Data is everywhere, and ever-increasing in quantity and complexity. But there’s a major difference between data and actionable insight. So, it’s no wonder many organizations are beginning to lose sight of what to do with all this data—and feel unsure as to whether they’re fully leveraging data for the deeper insights that can improve both technical and government outcomes..
This data dilemma is making artificial intelligence (AI), machine learning (ML), big data, and advanced analytics the heart of enterprise digital transformation. But, to make these data-driven initiatives a success, an effective, scalable data fabric and active metadata management is needed so the right people access the right data, at the right place and time to unlock smarter, surer insights more swiftly, and deliver better government and mission outcomes.
Data lineage is a foundation capability of a data fabric and active metadata management and a common key component for many different use cases.
Big data, machine learning, data warehousing, business intelligence, cloud migration and advanced analytics all need lineage metadata on the origin, rules, transformation, derivation, history, timing, context and background of the used and processed data.
With the average cost of a data breach exceeding $3.5 million, it is no wonder that safeguarding confidential information has become more important than ever. Furthermore, with stricter laws and governance requirements, data security is now everyone's responsibility across the entire enterprise.
Data masking, also known as data obfuscation, is a process that enterprises use to hide data. Generally, real data is obscured by random characters and a number of other techniques that IT cybersecurity professionals use when masking data.
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