Quality of data

There is no doubt that high-quality data (cleansed data) is an irresistible asset to an organization. But in the same way, bad quality or mediocre quality data, if used to make decisions for an enterprise, cannot only be bad for your enterprise but can also tarnish the brand value of your enterprise, which is very hard to get back. The data, in general, becomes not so usable if it is inconsistent, duplicate, ambiguous, and incomplete. Business users wouldn't consider using these data if they do not have a pleasant experience while using these data for various analyzes. That's when we realize the importance of the fourth V, namely veracity.

Quality of data is an assessment of data to ascertain its fit for the purpose in a given context, where it is going to be used. There are various characteristics based on which data quality can be ascertained. Some of which, not in any particular order, are as follows:

• Correctness/accuracy: This measures the degree to which the collected data describes the real-world entity that's being captured.
• Completeness: This is measured by counting the attributes captured during the data-capturing process to the expected/defined attributes.
• Consistency: This is measured by comparing the data captured in multiple systems, converging them, and showing a single picture (single source of truth).
• Timeliness: This is measured by the ability to provide high-quality data to the right people in the right context at a specified/defined time.
• Metadata: This is measured by the amount of additional data about captured data. As the term suggests, it is data about data, which is useful for defining or getting more value about the data itself.
• Data lineage: Keeping track of data across a data life cycle can have immense benefits to the organization. Such traceability of data can provide very interesting business insights to an organization.

There are characteristics/dimensions other than what have been described in the preceding section, which can also determine the quality of data. But this is just detailed in the right amount here so that at least you have this concept clear in the head; these will become clearer as you go through the next chapters in this book.