Data + Cloud = Money

Many organizations are realizing that great potential insight lies within the big data in their own networks and streaming through the social web.

And that means these enterprises should be focused on the money they could be making if they take advantage of cloud-based analytics.

Measuring Cloud Effectiveness

That’s the assertion of Mark Herman, executive vice president of Booz Allen and leader of the firm’s Value from Data Initiative.

In a Forbes blog post, he notes that just as public companies measure their financial performances on earnings before interest, taxes, depreciation and amortization (EBITDA), the driver for advancements in data analytics should be “cloud EBITDA.”

In other words, companies should drive data analysis by monetizing their data. He points to a client who was frustrated about the limits of its 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,” Herman notes. “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.”

That’s because the cloud opens up a new paradigm for firms to store and analyze massive amounts of data.

“Today, data scientists may spend as much as 80 percent of their time searching the data, and only 20 percent analyzing it,” he notes. “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.”

How to Change the Way You Use the Cloud for Data

Cloud EBITDA could be realized if these same data scientists can quickly search varied data and spend 80 percent of their time analyzing it.

He points to another Booz Allen client, an international airline that mined three years and 100 gigabytes of data on passenger behavior and flight connection times. Previously, the firm had tried data analysis on smaller amounts of data, but only limited results were available because the data was housed in disparate data sources.

However, by using data analysis tools in the cloud, the analysis showed how to better manage and support important, higher paying passengers. Thus, through changes in its customer service strategy, the airline was able to achieve bottom-line results, contributing to the cloud EBITDA.

In addition to direct revenue results from cloud EBITDA, Herman also highlights indirect benefits, such as more streamlined and cost-effective operations to improve profitability.

“Chief investment officers can use improved data analysis to build more robust peer analysis and better understand how to outperform competitors,” he says. “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.”

To realize these benefits, every company needs to beef up its understanding of data science, he suggests. A data science team should include a mix of math, computer science, statistics, and organizational domain experts.

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