Data points and data insights sound the same, but one is not like the other. We go through life being taught not to judge a book by its cover but in terms of data we can. In my last blog post, I touched base on dirty data and the importance of cleaning it. We’ve also discussed the importance of collecting data from everything like customer experiences to behavioral interactions with your website and the types of information your patients like. Hopefully, by now, you’ve decided who on your team will administer all the data you’ve been collecting. This post is for them, so go ahead and forward it; it might be helpful.
There is a Statistical Significance
Have you ever wondered if collecting your data was the biggest obstacle in your research journey? It very well can seem like it. But after collecting comes the task of cleaning your data and keeping it clean. A more important job than the first. Yet, clean data are data points and can be empty, even if they look like the busiest spreadsheet in the world. Data needs to be transformed into accurate information that is comprehendible and useful. However, it means very little to read that 49% of your patient base is comprised of 56-year-old women or that 52% of your entire patients have diabetes. With information, you can get percentages and summarize processed data. While this is information is useful, it’s still not data insights.
Data insights dig deep and allow for the analysis between differences. Using segmentation to breakout subgroups within your sample can give you that range to see if actual differences amongst your population occur. To test for differences, functions like a T-test or Anova tests can be performed to find contrasts between multiple groups and events.
When differences are found within your data, a key factor is to determine if there is a statistical significance that can be found by the tests mentioned above. If there is a difference, you can start to analyze and determine what the differences are, why they happen, and what they mean to you and your practice.
Now that you’ve been named the gatekeeper to the firm’s information start thinking of subgroups that you feel might have differences. For example, male and female diabetics? Perhaps you’ll find a difference between the groups and the way they manage their condition. The tests that you can run are vast and will help you uncover rich insights that can help improve the business.
If you’re looking for information on the importance of cleaning data? Check out my previous blog: Let’s Talk Dirty Data.