“Raw data are like raw sewage,” writes Andrew Hill, “toxic if not handled properly.” [“Less is more when it comes to ‘big data’, Financial Times, 12 November 2012] He warns, “The lure of ‘big data’ is perfect bait for fee-hungry experts hunting new business. It also poses untold risks to companies that fail to read the trend, or the data, correctly.” Turning data into actionable intelligence (i.e., knowledge that is useful for making business decisions) sounds easier than it is; but, Hill claims getting big data analytics right doesn’t necessarily mean “a big initiative, run by a big team, and backed by a big investment.” Of course, “big” is a relative term. A “big” investment for a small- to medium-sized business isn’t necessarily a big investment for a large business. Hill continues:
“Big data are daunting. Even if companies realise they can no longer merely mine static customer lists, they should not underestimate the technical difficulties of marrying large proprietary databases with the more valuable unstructured, dynamic information that comes from open sources, such as social media or mapping applications. The guidelines for chief executives, on the other hand, are relatively straightforward: verify, purify, simplify.”
Thor Olavsrud agrees with Hill that “Big Data [has] a powerful lure, promising to turn the massive and ever-increasing volumes of data inside an organization into a pool of intelligence that promises deep, actionable insight into every aspect of a business.” He also agrees that “that lure can lead you into an expensive trap if you don’t plan carefully.” [“How to Avoid Big Data Spending Pitfalls,” CIO, 8 May 2012] He continues:
“That’s not to say that harnessing the power of Big Data is a mistake, [explains Jeff Muscarella, IT spend management consultant with NPI Financial.] But it does mean that organizations seeking to base their decisions on data need to start by gathering real data on how a Big Data project will benefit the business. … When you’re exploring a Big Data project, don’t dive in head first, Muscarella warns. Start with open source tools like Apache Hadoop and build a test case. ‘You want to really pilot these things,’ Muscarella says. ‘Pick something that’s manageable. Start on a small scale to prove your hypothesis. For instance, if we could mine this sensor data or these Web clicks or these purchasing habits, would what we do with these results improve our business. … Don’t get trapped into building the infrastructure yet,’ he adds. ‘Prove it first and then go back and architect your solution. Assume that however you solve the problem, you’re probably going to throw it away and start over. That’s OK because at least you proved the business need before you spent a lot of money.’ Once you proven the business need, it’s time to look at the infrastructure required to manage Big Data. Big Data projects scale to petabytes and potentially exabytes of data, so making sure you get your storage infrastructure right is essential.”
Hill goes on to recommend three things that CEOs need to do to make sure their investments in big data analytics aren’t wasted. He writes:
“First, they need to identify the information they hold and ensure it is consistent and comparable. … Sean Carney, chief design officer of Philips, says, data are ‘like crude oil: it isn’t much use until you start to synthesise it’. Your company may have access to lots of data, but only some of it will be relevant. The second step is to know what you are searching for, and why. … Finally, go ahead and put the data to use. … Once the data have been properly marshalled, chief executives can test ideas cheaply and repeatedly. Innovations can flourish – or be allowed to perish without having wasted too much time or money.”
In the end, Hill recognizes that the benefits of big data analytics can far outweigh the costs involved — but only if the analysis is done correctly. Hill asks, “Are strategists redundant, then?” His answer is, “Not yet.” He explains:
“Unlike intelligent fridges that can buy their own groceries online, large data-driven companies can’t order their own strategic direction. Even if they could, they would need someone sitting in front of a dashboard to decide, on the basis of abundant data, which innovation to bless with scarce capital. That, at least, will come as a relief to CEOs, as they struggle to keep their heads above water.”
I agree with Hill. As I pointed out in a previous post, one of the benefits of the insights provided by big data analytics is that it better positions CEOs to think — those insights don’t do all the thinking for them. Travis Hessman agrees with Hill that, if not handled properly, big data can cost you money. He also agrees with Hill that big data is a subject in which CEOs need to be fully immersed. [“The Cost of Big Data,” Industry Week, 22 August 2012] Rod Johnson, vice president of Industry Strategy at Oracle, told Hessman, “Big data equals big revenue and big dollars. Dealing with it is not an IT strategy — it needs to be enabled by IT, but it is really a business strategy that should be on every CEO’s agenda.”
“Everywhere you look today,” writes Mike Smith, “the impact of data analytics is becoming more apparent. … Analytics are changing the way we live.” [“Harnessing ‘Big Data’ for business value,” Intelligent Utility, 11 January 2012] He continues:
“The Wall Street Journal, one of the best gauges of where business is today and where it is headed, has recently been featuring more analytics in its coverage. For example, the Journal recently reported that XO Communications experienced a cost savings of between $9 million and $13 million from a single analytics application geared toward reducing customer turnover. And in a recent article titled, “So, What’s Your Algorithm?“, the newspaper cited how The Schwan Food Company, ubiquitous in my northern California hometown with their colorful delivery trucks, increased revenues by 4 percent after four straight years of flat sales by applying analytics to customer spending patterns.”
Like Hill, Smith insists that CEOs need to understand big data if it is going to be useful to a company. “Nuances abound,” he writes. “Analytics include leveraging real-time data sources, bringing together multiple data sources, predicting (not just reporting) and merging new and existing data sources. The ability to predict the future with a degree of certainty is perhaps the most game-changing aspect of analytics.” McKinsey analysts Jacques Bughin, John Livingston, and Sam Marwaha agree that harnessing the power of big data is essential for most large companies. They write:
“Large-scale data gathering and analytics are quickly becoming a new frontier of competitive differentiation. While the moves of companies such as Amazon.com, Google, and Netflix grab the headlines in this space, other companies are quietly making progress. In fact, companies in industries ranging from pharmaceuticals to retailing to telecommunications to insurance have begun moving forward with big data strategies in recent months. Together, the activities of those companies illustrate novel strategic approaches to big data and shed light on the challenges CEOs and other senior executives face as they work to shatter the organizational inertia that can prevent big data initiatives from taking root. From these experiences, we have distilled four principles that we hope will help CEOs and other corporate leaders as they try to seize the potential of big data.”
Bughin, Livingston, and Marwaha assert that “too few leaders fully understand big data’s potential in their businesses, the data assets and liabilities of those businesses, or the strategic choices they must make to start exploiting big data.” As noted above, they make four recommendations to ensure that business leaders understand how big data can help them grow. The first recommendation involves big picture awareness. They write:
“1. Size the opportunities and threats — Many big data strategies arise when executives feel an urgent need to respond to a threat or see a chance to attack and disrupt an industry’s value pools.”
The second recommendation involves understanding available resources.
“2. Identify big data resources … and gaps — Framing the basics of a big data strategy naturally leads to discussions about the kinds of information and capabilities required. At this point, executives should conduct a thorough review of all relevant internal and external data. The audit should also consider access to analytical talent as well as potential partnerships that might help fill gaps. Such an audit will not only create a more realistic view of a company’s capabilities and needs but can also spark ‘aha’ moments—for example, as executives identify ‘data gems’ cloistered inside their business units or recognize the value of creating the right kind of partnership.”
Their third recommendation involves corporate alignment.
“3. Align on strategic choices — Once companies identify an opportunity and the resources needed to capitalize on it, many rush immediately into action-planning mode. This is a mistake. Data strategies are likely to be deeply intertwined with overall strategy and therefore require thoughtful planning when a company decides how its resources should be concentrated to achieve the desired results. In some cases, that could mean putting powerful data analysis tools in the hands of frontline workers. In others, it might mean amassing data and ramping up analytical talent to create a first-mover advantage.”
Finally, like Hill, they recommend that CEOs be deeply involved in big data decisions. They write:
“4. Understand the organizational implications — Because the means of securing competitive advantage from big data are still evolving, some CEOs believe that big data initiatives should be the sole responsibility of a company’s IT or marketing departments—the functional groups where large-scale data sets are most often gathered, analyzed, and applied. Bad idea. In our experience, big data projects need concerted senior-management attention to succeed.”
When big data collection and analysis is done correctly, the benefits can be game changing. Done poorly, it can turn into a money pit. I agree with the analysts cited above that CEOs have too much at stake to leave decisions about big data to others. They also need to appreciate how the insights that can be gained from big data analytics can help position themselves to make better decisions.
