How can a business leverage data for analytical insights if they don’t know the origin of that information? The reality is, if organizations want to produce quality, reliable business intelligence, there is a need to understand the provenance of the underlying raw data.
Thanks to big data and new data sources like IoT devices, the amount of information organizations consume continues to grow exponentially. Not only is it critical to know where that data came from, but it’s also important to understand where it has been within the data supply chain, and how it has changed along the way. Data’s path can be winding and complex, but understanding its usage and flow is a critical part of any enterprise data governance program.
As data passes through diverse systems and platforms, organizations must track, monitor and apply data governance standards to protect data quality and provide an audit trail throughout its lifecycle. If users don’t understand where data came from or how it may have been altered, they won’t trust the reporting results or insights generated. However, understanding different data lineage perspectives helps organizations build a 360 degree view of their data landscape, while building trust and encouraging data use.
Within an organization, there are various ways to view data lineage depending on the user’s role and business objectives, but they’re two sides of the same coin. Business users from marketing, sales, finance and operations require visibility into the data analytics channel. These departments require the ability to trace errors back to their source, to ensure accurate information that produces impactful business insights. This is often referred to as business data lineage, because it allows business users to understand their data’s origins and where it travels over time to properly apply business intelligence.
On the other hand, technical data lineage documents all the elements critical to compliance and operations, including procedures, transformations, artifacts and data joins. By tracking this information, IT resources can easily examine procedures and quickly search the entire data glossary and quickly determine upstream and downstream effects of any changes to the technical metadata. Technical lineage details the impact regulatory requirements have on various data environments by identifying where sensitive or business-critical data elements reside and how they have changed over time, who has access to it and how it is shared.
Once data lineage is understood from both perspectives, data stewards, business analysts and business users can effectively apply data assets to achieve their goals.
In any type of business, data consumers that come from divergent lines of the company have different needs. For example, business analysts, business users and data stewards have different responsibilities and want to derive distinct information from data lineage:
Data lineage forms the foundation of an enterprise’s data governance strategy, providing the information business users need to understand and take control of their data. Data lineage also describes the different processes involved in the data flow and their dependencies, establishing trust among business users to make critical business decisions that impact the entire organization.
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