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Big Data: Big Opportunity or Big Challenge?

By Barton Kaplan

We are undoubtedly living in the era of big data. For any doubters, McKinsey estimates that the amount of new data that enterprises around the world stored on disk drives in 2010 equaled 7 exabytes, growing at a compound annual rate of 40 percent.[1]

The opportunities for enterprises to leverage this data abound, at least in theory. In the retail sector alone, McKinsey estimates that big data could raise operating margins by a whopping 60 percent.[2] And in US healthcare, the potential benefits are as much as USD $300B annually.[3] Further, venture capital is flowing to a slew of new startups that are creating tools and algorithms that can parse data at scale.

But the reality inside enterprises paints a different picture. The biggest misconception with big data is that the sheer volume means there is more meaningful insight to be had. But as Nassim Taleb has convincingly shown, as the amount of data goes up, the signal to noise ratio goes down, by as much as 200 times.[4]

This phenomenon is reflected in corporate survey data. In a poll of over 8,000 employees by CEB, only a third indicated that the information they needed to do their jobs was available. And less than half said that corporate sources of information were usable.[5]

Meanwhile, at a time when over 80 percent of employees at the typical organization are now considered knowledge workers, fewer than 40 percent have the combination of analytical skills and judgment needed to use that data to drive better decisions,[6] without which no value can be gained. Data scientists alone can’t make up for a capability gap across the broader employee base.

So how do organizations tackle the myriad information management challenges that big data has exacerbated so they can realize some of the benefits? In my work with companies, the following four tactics have proved effective:

  1. Elevate the importance of information: One large financial institution put information on par with people, process, and technology as a discrete enabler of business capabilities, leading to a fully funded, five-year information management strategy and roadmap.
  2. Develop differently for data: At a major insurance company, IT leadership replaced their software development lifecycle with a data-centric development lifecycle for information-intensive projects.
  3. Make data analysis user-friendly: IT can no longer keep up with business demand for reports and dashboards. Instead of being the bottleneck, work instead to expose corporate data in a secure yet user-friendly manner so employees can quickly and easily generate their own reports. At one organization I worked with, they set up an interface modeled on Amazon’s with the motto “shopping for data.” The result was a 50 percent reduction in support staff time to deliver analytic services.
  4. Upskill your people: Rather than trying to land a few hard to find, expensive data scientists, focus instead on raising the skill sets of the 8 out of 10 employees in your organization who are considered knowledge workers. This starts with an understanding of the profile of an employee with high “insight IQ.” Try the following quiz to see if you fit the bill.

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Barton Kaplan is a business solution strategist with VMware Accelerate Advisory Services and is based in Maryland.


[1] McKinsey Global Institute, “Big data: The next frontier for innovation, competition, and productivity,” June 2011.
[2] Ibid.
[3] Ibid.
[4] Nassim Taleb, “Noise and Signal—Nassim Taleb,” Farnam Street Blog, 29 May 2012.
[5] CEB, Business Outcomes from Big Data, Webinar, March 2014.
[6] CEB Enterprise Architecture Leadership Council, “Overcoming the Insight Deficit,” 2011.

5 Ways to Make Your Big Data Strategy Reap Big Business Value

AUTHOR: Derek Lacks

How does the intersection of two huge opportunities—cloud computing and big data—impact data strategies, and how does the IT organization take advantage of them to create business value?

As an Accelerate strategist, I define actionable strategies within organizations aimed at accelerating the maturity and business impact of virtualization and cloud initiatives.  Many of the executives I work with are finding themselves at the intersection of two mega opportunities facing their organizations—cloud computing and big data.

To ensure common vernacular, “cloud computing” refers to an IT approach that allows the abstraction of computing workloads, so that they can be run across multiple environments based on organizational requirements (private, hybrid, or public clouds).  This is made possible by decoupling the data and software from the servers and storage systems running them, which enables IT resources to be dynamically allocated and delivered as services (as in infrastructure-as-a-service, platform-as-a-service, storage-as-a-service solutions, and so forth).

In the realm of data, cloud computing allows organizations to think outside the traditional data center constructs, which limit capabilities based upon finite capacity. Cloud computing provides the elasticity and flexibility to spin up or down compute capacity based upon the needs of the business. Now it’s possible to build applications that exceed the limits of existing compute power without worrying about significant CapEx investments to meet the demands of future applications.

But what about big data? Forrester Research defines big data as techniques and technologies that make capturing value from data at an extreme scale economical and estimates that we will exceed 2.7 zettabytes (equivalent of 27 million terrabytes) of global digital data this year.[1] The key difference between big data and our traditional analytics strategy is that the investments required to support the variety, volume, and velocity of today’s data available was previously prohibitive.

Big data strategies provide us with the means to collect and analyze this data in a way that is flexible and cost-effective. While many hear “big data” and think of the challenges of managing it, I prefer to focus on the opportunity to turn this growing data stream into business value.

The technologies—including applications and data platform—to leverage this new paradigm are still being created, but it’s inevitable that they will depend on integration with cloud computing.

The question is, how will these two topics impact your data strategy? Here are five best practices that I believe can help accelerate your journey:

  1. Build your application with tomorrow in mind – Big data offers significant benefits but is dependent on building applications that can leverage the key tenants of cloud computing.
  2. Strive to retain flexibility – Cloud and big data strategies need to be examined in concert with significant attention focused on maximizing elasticity and portability of workloads.
  3. All data is in scope – Big data is about extracting insight from the full body of organizational data versus the typical 10 percent that resides within your relational database management system (RDBMS).
  4. Staff for success  – Be aggressive in building data scientist capabilities, as this will be a critical requirement in driving your big data strategy.
  5. Don’t be afraid to start small – Leverage the expertise of early leaders such as Pivotal that can provide your efforts a jump-start by exposing you to the art of the possible.

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Derek Lacks is a strategist with VMware Accelerate Advisory Services based in Massachusetts.



[1] Forbes.com: Big Data Meets Cloud, Holger Kisker, Ph.D.