On the surface, it sounds easy enough to sprinkle some data science into your systems. Add a touch of predictive analytics plus a little machine learning and voila! The next leap forward in healthcare management.
Except... You can't skip steps. Before you can get to the shiny, exciting data science, you need clean data.
While your data may be workable for current operations to make it usable for data science, it needs a whole clean up job. In terms of data science and analytics, your data is likely pretty dirty. In healthcare payments, where data flows from multiple systems and standards are a moving target, data can be pretty filthy. This means mismatched formats, errors and inconsistencies. Having clean data is often the biggest roadblock to being able to reap the benefits of data science.
So what is clean data?
Clean data is identifiable. This means that in some regard, you know what data you have and what it should be telling you. This basic level of data literacy is needed to begin the bigger job of cleanup.
Clean data is organized and normalized. Your data needs to be organized into uniform .csv files or tidy SQL databases. All the data that you want to use needs to be in the same format. The files you use for EDI (think 837, 835) should link together in a way that usable in data analytics. This means key fields need to match.
Clean data has been scrubbed. Records in your data that are inaccurate, irrelevant or incomplete need to be repaired or removed.
Getting your data clean is absolutely necessary, but it can be a massive undertaking. It requires a data engineering skill set and an organizational commitment -- meaning it can be expensive and time-consuming. The payoff is worth it, but it can be a long road just to get your data ready.
That's why at Sift, we not only provide cutting-edge data science applications, but we also do the data cleansing for you. We take your raw, messy data and organize, normalize it and scrub it. We transform messy data into analysis-ready data. We set your data up to be parsed, queried, display vital information and identify patterns.
This cleaning work sets the stage for powerful data applications that make your organization smarter and more effective.
So while you may have messy data, we have the means to get you over the hump and onto the exciting parts of data science.