Herman Hollerith’s Electric Tabulator, US Census Bureau, Washington, DC, 1908, Photograph by Waldon Fawcett (Source: Library of Congress LC-USZ62-45687)
Ideas for Leaders #175

Big Data = Big Opportunities: If the CEO Takes the Lead

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Key Concept

Big Data has recently gained lots of hype, prompting organizations to consider the best ways to utilize it. In this Idea, researchers suggest the responsibility lies not within IT departments, as is typically assumed; rather, CEOs need to take charge of understanding, exploring and exploiting the opportunities presented by Big Data.

Idea Summary

By digitizing many business processes, organizations have begun to automate operations and how they interact with customers and suppliers. As a consequence, more and more aspects of our working lives are taking place in a virtual world—a world defined and shaped by “bits and bytes,” say James Petter (Vice President at EMC) and Joe Peppard (Cranfield School of Management). This expanding volume of data (now commonly referred to as Big Data) forms the focus of their paper, in which Petter and Peppard ask, how can CEOs ensure that their organization is harnessing the potential of Big Data?

There is no value in just having data; it must be exploited or explored. Exploration is using the data to better understand something, such as your customers or your supply network. Exploitation, on the other hand, is using data to take advantage of information asymmetries — in other words, making the invisible visible.

Currently, what organizations are doing with Big Data can be divided into five strategies:

  1. Do what we always do, but better: this generic strategy involves using data to generate insights that enable an organization to identify areas of its operations where there are inefficiencies that could be improved. For example, Ford is using data it collects from cars to redesign future models. In addition, they are looking into how to use technologies embedded in cars to assist with marketing campaigns, vehicle safety features, etc.
  2. Do something different by harnessing existing or new data: using data to shape a new business model.
  3. Do something new: this involves the creation of an entirely new business (i.e. an established organisation harnessing data creatively, or a completely new start-up business with no legacy.)
  4. Co-create value with customers: this strategy moves away from deciding internally what a customer wants to co-creating value with them. For example, BMW ran an Urban Mobility Services Idea Contest, encouraging the generation of ideas that could be applicable for its business in the distant future.
  5. Monetise data: organizations that harness data generated as a by-product can create additional revenue streams.

The challenge for organizations is to not only immerse themselves in the wealth of data generated from various sources, but to find ways to capitalize on it. Petter and Peppard suggest that this is not an IT issue; rather, CEOs set the tone, and harnessing data may require a complete overhaul of the organization to create structures and processes that can respond to any data in a short timeframe, and potentially even in real-time. This is something that can only be driven from the top.

Business Application

It is the CEO of an organization’s responsibility to ensure that their organization is harnessing the potential of Big Data, and as such, the researchers strongly recommend establishing a “data lab” to proactively seek out opportunities highlighted from data. This lab need not start out as a separate organizational unit; it can begin as a project bringing together a cross-functional team to explore data. Fostering collaboration in this way is most likely to result in new ideas coming to light.

The creation of a data lab also avoids the potential problem of addressing Big Data as an IT issue; Petter and Peppard advise against the latter. In fact, in a 2013 Harvard Business Review article, Peppard suggested that a Big Data or analytics project cannot be treated like a conventional IT project, with defined outcomes, required tasks, and detailed plans for carrying it out; the former is likely to be a much smaller, shorter initiative, and requires team members to be well-versed in cognitive and behavioural sciences, not just in engineering, computer science, and maths.

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Authors

Institutions

Source

Idea conceived

  • October 2012

Idea posted

  • July 2013

DOI number

10.13007/175

Subject

Real Time Analytics