Once you have performant data architecture that supports your data strategy, but you don't have the right culture to actually use the dashboards to base your decisions on and drive change in the organization you have wasted your time and money. If you're going to ignore the data and keep relying on your gut feeling to take the decisions, why bother in the first place. The introduction of data driven decision making can be called transformational change, and will not be easy.
All change is hard, especially the kind of transformational change that data can trigger. Sometimes the data seems wrong because it contradicts your gut feeling. Then, instead of just discarding the dashboard, talk to the analysts and data architects and find out why the data looks different than you expected. In a professional setup of a data project, this has to be done during the users acceptance testing phase. In this phase dedicated testers from the business look with a critical eye to the dashboards and tackle possible bugs together with the technical teams. Once enough confidence in the dashboards is built up, they can be published to a larger group of users to drive insights and actions. For an organization to be able to react appropriately to the change of using data to drive decisions a certain culture has to be present. If you start working at a company look for signs of this type of culture and if it doesn't exist yet, don't assume it will get better soon.
Culture always starts at the top. To become an organization that is propelled towards high performance you need a culture of accountability and psychological safety. If the leadership in your organization is not willing to admit mistakes, or doesn't set clear expectations, it will be very hard to get the full value from your data. So if you want to change culture, you have to change the habits and language of your leadership. The first, but necessary step in this case would be the self-reflection of said leadership that a change of their own behavior is desired for the organization to reach it's full potential. Self-reflection is something that's very hard to teach and requires someone to be vulnerable to the idea that he or she is not as perfect as he or she would like us to believe. This kind of psychological shift will probably only take place in situations of shock or great distress. Let's hope it doesn't have to come to this.
The loop that has to be closed looks as follows: once you have a dashboard and insights come out of the data, you use these insights to refine the business processes further. But when you refine the business process, the underlying business applications have to be adapted as well to be able to capture the changes to the process. The next step in the dataflow are the analytical systems, that also need to be adapted to make sure the new data that is generated by the changed process and application is integrated in the data model. Finally, the results of the changed process will also be visible in the data visualization dashboard. Here you can look at your KPI's to follow up if the change in the process has the desired results towards your strategic direction.