“There are billions of people sharing their opinions online every day – now we have the capability to process that data and make sense of it.”
Abhishek Jain has dedicated the best part of a decade to a career in analytics, delivering data projects across a range of industries from finance to retail. He talks to us about the challenges of the job, the latest capabilities and how data is shaping the world around us.
Learning the ropes at one of the world’s biggest banks
Armed with a master’s degree in business administration, Abhishek took his first steps into the world of data science almost nine years ago when it was still a relatively new industry.
While most graduates in his field went on to work in marketing, finance, or HR, Abhishek found himself at Citigroup where he used his mathematical knowledge to provide business-critical credit card analysis.
“Companies like Citigroup have to know everything about their customers,” he says. “Who’s likely to default, who may commit fraud, how much money they should lend to each individual. Behind every card feature there’s analytics – and that’s where it all started for me.”
The spice of life
Since Citigroup, Abhishek has applied his analytics expertise across a range of industries, from insurance and finance to retail and CPG. For some, this constant changing of hats might pose difficulties. But for Abhishek, the variety is something to embrace.
“Every day I have a new challenge,” he says. “I’m always learning about new industries and discovering new terminology. There’s no such thing as boring in this job.”
Most recently, Abhishek has worked with our large, international retail clients, using data science to help them streamline in-store experiences with the right configurations of checkouts and products at each of their individual locations.
“It’s amazing what data can tell you. Before, these decisions were mostly random or based on gut feeling,” he says. “But with the analytics engines we build, our clients can make truly data-driven decisions that deliver measurable returns.”
Keeping up with changes
Over nine years Abhishek has witnessed a lot of change in the field of data science. For one, the tools he cut his teeth on, SAS and SQL, have largely been surpassed by more flexible and affordable open-source equivalents.
“That’s one of the things about this job,” he says. “There are new tools and techniques appearing all of the time, and you have to keep up with them or you’ll be left behind.”
“The last three years have been all about R and Python,” Abhishek continues. “They do all the things that we used to do with SAS and SQL, but everything else too. We can deal with large data sets, data mining, AI and machine learning—and it’s all available open-source.”
Another big change has been the wide-scale adoption of cloud infrastructures. More than half of the companies Abhishek worked with today are running everything on AWS, which means costs are lower and there’s greater flexibility in terms of processing power and storage. Vitally, for data science teams, it means greater efficiency too.
“With the cloud, anyone can sign in from anywhere. So, if you have a team in India, a team in the UK and a team in the US, when one person clocks off another can clock in. You’re essentially able to work 24/7.”
Getting the sentiment right
The results of this technological progress can be seen every day in the work Abhishek does for The Smart Cube clients. The boundaries of possibility are always shifting, which means new insights and ever-growing returns.
“Five years ago, we never thought social network data would be used to impact business decisions,” he says. “We just didn’t have the capability to process it, but it represented such a big opportunity. There are billions of people sharing their opinions online every day—and now we have the capability to process that data and make sense of it.”
The ability to use sentiment analysis on social posts to figure out what the public really thinks, or what customers really want, is invaluable to the modern business.
However, as far as data science has come, Abhishek recognises that there’s still plenty of room for improvement:
“Right now, we still spend around 80% of our time unifying data and cleansing it,” he says. “I think in the future we’ll be able to do this a lot quicker, which means we can use more of our time to uncover the insights that add real value.”
Bringing everyone on board
The other thing Abhishek would like to see in the future is greater adoption of analytics by business users. Currently, there is massive buy-in at the top level of a lot of companies, but getting employees below the C-level to embrace data science can be an uphill struggle.
“Acceptance by business users isn’t even 10-15%,” he says. “It’s because we’re trying to challenge people’s expertise. Some of them have been in their business for 15 years, and no one wants to accept they’re wrong. But a lot of the time the data will tell a different story from the one they expect to hear.”
“Most of our clients now have a role dedicated to bridging the gap between analytics experts and business users, to make sure they get the most value from our data projects – which is where we often take a strong lead as an external partner,” says Abhishek.
And he believes this role will evolve over time. “People are still getting used to what we can do,” he says. “When everyone understands the benefits, I think you’ll start to see people working more collaboratively, with less silos.”
Want to meet more of The Smart Cube’s data scientists? Keep an eye out on our blog over the coming weeks and months!