Those of you familiar with my previous blog posts know that I’ve already taken a shot at defining big data and articulating a taxonomy of different types of data. It may not be possible to overstate at this point how important big data analytics could be to the business world: I want to reemphasize my view that what may look like a revolution is really an evolution—the next frontier of a trend toward greater data-driven decision making that began with the adoption of mainframe computers for business use in the 1960s. As before, gaining commercial benefit from data depends on a hypothesis-led process: carefully formulating business questions that can be addressed by larger data sets and more advanced analytics, acting on the insights generated, monitoring performance changes, and revising procedures in a continuous improvement cycle.
This may sound like a familiar and trusted managerial approach, but companies must be careful not to let this familiarity lead to complacency when it comes to big data. From the work that Strategy& and others have done, a broad consensus is emerging around two key themes:
• Companies that adopt superior capabilities in data-driven decision making significantly outperform their competitors, and this advantage will only increase over time.
• Most organizations, even those that have made investments in big data infrastructure and tools, are still trying to formulate the high-value business questions that new sources of data and more powerful analytics can help them solve.
Recently, along with a global team of partners at Strategy&, I contributed a chapter, called “Big Data Maturity: An Action Plan for Policy Makers and Executives,” to the 2014 Global Information Technology Report (GITR), published by the World Economic Forum. Our recommendations—about how companies and countries can make practical use of the untapped potential of big data, and, more generally, fact-based decision making—were picked up by several media outlets including The Times.
At the heart of the chapter is a big data maturity framework, which seeks to help companies formulate these high-value propositions in order to take full advantage of the promise big data brings. We identify four stages of an organization’s path to excellence:
1. Performance management: financial reporting, compliance monitoring, and performance measurement, as well as leveraging key performance indicators and dashboards;
2. Functional area excellence: more effective target marketing and industry-leading logistics efficiency;
3. Value proposition enhancement: personalized experiences, which customers value, that can be monetized via premium pricing, cross-selling, and retention; and
4. Business model transformation: data-centric business models. (Some digital companies like Google or Amazon started here without progressing through all the other stages.)
Based on our observations of many clients, we’ve found that most companies in developed economies have achieved some version of the first stage, a minority have reached the second and third stages, and only a small handful are at the fourth stage.
Why are companies so slow to make the most of big data, despite their awareness of the obvious benefits? The GITR report and our work in this space point to a few common reasons. The first is cultural: Decision makers need to embrace data-driven decision making, and this is often difficult for executives who are used to relying on their instincts and intuition . Second, there is a severe talent shortage of those able to use the latest techniques to analyze and derive insight from data. Third, governments and policymakers have not done enough to cultivate the infrastructure and talent pool required to create favorable conditions for widespread adoption of data-driven techniques by the private sector.
Data-driven decision making is often difficult for executives used to relying on their intuition.
Our chapter in the GITR presents a set of imperatives for policymakers to encourage businesses to wholeheartedly embrace big data, and for executives to use it to their best advantage.
Imperatives for policymakers:
• Formulate a vision for using data that is consistent with public interest, fostering common understanding and buy-in
• Create an unambiguous regulatory framework and set clear rules regarding data privacy, to promote harmonization on a global level
• Enable a big data ecosystem of analytics, service, and IT providers via tax and investment incentives; the creation of enterprise zones, especially near academic centers
• Speed up and scale up the education of talent both on the business and IT side; this is critical, as lack of skilled talent is the major limiting factor in big data excellence
Imperatives for organizations and executives:
• Develop a clear data strategy and identify which data is crucial to transform your business model
• Prove the value of data and analytics in pilot schemes
• Nominate an owner for big data in your organization (for instance, a chief analytics officer or head of data science)
• Recruit and train management talent to ask the right questions and technical personnel to provide the required systems and tools
• Position big data as an integral part of your operating model
• Establish a data-driven decision culture, accelerating the transformation from pure managerial intuition to acting on insights from data
What’s needed most now is action from both companies and policymakers to begin adopting some of these suggested mechanisms and learning from real-world experiences. Otherwise, they risk missing out on the advantages big data analytics can provide.