In this fifth and final episode of this season of Future Says Says, host Sean Lang spoke to Ravi Parmeswar, vice president of consumer business intelligence at Johnson & Johnson (J&J). To cap off this season, Parmeswar and Lang spoke about how to build successful data analytics approaches and teams.
First, more on Parmeswar. It was a surprise, even for him, to end up at some of the companies he’s worked for – including major players like Citigroup, Sara Lee, Coca-Cola, and now Johnson & Johnson – based off his education and training in chemical engineering. After all, he completed his master’s degree and thesis in petroleum engineering. However, he says he’s always kept his mind open toward new opportunities and taken chances when they’ve come his way. He believes his “soft” skills have helped him take these opportunities and forge the career he's had. “My advice is to build a combination of both technical skills and ‘soft’ skills,” he said. “It’s the combination that carries your career journey. These will give you the ability to recognize when opportunity knocks and take what’s ahead of you.”
Solving Data Science Challenges
That said, his engineering background has also been an invaluable part of his success – maybe not in the subject matter, but in the conceptual frameworks and tools his training imbued him with. “Engineering gave me the ability to solve problems – to see through a clutter of data, recognize patterns, and come up with solutions.” These skills, he said, are what make him successful. Moreover, championing this creative thinking approach on an organizational scale is what Parmeswar thinks differentiates average companies with excellent ones.
But Parmeswar also advises organizations looking to utilize data-driven and machine learning (ML) approaches not to get ahead of themselves. He says it’s important that companies learn to crawl before they attempt to sprint, and that it takes time to build winning data analytics frameworks and processes. Most importantly, he believes organizations must put data and models into action with clear objectives in mind. “The concept of saying ‘If you build it, they will come’ is not true. You must design data processes with an outcome in mind, rather than building them without outcomes in mind,” he said. It’s this mentality – striving toward a clearly defined common vision – that gives organizations momentum and the tools they need to gather, implement, and act upon the data that now surrounds modern organizations. As Parmeswar points out, “You want to be making sure that you're designed for the run stage, but you want to go ahead and measure today by the crawl stage.”
Building Data Successes
Ultimately, Parmeswar believes data success is a long-term idea that’s built by aggregating and improving upon short-term wins, a concept that’s come up many times this season. As he put it, “The enemy of good execution is complexity. The friend of good execution is simplicity.” Like other guests, Parmeswar thinks it’s these wins that unlock creativity and give people and teams the skills and experience they need to deliver on an organization’s data vision. “The way you build credibility is through short-term wins, through ‘low-hanging fruit’ projects,’” he said. His believes data science teams should aim to score short-term wins every three or four months because organizational memory – especially in today’s hyperfast, hyperdigitalized world – is incredibly short. Parmeswar showed how J&J is using internal and external data in these projects, while also citing companies like John Deere, who have revolutionized their business models using data.
The big value Parmeswar’s team is generating from data is around customer intelligence. As he says, “People now expect you to treat them as a person, recognize them for their individual needs. The more you empower me and allow me to make decisions by giving me the right kind of information, by understanding me and the unique needs I have, that's how you're going to earn my loyalty. And that’s enabled now by the access to individual level, household level, device level information and data that we have.”
He notes that it’s not easy to bring people together at first – at J&J he’s had to bring together people of engineering, marketing, sales, data, and other backgrounds in a single project, so he knows these difficulties firsthand. But he believes when you empower people, encourage them, and support them every day, that’s when they buy in and feel empowered to bring their strengths to projects. Getting these strengths to emerge, Parmeswar said, is the best way to build winning data science teams and projects. “It's all about people. I can't emphasize that enough. Your data science team's success is 100% linked to the quality of the people that you bring in.” One good way to build this is by establishing a common target for all companies to work towards – just like everyone in, say, a Formula One team, it doesn’t matter if you’re a driver, engineer, or member of the pit crew – everyone is working toward victory. Everyone has their roles and specialties, but the vision is the guiding light. Parmeswar agrees, saying “Everyone’s working toward a common goal, a common vision. Specialization will be rewarded, but sometimes people who get too close to what they’re doing get so into the weeds that they can’t step back and see the forest.”
The Future of Data and the Workforce
Looking ahead, Parmeswar is excited for the future – not just because he thinks we’ll see a new wave of data and machine learning capabilities, but because he believes the upcoming generation is special. For him, it’s their values and their way of looking at the world that will spark change. “Where, in my generation, the focus may have been on material things and moving up the corporate ladder, the new generation is more focused on ethics, the environment, and other values that will change the world,” he said. He thinks this will foster a new generation of companies that treat people better, give them more agency, and help them act upon their values in new, easier ways.
Of course, there will always be challenges, especially when dealing with technology as powerful as machine learning and artificial intelligence (AI). “As with any technology, you could put it to good use, or put it to bad use,” he said. “I think everything that we have is a double-edged sword. But I think there's one thing that keeps me awake at night – being able to understand the bias we have in our data and algorithms and making sure that we’re paying special attention to that.
However, he has faith that the current young generation, and those to come, will meet these challenges and more. “AI is going to be AI and ML is going to be ML,” he said. “How we use it is what differentiates the impact we have. I’m excited that this generation, frankly, is going to do a heck of a lot better than my generation in making sure we use technology for the good of society.”
Click here to watch the full interview with host Sean Lang and guest Ravi Parmeswar and to revisit this season and seasons prior. And thank you for tuning into the latest season of Future Says – stay tuned for Series 4!
- Episode 1: "Creating the Identical Digital Twin" with Vijayakumar Kempuraj, digital twin lead, Ford
- Episode 2: “Building Bridges Toward a Better Future” with Geertrui Mieke De Ketelaere, adjunct professor of AI at Vlerick Business School and strategic AI advisor at imec
- Episode 3: “Fostering Data-Driven Innovation and Skillsets” with François Deheeger, senior AI fellow, Michelin
- Episode 4: “Using Data to Move from the Physical to the Digital” with Jan Chirkowski, vice president of analytics and fleet operations at Kongsberg