Data governance programs must be an integral part of the pharmaceutical quality system and are a fundamental part of the company. What are the pitfalls & best practices from which we can learn?
These following weeks, our Data Integrity Expert, Nathalie Wellens, will explain the concept of data integrity, its evolution, data governance strategies or programs, how to implement a data governance framework in your company and remediation solutions.
Nathalie, which best practices can you share with us?
- Management engagement is crucial: top management needs to understand and support the concept of data governance. If this is not the case, the program is at risk because insufficient budget, time and resources are allocated. In addition, success is not guaranteed in the long term.
- Introduce the program with as little disruption to the normal course of business as possible. Instead of forcing employees to perform new tasks, it may be wise to build on existing roles and responsibilities. Otherwise, there is a risk that the program will not be ‘accepted’ by your employees. Data owners are recognized for their relationship with the data, which reinforces their commitment.
- Vision and training is important. Demonstrate what the added value of data governance can be for someone’s job and how it can help that person do a better job. Emphasize the importance of their contribution and that they are an important part of the data governance program.
- Benchmark your data governance program with industry best practices. This can answer the question ‘Is my company ready for this?
- What are you currently doing in support of the best practices?
- Where can you make optimizations in relation to these best practices?
- What is the gap between what your company does and the best practice?
- What is the risk of this gap?
This process can help your company to draw up a list of risks and ‘quick fixes’. The benchmark can be an action plan to address the pain points of your current data integrity plan.
- Get rid of bad data circulating within your company. Bad data is:
- inaccurate: the data has spelling errors, missing information, empty fields,…
- non-compliant: the data does not comply with legal standards.
- not checked: data that cannot be continuously monitored cannot be used.
- unsecured: data vulnerable to hacking or breaches.
- static: data that is never updated becomes outdated and cannot be used.
If your business strategy is data-driven, bad data can lead to bad decision making. The same goes for product quality.
- Document and measure your data governance efforts. Define basic KPIs to evaluate the success of the strategy and your efforts.
Nathalie, can you identify some pitfalls we need to watch out for?
Certainly, a common pitfall is the assumption that data governance is a project that needs to be assigned to the IT department. Data circulates throughout your company, not just the IT department. It is important to have representatives from all departments in your Data Governance team.
In addition, data security should not be confused with data governance and it should not be assumed that this is only a topic for IT service providers, multinationals or data warehouses. Data has several sources, it can be digitally recorded and stored in the cloud, but it can also be written down on paper and stored in an archive.
Sometimes there is an assumption that data governance stops once all remediation actions have been performed. Of course, this is not the case, data governance and data integrity is not a one-off project. The amount of data that is generated and the way in which we deal with it continues to evolve. It is therefore important to ensure that the data governance framework is correctly applied and regularly evaluated.
Need help to implement your data governance program correctly?
Our experienced Data Integrity experts would be happy to assist you.
A risk-based and pragmatic approach
In order to address the growing need of the industry to implement data integrity in a smart and efficient way, Pauwels Consulting has developed a unique data governance program. The program combines a thorough pre-assessment with subsequent transition according to the well-known PDCA cycle. GAP assessments and associated actions are carried out in parallel across each department with continuous feedback loops and include alignment of all DI-related documentation (e.g. URS, data handling procedures, etc…).
Our risk-based and pragmatic approach embeds strong leadership and proper behavioral management. It is designed to achieve and sustain DI cultural excellence across the entire organization. Proper training is crucial so our Data Integrity team has compiled a variety of training modules that can be tailored to achieve these goals, together!