5 Practical Tips to Improve Your Data Quality
Data quality describes how well your data is able to serve its defined purpose, generally measured in terms of validity, accuracy, consistency, completeness and relevance. In other words, businesses know they have high-quality data when they are able to use it effectively to determine key business decisions. Data is considered “bad” or of “poor quality” when it’s inaccurate, which, unfortunately, is the norm rather than the exception in various industries.
The state of data quality report reveals:
- 33% of organizations believe they have a lot of bad data
- 49% point to human error as the main cause of data inaccuracies
- 89% of the C-suite admit bad data hurt their ability to provide excellent CX
If you’re also experiencing losses due to bad data, here are five tried-and-true tips to help you improve it:
Streamline your approach
Look at how data is collected, processed, stored, consumed and distributed to come up with a streamlined approach. You can choose to work with your existing setups, get completely new ones or create a hybrid solution. The idea is to come up with one that provides visibility into your data, the level of quality, its processes and ownership of said data.
Break down data silos
Having different and separate data silos discourages collaboration between internal and external stakeholders. This can be solved by having a central data repository where users and workflows are defined, and processes and progress are visible. This ensures you’re working with a single source of truth and not multiple sets of the same data.
Automate data onboarding
Building on the previous point, having vast amounts of data in different silos can lead to inaccuracy of published content, which leads to a poor customer experience. To bridge the gap among these silos, companies need to automatically onboard product information from legacy systems, data pools, suppliers, third-party content aggregators and other sources quickly and efficiently. This eliminates errors caused by manual entry and maintenance.
Enforce product data quality checks
Manual quality checks and maintenance is not efficient for large volumes of data. You need to set up data cleansing, standardization, normalization, classification and categorization rules to transform your data so that it is accurate, complete, consistent and up-to-date.
Utilize a version control system
Track and manage all versions of a dataset to create a seamless audit trail for full traceability. With this, you can get rid of redundancies and duplicates to ensure up-to-date, audit-compliant information at all times.