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Data management plan with integration focus drives healthy analytics

Don't let the buzz around analytics fool you. Solid data management capabilities are required to enable advanced analytics, as an example from the healthcare sector attests.

Advanced analytics these days requires special attention to data sources and a robust data management plan. That shouldn't be obscured by the excitement over the latest graduating class of model-wielding data scientists. To some extent, things haven't really changed since the days when the phrase garbage in, garbage out was coined.

High-powered analytics tools can uncover inefficiencies and disclose opportunities. But, as a conversation with the head of analytics at a specialty pharmacy company shows, being able to cope with the incoming data is still the first order of business.

First, some background. Health organizations are at the center of a lot of the latest big data activity. That's because there is a very concerted effort now underway to operate more efficiently and reduce costs in the healthcare sector. You may have noticed that the times, they are a-changin' if you had to do something extraordinary -- like, oh, take a physical, or fill a prescription. Depending on your medical coverage, you may be channeled toward a particular test service, or a mail-order medicine dispenser. Be sure and have your insurance card with you!

That's how it plays out at the micro level. At the macro-level, a lot of diverse data has to be churned to streamline the process of health delivery, and that has to happen before analytics ever begin.

Healthcare changes drive new analytics needs

Basically, healthcare is starting to resemble a regular business. As such, healthcare organizations require more sophisticated data analytics. Goals such as shorter hospitalizations and cost reductions on medications are behind the drive for stronger analytics. It's also intended to help industry players achieve better outcomes on patient care.

But achieving broad success with analytics requires special attention to data management, according to Craig Willis, director of analytics at Physicians Pharmacy Alliance (PPA), a company based in Cary, N.C., that provides pharmacy services to patients with complex medication needs.

One pixelClaudia Imhoff on analytics-driven orgs

Willis comes from the operations side of PPA. He began in patient services, without any particular background in statistics. What led him to what he's doing now, at least in part, is the fact that, in his words, he has always been "data-driven." His closeness to the company's data enables him to work with the IT team to manage a moveable feast of data -- clinical intervention, prescription and telephone records, medical claims data and more -- that holds the key to improving care for chronically ill individuals who can account for the lion's share of medical spending.

PPA has expanded its initial analytics efforts around business intelligence and data visualization tools from Tableau Software and is now working with SAS Institute's Visual Analytics platform, Office-based BI software and metadata management tool.

"We used Tableau for a year, in a small deployment," Willis said. "The results were great. But we wanted an enterprise platform that provided especially good back-end processing. What we really needed was something that could handle data management."

Variety, velocity pose data management demands

He added that the variety of disparate data PPA collects and the speed at which it is generated were factors leading the company to seek more advanced data management capabilities to complement the advanced analytics it's looking to do. "Due to the amount and velocity of our data, it wasn't possible to achieve our goals without powerful computing in the background," Willis said. "If you're working with small data sets, that's not a requirement. But we have 'gigs and gigs and gigs' of data -- thousands of rows of data that can't be successfully viewed otherwise."

Also among the drivers for better data management tooling at PPA are new measures that estimate the effectiveness of health plans. Like other healthcare companies, PPA uses the National Committee for Quality Assurance's Healthcare Effectiveness Data and Information Set (HEDIS) performance metrics to help ensure that their quality of care is as high as possible. But Willis said the metrics introduce their own complexity, calling for flexible tools that can handle a changing data pool.

"The difficulty is that these measures change annually -- they're evolving," he said. Moreover, the data required to calculate HEDIS scores comes from "a lot of different places," including clinical applications and pharmacy and medical claims systems.

That data is diverse, evolving and requires integration may be obvious. But stories like PPA's bear repeating. Indications are that integrating data will continue to be the precursor to successful BI and big data analytics initiatives. That's part of the reason research and consulting firm Gartner Inc. forecasts annual growth of 9.6% between 2013 and 2018 for the data integration tools market, pegging it to reach $3.6 billion annually at the end of that period.

The temptation on some people's part to overlook the importance of data management is natural. Hadoop hoopla has been near-deafening for several years now. The inclination to get on with the work and to do analytics that lead to beneficial business outcomes is valid. But many of the battles will only be won if an organization's arsenal includes a solid data management plan.

Jack Vaughan is SearchDataManagement's news and site editor. Email him at jvaughan@techtarget.com, and follow us on Twitter: @sDataManagement.

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This was last published in January 2015

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For successful data analytics, appropriate data modeling should be done. This ensures that the data collected serves the analysis goals and can be efficiently analyzed.
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