Big data strategy: how to develop a winning strategy

Howard Elias, President and COO, EMC Information Infrastructure and Cloud Services

Developing a big data strategy is challenging because it is as much about getting ahead of the trend and acquiring talent as it is about investing  in new technology. To be successful you will need data scientists on your team, professionals who are adept with the analytical and visualisation tools required to process and recognise patterns in data, and who are equally comfortable with business concepts and operations. EMC’s Howard Elias recommends three steps to help ensure your organisation has the people it needs.

Big data is exciting stuff, but don’t take my word for it. Ask the Academy of Arts and Sciences, who nominated Moneyball — a movie about big data — for six Oscars. If Hollywood is on board, you know big data has gone mainstream. What’s more, Tinsel Town managed to do what most industry commentators haven’t: Spin a tale that is both interesting and illustrative of big data’s transformative potential.

And yet, while a baseball team’s creative use of statistical data may have helped it to go from perennial doormat to scrappy contender, it doesn’t begin to describe what Big Data can do for an organization.

What is Big Data, anyway? Gartner defines Big Data as having three primary characteristics: volume (amount), velocity (speed of creation and utilisation), and variety (types and sources of unstructured data, such as social interaction, video, audio — anything that isn’t neatly categorised within a database). I describe big data as datasets so large and diverse, they break traditional IT infrastructures.

What big data is, however, isn’t as important as what you can do if you harness its potential and uncover new business opportunities through big data analytics.

Even in these early days of big data, its applications across diverse industries are compelling. Retailers embrace big data to combine RFID sensor data, social media data, and GPS coordinates to evaluate location, product selection, and individual profiles in order to deliver geo-specific product promotions to a mobile device. Big Data has been used to analyse entire forests in order to identify individual trees for harvest in order to maximize health, yield, and profit.

We are using Big Data to better understand massive amounts of data associated with processes and costs related to supplier parts, manufacturing, logistics, quality control, customer service, and more, and have used it to establish predictive performance models to address quality issues before they can have a negative effect on customer satisfaction. Yet even these examples only scratch the surface of how big data can effect business transformation.

Big data’s potential goes beyond traditional “rear view” business intelligence, revealing patterns in near real time to facilitate making a quantum leap from incremental improvement to predictive business processes and even entirely new business models — what I call the Art of the Possible.

But the Art of the Possible requires practitioners who understand the unique mix of “art and science” that characterizes the most transformative big data breakthroughs. It requires people who are as comfortable with business concepts and operations as they are adept with the analytical and visualization tools required to process and recognize patterns in the data. We call these professionals data scientists. They’re able to make quantifiable connections between previously unknown causes and effects; they’re adroit at seeing associations others have missed; and they’re able to understand how these new insights can be used to fundamentally change operational practices and business and organizational models.

But there are too few of these practitioners to satisfy industry need — and that talent deficit is quickly widening. A recent McKinsey report on big data estimates that in just five years demand for data scientists could outstrip supply by more than 1.5 million.

If you are an executive considering a big data strategy, you are fishing in a small talent pool — the banks of which are becoming crowded with a growing number of anglers casting their lines in the water. Gartner recently reported that one-third (33%) of business have already begun or are actively considering the implementation of big data projects. And with less than one-third (31%) of organisations “confident they can execute a big data strategy with their existing staff,” the rush is on. That means the big data challenge is as much about getting ahead of the trend and acquiring talent as it is about investing in new technology.

Until universities embrace the creation of data science disciplines, like computer science decades ago, how do you compete for such limited resources and achieve the Art of the Possible? I recommend three steps for ensuring your organisation has the people it needs.

Educate: Your first step should be to identify people from within your organisation who have proven themselves as both technically adroit and analytically creative. Remember, big data is about the Art of the Possible; you need creative thinkers. Don’t confine your review to IT professionals; it’s vitally important that your Big Data team draws heavily from disciplines able to think beyond typical linear problem solving. Once you’ve identified your future Big Data rock stars, invest in them through training and certifications in big data analytics and data science.

Acquire: A major part of your Big Data talent acquisition strategy should involve bringing in individuals from outside the organisation who not only possess skills complementary to your strategy, but who are not burdened by industry and organisational biases. Part of what makes big data transformative is its focus on discovering new approaches to solving old problems. That’s hard to accomplish when an organisation is saddled with the inertia of conventional wisdom and a “that’s not how we do it here” mindset. Look outside your walls and outside your industry.

Empower: Nothing will derail a big data strategy faster than building a team that is not given the chance to succeed. Conversely, nothing will energise your team more than the knowledge that they are being challenged with creating measurable impact and supported by your organisation’s senior management. Without this commitment, the time and resources you invest in training the team will end up benefitting your competitors through defection. This will be especially important when your big data team challenges the norm and runs up against others in your organisation who feel threatened by change (and they will). But by creating an environment outside of the constraints of the IT department where data scientists know their contributions are valued, you’ll make recruitment and retention easier.

This advice is born of my experience implementing a big data analytics strategy to transform business processes based on the quantifiable insights big data affords. Our achievements to date have resulted in significant and measurable improvement in areas such as quality control, logistics, and customer support. They didn’t happen overnight, and they didn’t happen without conflict, but we were committed to identifying and then acting on these new insights. We’ve also invested in the development of robust data science curricula that are used within industry and academia to train and certify tomorrow’s data scientists. I believe strongly in investing in the skills and people needed to meet the challenges of the future.

The Moneyball approach didn’t win the Athletics a world championship, but it did prove successful enough that, within a year, every major league organisation had adopted a big data strategy in some form. And in your industry right now there’s a Billy Beane who is analysing data in new ways, looking for a competitive edge that will become the next big data blockbuster. Getting started with big data now can mean the Art of the Possible becomes a significant competitive edge for your organization. Waiting until big data analytics are table stakes means you’ll be playing from behind rather than with the lead.

Howard D. Elias is President and Chief Operating Officer, EMC Information Infrastructure and Cloud Services. He oversees EMC’s Consulting and Big Data Advisory services, Educational Services, Technology Professional services, and award-winning global Customer Support organizations. Elias is a director of Gannett, one of the USA’s leading media and marketing solutions companies, and serves as a Director of the National Action Council for Minorities in Engineering (NACME). EMC is a corporate member of Change the Equation and numerous other educational foundations.

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