Is your data a liability? Data should support knowledge workers and empower executives to make the best decisions. In this post, we will cover the key characteristics you can use to decide if your data is an asset or liability.

 

Data Asset versus Liability: The Key Indicators of Data Value

The value of a data set goes beyond just the information it holds. It’s determined by the data’s ability to address a specific need. Data quality is the foundation of its usefulness. Below are the characteristics that define data value. A deficiency in any of these can render the data useless, sometimes leading to unknown expenses and problems. Understanding these indicators will help you assess whether your data is an asset, a liability, or a risk.

1. Relevance: Data must be relevant to the needs of the data consumer. For instance, a custom tailor expanding into shoes may not know customers’ shoe sizes but can use height data, which correlates with shoe size, to make inventory decisions.
2. Completeness: Completeness means having all necessary details. Missing columns in a table, incomplete rows of data, or a cut-short video are examples of incomplete data.
3. Timeliness: Timeliness refers to providing data by the required time. While some systems offer real-time results, others operate on batch processes that can delay critical information.
4. Accuracy: Accurate data is crucial. Inaccuracies can spread across systems and models, leading to misguided decisions. Unknown inaccuracies can make data seem like an asset when it’s actually a liability.
5. Precision: More precise data can be used in a wider variety of applications. Measurements in inches are more precise than those in feet, and high-resolution images are better for zooming and large-format printing.
6. Consistency: Consistency means maintaining the same data types and precision across all fields. Changes in data types, distribution formats, or computation formulas can lead to additional costs for the consumer.
7. Uniqueness: Data should be free of duplicate information. Multiple entries for the same person, for example, can be confusing and costly to resolve.
8. Accessibility: Users should be able to easily discover and access the data. Features like semantic search, workflow authorization, and easy-to-use interfaces enhance accessibility.
9. Understandability: Data should come with metadata, detailed documentation, and lineage to help users understand it. Incorrect documentation can be as harmful as inaccurate data.
10. Interoperability: The format of data distribution affects how easily it can be used with different technologies. Using industry-standard formats ensures high interoperability.
11. Community: An active community of contributors and users enhances the value of data. Collaboration and feedback make data more reliable, comprehensive, and understood.

A synergistic relationship exists between accessibility, understandability, interoperability, and community. Modern data catalogs that utilize knowledge graphs and semantic search, like data.world, leverage this relationship to empower data-driven organizations. Knowledge workers can find, understand, use, and share data assets effectively.

By understanding and evaluating these key indicators, you can better determine whether your data is a valuable asset or a costly liability.