As enterprises’ data stores have continued to grow exponentially, managing that big data has become increasingly challenging. Organizations often find that the data they have is outdated, that it conflicts with other data in their systems or that it is just plain inaccurate.

The impact of that lack of trust can be significant. Organizations without accurate data can miss out on opportunities or even suffer decreases in brand value or customer satisfaction. The Experian report added, “Nearly one in two organizations globally (52%) say that a lack of confidence in data contributes to an increased threat of non-compliance and regulatory penalties, and consequently, a downturn in customer loyalty (51%).”

To avoid those consequences, organizations often turn to the discipline of data management. They set up data policies and invest in a variety of tools designed to help them handle their stores of big data.

Big data management is a broad concept that encompasses the policies, procedures and technology used for the collection, storage, governance, organization, administration and delivery of large repositories of data. It can include data cleansing, migration, integration and preparation for use in reporting and analytics.

Big data management is closely related to the idea of data lifecycle management (DLM). This is a policy-based approach for determining which information should be stored where within an organization’s IT environment, as well as when data can safely be deleted.