The data quality role is once again ranked as the most important full-time role staffed in the CDO’s office. But despite the importance CDOs place on data quality, little has been done to actually solve the issue.
Data quality has always been perceived by organizations as a difficult thing to achieve. In the past, the general opinion has been that achieving better data quality is “too lengthy” and “complicated.”
There are many assumptions and opinions that disrupt the understanding of data quality strategy and what is required to implement, maintain, and succeed with it. To truly understand what is standing in the way of succeeding in a digital world, we’ve gathered some of the most common misconceptions plaguing digital companies today.
Myth #1: It’s a one-time affair
Fact: Data quality is a journey, not a destination.
Up to twenty percent of contact data is flawed at point of entry. Even if you spent a year cleaning your database and deduping records, a good percentage of those records will inevitably be out-of-date.
Individuals change jobs, companies change names, families move; the velocity at which your data changes is staggering. To get ahead, the focus needs to be on real-time data quality and ongoing maintenance. When left untouched and without regular maintenance, that data will naturally degrade by another twenty-two and a half percent over the course of a year.
Data quality is nothing like the collective project that goes from an idea to the end product. It is an ongoing process that requires effort and investment. Being “digital” as an organization means an ongoing search for new ways to optimize business models, provide even better customer experience, and explore new business vectors.
Myth #2: Any data quality software will do
Fact: Success in data quality is directly connected to the budget allocated
As we have previously written about, a correlation exists between data accuracy effectiveness and the amount of budget allocated to the discipline. As businesses continue to prioritize customer experience and innovation, they’re looking to increasing technology budgets to support that growth. Globally, technology leaders expect to increase budgets by 52% in the next year – the highest reported in 15 years. And this growth is happening fast: in just four years IT budgets have increased by 10%.
Before jumping onto the tech bandwagon, consider what will make the biggest difference in your company’s handling of data now and in the future. A deeper understanding of your data shortcomings and challenges will inform the selection of the best solutions or tools for your needs. Accuracy is the fundamental measure by which a data quality project should be measured, so there’s no such thing as ‘close enough is good enough’.
Budgeting for intuitive resources is paramount to achieving optimal success with data innovation, not just today but as the business transforms and grows. Budgets should identify all resources to clean, manage, and maintain the integrity of data effectively, otherwise, revenue, brand reputation, customer experience, or R&D can stagnate and possibly postpone the general positive changes within the company.
Myth #3: If it ain’t broke, don’t fix it
Fact: Rapid digital growth demands transformative data quality
Many competitors in the digital space have learned the value of data and analytics, and having made investments in popular data management software, consider themselves to be digitally transformative and on the cutting edge of growth. But what worked once will not necessarily guarantee success in years to come.
When it comes to keeping up with the fast-moving pace of technology, companies quickly become complacent, adopting the “good enough” approach that ultimately halts innovation and growth. Blockbuster Video, Kodak, Toys R Us, Nokia, Atari, Motorola were all once gigantic and thriving, yet despite their size and once-powerful positions, failure to innovate resulted in their downfall. Constant innovation is the linchpin of success in technology.
Time to Face Reality
As fast as we see more companies adapt to technological and digital transformations, the potential for rapid failure is even greater. Companies are acquiring and innovating with data management at such a historic rate that figures make it clear that failure to adapt will quickly backfire.
Fortunately, change is on the horizon and data quality is becoming increasingly more accessible for the company’s that recognize the need. Over the last two years, data quality tooling and procedures have dramatically changed, ignoring them is inexcusable.
Companies can no longer afford to ignore data quality initiatives; now is the time to take the data bull by the horns.