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Data-driven Strategy, or how to build on Big Data

Giuseppe Donvito, Partner P101 | September 15th, 2016

Big data – i.e. data that companies collect so extensively in terms of variety, volume and speed as to require specific technologies and analytical methods – are one of the most interesting products of the digital revolution. It seems that Italian companies have now understood that the analysis of Big Data represents a source of competitive advantage and a key instrument for the development of their business models. A new research by Microsoft-Ipsos Mori shows that 66% of Italian SMEs has the right expertise and tools to manage business information and 50% plan to invest in data analytics.

According to a research by the Italian Observatory for Digital Innovation, in 2015 Big Data Analytics grew by 34%, followed by Business Intelligence (+11%). The business functions that more frequently use big data are marketing and sales, finance and control, information systems, purchasing, production and supply chain. 26% of organizations hired a Chief Data Officer and 30% hired Data Scientists, although the responsibility of dealing with Analytics most frequently concerns the CIO or some other IT decision-maker.

However, too often there seems to be an impasse on how to build on the valuable information provided by Big Data, once they have been collected and analysed. Indeed, Italian companies have realized the importance of extracting insight from data, but are still far from implementing data-driven business strategies.

According to a research by McKinsey, fully exploiting data and analytics requires three mutually supportive capabilities. First, companies must be able to identify, combine, and manage multiple sources of data. Second, they need the capability to build advanced-analytics models for predicting and optimizing outcomes. Third, and most critical, management must possess the muscle to transform the organization so that the data and models actually yield better decisions. In short, to analyze data for measurement purposes only is no longer sufficient: predictive analysis that supports the company’s decisions is also essential, since true value lies in information that is hidden in data. A company’s strategy should encompass the collection, processing and proactive use of data.

A strategy that is based on real information is a strategy that is not afraid of running changes. Virtuous implementations of data-driven marketing strategies include, for instance, retail strategies that, from the analysis of sales and other in store data, allow to derive actionable insights, which are then used to modify the strategy almost in real time. Another example of virtuous data-driven strategies are those that allow to use software tools that are aimed at monitoring the prices of e-commerce consumer goods, with the consequent possibility of redirecting the pricing strategy.

To implement data-driven strategies therefore means to change the way companies are internally organized: data-driven marketing encourages companies to maintain a continuous cycle of interaction with their customers, to keep learning and evolving as the customers’ needs change.