Successful 21st century healthcare organizations do not thrive based on the intuition of a few brilliant leaders; rather, they become modern data-driven businesses. Delivering better care, fostering population health and greatly reducing the per capita cost of care does not happen without targeted investments in big data analytics.

In my experience with healthcare analytics, I’ve observed several best practices in the big data space that have consistently boosted return on analytics investment for both health plans and health systems. Each one of these imperatives acts as a multiplier for big data analytics investments, which can shape business fitness – and patient health – for years to come.

Before you sign off on a big data analytics project, consider these five tips for making it even more successful:

1. Invest in discovery. Big data analytics is really discovery analytics. Just like lab tests inform diagnosis and treatment, you need to conduct discovery analytics that will inform, change or improve the health of your business. This is quite different from reporting. It involves using new algorithms and data visualization techniques to let the data speak for itself. While it doesn’t replace legacy evidence-based research, it does help you take the first step to separate the signal from the noise in the vast data lake.
2. Use all your data. Don’t overlook the cleansed data that has already been integrated in your enterprise data warehouse; there are probably answers hiding in your structured data. Once you figure out those answers, you can (and should!) also layer in multi-structured – text, sensor, web-log, social media data and so on. Take stock of your data assets and, to paraphrase a popular commercial, set up a sleeping data alert!
3. Make it easy. Choose big data analytics technologies that support iterative analysis with very short cycles, and that allow you to connect to familiar tools -- e.g. BI, stats, data visualization. That way, you won’t limit analysis to a few highly paid data scientists. Easy means that business analysts without advanced degrees should be able to use big data to do their jobs better too!
4. Set clear business objectives. Know what you’re going to do with big data technology – and give it a test run -- before you buy it or launch into your project. Have some business questions in mind that are vexing, for example, or that the combination of traditional approaches, tools and staff have had a hard time solving. If you skip this step, you run the risk of your big data analytics project becoming an IT science experiment.
5. Ramp up with an experienced team. You probably already have a great IT staff and a solid healthcare analytics team, but that doesn’t mean you couldn’t use specialized support when getting started with big data and discovery analytics. New data, new analytics and new analytics processes will go much more quickly and predictably by starting with experienced data scientists. Be clear that your team will be shadowing and learning from folks that have specific technology, tool and data scientist experience. Clarify that their role is to pass along this expertise, not just deliver analytics.

If you want to get the most out of your big data analytics investment, choose technologies and partners who can deliver on these best practices. The great news is that everybody wins with smarter healthcare – payers, providers and patients.