Effective data management is crucial when building an enterprise that is dependent on data. This requires a mixture of functions that work together to make sure that all systems of the company have reliable, clean data. It includes steps like data cleansing, integration, and standardization.
A well-designed data management strategy requires the right team to supervise it. It’s usually a mix of data management professionals as well as data governance teams, departmental analysts and data collectors. This includes people who can analyze and improve the technology platforms that store and collect data. The right team will ensure that employees have access information that is consistent and clean, and build a complete picture of customer and product data to aid in the operation.
There are several characteristics of data that are quality: completeness, consistency and uniqueness. Completeness refers to the amount of data collected, including missing values. Consistency is the same values across all networks and applications. Uniqueness is the term used to describe whether data sets are not contaminated by duplicate values. Duplicates can be eliminated to reduce storage costs and make it easier for data scientists and analysts to find the information they require.
Another factor in data quality is relevance. It is important that you only collect data pertinent to your business. You could waste time and money if don’t. Compliance is an essential aspect of data management. GDPR, CCPA and similar laws have made it possible for consumers to pursue legal action against companies that don’t obtain informed consent at the point of collecting data, exercise appropriate control over use and the location of data, or meet the erasure or portability requirements.