How to Connect Analytics to Strategy to Avoid the Big Data Bridge to Nowhere

Businesses are struggling to build a meaningful bridge to the terabytes of data that are now available to them through the tools of the information revolution. Even companies born in the digital age often struggle to keep up with the swiftly evolving possibilities of big data.

To transform into an organization that effectively bridges strategy and data analysis so that big data starts influencing execution, Professor Raj Venkatesan recommends businesses follow “five enablers of the data driven process.”

1. Place Strategic Emphasis on Brands or Customers
   Big data has the greatest positive impact on strategic execution for companies with the sharpest focus on their brands and customers, Venkatesan says.
   
   For example, in the Interbrand ranking of best global brands, Samsung rose from No. 43 several years ago to No. 7 in 2015 because the company made a strategic decision to move from being a product-focused company to a customer brand company. Venkatesan said the company had a core of “data-driven analytics people who were passionate about customers and brands” in the early 2000s. At that time, before advanced use of data analytics became a trend, Samsung used data and analytics that helped it predict the ideal allocation of marketing assets in various countries to best promote its brands.
2. Clarify Strategic Challenges and Key Performance Metrics
   If an organization is not crystal clear about its business model and strategic challenges, it won’t collect the right metrics that yield useful analytics and lead to better execution, Venkatesan says.
   
   A media organization in early days transitioning from a B2B company to a B2C firm had to learn about developing a marketing capability that directly targeted consumers for subscriptions instead of relying on the cable partners. Venkatesan says part of this learning involved identifying the key performance metric (KPM) to access the success of their different initiatives. The struggle of this media company to find its new KPM reflects a lack of clarity around the business model. 
3. Adopt a Design Thinking Approach
   Venkatesan says there’s a troubling conventional wisdom in the business world that says data analytics are best used to solve problems that are easy to define, not those that are too complex. “Shouldn’t it be the other way around?” he asks.
   
   One company Venkatesan examined in the online auto trading space used a design thinking approach to rapidly develop prototypes with inputs from multiple stakeholders and run quick experiments to obtain feedback on each new prototype. The team deployed data analytics effectively by starting with small questions and running fast experiments in two week cycles.
4. Allow Analytics to Be Flexible
   Leaders in data analytics, like a financial services firm in the United Arab Emirates, Venkatesan says are “scrappy” and incredibly good at getting many things done with relatively few resources.
   
   The key to making sure organizations get the most out of the scrappy potential of their analytics groups is to also give those teams and their business sponsors a flexible budget, Venkatesan says. When analytics teams have budget flexibility, it makes it possible to make extra funds available for experimenting with the prototypes suggested by the analytics.
5. Make Data Scientists Data Curators
   There is a power in storytelling, Venkatesan says. Stories have the ability to influence people in ways that raw data or even a dashboard of refined analytics cannot. As such, companies that want to get the most out of what their analytics are trying to tell them need data scientists who can present a story of the implications of analytics for the stakeholders, such as customers and business managers.
   
   There’s a reason satirical news shows like Last Week Tonight With John Oliver or The Daily Show are so effective at guiding their audiences through complex topics each week, Venkatesan says, and it’s not just the ability to tell a good joke. The hosts of those shows present stories that “talk about you and me, not something somewhere.” In the same vein, an effective data scientist will harness data analysis to tell a story about one customer or brand and one customer’s journey with a product.

Professor Venkatesan presented the five enablers of the data driven process during his Bridging Analytics and Strategy Hot Topics presentation to Darden alumni in San Francisco on 12 April.