Discover Performance

HP Software's community for IT leaders // April 2014

Putting real analytics into the hands of the business

Until you master all of your data, you don't know what you're missing. Take a peek into how HP Labs will shape the future of analytics.

The bottom line

What: HP is driving toward Big Data tools that will mean “analytics for everyone.”
Why: Advanced analytics will have true impact when it doesn’t take a data scientist to understand it.
How: Two key factors are making the right choices up front, and ending with easy-to-absorb visualizations.
More: Check out  for the cutting edge of Big Data power.

By Mike Shaw, HP Software Strategic Marketing
I was a product manager for ten years. It can be an exciting job, but also frustrating. One of my main frustrations was getting access to and doing the analysis on the data I needed to chart the course for my products. I always bumped into the issue of "constrained resource" within IT—the IT department simply didn’t have enough data analysts for all of us product managers. And so I had to make do with limited data sources and mediocre, relatively uninsightful analysis.
The problem is only going to get worse as we move into an era of 360-degree Big Data. Back in 2011, McKinsey estimated that the U.S. was already 190,000 data scientists short. The answer is obviously not to throw more PhDs in data science at the problem.

Mike Shaw
Instead, we need to empower subject matter experts to create their own 360-degree Big Data analyses.
HP Labs and HP Software have been working for over a year on a project that allows subject matter experts to create their own Big Data analyses. They’ve defined a series of steps that allows a user well short of being a data scientist to benefit from data science. Although this tool is still in the prototype stage, the lessons are clear:

  • Stage 1: Choose your information sources. Users must be able to select a combination of structured, human interaction and machine-to-machine data.
  • Stage 2: For any data set, there are a number of different analysis algorithms that could be used. Do you want to do prediction? Do you want to find patterns in the data? Once chosen, an effective tool would apply a set of standard cleanup processes to your data—cleanup processes that any data scientist would routinely apply. Then it would compare the different analysis options and choose the one that provides the best results, given your data and your analysis needs.
  • Stage 3: Visualize the data. Decide the best way to display the insights you glean. A geographical map? Heat maps?
  • Stage 4: Decide on your presentation clients. Do you want the analysis insights to be available on laptops, tablets, and/or smartphones? The HP Labs prototype can even provide access to the results programmatically, so that other standard organizational software can access them.

 One of the key design objectives of the research is to give the subject matter experts the ability to experiment with their 360-degree Big Data analysis. Try one set of data sources, one algorithm, and see how your "customers" like it. Add another data source, try another algorithm, or adjust the visualization. This is important: data analysis is not an exact science. Just a small change to a visualization, for example, can make a massive difference to the perceived value of the analysis. In other words, we need to view the analysis modeling as an experiment.

A demo video of this prototype in action can be found here.
I believe that by 2020, subject matter experts will be creating their own 360-degree Big Data analyses, using tools similar to those that HP Software is now working on. As enterprises acquire new tools and new talent to fulfill their analytics goals, it’s important to keep an eye on how the field is evolving, and plan for what is—or soon will be—possible. 
In the coming months, Mike Shaw will be writing in Discover Performance on the future of Big Data—ahead of an Enterprise 20/20 chapter on the topic. For more on the tools and techniques that can improve your view of your data, check out


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