Article

From Quick Fixes to Long-Term Success

In the rapidly shifting digital landscape, the importance of data quality for becoming a truly data-driven organization cannot be overstated. The quality of your data impacts every decision you make, and even the smallest error can have significant downstream repercussions. Recognizing this fact, many companies have implemented various measures to get in front of this by monitoring and improving data quality.

However, the journey towards high data quality is not a straightforward one. Companies tend to start with tactical, home grown solutions, utilizing tools and technologies already at their disposal. These solutions often include using Excel and SQL queries to sift through data, or incorporating data quality checks into Business Intelligence (BI) dashboards, allowing for some level of automation, monitoring and control over the data quality.

While these solutions add short term value they have difficulty evolving and scaling as an organization matures and its data needs change over time. In this article we discuss important limitations organizations face when relying on custom and home grown data quality solutions and why they might want to think more strategically.

Tactical data quality solutions, as discussed above, have their place in an organization's toolkit: they provide immediate insights and help teams react quickly to data quality issues. But these solutions come with their own set of limitations around metrics, communication and collaboration, and scalability.

Metrics

Tracking data quality and trends over time quickly gets increasingly complicated when just relying on queries and reports. SQL queries might pinpoint data quality issues, but they are not be able to identify patterns or trends over time. This makes it hard to predict and manage future issues.

While it's important to identify trends over time, it's just as important to ensure data quality issues that have been detected and resolved don’t reappear later on. Monitoring regressions like these is complex when you're just doing point in time analyses. Instead, to be able to alert teams of regressions, organizations must keep a full record of their data quality incidents.

Tracking data quality, trends and regressions often requires rethinking the framework and involves adding more complexity to an existing solution. Before you know it, your team is spending more time developing a system for data quality management rather than focusing on their core business operations.

Communication and collaboration

Specialized scripts, SQL queries and BI dashboards are often managed by specific individuals or teams. When a data quality issue is detected, the process of a) notifying the relevant stakeholders, b) discussing the problem, and c) deciding on a solution is a largely manual one and often involves sending a string of emails back and forth. This often leads to delays in resolution, poor communication, and a lack of coordination among teams. Integrating these custom solutions with incident management processes and tools are also frequently complex and unwieldy undertakings.

In short, without a streamlined process and shared view on data quality, it becomes difficult to respond promptly and effectively to data quality issues when they arise.

Scalability

Tactical solutions are effective for handling small to medium volumes of data. But as the number of data checks, frequency and scale increases, these solutions can easily become overwhelmed. They struggle to process large data sets efficiently, and their performance suffers as a result. What was initially a quick and easy solution becomes a bottleneck, limiting the organization's ability to handle and analyze data effectively.

The technical nature of custom solutions make them less accessible to non-technical users and make knowledge less transferable. This limits the number of people within the organization who can effectively maintain, update and utilize a data quality solution.

A strategic approach to data quality management is about more than just fixing problems. It’s about anticipating issues, preventing or containing them, and continuously improving your data quality. It's laying the groundwork for your organization to become truly data-driven.

The challenges posed by custom solutions underscore the need for a shift in approach. Instead of relying on tactical short-term solutions, companies should aim for a strategic and long-term approach to data quality management. This shift is not just about better tools, but also about adopting a new mindset &embdash; one that values proactive measures, scalability, and collaboration.