Nitin Vengurlekar – Oracle Public Sector
- Written by: Bob Mentzinger
- Produced by: Liz Fallon
- Estimated reading time: 5 mins
When we talk about data, there’s a tendency to view it as an end in itself—the great equalizer the world has been waiting for.
In reality, data is merely an asset. It’s how we use it—the technologies we tackle, the efficiencies we seek to gain, the solutions we choose to pursue—that tells the real story.
In other words, the real value of data needs to be unlocked. One of the sectors that stands to benefit the most from this effort is our public institutions—from local to international.
As the chief technology strategist for Oracle Public Sector, Nitin Vengurlekar has been diving into systems architecture and data management for more than 30 years, creating actionable impacts for large organizations with huge data troves.
For his part, Vengurlekar is excited for the future of data architecture, especially at the intersection of machine learning, analytics and autonomous databases.
If you haven’t heard of the last one, you’re not alone. In short, autonomous databases are self-managing and self-healing, allowing their stewards to focus on more high-value tasks like machine learning and business analytics.
“Analytics have always been able to solve questions like ‘what happened?’ and, to a certain extent, ‘why?’” he says. “Today’s managers want to know, ‘What if’ scenarios and ‘How do I make intelligent decisions, proactively, out of the existing enterprise data?’”
Everyone knows Oracle, the American multinational technology corporation that sells database software, cloud systems and enterprise software solutions.
Oracle Public Sector focuses on secure data-management, solving business challenges and connecting citizens and employees at the federal, state and local levels—as well as in higher education. Clients also include large health care and national security organizations, colleges and universities, and other global enterprises.
Turns out, even some of the biggest organizations in the world—those with petabytes of data—simply don’t know what to do with it, what questions to ask about it, or even what data exists.
That’s where Vengurlekar and the analytics team get involved.
“We start with some key business questions that need to be answered,” he says. “The customer is trying to solve some business problem. A common thread is to understand the data, take out routine tasks that are error-prone or costly and make these operations more self-servicing, and embed machine learning into the technology to produce interesting observations.”
But crafting technology solutions for his clients’ complex needs is about more than mining through terabytes of information; it’s also about ensuring the data can answer the organization’s most pressing business question.
In other words, executives don’t just want to know what the data says. They want to know how to act.
Take autonomous vehicles. Just as cars evolved from stick shift to automatic, they now feature lane-departure systems that actually take the wheel for you. The car is self-learning, self-tuning, self-adjusting.
“You didn’t have, all of a sudden, a self-driving car,” Vengurlekar explains. “You had an evolution of several technologies, all built from the foundational stack, none of which stands alone, but when integrated together, create a powerful data architecture stack.”
Now, with the right tech stack, organizations can answer myriad questions: When (and why) a specific scenario will happen, how to prevent others from happening—issues we simply couldn’t answer before.
“It’s looking forward and leveraging augmented analytics that excites our clients,” Vengurlekar says.
That, in turn, brings Vengurlekar to talk about “multi-dimensional data sets.” If a company only has one set of data, it can likely only answer a limited number of questions: say, how long it takes to produce or ship a product. But the more corollary data you have—information such as weather, spatial and transactional data, customer sentiments and so on—the more interesting insights and predictive analysis you can develop.
Especially in the federal space, where data pools are as large as the responsibility to run programs affecting millions of citizens, it takes massive computer power and smart algorithms to sift and find data that can be integrated—and mined for meaning and value. This is where autonomous infrastructure and systems come into play.
If you can do that, Vengurlekar believes anything is possible. Police can figure out how to improve crime prevention with predictive policing or crime mapping; growers can stop potatoes from rotting; and cities can use 3D technology to better model infrastructure.
“If you assemble data sets with corollary, you create all sorts of predictive models that says to police: ‘On this certain Saturday, on certain months, this thing is likely to happen,’” Vengurlekar explains. “Or, if you’re a grower trying to figure out why all your potatoes are rotting, we can isolate down to a pallet. But if you have corollary data like human capital or time series datasets—who was working that shift and so on—along with location, destination, weather, spatial data, we’re able to answer more specifically.”
In other words, if you know your workers weren’t trained properly, you can address it. If you know the humidity was at a certain level, you can adjust it. You get the idea.
The new gold
His years-long immersion in the world of data made Oracle an obvious landing for Vengurlekar, who says he used to go home after his day job and read Oracle and system architecture manuals.
“There’s a very gray line between my day job and after hours,” he says. “I ride my bike, I work out, but then I come home and read about data management.”
He started with a degree in chemistry, but found his heart wasn’t in it. Rather, it was in information systems—and, more broadly, solving problems.
Indeed, this is Vengurlekar’s second stint at Oracle. He spent more than 17 years there in various capacities working on database cloud architect in the Cloud Strategy Group and emphasizing virtualization and third-party storage as a member of Oracle’s Real Application Cluster engineering group and Product Management Team. He was also a key contributor in building the Oracle-EMC Joint Escalation Center.
He caught the entrepreneurial spirit in 2012 as co-founder for Viscosity North America before returning to Oracle, which “kind of hand-crafted” his new role.
“I wanted to be an evangelist, an enabler” he says. “Sales enablement is important, deals have to be closed. I’m there to help talk to customers about solutions. We have so much data from so many sources. Data is really where it’s at now. It’s the new asset, it’s the new gold.”
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