Data Quality Issues Are Costing Organizations Trillions
Learn How to Avoid Data Quality Issues to Ensure Better Decision Making
From the moment data enters an organization, it will move through various processes, systems and environments, all of which can threaten its integrity and pose operational risks. Given the critical importance of these data assets, ensuring its quality, confidentiality and availability needs to be a top priority. When organizations use low-quality data, they risk making flawed business decisions, losing revenue and frustrating customers.
Avoiding Data Quality Issues
According to an article in Harvard Business Review, the yearly cost of data quality issues in the U.S. alone is $3.1 trillion. Poor quality data is a problem that presents itself in many ways, ranging from system errors and migration issues to data entry mistakes. Whatever the cause of bad data may be, if organizations don’t correct the problem and improve their data quality, the added expense and lost revenue can devastate a company and even put it out of business. So, how can organizations address bad data?
In data management, data quality is critical to enabling data-driven business decisions. But business users won’t utilize data if they don’t trust it, so organizations must find a strategy that builds data quality, as well as trust among data consumers, which is no small challenge. Knowing where to start can be the hardest part.
Ensuring End-to-End Data Quality
Let’s take a step back and look at key dimensions of data quality, such as completeness, type conformance, value conformance and consistency.
- Completeness – looks for null values or empty fields and makes sure that there isn’t anything missing from the data
- Type conformance – looks at the fields making sure that it follows a specific pattern and that it is the right value type for that field
- Value conformance – checks a field by cross-referencing it with another table that would have a list of valid field types
- Consistency – makes sure that the data makes sense in the relationship between the fields
Supporting Data Quality through Data Governance
Every organization needs to have a data integrity process in place that not only checks the key dimensions outlined above and validates data accuracy, but is also standardized and auditable to help catch errors before they occur. While some organizations have data quality tools, they fall short because they’re not end-to-end, automated and in real time. Other organizations deal with data integrity as problems arise, when instead they should be continuously monitoring and tracking the data. This is where analytics-enabled data governance can come into play.
Data quality is often thought of as simply the accuracy, consistency, and reliability of data, and this is true to a great extent. But when it comes to practical applications of data by business users, applying accurate but inappropriate data to solve a business problem can have just as catastrophic results as applying the appropriate but inaccurate data set. That’s why data understanding is so important among data consumers, and why data governance is so important. An organization’s data governance program provides a foundation of policies and processes for data usage, meaning and ownership, so that data can be used to perform critical business functions.
Using Analytics with Governance
By adding analytics to a data governance framework, an organization can continuously monitor data integrity to ensure it is error free. When data is clean and correct, business users can trust the information they pull and use it to help drive better business decisions. Analytics-enabled data governance automates essential tasks that would typically take large teams of people to accomplish. If organizations cut back human input, they can restrict human errors.
Finding the Right Data Governance Solution
While there are a lot of data governance specific solutions in the marketplace, an organization should look for a solution suite that includes data governance, data quality and analytics capabilities. It should perform critical data integrity checks and balances to ensure data integrity remains as data moves across the enterprise. The analytics capabilities can then be used to ensure data anomalies are automatically detected instead of being forced to comb through data to find any irregularities manually.
In addition, the solution should also deliver complete transparency into an organization’s data landscape, allowing organizations to easily define, track, and manage all aspects of their data assets. This enables collaboration, knowledge sharing, and user empowerment across the enterprise.
If you would like to learn more about what an organization can do to restore trust in their data, check out this datasheet.Download the Data Sheet