand changes. Hence everybody is going to look to build an adaptive enterprise more agile and more nimble like never before. A few big things that I can think of as we converse we’ll see more and more touchless and immersive customer experiences becoming the norm and that will enable enterprises to become adaptive and those touchless and immersive customer experiences in turn will be enabled by technologies that help in digital leapfrogging and also helps in ensuring resilience on cloud.
Vaidya JR [00:26:39]:
We’ll see enterprises engaging the anywhere employees. I’m seeing that most enterprises are already doing that during Covid and on the other side of Covid it’s going to be not engaging with anywhere employees. But all of these initiatives, the touchless immersive customer experience engaging with anywhere employees are going to be funded by automation led sustainable cost takeovers. Those initiatives are going to be coming out ahead of the rest of the initiatives for any enterprise. So data strategies, data architectures will be very critical as they are the backbone that will enable anybody to build an adaptive enterprise as we come out on the other side of the code. And an adaptive enterprise architecture is what is going to differentiate between the leaders and the laggards. That is my view going forward.
Sanjog Aul [00:27:48]:
Once again, thank you Vaidya so much for sharing your thoughts and insights about creating a winning data and analytics strategy on our brandcast segment.
Vaidya JR [00:27:59]:
Thanks Sanjog. It’s always been a pleasure talking to you.
Sanjog Aul [00:28:03]:
Thanks again. And listeners I invite you to find related conversations on our website at ciotalknetwork.com.
Download Podcast
Explore More
