Multi agent development

Data Tips #06 – Key factors for real self-service analytics

Self-service analytics was the holy grail for analytics a 10 years ago. Since then it has been a bit of a lost art or something taken for granted.

At the same time it is vitally important for any datadriven company to make sure that it’s employees have the right information at the right time in order to take the right decisions.

We can integrated this information or data into the operational system if we know what the process looks like, if we can predefine how the decision is made and we know the data that the decision is based on. But a lot of times this is not the case

In order to give our employees the right data and information and the ability to analyse it there are a few things we need to set in place.

  1. Access to harmonised data – As simple as this sounds it may require a lot of effort. It means gathering the data in a Data platform, making sure that the data is harmonised on the dimensions and facts so that the data can be used for many cases with the same results.
  2. Data descriptions & definitions – Without proper definitions of the data it is very easy to use the data incorrectly, or end up with different results even though both analysis are meant to be the same. Therefore it is very important to have proper data definitions and they should be easily accessible and the data should be described.
  3. Known data quality – In order to trust the results of the analysis you need to know the quality of the data that you are using. The data quality may not need to be perfect, it is much more important to understand the quality.
  4. Tooling to work with the data – In order the be able to work with the data you need effective tooling. Key tools are: Visualisation tool to create dashboards and analysis. Semantic layer to always have a uniform definition of the data. Data Sandbox in order to be able to add external data to your analysis.
  5. Data Literacy – To be able to be effective in analysis it is important that everyone understands how data works, understands data quality and really “speaks data”.
  6. Analytical competence – Just speaking data is not enough. Analytical competence is how to perform analysis and the experience in how to transform a business problem into an analytical problem.

To sum it up, there are a number of things to set in place to reach all of the way to Self Service analytics, do you need to have everything in place to get started? Definitely not. But being aware of different dimensions and your shortcomings in them means you can handle the consequences.

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