Data. It's in the news and on the blogs. It's all over the trade show floors and in all of the industry conferences.
But while data initiatives continue to gain momentum and importance, data quality still holds back more data initiatives than it empowers. In many cases, it can damage the organization's ability to compete, as well as meet project and earnings goals.
The Study Assessed Where Companies Believe Their Data Problems Come From
According to survey respondents, data quality issues mainly happen internally and need to be addressed through programs and processes.
This isn't just hear-say; it's what was found in the latest study by Experian, entitled, "The 2016 Global Data Management Benchmark Report." In this report, Experian determined that though most organizations still believe data use is evolving enough to support significant improvements in business outcomes -- especially in regards to customer experience, decision making, and governance -- that the data is not where it needs to be in terms of the quality level necessary to achieve all of these lofty goals.
About 23 percent of organizations surveyed feel that their data is not accurate, and that the data inaccuracies are undermining their ability to deliver a superior customer experience (75 percent indicated this was a problem). The study also found that most of the problems with data inaccuracies stem mainly from internal challenges, not from external sources. One of the primary concerns expressed in the report was that, "Organizations lack the knowledge, skills and human resources around data to manage and govern it properly."
In the study, 84 percent of companies indicated that data was an integral part of their business strategy, and 75 percent feel that their business is more likely to quantify and measure their data by department instead of as a whole, across the entire company. Seventy-five percent indicated that it was hard to determine when and where the next challenge associated with their data was going to pop up, meaning they are not doing a great job at forecasting needs and determining where quality might become problematic.
The Key to Overcoming Data Quality Obstacles is Establishing Ownership and Accountability
When data quality is poor, all of the decisions and actions taken based on that data are also off base. Data cleansing comes first, followed by analytics, and finally decision-making based on the data.
Businesses are working towards making smart decisions based on their data, but have not sufficiently updated their data management processes in order to make sure their data is of the highest quality. When businesses put data initiatives into practice before assuring the quality of the data, it causes issues across the company. Some of this is due to the higher volumes of data and greater data diversity, but much of the problem is directly associated with a lack of processes and procedures to cleanse the data and manage its quality over time.
For instance, some companies lack the process of assigning ownership, responsibility, and control of the data to particular people within the organization. If data is going to be used to support strategic business operations, it is essential to assure the quality of the data and establish good data management strategies. It all comes down to having a culture that is founded in the data.
What does it take for a business to achieve high quality data so that the initiatives they base on the data are, in turn, successful? Visibility. and see how it can start giving you the visibility you need build trust in your data.