Data Lineage :

Data lineage is generally defined as a kind of data life cycle that includes the data's origins and where it moves over time. This term can also describe what happens to data as it goes through diverse processes. 

Data lineage can help with efforts to analyze how information is used and to track key bits of information that serve a particular purpose.

One common application of data lineage methodologies is in the field of business intelligence, which involves gathering data and building conclusions from that data. 

Data lineage helps to show, for example, how sales information has been collected and what role it could play in new or improved processes that put the data through additional flow charts within a business or organization.

All of this is part of a more effective use of the information that businesses or other parties have obtained.

Another use of data lineage, as pointed out by business experts, is in safeguarding data and reducing risk. By collecting large amounts of data, businesses and organizations are exposing themselves to certain legal or business liabilities. These relate to any possible security breach and exposure of sensitive data. 

Using data lineage techniques can help data managers handle data better and avoid some of the liability associated with not knowing where data is at a given stage in a process.

 


Data Quality :

Data Quality is a relative and never-ending judgment, one that needs to be defined by the business (or business unit) that’s consuming the data. 

An essential element of holistic data governance, trustworthy data serves critical business needs across the enterprise—from legal to finance to marketing and beyond.

Driving data quality requires a repeatable process that includes:

Defining the specific requirements for “good data,” wherever it’s used.

Establishing rules for certifying the quality of that data.

Integrating those rules into an existing workflow to both test and allow for exception handling.

Continuing to monitor and measure data quality during its lifecycle (usually done by data stewards).

And because rules and needs change and new systems can be added to the mix, truly successful data quality initiatives need to be scalable to address those new requirements.

 

Data Governance :
Data governance means you’re managing data completely across the organizational, architectural, and political silos in your company. This requires aligning people, process, policy, and technology to ensure the delivery of trusted, secure data so you can meet industry regulations, reduce the cost of doing business, and grow revenue.
If you ignore the need for data governance and management, you’re missing out on revenue, market share, and product portfolio growth, as well as optimized customer experiences and more efficient supply chains


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