Junk data defines data that does not serve any real purpose. Companies and organizations tend to keep such data not restricted by data protection regulations because they presume those might be helpful in the future, although sometimes this does not happen. One of the problems of storing junk data is that it generates expenses as it has fixed costs to be maintained on the cloud, as it’s common to store data in the cloud. This cost-creep is gradually adding a burden to budgets. Other problems might be associated with legal or security issues that might derive from storing data that is not adequately managed. Junk data can be missing metadata about data assets, inaccurate information, outdated records, and duplicate data.
Junk data can also lead to legal, security, and compliance challenges, for example, companies operating and gathering information in EU countries. GDPR obliges them to collect only the necessary, minimum amount of data from their clients and users. Furthermore, these data must be accessible if they are required to be deleted or edited and protected from malicious attacks or data breaches.
Organizations can mitigate problems derived from the accumulation of junk data by implementing a proper data management strategy and data governance. These initiatives will keep data organized, accurate, usable, and protected. In addition to eliminating duplicates and standardizing formats, good data management lays the groundwork for data analytics that can create trusted outputs and analytics that analysts can transform into actionable insights later.
Steps in Prevention of Junk Data Accumulation
- Implementation of data governance policies to ensure availability, usability, consistency, integrity, and data security.
- Clearly defined retention policies.
- Adopting a data architecture with an understanding of your data structure and how it fits into a larger organizational structure, leaving no subset of data linked to a business application.
- Optimized data modeling to ensure maintenance of data assets.
- Implementation of metadata repositories and data catalogs.
- Monitoring data quality and data sources to ensure data quality and integrity.
Data cleaning is one of the essential steps for an organization to create a quality decision-making culture and avoid data junkyards. Data cleaning is the process of fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset. When combining data from several sources, many errors produce duplicated, mislabeled, or wrongly formed data. If data is incorrect, analytics are unreliable, even though they may look correct. The data cleaning process will vary depending on the shape of the dataset. It is vital to establish a template for data cleaning processes during data governance initiatives and policies.
Cloud Data Security with Satori
Satori, The DataSecOps platform, gives companies the ability to enforce security policies from a single location, across all databases, data warehouses and data lakes. Such security policies can be data masking, data localization, row-level security and more.
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