Article

Avoid the pitfalls of data management

The need for performance management

Published

June 2020

Author

Julie Blom Hansen

Several studies show that the success rate for launching and maintaining a successful data management and governance initiative is as low as 10%.

At the same time, the importance of data management grows exponentially as:

  • data volumes from more and more sources are skyrocketing, causing data inconsistencies that need to be identified and addressed before decisions are made using incorrect information or RPA can be used on the data.
  • self-service reporting and analytics demands are increasing, creating the need for a common understanding of data across the organisation.
  • impact of regulatory requirements, such as Basel III, GDPR, Solvency II, are steadily increasing, making it even more important to have a strong handle on what data is located where and how it is being used.
  • the need for a common business language is increasing in order to enable cross-departmental analysis and decisions.

In our experience, there are six main pitfalls that organisations fall into trying to introduce data management:

  1. Unclear scope. Organisations often initiate their data management efforts without a clear scope or with too broad of a scope, making the task insurmountable and unclear. Make sure that the data business case and hence the scope and objectives are clear and agreed to.
  2. Data management and governance as an add-on. Data management and governance frameworks are often established outside the normal business governance framework as an add-on. This drives governance ambiguity and even conflicts. Data governance that is integral to the business and IT governance is easier to accept and has a longer life span.
  3. Lack of end-to-end view. The real source of performance difficulties does not lie in how individual tasks are or are not performed (efficiency) but rather in how they fit together. However, many organisations are too biased, e.g. through incentive structures, to prioritise end-to-end data problems across business barriers. Dare to challenge.
  4. Too theoretical. It is easy to make data management theoretical and over-engage in data definitions, processes etc. without really having identified what business impact is intended. Start by identifying the indented impact, understand the root causes of why it is not working and solve these. Eventually, the gaps will be closed. The only difference is that it has been set in a context.
  5. Not building new habits. Data management programmes can be successful at go-live but not have a permanent effect, e.g. when the charismatic programme manager has left, and the top management’s focus is directed elsewhere. To mitigate this, focus on building new habits as much as on immediate business benefits.
  6. One type of data ownership to fix it all. Many companies try to merge too many different aspects of data ownership into one and the same ownership role. By differentiating between different types of ownership, a data governance framework becomes easier to understand, implement and live by.

At Implement, we have developed a tried-and-tested framework to avoid these pitfalls, which allows us to help our clients implement data management setups that create lasting impact.

Reach out to learn more about how we can assist you with your data management concerns.