Now that you understand what a Data Culture is and how well-intentioned Senior Leaders can derail it, it is time to consider the wider angle of your teams’ roles and responsibilities and what controls. Governance layers you will be putting in place and, of course, the core reason you are pushing for a Data Culture.
Each of the main roles (Data Owner, Data Engineer, Modeller) needs to be well aware of what is expected of them and how they can maximise their interactions with each of the other roles, i.e. how can a modeller identify a requirement for more data and so engage engineering to then track down the data owner and get the data ingested, curated, modelled and finally presented to the business. The core thing is to make it clear that your Analytics functions cannot “request” new content that needs to come from the customers; these people will actively drive all the activities and need to be engaged throughout the process.
Starting to understand that a typical request says for “Monthly Sales by product and store” will require the list of stores, the list of products, a clear definition of what is sale is etc. However, bombarding the requestor with technical questions will not deliver the result. The act of prioritising your requests, be they for new reports, an enhancement to reports, or any other requirement, is key. Over time, you will find a move from the simple to the more complex reports as it becomes clear that more data is already available. Remember that as you mature in your Data Culture, it will become more likely that people will begin to build their own reports. To do that, there is clearly always a requirement to consider how you empower your whole community and enable levels of self-service.
When you start to think about all of this together and what you are comfortable with you will start to find you have a Vision of the future!