5 tips to properly integrate Data Quality Management into your organization

Data Quality Management has become a must. But what does that mean and how do you create actionable insight into the quality of that data?

Most organizations continue to collect, store and manage more and more data. Since they make decisions and take action based on  information and insight coming from this data, poor data quality can have disastrous consequences….

This begs the question: what is “poor (or high) quality data”, and how do you measure it?

This article sheds a light on the topic and gives some tips & tricks for successful Data Quality Management in your organization.

2. Tips & tricks for an effective Data Quality Management

Once you know what Data Quality Management means and requires, it all comes down to one question: what makes a Data Quality Management program succeed or fail?

TIP # 1 Tailor it to your needs

Every organization is different, which means needs, challenges and priorities vary from one business to another. Similarly, each organization needs to design its own Data Quality Management, taking into account its constraints and realities. This journey starts by correctly identifying what is critical to realize business value and what is not, and by identifying the most valuable data as well as defining their minimum quality requirements.

TIP # 2 Define and rollout your Data Quality framework

This may seem obvious, but you should take time to design simple, repeatable and scalable practices, processes and ways of working that are fit for your purpose. Define clear roles and responsibilities for data quality, and take time to make sure your people can develop the necessary skills. Also, you can set up appropriate ceremonies to regularly review your critical data quality, discuss the common issues, recognize and share good data quality practices. This will foster a good visibility on the situation and fast update of practices when necessary.

TIP # 3 Involve all your stakeholders

Data Quality Management is not a stand-alone activity, and will be most effective when integrated to your general way of working, processes and procedures. Everyone in your organization should be – and should feel – involved. Issues affecting the quality of your  data can occur during its whole lifecycle from data entry to data collection, use and reporting. This means that everyone should understand their role and responsibility in reaching high-quality data. Everyone who uses data to perform their job within your organization need to drive this.

TIP # 4 See it as a culture, not a project

As for your data management as a whole, your Data Quality Management is not a project, it is a work culture. It is made of iterative and repeatable activities that cannot exist in isolation, but must be fully integrated within your business. As mentioned under TIP # 3, it stems from a true commitment, not only from the leadership team, but from people of all levels. Data Quality Management should become a cross-functional engagement of your whole organization, as high or poor quality data impacts various areas of your company. This will also foster a proactive approach, rather than a reaction to the identification of bad quality data.

TIP # 5 Be transparent

Transparency leads to trust. We encourage you to be transparent about your data, create a culture where poor data quality can be talked about, where people can share their experience with quality problems and the solutions they implemented. Also, communicating on your data quality practices and how they impact your business helps everyone understand why all of this matters, and feel involved in the program. By being transparent, you can reach a quality mindset and, in time, a culture where high data quality is achieved from the start.

3. Conclusion

Much like any endeavor one wishes to carry out, Data Quality Management rests on one crucial basis: purpose. There is no such thing as reaching your destination if you do not know first where you are going. Why do you need the data? What goal does it serve? Only by answering these questions can you strive to reach high-quality data. And once you have done this, the real journey begins: fostering a culture that onboards all internal stakeholders on the way to high-quality data.