Close

How to Take Proactive Action for Better Data Quality

Share

Many of our customers reach out to us in the aftermath of a major issue with their data. It could be a disastrous board meeting in which the wrong insights were presented or a never-ending migration process that becomes a costly exercise when companies keep two data warehouses running because they’re not fully confident in the new one.

While these issues happen to many companies, there comes a point in which the cost, pain, or hassle becomes too big to bear. However, more experienced CIOs and data experts look for data testing solutions and alternatives proactively. On the one hand, they know that if they don’t implement the right processes, sooner or later they will face a crisis as a result of data inaccuracies or other issues. On the other hand, it is cheaper and more efficient in the long run to proactively manage data tests which can be easily be implemented to routinely verify and guarantee data quality across an organization.

The role of data in itself has evolved enormously over the past decade. From embracing big data to the cloud, the way in which we manage, store, transfer, and present insights has improved exponentially. Companies are a lot more data-driven than ever before and rely upon data from multiple sources, both internal and external, to craft sophisticated business insights. As a result of this, the industry for tools and platforms to optimize data management is booming.

Of course, no supplier of an analytics console or data tool will ever mention accuracy issues within their platform. Since companies are responsible to orchestrate and manage the flow of data among all their warehouses, platforms, and reporting tools, it is ultimately their responsibility to ensure accuracy remains intact.

The main purpose of quilliup is to help companies take a proactive approach to data quality. In a similar way in which hitting the gym weekly is a better and more efficient way to tackle coronary heart disease before a bypass intervention, managing data quality upfront will save you from serious issues further down the line. All these processes can be optimized so all your data is checked adequately, frequently, and automatically.

A big benefit of our platform is what we call our ‘alert system’. The ability to receive email notifications when a data issue is found is a game-changer for BI teams. They no longer need to look for issues – just automate the process to ensure issues come to them if and when they happen. This gives them the upper hand, being first to know when there’s a discrepancy that needs to be reviewed.

As the world of data management evolves, quality throughout the process is going to take center stage. After all, wrong data can seriously damage a business and no company should compromise on the accuracy and integrity of their insights.