For True Big Data Success, Invest in People as Much as Technology

For True Big Data Success, Invest in People as Much as Technology

Big data analytics delivers increased profitability and return on investment, but there is a caveat. While many executives are keen on investing heavily in technology, those that do not make complementary investments in training, skills development and recruiting are only seeing lukewarm results from their investments.

That’s the word from Jacques Bughin, a McKinsey consultant, recently put together an analysis of big data success to date. Bughin led a study of 714 companies that confirms big data analytics delivers great “profitability and value-added productivity benefits,” he notes — similar to “those experienced during earlier periods of intense IT investment.” Big data analytics added at least six to nine percent to companies’ bottom lines, he estimates.

However, those companies not making corresponding investments in developing employees’ analytics skills are not seeing such returns. “Many companies still compartmentalize their data-analytics initiatives,” Bughin writes. Big data technology investments cannot be made in isolation, he observes. “About 40 percent of the profit improvements we measured resulted from complementary and coordinated investments both in IT and in big data talent.”

Bughin’s team was able to make a correlation between coordinated IT and skills investments, which led to high performance and impressive results. “Our results indicated that to produce these significant returns, companies need to invest substantially in data-analytics talent and in big data IT capabilities,” he states.

When adequate investments aren’t made in people, watch out. Bughin relates how “one company’s large investment in database-management software foundered when HR neglected to hire the analysts needed to support the new data-driven business priorities.” The lesson is talent needs to be part of any big data analytics initiative.

“Investing in big data talent at scale is a must,” Bughin writes. “Skilled employees across the spectrum of data-analytics roles are in short supply, so aggressive actions to address this problem are critical.”

Accordingly, the study found that 15 percent of operating-profit increases from big data analytics were linked to the hiring of data and analytics experts. This requires a well-thought-out effort to bake skills and training into the organization’s structure from the get-go. “Best-practice companies rarely cherry-pick one or two specialist profiles to address isolated challenges,” Burghin says. “Instead, they build departments at scale from the start. With a broad range of talent, these companies can use data analytics to address the current challenges of their functional areas while developing forward-facing applications to stay ahead of competitors.”

A detailed summary of the team’s research is found here in the Journal of Big Data, here.

Building a base of big data analytics talent is something that needs the involvement of the entire organization. First, there are likely to be many skilled individuals already in the enterprise – both from within IT and without – that can rise to the occasion, with the right amount of training.

Organizations also need to find ways – both formally and informally – to bring data teams and business teams together, to enable cross-pollination of knowledge and ideas. This could be through regular workshops, or even something as simple as bowling nights.

Lastly, organizations need to sharpen their recruiting efforts, identifying potential candidates by partnering with colleges and universities, as well as being active on social media networks.

Finally – and this may be the most complex challenge – employees working with data analytics need to know that they have the support of management. They need to know that their findings will be considered, and that the organization is open to the innovation that data analytics will offer. This will ensure that the organization is attractive to the talent they need to compete in today’s hyper-competitive data economy.