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All the Data, All the Time

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At Dell’s recent Big Data 1-5-10 event, I kicked off my introduction by saying my goal is “to help customers use 100 percent of their available data all the time.” This remark caused a few heads to turn, and later prompted Jeff Frick, GM of SiliconANGLE and host of theCUBE live interview show, to ask me for more insight into what he called a “provocative statement.”

Shouldn’t we all be driving toward collecting, analyzing and utilizing data to its fullest? As I explained to Jeff, we’re nowhere near ready to deliver all the data, all the time, but we need to make steps in that direction so we’ll be ready to clear the hurdles and take full advantage of opportunities as they become available.

 map of the earth with zeros and ones to illustrate land mass

Technology is still siloed, unfortunately, which makes it difficult for people to build out all the analytical models today that can deliver answers to their most critical questions. Structured and unstructured information isn’t analyzed together, which creates another barrier to getting one single view of the truth. Another barrier: people doing the analytics address very specific, often narrow areas of focus.

Currently, most companies use only a subset of their data for a very specific purpose. But, you can discover so much more if you step back and take a larger view. For example, instead of only looking at revenue trends over the past 12 months, what could be learned if you look more broadly at the health of your company’s customer base or the social factors driving trends and behaviors that either accelerate or moderate a drop or move in your business?

Delving deeper into the data delivers so much more insight. At the University of Iowa Hospitals and Clinics, for instance, Dell Statistica is used to pull data from a wide variety of data sources to help lower the rate of infection for surgical patients. As reported in the Wall Street Journal’s CIO Journal, the University of Iowa takes information from patients’ medical records, and surgery specifics, such as patient vital signs during operations, to predict which patients are face the biggest risk of infection.

Armed with this valuable insight, doctors can create a plan to reduce the risk by altering medications or using different wound treatments.

Thanks to the evolution of analytics, other organizations will be able to follow University of Iowa’s lead in more fully utilizing their data. We’re at a tipping point—compute cycles now are affordable enough and can keep pace with data proliferation while plentiful bandwidth and cloud services make ubiquitous data access a reality. Today’s infrastructures enable us to do things that weren’t possible five years ago.

While environments now are ready to accommodate a more holistic view and broader conversations about data, most companies are just starting to buy-in conceptually. Sure, companies want access to all their data, all the time, but most folks I speak with see this as an aspirational goal still to be achieved. When it comes to the here and now, they’re pretty pragmatic and taking the first steps to realizing their data’s full potential.

Since focus is the hallmark of success, I recommend putting customers first. Start by taking all the steps you can to get all the data on your customers. Then, gather all the data on your product areas, supply chain, manufacturing, etc. In each respective area, there likely will be a dozen different data sources that are interconnected and interrelated. For instance, in compiling data on customers, you’re likely to encounter exposed interfaces that take you to product, which can be integrated with manufacturing, and so on. It’s kinda like assembling LEGO blocks or deciphering fractal patterns as all the data elements are nested and interwoven.

Another major step is determining how best to empower your data analysts by providing them with the right tools for producing everything from simple reports and visualizations to complex analytics. But don’t stop there. If your data is locked away and only useful for PhD modelers and data scientists, you’ll only solve part of your problems. Getting data into the hands of your subject matter experts and line-of-business decision makers is crucial because they too must be empowered to build their own analytical models.

The day when employees become their own data analyst isn’t too far out on the horizon. Once everyone has access to all the data, all the time, they can create their own hypotheses. Training your employees to think more analytically is something every organization should already be doing to stay ahead of the curve.

What steps are you taking to ensure your company gets the most from all its data, all the time? Drop me a line at [email protected] to exchange ideas on how to unlock the power of your data.

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