Four ways to get your data analytics team up to speed

December 1, 2016

Does your analytics team have what it takes? Experts share their top recommendations.

The costs and resources needed to operate and maintain a data analytics team at a healthcare organization will continue to rise. But experts say the benefits should have a positive effect on all areas of business.

“The data analysis team can work with the rest of the organization to find the right questions to ask. This will produce the outcomes and deliverables that are valuable to the whole organization,” says Gerard M. Nussbaum, director of technology services at Kurt Salmon in New York City. “Having an analytic staff to draw valuable data that helps the business learn, decide, and act is invaluable to moving the business forward.”

And, for many organizations, having data scientists on-site is no longer a choice; it’s the standard, says Nussbaum. “People on your team understand your business, the type of data you need, and are quicker access,” he says. “It’s tough to find good people in this field and keep them-people who understand not just generic health plan information, but your business.”

Find the right structure

VenneraWhile it’s a good idea to have some in-house staff dedicated to analytics, some organizations find that mixing in some outsourcing can be effective, says Michael Vennera, chief information officer and senior vice president of Independence Blue Cross.

For example, one team might use a mix of outsourced analytics from a vendor, and also have one or two in-house scientists and other existing staff who are trained in analytic-thinking, he says.

“One of our value propositions is the data we have about our members,” Vennera says. “That’s one of the big places that we can make a difference in the marketplace. So we have to be very careful in making [outsourced data] decisions. You have to be able to balance what you need with what you can afford.”

Still, he says, a lot of analysis is available in the market, so it doesn’t always make sense to “reinvent the wheel.”

Find the right staff

PeeleAnalytics teams can have several makeups depending on the needs of the organization. For example, Pamela Peele, PhD, chief analytics officer at UPMC Health Plan, Inc., says their analytics department is its own department (separate from IT) and has its own budget. As the department head, Peele reports directly to UPMC’s CEO.

She is a champion of this set up, saying that having a budget and staff solely for analytics allows the department to set its own strategy.

“I don’t think the analytics team belongs under the CIO. The CIO is looking at technology, security and privacy, not looking at secondary analytics and knowledge discovery,” Peele says. “Being separate makes analytics a distinct and integral part of the organization.”

The UPMC analytics department is made up of a range of specialists, including scientists, mathematicians, and epidemiologists. Forty team members work specifically in the analytics department, and other analysts work in other departments, such as pharmacy, marketing, and maternity. In total, Peele says UPMC staffs approximately 100 data analysts.

“People who are hired to do analytics aren’t just IT. They are true analysts, scientists, physicists, and statisticians. We even have an epidemiologist on our team. It’s not usually a team of people that would be in an IT group,” Peele says.

Nussbaum says a more traditional structure for an analytics team it is to position it within the IT department, while working with other departments.

“The data analysts must work with IT and business groups at carefully defining the right questions and answers that will help the business,” he says.

Creating goals and measuring success of the data analytics team is critical, says Peele.

“Short term and long term, front and center of our goals is to influence the organization,” she says. “The value of analytics at any organization is how much it influences the organization. If you are investing money and never change decision making, you’re wasting time and money.”

Next: What training does staff need?

 

 

Find the right training

An analytics team may require more training than other departments because of the rapidly changing industry, Peele says.

“Ongoing training is important, whether you have an entry-level or very sophisticated shop. We train a couple of days a month. Healthcare is changing rapidly, and requires training on these changes surrounding CMS, [the Merit-Based Incentive Payment System], and these other quality measures,” Peele says. “We live in an industry that metrically is changing at a rapid pace. Analysts can’t interpret what to do if they don’t understand the operations.”

Nussbaum suggests conferences that focus on the business of healthcare and data analytics so that staff can meet face-to-face with peers.

“They also need exposure to tools,” he says. “Vendors are rapidly adding capabilities and features. Knowing what tools can and can’t do adds to efficiency and new approaches to look at data. Self-learning new technology is a sorely underfunded area of analytics training.”

He adds that self-learning can include investing in new tools and applications to test for business viability. “Conferences two times a year to interact with peers in person, plus self-learning, is an expected investment.”          

Find the right communication style

An important and often overlooked aspect of building an effective data analytics team is determining how numbers and concepts will be communicated to the C-suite. That’s why Peele hired two journalists to her team.

“We use the skillset of storytellers. Their job is to craft the narrative of the data. A strong analytics team will have someone trained in communications who can take results of analytics and craft it in a way that is digestible to senior management,” Peele says. “We influence our organization by providing unbiased information in a way that leaders are used to receiving it.”

Peele says that without the right communication tools, executives may be prone to negating data that is counter to what they believe.

“All organizations have their own stories,” she says. “In the absence of data, we tend to have a selective memory, usually of outlying situations. It is normal, as it becomes more sophisticated in using and consuming data, that the data is at odds with the corporate story and myth. Communication is important to reshape the organization’s story to make it more true.”

Donna Marbury is a writer in Columbus, Ohio.