Technology & Innovation

Data become infrastructure

November 24, 2014

Global

November 24, 2014

Global
Philip Evans

Senior partner

Philip Evans is a senior partner in the Boston Office of the Boston Consulting Group and a BCG Fellow. He founded BCG’s media and then multimedia practices and has consulted to corporations worldwide in the consumer goods, media, financial services and high technology industries. He has also advised governments on military organization, homeland security, and development policy.

How do we close the gap between those who understand analysis and those who understand the business?

It is almost an axiom of business that data should be placed as close as possible to the locus of managerial decisions. The supervisor in the factory needs to see the quality report; the salesperson in the field needs the integrated view of customer purchases and profitability.

But big data fly in the face of this principle—with the very point of them often being the integration of data sets across different operations and product lines. Moreover, only a few specialists have the advanced analytic skills necessary for such operations. As the requisite computations cannot be performed on the average workstation, any query must be uploaded to a centralised data centre. Management of such a facility is itself a specialised skill.  

So the reality of big data in many organisations is that they compel a “new functionalism” whereby decisions on marketing, pricing, webpage design, scheduling, underwriting and so forth are made not by seasoned professionals who “know the business” but by a specialised unit of technical analysts.

Not surprisingly, this creates pervasive disagreements about and intergenerational resentments around whether all this fancy analysis is actually telling us anything new and useful. 

Closing the gaps

In part, this is a transitional problem caused at least in part by the fact that these analytical techniques are so new. As generations of managers mature in their understanding of the interpretation of advanced analytics, if not the technical execution, the gap will close between those who understand analysis and those who understand the business. Visualisation techniques in particular will help to bridge the gap between technical experts and general managers.

But the problem of the data themselves remain. Big data’s scale and scope are larger than the traditional operating unit. Big data want to be big; many of the most valuable applications have come from exploiting economies of scope across lines of business and, indeed, often beyond the boundaries of the corporation itself. So the segregation of data from the specific business in which they may originate is not a transitional phenomenon, but a permanent shift. Data need to be treated as infrastructure and managed accordingly.

Tomorrows big challenge

The principles for managing infrastructure are well understood: It is a long-term, general-purpose asset, high-fixed-cost/low-variable, not optimised for any one application, low-risk, pervasive in its benefits, managed for reliability and simplicity, open and interoperable. Good infrastructure is not itself innovative, but it is cheaply available to the innovators. Yet these principles are entirely new for many, if not most, organisations.

While the greatest managerial challenge today is the acquisition of highly specialised analytical skills, that problem will be solved very quickly. These skills are “table stakes”. The greatest managerial challenge of tomorrow will be building data as infrastructure. Those organisations successful in such building will enjoy sustained competitive advantage.

Philip Evans is a speaker in our upcoming live event, The Virtuous Circle of Data on Thursday, December 4th 2014 at 1PM EST/ 6PM BST  REGISTER YOUR PLACE

 

The views and opinions expressed in this article are those of the authors and do not necessarily reflect the views of The Economist Intelligence Unit Limited (EIU) or any other member of The Economist Group. The Economist Group (including the EIU) cannot accept any responsibility or liability for reliance by any person on this article or any of the information, opinions or conclusions set out in the article.

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