Although businesses can now access vast amounts of data, enabling them to profile the behaviour of customers, employees, competitors and more, many struggle to make meaningful use of this data. In theory the possibilities are endless. In practice leveraging this flood of data to help execute successful corporate strategy is difficult.
To provide a bridge between ‘big data’ and effective strategy, Darden School of Business professor Raj Venkatesan recommends 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. He quotes Samsung as an 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.
This strategic move was enabled by data-driven analytics people who were passionate about customers and brands. 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, says Venkatesan.
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
Conventional wisdom in the business world 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?” Venkatesan asks.
He quotes the example of a company in the online auto trading space which 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
Venkatesan says that leaders in data analytics 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. 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. 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. 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 these five enablers of the data driven process during his Bridging Analytics and Strategy Hot Topics presentation to Darden alumni in San Francisco in April 2016