The North Star of the Data-Strategy Journey

Peer Practices
Written by Drew Lazzara

Phanii Pydimarri

Head of Global Data Strategy and Analytics


Phanii Pydimarri, head of global data strategy and analytics at Bose, pursued this particular role because he views data as uniquely indispensable to running a business. “I’ve never believed that a company can survive with only one kind of leadership,” he explains. “Leadership has to be in conversation with every line of the business. And data reaches out horizontally like that across organizations; it can solve any business challenge and it can bring companies together.” 

For Pydimarri, this essential belief in the role of data isn’t just an article of faith. Over the course of his career, he’s taken care to build foundational data strategies that lead to meaningful value creation. He’s learned that both success and failure in data strategy depend on a few critical organizational attributes.

Strategies of Extremes

“I’ve seen lots of organizations have the initiative, lots of talk about being data-driven,” says Pydimarri. But for most businesses, “There’s always a missing link somewhere.” This missing link can take many forms: “Some companies will say, ‘Look, data is our priority.’ But then they don’t follow up with a clear roadmap. Or within some organizations, all data efforts feel too ‘long term,’ and they lack the clear, measurable short-term goals that are essential and justify the investment in actually achieving long-term plans.” 

Strong data strategy reconciles long-term vision with tangible, short-term value, but for organizations to thread that needle, Pydimarri says they must locate themselves more precisely along the maturity curve.

“It is highly critical to know where you are in the data journey, where your ground floor is,” he says, “because things are often viewed as being at the extremes of the data spectrum: organizations either feel they are just starting out on their data journey or they are fully mature and are looking for more innovative opportunities.”

Organizations struggle to calibrate their place along this spectrum, leading to assumptions that distort their strategic outlook. As Pydimarri says, “The majority of organizations, of course, are somewhere in the middle, and this is a really challenging place to be – many don’t know how close to the starting line they are. Some think, ‘Oh, I’m close to the beginning,’ which makes it easier for them to constantly start over or revisit foundations, resulting in multiple attempts of trying to start an effective data program.” 

“Other companies think, ‘We’ve invested in this for three or four years; we must be getting close to the finish line.’ They can start to see some returns on data strategy and think they have all they need without realizing the true value they could actually achieve.” 

Finding yourself on the data-maturity curve can be tricky because, as Pydimarri says, businesses define business objectives but struggle to understand the enablers of those objectives. “We think of technology or funding sources as the drivers of strategy, but the one element that is actually gluing all these drivers together is data. They are your true asset, your enabler.”

Pydimarri knows this realization doesn’t come easily, but he’s developed an outlook for greater clarity.

Pillars of Data Strategy

Pydimarri has identified four areas of focus for understanding the organization’s place on the data-maturity curve and defining the right next step.

  1. Find Your North Star – Every organization must know where they are now and where they want to be. This orientation piece is straightforward, but it’s essential for Pydimarri because “data will help you get there faster, more effectively and more efficiently.”

  2. Understand Your Ecosystem – This is a macro understanding of the key elements impacting your success as a data-driven organization: organizational culture, the acumen and data appetite of your user community, and infrastructure.

“I need to understand if there’s an analytics-driven mindset,” Pydimarri says about this ecosystem. “Are people happy using spreadsheets, or are they interested in tools that make their life easier? Are people empowered to use the data right away, or do we need to take a step back and focus our time and efforts on data literacy as a foundation?”

Finding data solutions to business challenges also requires alignment between good use cases and current infrastructure. “Let’s say I want streaming data from our Twitter feed so that I can understand what influencers are really saying about my company and my product. If I don’t have the infrastructure to get that real-time data from Twitter, I can’t address this use case."

  1. Ensure Continuous Executive Support  – Pydimarri says that data leaders need to be great communicators and salespeople or they risk losing momentum for their initiatives. “I’ve been in situations where I had the support of one executive and assumed it was there across the C-suite. But just as things started to get interesting, there would be a decline in that support due to the misaligned support across the C-suite.”

Maintaining momentum requires diverse communication. “I never consider only one avenue for passing along the message,” Pydimarri says. “I use everything from our internal portals and town-hall sessions to simple PowerPoint presentations. The ideas like ‘Data Days’ can have a significant impact in identifying internal data talent that you may not be aware of. I’ve even printed out internal pamphlets to distribute to my business community for marketing my team’s accomplishments saying, ‘Hey, this is what we’ve accomplished. Take a look!’ And people see that and say, ‘Wow, I like that idea.’ It becomes a call to action for the business to engage.”

Pydimarri also believes in setting clear expectations and having a back-up plan. “From the beginning, I have a clear path of communication established that assumes something will go wrong. Transparent communication ahead of the game gives you leverage with your business partners, because a change to our best-case scenario is likely. But I always have a ‘Plan B’ ready.” This approach builds a trust and accountability that helps tie business support to a project.

  1.  Identify and Deliver the Right Opportunities Quickly   – Of course, nothing builds trust like delivering results, so finding immediate return on your effort is critical. Enterprise-wide data strategy falters if the wins are too long-term or abstract. People need to see themselves in the projects you target. “If I’m a supply chain person or manufacturing person, my focus is my own line of business, my day-to-day challenges,” says Pydimarri. Strategy takes off when you can tangibly address these day-to-day problems.

But you also must be intentional about the low-hanging fruit you target. “Picking the right ‘win’ can be a multiplier if you choose areas that are highly influential,” says Pydimarri. “I look for areas that have direct impact on our customers, because that helps create an impactful story for other business lines.”

You must also be intentional in how you manage your success; demand for data will multiply quickly, but not all projects can be a priority. “Once people start seeing results, you’ll definitely see the floodgates open. Everybody wants those cool dashboards or fancy visualizations. But everything we do must tie back to that North Star to be able to justify the value,” says Pydimarri. 

Short-term and long-term must be balanced to hold strategy together, and Pydimarri can change the thinking of a peer to align better with overall business vision. “I take that longer view that comes from having a North Star. I want to build relationships with the business community for down the road; I give them an opportunity to improve the value of their request by helping them frame it in a way that actually helps multiple groups and fits one of our larger corporate goals.”

A Data-driven Future

Data utilization is constantly evolving. “I see all the advancements in how we are approaching data,” Pydimarri says, “and there are organizations with technology in place that can already predict things efficiently – in fact, completely automated predictive modeling is our goal right now.” 

But that won’t simply happen just because the technology exists. “The human element has to be there; I don’t see that going away, because no matter what insights the data provides, there has to be that human intervention for decision-making,” Pydimarri says. That basic truth is why the four pillars are so important for moving the organization along the data spectrum – they ensure people can see their place in data strategy. “People are connected to data through business processes, and your processes drive the quality of the data.”


Special thanks to Phanii Pydimarri and Bose.

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