Stay Ahead of the Game: Top Trends Transforming Data Quality in 2023
15 February 2023
We live and work in the age of big data. And as organizations continue to amass a wealth of data, the challenge of ensuring the quality of that data is becoming increasingly apparent.
Since data quality can make or break the effectiveness of data-driven decision-making processes, it will remain a top priority for any organization in 2023. So, let’s kick off the new year with an overview of the key data quality trends that will shape how you manage and leverage the quality of your data.
Acceleration of data quality processes empowered by Machine Learning
A traditional approach to data quality tends to be somewhat technical and manual. Defining and identifying data quality rules can be a time-consuming process and is never really complete. That’s why data engineers and business owners need to proactively look for new issues not detected by an existing set of rules. In addition, setting a failure threshold for a data quality rule can feel arbitrary and disconnected from your actual data.
In recent years, we have seen data quality tools evolve to include machine learning (ML) technologies. While there is no discussion that the traditional approach is still needed (because it relies on domain knowledge and ensures specific assumptions are tested), machine learning can help with day-to-day tasks such as detecting anomalies, automatically suggesting new data quality rules, detecting data duplication, and predicting missing values. Even if machine learning might not entirely remove the (much-needed) human element in data quality processes, it can make those processes much more efficient and easier to use.
As demand for a scalable approach increases, machine learning will continue making headway and improve the efficiency of data quality tasks and processes. It’s not just us saying that: Gartner has adapted its magic quadrant for Data Quality solution leaders, introducing capabilities related to ML-augmented data quality.
Data quality management success driven by data literacy and democratization
Writing business rules is never entirely independent of the data you are working with. Having a good understanding of your data and being able to appropriately interpret and communicate about them is essential to write relevant business rules and to assess the impact of poor data quality. Since data knowledge tends to be stuck in people’s heads or buried within technical documentation, there is a strong need for data literacy to ensure good data quality management. As is the case with reporting, you need to make statements about your data and capture well-chosen metrics to properly track and interpret the quality of your data.
And as more and more organizations seem to go through a data transformation, good management of data and data quality becomes essential. A common challenge for such data transformation is often on the people side, where data literacy and a stronger data-driven culture are needed to drive the change. That is confirmed by the Talend Data Health Barometer. This survey revealed that about one-third of data experts have relatively low confidence that people in their companies understand data well.
Leveraging existing practices through an enterprise-wide data quality strategy and scalable framework
Many organizations have taken some first steps on their data quality journey, defining a standard approach for the organization, determining what good data quality is, and embedding it into data lineage to easier assess the impact of poor data quality. Organizations will look for an enterprise-wide strategy and framework to further increase maturity, enabling them:
to achieve a faster feedback loop between issue detection and remediation through the identification of focus points that help them build a data quality dashboard on critical assets,
move to a trust-based semantic model where data is trusted because it’s known where it is coming from, rather than because all implemented controls and rules are exposed to the end-users,
ensure accountability of business users to set a data quality standard.
Stay tuned for more insights on the latest data quality trends
In conclusion, the 3 key trends discussed before will play a crucial role in enabling organizations to effectively manage and leverage the quality of their data, increasing the effectiveness of data-driven decision-making processes. But as trends evolve over time, we will see some other data quality trends come to the fore in the near future, such as:
- a holistic Data Quality by Design approach to address data quality up front and embed data quality features and functions into the system as part of the modernization of data warehouses (in the cloud) and the move towards data hubs,
- a self-service approach to data quality processes, to empower all types of end-users,
- a stronger collaboration between business and IT teams, facilitated by role-based data quality workflows.
So, stay tuned for more insights on the latest trends and best practices. Or drop us a message if you want to learn how we can help you better manage and leverage the quality of your data.