Untangling Seven Myths and Truths about Data Quality
Infogix, a leading provider of data management tools and a pioneer in data integrity, debunked seven popular data quality myths that are doing businesses more harm than good.
According to a recent survey from O’Reilly Radar, “Organizations are dealing with multiple, simultaneous data quality issues. They have too many different data sources and too much inconsistent data. They don’t have the resources they need to clean up data quality problems. And that’s just the beginning.”
In order to solve data quality challenges that plague organizations today, businesses must understand the most prevalent data quality misconceptions. Here is the truth behind the seven most common data quality myths:
Myth one: Data governance and data quality are two different initiatives.
The truth: Data governance without data quality is data governance light.
Data governance ensures people, processes and technology involved in managing data also establish trust in information to generate reliable insights that benefit the business. This is impossible to do without data quality.
When data travels through the data supply chain, it is exposed to new processes, uses and transformations, which impact its integrity. By scoring and monitoring data quality and enacting data integrity controls within a data governance program, businesses establish data trust by preventing downstream data issues that could adversely affect audit, risk and compliance reporting, management presentations and general decision making.
Myth two: Solving data quality issues is extremely costly and time-consuming.
The truth: Advanced data quality checks and integrated machine learning capabilities allow companies to automatically monitor and improve enterprise data trust.
Machine learning and analytics capabilities continuously monitor data integrity, automate data quality tasks that typically take large teams of people to accomplish and ensure data is error-free. When business users know data is accurate, they trust the information to help drive better business decisions.
Myth three: Knowing if data is either high or low in quality is the only thing important.
The truth: Data quality is a moving target and simply categorizing it as low or high won’t cut it.
It doesn’t matter if data is somewhat inaccurate or very inaccurate. For those consuming the data, if it is inaccurate, at any level, it is useless. However, there could also be times that data is accurate, but not exactly useful. For example, if an organization has accurate data that is six years old, business users will likely consider that information unreliable because it isn’t timely, even though it may be useful to different departments in other ways. Being able to characterize, catalog, and provide lineage for the data might be more important to a user of the data than actually knowing if it’s high or low, depending on how the consumers intend to use it.
Myth four: Third parties are solely responsible for the quality of their own data.
The truth: Whether it was created internally or not, it is critical to verify and maintain data quality across all data sources.
