Big Data is complex and in order to maximise its potential, organisations not only need specialists who understand the data, but also “translators” who can analyse and clearly communicate the retrieved information to the executive level, says McKinsey & Company. By treating the process as a value chain, and attracting and positioning two or more “translators” that can effectively work together on the translation between their respective groups, transforming raw Big Data as input into business decisions as output, the maximum potential can be derived from the process as a whole.
Big Data is almost synonymous with data sets so large and complex that it becomes difficult to process it using traditional data processing applications. Instead, to get at the insights contained in the data people are needed at each step of its transformation. A recent McKinsey & Company survey finds that companies continue to face this issue, with only 18% of firms believing that they have the skills necessary to unlock the insights contained in Big Data effectively. At the same time, only 19% of companies are confident that their insights-gathering processes contribute directly to sales effectiveness. The crunching of numbers, while being able to create relevant insights derived from advanced analytics, is not enough if they cannot be understood by the decision making executives.
McKinsey suggests that raw data needs to be translated into relevant insights by analytics, as refined relevant data. The refined data then needs to be translated and communicated to the executive level for business decisions. While in the background complex IT systems facilitate the process as a whole. Each of these functions, from the initial data input to the decision potential contained in the relevant outputs, requires both specialists doing and supporting the transformations, as well as those that are able to communicate the information between functional working groups. According to the consulting firm, an effective “Big Data to decision” conversation process requires the seamless interplay of strategically well placed personnel that bridge between working groups.
Yet looking for a single translator at the right intersection of all the various skills needed, McKinsey suggests, is like looking for a unicorn. It’s more realistic to find translators who possess two complementary sets of skills, such as computer programming and finance, statistics and marketing, or psychology and economics. In almost all cases a business will need at least two translators to bridge each pair of function. By having an interplay of relevant skills a more robust output may develop than if one person makes executive decisions about the translation; collaboration rather than straight translation adds potentially valuable insights.
The authors remarking that: “In effect, translators form the links that bind the chain of an effective advanced-analytics capability. On the business end, that requires people who can define a strategy and run the economic and financial analysis to determine the value of the opportunities to pursue. Translators turn those analyses into requirements that guide IT’s development of an analytics environment to perform, validate, and ultimately scale analytics. When the data are rendered into insights, business managers need to then translate them into messages and offers to be delivered to the marketplace.”