Like virtually every other industry, success in the healthcare sector these days relies primarily on becoming more data-driven. Leveraged properly, big data can help deliver better patient care while at the same time reducing the per capita cost of that care. A targeted investment in big data with regards to healthcare analytics combined with best practices in the big data space can be a recipe for analytic success. Here are a few tips that can help you get started.
1. Set Clear Goals
The first step in a successful big data analytics project is to define your business objective. Knowing exactly what you want to accomplish with big data at your back is a must before launching into a new idea. For example, are you trying to answer specific business questions, the scope of which exceeds traditional tools? Or do you want to make future predictions that could shape the way you make business decisions next quarter? Without taking the time to set definitive goals ahead of time, you run the risk of creating a very expensive failure.
2. Take a Comprehensive Approach
It’s natural to assume that analysis only applies to previously unstructured data, but don’t forget to take into account the answers that are probably hiding in data that’s already been processed and cleansed. You also need to include data from not-so-obvious sources, like social media and web logs. Any data analysis project has to be all-inclusive in order to establish a meaningful big picture.
3. Embrace Discovery Analytics
Big data doesn’t exactly replace legacy evidence-based research, but effective analytics are essential to separate out the chaff. There’s really no difference between discovery analytics and big data analytics. Big data analytics aren’t just about reporting; they help inform diagnosis and strategy. Through the use of new algorithms and data visualization techniques, big data can speak volumes—and far more clearly.
Big data doesn’t have to be overwhelming if you take a simplified approach. Choose analytics technologies that help you connect using familiar tools, and that also support short-cycle iterative analysis. This helps open the analysis field to more minds than just a handful of highly paid specialists.
5. Engage Outside Experts
Managing big data is no small task; no matter how skilled your IT staff and existing analytics team, your big data project can surely benefit from some specialized support. Working closely with an experienced vendor can shorten the learning curve tremendously when it comes to figuring out new processes for big data analytics.
Written by Dean Van Dyke
Dean Van Dyke is the Vice President of Business Process Optimization for iBridge. He brings more than 18 years of customer relations, business process outsurcing, lean six sigma, program/project management, records management, manufacturing, and vendor management experience to iBridge. Mr. Van Dyke was the former head of Microsoft’s corporate records and information management team, and served honorably for over fourteen years in the U.S. Navy and Army National Guard. He received his Bachelor of Science in Business Administration from the University of South Dakota and his Master’s in Business Administration from Colorado Technical University.