When you look at self-service analytics, people generally focus on the outcomes – the charts and tables they can create, or analysis or computations. People often forget about the data, which is often the biggest challenge of self-service analytics.
This is because data has always been someone else’s problem. And it’s only getting worse.
Years ago, data was simpler – in that all my data lived within the universe of my company. If I needed access to a data set, I could go to somebody. It might take some time, but eventually I could get some representation of data that would be useful, and I could do some analysis within Excel or similar tools.
Today, the data problem has only gotten worse, because the applications that people use to run their businesses are no longer in control of a professional IT organization. My CRM system, my financial and HR applications, my marketing automation tools are all in the cloud. All of this data lives outside the four walls of my company, and I no longer have a single view of the universe.
If I am lucky, I am querying this data with some degree of success through the native tools of the applications that have been provided to me, but then I’m downloading it into Excel. Now Excel is a good tool for certain things, but it’s hard or nearly impossible for the average business user to take all of this disparate data and merge it together into a coherent data set using Excel.
People often complain about self-service analytics, but when you dig into their frustration, it’s almost always about the data. If you want to offer self-service analytics, you need to allow people to own the data problem.
What’s needed in the marketplace today is not another infrastructure tool designed for IT or data architects. What’s required is a tool that allows users to access data in various systems (e.g. on-premise, cloud, spreadsheets), and provides the facility, through a very simple but smart experience, that allows users to identify the data sets needed, blend the data, and get the data into the right format. From there, users can enrich the data further, combining like pieces of data into common fields, or adding new calculations as a new field. Basically, you’re doing the data prep work as you’re getting the data ready for analysis.
Data prep also needs to be appropriate for most any business person who is required to do analysis today, which is pretty much everyone at this point. This will allow users to get their answers much more quickly, and on their own. IT can help here and there, but you are not dealing with changes to data infrastructure.
What people need is the ability to answer their own questions. Data can no longer be someone else’s problem. By providing a way for users to take charge of their data, we take an important step forward for self-service analytics.