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Data-Driven Work Cultures: Abhishek Mehta Of Tresata On How To Effectively Leverage Data To Take Your Company To The Next Level

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blog-details-eye-slashNov 3, 2022

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An Interview With Fotis Georgiadis

Distribute Data Assets: When you’ve connected all your data and linked their one-to-many relationships, the assets aren’t limited to a single use but can benefit many parts of the business. This usable data must get distributed across your many customer, business and enterprise systems and processes. In essence, you liberate the data so every last bit can be used and monetized to achieve transformational results. For instance, we’ve helped one global bank attain usable data from 300 million entities across its transaction and payment systems. From these assets, the bank has powered over 30 use cases — from cross-selling and ESG monitoring to fraud detection. In the first year alone, the bank has seen three times the return on investment.

As part of our series about “How To Effectively Leverage Data To Take Your Company To The Next Level,” I had the pleasure of interviewing Abhishek Mehta, Chairman and CEO of Tresata.

A radical technology expert, disruptor, and in-the-trenches, outspoken leader, Abhishek founded Tresata in 2011 with a mission to help businesses harness the power of data to Enrich Life for every customer. He has since grown Tresata into the first profitable unicorn company in analytics with successful software implementations in nearly every major industry for the Fortune 100. In the following interview, we sit down with Abhishek to discuss how companies can leverage data to become digital businesses.

Thank you so much for joining us in this interview series. Before we dive in, our readers would love to “get to know you” a bit better. Can you tell us a bit about your ‘backstory’ and how you got started?

I am fortunate to have the luxury to call two of the world’s largest democracies home — I was born in India and bread (as in earned my bread) in America.

I have always believed that life is better lived when manifested in decades, which is how I live mine. Since I was very young I have had a burning desire to build something and I was willing and able to seek experiences that have allowed me to do so.

In my twenties, I experimented and challenged myself to learn as broad a variety of skills as I could so that I could leverage them to build what I wanted to build in my thirties. These experiences allowed me in my forties to help others build what they wanted to, leveraging all I have learned.

Can you share a story about the funniest mistake you made when you were first starting? Can you tell us what lessons or ‘take aways’ you learned from that?

One of my favorite life lessons comes from a Charlie Chaplin quote: “Life is a tragedy when seen in close-up, but a comedy in long-shot.”

With that, and in hindsight, everything seems funny in those early days. If I was to pick one moment, it would be the first three months when we decided to raise capital. It was like the Sand Hill Shuffle episode in the show “Silicon Valley” had been scripted based on us. It’s Season Two, Episode One, in case anyone’s wondering — minus the Hollywood-hyperbolic additions.

The funny part was we decided not to raise money from VCs at that early stage but we learned so much in the process so I don’t know if I’d call it a “mistake.” The lesson learned from it was simple: Believe in your vision as no one else will — but also don’t be obstinate. Use questions and critiques as inputs to bulletproof your plan.

Is there a particular book, podcast, or film that made a significant impact on you? Can you share a story or explain why it resonated with you so much?

This may sound nuts, but the movie I bring up the most when reflecting on entrepreneurship is “The Croods.”

The entire movie is summed up in this one scene when the family is trying to escape the destruction of the planet. The father, who has been resistant to his daughter’s risk-taking, adventure-seeking, and ‘dreaming of new possibilities’ ways, finally realizes that the only way to survive the explosions is to make a leap through the fog to the other side that may not have solid footing…but “THERE’S A CHANCE.”

This sums up the life of every entrepreneur — the willingness to make the ultimate sacrifice and do everything it takes to realize a dream, not knowing what lies on the other side…simply because it represents a chance to do something no one else would.

To me, the fact that there are few among us who, like all of us, were also taught to not play with fire, not jump off cliffs, not dive into the unknown and play it safe, but are still willing to make that ultimate sacrifice is deeply humbling. It’s a reminder that these few brave souls deserve our ultimate respect, compassion and pride, as without them, we wouldn’t evolve.

Are you working on any new, exciting projects now? How do you think that might help people?

I’m very excited about the new technology that Tresata is introducing in the market, our Digital Business Platform. DBP, as we lovingly call it, was designed to deliver to every company in the world the ability to use data to better understand and serve their customers, employees, partners and products as a unique segment of one.

DBP enables companies to obtain usable data faster and more affordably and of a much higher quality. And it does so by alleviating the pressures that companies of all sizes and scale face today: lack of talent, a massive influx of data, and rapid technological shifts to becoming “digital.”

We believe that the best in the ‘digital business’ biz are data-first companies, like Google and Netflix. By accelerating the rate at which more traditional companies can create usable data, DBP allows for the creation of data assets that are critical to the foundations of building and evolving successful businesses. For instance, what would take a company two to three years to deploy through traditional, manual data engineering can now be delivered, with DBP, in just a few months.

For organizations struggling with talent shortages in data engineering, analytics and sciences, DBP has the added benefit of both (a) maximizing the productivity of existing talent and (b) freeing up existing talent to focus on higher value delivery. Moreover, DBP is delivered via an extremely cost-effective consumption-based model, where companies only pay for the data that is made usable.

We believe that DBP can usher in a new Data Engineering as a Service (DEaaS) paradigm that has the ability to help enterprises brace for all economic cycles, by helping to control the rising costs that we are all facing in the race to become more data- and digitally driven.

Thank you for all that. Let’s now turn to the main focus of our discussion about empowering organizations to be more “data-driven.” For the benefit of our readers, can you help explain what exactly it means to be data-driven? On a practical level, what does it look like to use data to make decisions?

In October 2010 I announced in a speech at Strata that the world was about to enter a new Industrial Revolution, the raw material for which would be data.

While I was the first to go on record with this proclamation, the concept of data being the new fuel for organizations to transform or build businesses has since become ubiquitous.

Today, being data-driven isn’t a matter of choice. It’s existential. To become data-driven, organizations must do three things:

  1. Understand what data they are creating in their business, across all products, processes (sales and service) and people.
  2. Engineer capabilities that can use that data as the key to unlock the “secrets of their business” — and enable the understanding of customers, employees, partners and plans better, cheaper and faster.
  3. Apply that intelligence to build and create products and services for their customers that help enrich, simplify and improve the quality of their lives.

Organizations that can achieve that level of “data IQ” have a once-in-a-lifetime opportunity to fundamentally transform their business models. Just look at what the MAANA’s (Meta, Alphabet, Apple, Netflix, Amazon) have been able to do in their industries. They have fundamentally transformed advertising, retail and entertainment. Operating with a data-first model is what has enabled them to dominate and disrupt entire markets.

The secret sauce to the success of these “digital-natives” is their ability to capture and use all of their data — as it’s generated — to gain dynamic insights about their products, processes, people, supply chains and most importantly customers. Think of how Netflix knows what shows to not just recommend but script so you continue to binge watch…or how Amazon knows what products to suggest at checkout based on not just price, but the time of delivery, so you add even more to the shopping cart.

For companies that want to be more data-driven like the MAANA’s and evolve into digital businesses, I always say the foundational step is to create “segments of one” of their customers. Only then can you optimize every process and interaction to serve each customer — not as target segments — but as unique “segments of themselves.” This drives better decisions, builds more meaningful relationships and inspires unparalleled customer loyalty.

Which companies can most benefit from tools that empower data collaboration?

I don’t necessarily agree that companies need more data collaboration tools. The problem is we have too many collaboration tools and processes, and not enough usable, good quality data to collaborate on.

Practically any company can benefit from having real-time, quality usable data ready to support their business. In fact, regardless of industry, many organizations face the same data challenges. They have these raw bits of data riddled with errors, duplication, inconsistencies and other variabilities that make it incredibly hard to extract any actionable intelligence from them. And most of it exists in many, siloed systems. The sheer volume and variety of data is overwhelming — for businesses of all shapes and sizes, from the smallest ones to the global behemoths.

Trying to capture, clean, reconcile and understand the inherent relationships between these vast, disparate bits is an enormous obstacle. Organizations have tried to overcome it by pouring money into massive storage platforms and data discovery and preparation tools. Or, they’ve tried hiring departments of people to do the cleansing and integration manually, only to come up short on available skilled talent and variability of output.

When you have a dearth of human resources and a daunting task that needs to keep up with the speed and scale of your business, automation is the only answer.

At Tresata, we were one of the first to recognize the challenge and decided to solve it by applying AI to automate the entire data engineering lifecycle — from collecting raw bits of data from multiple systems to creating data assets that are instantly usable and relatable for immediate business value. It’s the same technology principles that have empowered the MAANA’s to reign supreme. We’re bringing these principles to traditional industries and helping businesses become digital.

We’d love to hear about your experiences using data to drive decisions. In your experience, how has data analytics and data collaboration helped improve operations, processes, and customer experiences? We’d love to hear some stories if possible.

A stateless bit of data on its own doesn’t provide much value. But when you link all of the bits to find the relationships your data can show you — across customers, products, services and interactions — then you start to uncover intelligence about your business.

The trouble is most companies assign each bit to a single use case at a time, which limits the insights that can be gleaned, causes massive duplications in effort and not much re-usability. In addition to being unscalable, this limitation leads to many one-off exercises where data teams need to start from square one with each project.

DBP breaks that cycle of inefficient data processing. Instead of using one bit for one use case, DBP allows for raw bits to be assigned and used for multiple use cases. For instance, we allow raw bits of data to be linked to create a common, unique and tokenized customer identifier across multiple systems and data feeds. Not only does this allow for efficient movement of data, but also protection and privacy that meets and exceeds regulations and customer expectations.

This also allows organizations to take their analysis further, by linking duplicate accounts, multiple accounts, family and business relationships etc. The result is a complete, secure and multidimensional understanding of each customer that can be applied to multiple use cases and analytics processes.

Take for instance an airline that has fragmented information about its passengers in different sources like their ticketing, payment, frequent flier and baggage check-in systems. Small nuances in how their name is entered into the systems (e.g., Jane Doe vs. Jane W. Doe) or if a passenger doesn’t enter their frequent flier number can prevent an airline from connecting the dots that the records all relate back to the same customer.

We helped one of the world’s leading airlines weave together 4 billion daily records to capture 102 million unique customer profiles. From these profiles, the airline provides every passenger with a positive and personalized experience based on their recent activity and demonstrated preferences — even if they’re not part of the frequent flier program. Customer support representatives can easily get up-to-date travel information on each passenger to assist with rebookings or refunds. Gate attendants have been able to proactively contact passengers to assist with sudden schedule changes and mitigate frustrations. The airline is treating everyone like they are royalty to inspire a different level of loyalty.

And I say often — loyalty IS royalty.

Has the shift towards becoming more data-driven been challenging for some teams or organizations from your vantage point? What are the challenges? How can organizations solve these challenges?

I think becoming data-driven has been challenging for some because so much emphasis has been placed on one aspect of it — CLOUD. Some have come to believe that having all your data in one place will make it instantly ready for analytics. But in fact, as highlighted in a recent TDWI Research study, 76% of the companies said they’re seeing most of the same challenges with their cloud data warehouses and lakes as they did on-premise. This isn’t surprising because the cloud only addresses the issue of data storage and access. What’s more, companies often just port their data as is — with all of its existing issues and siloes — straight over to the cloud. It’s like pouring the same wine into a new bottle and expecting it to somehow taste better.

To become data-driven and evolve as a digital business, companies need to not just move their raw data, they need to now integrate, enrich and distribute it via these cloud frameworks and automate the creation of usable data — in the cloud.

Given the speed and scale of data generation in the modern enterprise, automation is the only way forward. Only then can you link and make sense of all the information from across the enterprise at absolute scale and gain a deeper, richer understanding of your business.

Ok. Thank you. Here is the primary question of our discussion. Based on your experience and success, what are “Five Ways a Company Can Effectively Leverage Data to Take It To The Next Level”? Please share a story or an example for each.

Great question — I believe there are five things that a company needs to do to effectively leverage their data:

  1. Be Intentional With Your Data: There should be a clear intention for why you want to leverage this data. What ultimate goal do you want to achieve? What value do you want to derive from these assets? Setting a prescribed goal helps to map out the steps for success. For instance, in 2021, Tresata launched a free tool Bad Actor Discovery as a Service (BADaaS) with the goal of applying our record linkage technology to uncovering hidden financial crime networks. We knew what data we needed, scanning and linking trillions of data points on beneficial owners sourced from legal, corporate, offshore leak and sanctions data. We made BADaaS totally free to aid governments, banks and journalists in exposing the hidden tracks of bad actors and preventing further exploitation of the world’s financial resources.
  2. Don’t Overlook the 80%: In the data analytics lifecycle, companies get excited about the last 20% of the journey — where you’re extracting intelligence from the data. This last 20% is viewed as the sexy part of analytics and where you get the most returns on investment. The preceding 80% of the journey — all the unsexy data discovery, preparation and plumbing work — gets less attention because it’s frankly arduous. But I would argue that it’s even more vital to get this 80% right because otherwise, you’ll be making critical decisions based on incomplete or inaccurate data. At Tresata, we developed our DBP solution to automate that 80% of the analytics lifecycle and simplify the process down to three steps: ingest, enrich and distribute. DBP gathers data from all enterprise sources, fuses them together for instant usage and makes it available on demand for analytics processes to empower digital businesses.
  3. Go All In With Your Data: To be data-driven, it’s not enough to leverage piecemeal versions of your data or to make decisions based on the information in a single system. Think of the many parts of a business and supply chain that go into the launch of a smartphone. There’s the raw materials supplier, the manufacturer of the phone’s components, final assembly, marketing to promote the phone, shipping, sales and distribution. Contextualizing such a supply chain and making decisions to optimize processes upstream and downstream requires data from all of these sources. Currently, one of our important projects is “susAIn,” where we’re taking financial data and overlaying digital, physical and environmental, social and corporate governance (ESG) data to understand supplier relationships across supply chains. Our goal is to use the data to help companies know if their suppliers are following ESG best practices and improve accountability and sustainability for the future of our planet.
  4. Don’t Get Lost in the Hype: Every few years a new platform or approach emerges that purports to be the solution to the world’s data management woes. First came enterprise data warehouses, then cloud data warehouses, lakes and lakehouses, and now the hype is all around data fabrics and meshes. With so many approaches to choose from, it can be difficult to narrow down what’s best for your organization. Anything hyped up and new naturally appears enticing. My advice is to not get lost in the hype. Don’t lose sight of the fundamental fact you ultimately need a way to integrate and make sense of all your bits — whether they’re housed in a data lakehouse or data mesh. You will always need a usability layer within your data stack to reap the full benefits of your digital investments.
  5. Distribute Data Assets: When you’ve connected all your data and linked their one-to-many relationships, the assets aren’t limited to a single use but can benefit many parts of the business. This usable data must get distributed across your many customer, business and enterprise systems and processes. In essence, you liberate the data so every last bit can be used and monetized to achieve transformational results. For instance, we’ve helped one global bank attain usable data from 300 million entities across its transaction and payment systems. From these assets, the bank has powered over 30 use cases — from cross-selling and ESG monitoring to fraud detection. In the first year alone, the bank has seen three times the return on investment.

The name of this series is “Data-Driven Work Cultures”. Changing a culture is hard. What would you suggest is needed to change a work culture to become more Data Driven?

Change is a human superpower. But it is also the only constant in life (or so said, Heraclitus). To create a culture that is truly data-driven, people across the organization (whether they are customers, employees or partners) must be educated and bought-in to adopt a data-first mindset.

And I believe this is a unique mindset where it must be engendered across all levels of an organization. It definitely needs complete, unanimous buy-in at the very top (the C-suite and the board). But equally, it needs the creation of capabilities where access to good quality data is democratized across all levels, processes and teams, with a clear, defined vision of how intelligence from data can help improve, simplify and enrich the day-to-day lives of employees, partners and eventually customers.

The key is to invest in capabilities that don’t rely on large human efforts to create usable data, as those efforts will fail and thus become demoralizing. Secondly, it should show quick value so the investment needed to enable this change has a ready supporter in the CFO.

Everyone can agree that putting data-driven intelligence in the hands of employees and customers should result in positive outcomes for all — across every interaction, transaction and business decision. I believe once one person or team starts to demonstrate the impact of having usable data on demand, then it’s easy for a data-driven mindset to go viral as everyone wants to win.

The future of work has recently become very fluid. Based on your experience, how do you think the needs for data will evolve and change over the next five years?

From online shopping and food delivery orders to social media, so much of our daily interactions are happening in the digital world. Over the next five years, I expect 95% of all human activity will leave a digital footprint — and bring with it an exponential proliferation of data.

At that point, unless businesses of all shapes and sizes are adept at using this digital exhaust of data, they will “drown” in their own data lakes.

Does your organization have any exciting goals for the near future? What challenges will you need to tackle to reach them? How do you think data analytics can best help you to achieve these goals?

One of Tresata’s biggest goals is to bring our technology and intellectual property to the masses. We want to empower every business — not just the Fortune 100 — to become data-driven, digital businesses. This includes your neighborhood mom-and-pop stores, local restaurants and every other small- to medium-sized business. Many of them were already forced to become digital during the pandemic, across POS systems, delivery platforms, social media, mobile apps and the like. These platforms contain a veritable treasure trove of customer data that belongs to the businesses, not the platforms, and can help the business understand and better serve each customer. They just need an easy, affordable way to connect all those bits and bytes. That way, for every customer that walks into a corner coffee shop, the barista already knows if their go-to order is the chai latte or the vanilla americano.

How can our readers further follow your work?

To learn more about how Tresata is powering digital business of tomorrow, I invite all interested readers to check out tresata.ai and explore our resources section. You can keep up with what’s happening in our global offices on Twitter at @tresata or follow me at @ab_hi_ and on LinkedIn. Here’s to unleashing the power of data to enrich life!!

Thank you so much for sharing these important insights. We wish you continued success and good health!

Source – Medium