Without enough information, making a decision can be impossible. Thankfully, today’s technologies allow us to have access to data quickly and easily. However, when you are faced with information overload, making a decision can seem impossible as well. Having a process for extracting meaningful information from random data can be very powerful for making an informed, timely, and logical decision.
A good starter process is to Simplify, Filter, Analyze, and Decide. Let’s go through the process with the example of Customer Service data. Assume you have a database full of customer information, queries, response times, and resolutions as well as information from satisfaction surveys.
1. Simplify – Forget the data and consider the big picture.
What is the business issue that matters? What is the question that you want answered? Example:
- Are we providing good customer service?
- In which areas do we excel?
- Where can we do better?
2. Filter – Go from meaningless to meaningful.
- Are we providing good customer service? Consider satisfaction ratings over time.
- In which areas do we excel? Look at your high satisfaction ratings and your quickest response times.
- Where can we do better? Check out low satisfaction ratings, repeated queries, and longest response times.
3. Analyze – Extract meaning from your data.
Next, you want to take your data and put it into context. Example:
- Are we providing good customer service? Satisfaction ratings average a 4 out of a possible 5 points. So what? Our users are satisfied. Compared to what? Last year our rating was a 3.5. Our users are more satisfied than they were a year ago.
- In which areas do we excel? The highest satisfaction rating was for Product B. What’s so special about Product B? The quickest response time was from Customer Service Rep Melinda. What is Melinda doing right and how can we replicate that?
- Where can we do better? Product D received the most queries. Where is the confusion and how can we proactively address that?