Are you the servant or master of your analytics?

Associate Partner, Distribution Sector Europe, IBM Global Business Services, IBM

A recent article (and discussions in social media) speculate how much Tesco's current challenges are based on the company's strong trust and use of analytics. Harvard Business Review recently published "Tesco’s Downfall Is a Warning to Data-Driven Retailers.” Michael Schrage, a research fellow at MIT Sloan School’s Center for Digital Business, raises the question: "is it a market signal that big data, predictive analytics and customer insight aren’t the sustainable competitive weaponry they’re cracked up to be?"

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I personally believe that analytics is a good servant, but a bad master. Let me explain: it is the people who define the business questions that need to be solved or supported using data, predictive analytics, optimization and cognitive analytics. It is the people who design the solution, mine the data, develop algorithms and strategize how the outcomes are used in decision making or automated into processes or solutions. In most cases, the analytical models are not static, but need to be monitored and developed as we face changes and fluctuations (such as new products or services) in customer behavior, competitive landscape, market and within individual businesses. Also, new sources and types of data emerge and need to be considered when deciding how analytics is being used and what type of analytical model may deliver the best outcome.

Today, because technology increasingly enables the use of large sets of data and sophisticated analytics, an understanding of the value of analytics is increasing dramatically within companies. However, I have seen few companies across industries (retail, for example) which seem to have a clear vision and strategy for analytics.

So how do we turn analytics into a competitive advantage? In most cases analytics are used in a fragmented way on a very operational level, for example: use of multiple customer analytics methods and models from various segmentation models to cross- or up-sell, score models for various target group criteria and create mailing lists to push out promotional campaigns. Internally, at the company level, analytics helps produce all levels of  "slice and dice" reports to view, for example: the sales figures from multiple perspectives at multiple levels.

A clear analytics strategy, that would define what we are aiming to achieve with analytics based on our business strategy and market situation, is missing. For a retailer this would mean a proper assessment of where the value of analytics lies: is it in optimizing the assortment to meet customer needs or is it to send targeted product promotions? It is easier to develop marketing analytics, but unless a retailer's assortment and overall concept is competitive and meeting the customer needs, there is a limited value marketing analytics can offer. With a defined strategy in place, analytics adds great value and deliver relevant personalized content to the customers.

Also, it is important to realize the value of people skills in the organization, all the way from data management, governance, privacy and security to data mining and modeling. It’s the people who can best interpret business and combine their understanding with the analytical models. Increasing data sources, volume and velocity, growing business needs for analytics and expanding the number of analytics experts and users require a wide set of new skills, competencies and roles. The chief data officer is becoming a standard core role. With even more data-specific roles becoming relevant (such as privacy officer, security officer and chief data scientist) it is truly a skills game, meaning that if a retailer wants to turn analytics into a competitive advantage, we are talking about quite a large investment in people, skills and technology with a clear vision, strategy and business case.

Analytics provides significant benefits (and a competitive advantage) when used wisely and based on a clear vision of where it adds value. As markets evolve and customer behavior changes, the analytical models, as well as the way analytics is being used, needs to adapt and change. Many of the leading, and most effective, users of analytics have teams in place constantly monitoring and analyzing new data sources and changes in customer behavior, as well as following competitor actions, updating their own approach and models in tandem.

Will you be the master or the servant of your analytics?

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