Most organizations are failing to turn Big Data investments into a Big Value return. In our latest research, we found that only 27% of respondents described their Big Data initiatives as “successful” and only 8% described them as “very successful”.

There are many factors that go into the making of a successful Big Data implementation. However, we observed a number of factors that are critical to getting an impact from Big Data.

A Well-Defined Organizational Structure. The success rates of organizations with an analytics business unit are nearly two-and-a-half times those that have ad-hoc, isolated teams. There are significant merits to a centralized set-up. The centralized approach can bring together technology and business executives to conceptualize new use cases and define best practices that other teams can leverage. US-based retail chain Nordstrom, for instance, has set up the Nordstrom Data Lab to develop new offerings backed by data-driven insights. The lab is a multi-disciplinary team of data scientists, mathematicians, statisticians, programmers, and business professionals. It follows a continuous deployment model to build and test prototypes, and take new products to market rapidly.

A Commitment to a Systematic and Structured Implementation. Our survey shows that a systematic and structured approach to implementation is rare. Seventy-four percent of organizations did not have well-defined criteria to identify, qualify and select Big Data use-cases. Sixty-seven percent of companies did not have clearly defined KPIs to assess initiatives. The lack of a systematic approach has a deleterious effect on success rates. For instance, fifty-one percent of initiatives that have well-defined KPIs are successful against only twenty-eight percent for those that do not.

A Strong Leader at the Top. Previous Capgemini Consulting research into digital transformation, with the MIT Center for Digital Business, established the importance of strong top-down leadership. Big Data is no exception. Our research showed that organizations that have successfully implemented Big Data initiatives usually have clearly defined leadership roles for Big Data and analytics. For instance, US-based Bank of America, a pioneer in the use of data in the banking industry, appointed a Chief Data Officer to champion data management policies and standards, set up the bank’s data platform, and simplify tools and infrastructure. Organizations can choose from multiple approaches, but the secret lies in ensuring that Big Data initiatives receive the necessary stewardship. Our research showed that only 34% of companies have a Chief Data Officer, or an equivalent role.

Use Innovative Approaches to Find Big Data Talent. Smart organizations not only invest more on hiring and training, but also explore unconventional channels to source talent. Consider, for instance, how P&G has partnered with Google to enhance its employees’ analytics skills. The two companies have engaged in employee exchange programs for the past five years. While employees from Google gain from P&G’s expertise in advertising, those from P&G get to learn from Google’s expertise in data analytics. Other mechanisms to acquire Big Data talent include partnering or acquiring Big Data startups, and setting up innovation labs in high-tech hubs such as Silicon Valley. Walmart has set up “@WalmartLabs”, an innovation center based in Silicon Valley, which is helping the retailer enhance its customer experience through innovative uses of Big Data.

The majority of organizations fall far short in securing the value of Big Data. Solving this conundrum means tackling the basics of the operating model. You need the right structure, a disciplined approach to implementation, truly determined leadership and a willingness to innovate in looking for talent. With those factors in place, Big Data can deliver Big Results.