Deepskydata

Purpose - What do we want to achieve?

Data can be powerful. It can speed up decisions and by that enable teams to experiment more and quicker and by that find better marketing opportunities and build better products and services.

But data is also hard. It’s hard to implement, to maintain and especially to get it in shape in which it can support decisions.

9 out of 10 data projects fail at least some weeks after the initial implementation, because they don’t evolve or even get a basic maintenance.

We want to spread good data setups and we want to get everyone into the position to start to use data in their decisions processes. And in the end make quicker and more confident decisions and enable more and (most important) better experiments.

Importance - What is the biggest difference this will make?

Business with good data setups and which are using data in a lot of daily decisions move and learn faster. They are naturally ahead of their competition and are impossible to copy and challenge.

Ideal outcome

People get first insights from the data about what marketing works, which product features look essential and how their business is growing (and which changes can improve the growth). By that they start to understand how to use data in more decisions. And they learn their data superpower: Their business context they can combine with the data.

All this enables business essentially to try out more things and decide if they work or not in a better, confident and quicker way. This enables more revenue by less costs.

Success Criteria - What has to be true when we have worked with a client

• There is a good data setup → Good means:
  • The data collected can show how business goals are performing
  • New people easily understand the data setup and can work with it without huge trainings
  • Tracking events track what their definition says
  • There is one tracking plan which defines the tracking events and they are implemented based on this plan
• The teams have initial charts and dashboards which helps them to track the business performance and to plan and monitor new experiments (or feature launches)
• The setup is maintained and evolves with new insights and with new features implemented