Making Money in the Cloud — There’s a Word for That


CIO Central Guest, Contributor

November 19, 2013

By Mark Herman

Industry and government have untold amounts of data at their disposal and, like flecks of gold, there are valuable pieces that are frustratingly hard to access, given the limitations to common approaches to data analysis.  But aside from losing potentially priceless knowledge from this data – a deficit that is increasingly the focus of “big data” discussions – these institutions are leaving money on the table, potentially lots of it.

Just as public companies measure their financial performance on the income measure Earnings Before Interest, Taxes, Depreciation and Amortization (EBITDA), the driver for advancements in data analytics should be “Cloud EBITDA.”  More decisions about data analytics should be driven by the bottom line question:  how can I monetize my data?

Case in point, not too long ago, an industry client, well known for its focus on efficiency, expressed frustration to me about the limits of the organization’s on-site technology. The firm was sharing about 40 percent of its data with financial exchanges, only to be scooped up by third-party firms that repackaged it and then sold it back.  Essentially, the organization was buying back its own data because it had no other choice – the data couldn’t be processed internally.

If you’re wondering what the cloud can do for you, this is the big score.

The cloud opens up a whole new way in which companies can store, manage and, most important, analyze the untold amounts of data they have at their disposal.  Today, data scientists may spend as much as 80 percent of their time searching the data, and only 20 percent analyzing it. As analysts are freed from the constraints of specific data structures that limit their queries, they can ask more intuitive questions of the data, or ask different, new questions, with a focus on what might be profitable. They can flip the ratio, conducting quick searches across all varied data, and spend 80 percent of their time analyzing it. Interesting and potentially powerful combinations of data from areas not traditionally included in the mix can lead to breakthrough insights and ideas for greater revenue and value – I’ve seen this happen firsthand. This is Cloud EBITDA realized.

For example, one of our international airline clients mined three years and 100 gigabytes worth of data on passenger behavior, the airline’s performance, flight connection times and more.  The airline tried analyzing smaller data sets, but this yielded limited results because the data was siloed into separate databases.  Yet, once the airline began to use big data analytics tools within its own cloud, the results were more robust.  The analysis indicated, among other things, how to better manage and support important higher-paying passengers.  In the end, these findings and the subsequent changes in their customer service strategy contributed to the airline’s bottom line and helped them achieve Cloud EBITDA.

And, that’s just the direct revenue element of Cloud EBITDA.  Indirect benefits of advanced analytics also can contribute to better bottom line results.  Improvements to data collection and analysis leads to more streamlined and cost-effective operations, improving profitability.  Chief investment officers can use improved data analysis to build more robust peer analysis and better understand how to outperform competitors.  And finally, better data collection and analysis can generate more helpful information for investors, building a stronger case for a company’s long-term performance.

So, if the benefits are clear and the technology is there, why aren’t organizations adopting advanced analytics at rapid speed? Concerns about security are a major obstacle; so too is a lack of understanding around the role of data science. With more data available to analyze, it’s all the more important to understand what questions to ask.  A strong data science team should include a mix of math, computer science and statistics experts, as well as domain experts from the organization. These domain experts contribute to a stronger understanding of the mission at the data science team level, and carry back new insights to organization decision makers.

In an environment where there is tightened spending and a drive for efficiency in nearly every market sector, the bottom line benefit of Cloud EBITDA can’t be ignored. But like any innovation in any good organization, it will take risk, innovation, and a forward-thinking approach to achieve – something successful leaders are known for.

Mark Herman is an executive vice president with Booz Allen Hamilton and leads the firm’s Value from Data initiative.

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