The Role of Data Quality in Digital Business Transformation

Companies have always struggled to leverage data quality to the extent necessary to run their business at an optimal level. Their challenges in the digital world are accelerating operational pace, increasing volume of data, variable modification rates and lastly, wide-spread consumption. In the modern enterprise, data is pervasive and these factors have become a difficult challenge in driving digital transformation initiatives because they can’t be managed with old analog procedures. Previous methods and workarounds are insufficient; in the digital business, the weaknesses of the old methods are magnified and inhibit business growth and success.

Today’s fast paced digital economy requires real or near real-time data to properly respond to events. Companies cannot effectively manage business operations to enable informed decisions if their data is incomplete or inaccurate.  Information which was gathered, manipulated and reported historically may no longer accurately portray what is going on within IT or business units. To be effective, decisions must be based on the data being current and reflect what is happening internally at that time.

Compounding the organization’s transformation challenge is the rate at which data is generated. Data volumes and change rates are staggering, accelerating, and further complicated by the adoption of new technologies which continue to spawn additional data sources. This results in more unsynchronized data sources that need to be managed while continuing to grow in volume and complexity.

Don’t assume your digital transformation endeavor will guarantee seamless transmission and sharing of information. With increased volume and availability, there will be increased consumption and use of data by a broader audience. By making the data available to users, the assumption is that it is of high quality as well as accurate. This is where problems surface; users tend to trust the data without validation from any other data source because it is their system of record, and they therefore consume the data without hesitation. This is where bad decisions start that can cause major operational challenges.

The adaptation to a data-driven world has had its success, but has also had its challenges. There needs to be zero tolerance for poor data quality within a data-driven business. Understanding this is the key to addressing it; recognizing that this needs to be tackled is the starting point to minimize the negative impact of these factors.

Well thought-through solutions address enterprise data quality improvement efforts by presenting the most accurate, current, and contextualized state of IT data. Data Quality initiatives must be enabled and carefully monitored if substantive operational improvements are to be achieved. You can no longer rely on the old methods of periodically mashing data together into a report and sending it out across the company. Without changing archaic methods for managing data, your organization will become just another casualty of a failed data-driven transformation initiative.