- No data program or initiative without a purpose. Sounds like basic but so true. It’s not the first time that that a data initiative is kicked off because it’s innovative but at the end doesn’t address any real business need.
- In order to be successful you need to inform business leaders at all levels. Not just C-level but all leadership in your business stakeholder community. Of course, these business leaders also need to be open to listen. Reading tip: Also have see our post about MDM business case building.
- Correctly translating business needs is important but also understanding the right priorities is key. What are my real burning data platforms?
- In the AI, ML, …. Basically the data science context, we are happy to see that a substantial number of business leaders managed to gain understanding of the most important principles of data science. Pay the necessary attention to this in your organization and make sure that you also remove the data & data science ‘language barrier’.
- Don’t forget the company politics. In some organizations you will need to cope with individuals attempting to sabotage constructive change if it was ‘not invented here’ or ‘owned by us’. This point of attention was valid before, but unfortunately is still there and definitely present in the context of data programs.
- Ethics is not about a choice, it’s an obligation. Data can be powerful and can potentially deliver huge value. Besides this potential, it also comes with a duty of care. In an era where customer centricity is vital, you need to make sure that your data management is objective, trustworthy and transparent to your customers and other stakeholders.
- Ethics carry a cost. However the cost of not doing things right is much higher. Think about the overall and longer term reputationally and commercially cost.