Data is everywhere.
In fact, some experts estimate that we will have some 200 trillion gigabytes, or 200 zetabytes, of data on our hands by as soon as 2025. For those of you with a CD collection, that’s the equivalent of having your CDs span the world twice over.
So what’s a business to do with all of that information? According to a recent survey of data leaders at our Thought.Leaders event series, only one third of global businesses have moved their data warehouses to the cloud. Of that group, an even smaller fraction has adopted cloud-based BI and analytics tools to turn that data into fact-driven insights. This is a missed opportunity, and one we were curious to dig into deeper at Beyond 2020.
During the digital conference, ThoughtSpot’s Chief Data Strategy Officer, Cindi Howson sat down with Bayer Chief Analytics and Insights Officer, Manik Gupta to learn how he’s driving Bayer’s cloud transformation. They also discussed how search and AI-driven analytics are helping the team solve business challenges faster, and how Manik thinks about prioritizing data and analytics investments. Read on for more insights from their conversation.
Cindi Howson: So Manik, one of the things I love about your title is that it goes beyond analytics. It also includes insights. Tell us more about your role at Bayer.
Manik Gupta: I run several of our centers of excellence here at Bayer including the ones for human insights and consumer engagement, our enterprise business intelligence organization, and the advanced analytics and data science team for all of North America. I wear my title with a lot of pride because I like to think about my role here as overseeing analytics in service of insights. It’s not just about the data and analytics themselves, but the specific actions that come out of that work—the insights and foresights—that make the most impact.
CH: If you think about Bayer’s cloud journey, what role have you played in driving this journey and what’s been the impact of the pandemic?
MG: To understand our cloud journey I think it’s helpful to take a step back and appreciate that a lot of enterprise companies grow through M&A. When you do that what you’ll often end up with is a wide range of on-prem, heavily engineered legacy systems. At some point, you realize that the value of the data coming out of those systems is significantly lower than the cost to maintain them, and you have to evaluate whether you have the right platforms and the right purpose or not.
Recently, there’s been a seismic shift in the way data and analytics leaders strategically think about platforms, systems, and technologies. It’s less about managing different applications and more about monetizing the data coming from those applications. A very key part of any enterprise’s cloud journey, including Bayer’s, is how you manage that agile transformation. That’s where we see cloud coming in and playing a very critical role.
With specific regard to the pandemic, I’ve spent a lot of time over the last year thinking about COVID-19 and how we leverage our data to weather the storm. A lot’s happened. One thing that stands out to me is how much quicker we’re all moving. Data and analytics capabilities we thought we were going to have in two to three years have now launched in 6 months. And that I believe is here to stay.
CH: The acceleration you just described is pretty consistent across the industry. It’s clear that cloud is an enabler of this, but with so many opportunities how do you prioritize your investments?
MG: There’s no one recipe for success, but I do come back to three main principles when thinking about prioritization:
- Allow business needs to drive the data analytics and technology roadmap—not the other way around. This is a critical nuance. At the end of the day, our job as data and analytics leaders is to cultivate a deep understanding of the business, and its challenges, to create business use cases. I define use cases as efforts against a specific business challenge to get to a specific business outcome.Orienting your data and analytics roadmap to solve for those business use cases is key.
- Adopt an entrepreneurial mindset. Whether you’re a one-person shop or at a fortune 500 company, you must adopt an entrepreneurial approach to your analytics program. What I mean is, when you start on a project with your initial “seed money” much like an entrepreneur, your next steps are to address the business need, prove your concept, scale, rinse, and repeat. The more you succeed in a particular area, the more energy and resources you should spend on it. If a program doesn’t work, cut the investment and reinvest in something else.
- Agility triumphs over perfection. Like I mentioned earlier, the days where our data and analytics teams would prepare for 6-18 months are now gone. You have to run 2-6 week sprints to come up with new programs or new functions to maintain momentum.
These fundamentals should help guide you in the right direction, and I really can’t stress the first one enough. It’s so important to tie your group to a business outcome. Some people think of data and analytics as a cost center, I don’t see it that way. I see it as a profit center. Your KPIs might vary from business to business, they might be top line or bottom line, it really doesn’t matter. What matters is that you’re able to say, ‘Hey, I’m going to drive a couple of these, I’m going to track the work I do, and I’m going to show you how it impacts the entire business.’
CH: That’s a great point. It’s not about technology for technology’s sake. Which leads us to why ThoughtSpot? And why now?
MG: The thing I care about the most is getting to the 1% of data that’s relevant to the context I’m in. That’s where I love the promise of ThoughtSpot. We’re still early in our journey with the product, but I love that it’s an advanced BI tool with a query-based interface, and that it sits so nicely on our enterprise data management architecture. We didn’t have to re-architect anything to develop and deploy ThoughtSpot inside of our company. The analogy I like to give is Google versus Sharepoint. One can argue both are systems to share knowledge, but Google is ubiquitous because everyone has a smart device and everyone knows how to type questions into a search bar. What it boils down to is access, agility, and experience. That’s the promise I see, and that’s why we’re so excited about ThoughtSpot right now.
CH: As you look ahead to 2021, what’s your vision for data, analytics, and AI-driven insights? What are you hoping to achieve?
MG: I say this often to anyone who’s willing to listen: Every problem is imminently solvable. All it requires is leadership, focus, and resources. I’m of the belief that we’re only limited by the size of our ambition. We have a range of AI use cases that we’re deploying, accelerating, and scaling now at Bayer, and I believe our purpose in the consumer health space in 2021 and beyond is to accelerate care, specifically for underserved communities, through fact-based decision making at every touchpoint.
Better business decisions with ThoughtSpot
If you’re curious how other healthcare and life sciences businesses use ThoughtSpot to unlock the value of their data, visit our Healthcare & Life Sciences Analytics solution page today.