Data Maturity: Reporting
Where are we on our analytics journey? It’s a question we get asked all the time. In this series we will look at key questions to help you determine just this! We’ve graded common responses based on three levels of maturity: walk, run and fly.
For automated reporting you have:
Walk: We have nothing set up, currently all our reports from different channels have to be put together manually.
Run: We have automated dashboards but not being widely used due to data quality issues or lack of data adoption training.
Fly: We have automated dashboard, set up and trained the team how to use them and send modelled data back into the applications for better democratisation.
See which answer most closely matches your current situation and scroll down for some crucial advice.
Your answer: We have nothing set up, currently all our reports from different channels have to be put together manually.
Our advice: When starting with data, it is advisable to think about the top-level daily reports you will need. These reports provide a snapshot of your business’s performance and can serve as a foundation for further analysis. Define your Key Performance Indicators (KPIs) and establish clear definitions for the customer journey and other important business metrics. This initial focus on high-level reporting will help you gain valuable insights and set the stage for more advanced analyses in the future. While all of this may seem simple in principle, it’s always difficult to create realistic customer journeys that are broad enough so that you can easily understand where customers are on it, but detailed enough so you can understand what actions to take.
Your answer: We have automated dashboards but they are not being widely used due to data quality issues or lack of data adoption training.
Our advice: Data quality (or the perceived lack of) can be a major hindrance to adoption. This is why getting the fundamentals right in terms of a data dictionary to define your metrics and data modelling to create them, is so important. These models feed directly into your dashboards and help establish data quality. Further to this, it’s important that your team gets regular training on your visualisation tool. Track their usage and ensure that everyone is using the solution or your democratisation efforts will suffer. When it comes to culture change, you can only go as fast as the slowest person and without successful tool adoption, you may find that your team slip back to their old, more comfortable ways of doing things.
Your answer: We have automated dashboard, set up and trained the team how to use them and send modelled data back into the applications for better democratisation.
Our advice: You’ve done a great job of embedding data into your decision-making! What we often find at this stage of maturity is that organisations start to struggle with governance and control. They suddenly find that they have 1001 dashboards and too many models which has places a burden on your analytics team and increases costs. It might be wise at this point to ‘take inventory’ of who in your organisation has access to what, which dashboards and reports are actually being used and to try wherever possible to have 20-50 data models and not any more. If you don’t keep a firm handle on this, then you may get to a point in which starting afresh with new infrastructure is better than trying untangling what’s been built – and migration can be a painful project. There’s a careful balance between getting your team excited about data, giving the power but still keeping an eye on them.
If you want to understand where you are on your analytics journey. Our Data Maturity review offers a holistic and independent assessment of the strengths and weaknesses of your current technology, processes and people.