Data remediation forms a crucial element of an effective data management strategy helping organizations manage their data risk while lowering their overall operating costs.

Explaining Data Remediation

An effective data management strategy must include steps to treat the risks associated with inappropriate handling and storage of certain types of non-public data — such as personal information (PI/PII), sensitive data or business critical information.

Data remediation is the process of applying treatment to this data to minimize any risk of unauthorized or inappropriate disclosure, theft or misuse. 

Ground Labs’ award-winning data discovery solutions come with four main forms of data remediation built-in:

  1. Quarantine — moving data files to a specified secure location for review. This allows the data to be protected while its long-term treatment is decided.
  2. Encryption — encrypting data in its current location using a strong encryption algorithm, such as AES-128 and greater. 
  3. Masking — sometimes referred to as truncation, masking replaces digits of data strings such as payment (pre-pay, debit or credit) card numbers with a static character, e.g. “1234 5600 0000 1234” when masked with a static character “X” would become “1234 56XX XXXX 1234.”
  4. Deletion – permanently delete the data from its location.

Why Data Remediation Matters

Organizations store vast quantities of data, of all kinds, in many different formats across increasingly disparate and fragmented networks. According to the IDC’s Global DataSphere Forecast, 2021–2025 around 80% of all data stored by businesses is unstructured. This means that it isn’t stored in formats that can be easily searched, such as databases. Instead, it’s distributed across a wide range of systems, files, emails, images and spreadsheets that are far harder to monitor and control. 

Much of this data is personal data and sensitive information not authorized for storage in such systems. Other sources of high-risk data stores include rogue data – such as payment card data captured within a notetaking app, for example – could appear because of primary system failures, when personnel create workarounds to continue helping clients and customers.  

Across both structured and unstructured data stores, many organizations retain huge volumes of redundant, obsolete and trivial data (ROT data). As the cost of data storage continues to climb, there are significant savings available for businesses who routinely cleanse this data.

To see Enterprise Recon’s remediation and data management capabilities in action, book a demo with one of our experts today. 

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