Question…… How many of you at community hospitals have a few million dollars laying around for analytics?
To quote Ben Stein, “Anyone….,Anyone…..”
Didn’t think so….
Another question….. How many of you need to answer basic questions regarding re-admissions, quality metrics, population health, etc.?
So, how do we get these questions answered in the face of budget constraints?
I would like to propose one method.
The basic problem….
Most of the questions that come my way are fairly straightforward. The complexity arises largely because systems don’t talk to each other. Imagine a world where every piece of data was in ONE SYSTEM! How easy would analytics be then! The reality is that we are faced with many systems and sources of data in healthcare and we don’t have an easy way of getting at the data.
Enter the Data Lake
One tool that we use to address data gathering is a Data Lake. I am often asked how a Data Lake and a Data Warehouse differ. A Data Lake is simply a repository where data is stored in its native format. Data Warehouses are defined data structures that model a business area or need. The better, more complete the model, the better the warehouse.
Data Warehouses are at their best when they have an objective. When the business area they deal with is fairly well defined. That isn’t the case in modern healthcare. The questions come hot and heavy from all areas…. The data does like-wise.
Data Warehouses are “Pay Now” systems: You pay upfront to define and optimize the structure, normalize and load the data.
Data Lakes are “Pay Later”: It’s a comparatively straightforward matter to “Take what your given” and put it somewhere. You pay when you need to use the data. You then have to pull what you need, relate it somehow, normalize/ optimize the data, and visualize it.