What is Data Lineage? How does it Solve Business Problems?
Data lineage is a tool whereby businesses are able to gain an end-to-end view of the entire lifecycle of a data asset, its relationships with other data, and the transformations it has undergone. This promotes trust in the organization’s data as stewards are able to see the sources of information and what processes have created its current state. It also provides valuable insights and an easy way to visually understand relationships, which can become very complicated in the modern hybrid enterprise. The most general ways data lineage benefits businesses are:
Why do Data-Driven Enterprises Choose Alex Solutions Data Lineage?
Alex Solutions is a global Leader in metadata management systems. Among our solutions suite, it is our data lineage that makes us elite. Businesses without data governance, that choose metadata tools with suboptimal features, or that are unable to easily implement struggle to keep up with the pace of change. We are aware of how stressful it can be when shopping for data solutions because of uncertainty about what features will be needed, additional costs and disruptions that occur in its implementation. Alex differentiates itself by providing all features, including a world-leading marketplace of in-built connectors which can automate up to 95% of lineage out-of-the-box. Thus, all of Alex’s functions can be seamlessly integrated into your systems to be used quickly, giving companies a head start.
Some Alex Data Lineage Use Cases
Alex works closely with customers to assist them in leveraging our software to solve real business problems. Some of the most prominent Alex Data Lineage use cases include:
1. Certified Lineage
It is often a necessity that businesses can validate the history of data assets back to the original source. Whether it is to ensure compliance for data protection, to make sure that the data’s quality is trusted so it can be used, or just to be able to quickly track data whenever the situation calls for it.
In one case, the Chief Financial Officer of a major bank wanted to be able to confirm to APRA that all reports that were submitted had a certified lineage. Alex was able to assist by using technically harvested metadata to provide an efficient and reliable line, age of the data within the reports. The completeness of the data that made up the reports were validated so that the CFO was confident in the data to be at a standard that was compliant.
2. Impact Assessment
Data lineage can be considered a bible for business users who need to predict consequences of business decisions. A common use case is that business analysts need to break down data to make predictions.
For example, a data engineer wanted to investigate the downstream effect of decommissioning a data asset. They wanted to be sure that such changes would not affect indirect users of the data. Alex enabled them in two major ways: (1) key usage statistics that identified areas for decommissioning were provided, (2) where the data asset lay within the overall data landscape was contextualized.
The engineer had downstream impacts visually rendered on their screen so they could visualize downstream impacts of change. Additionally, they used Alex’s features to access a catalog that defined which assets would be affected by the change and their associated owners that would be impacted. Armed with this information, which was quickly and easily gathered, the data engineer was able to decommission assets knowing that there would be no unexpected downstream impacts.
Data lineage is useful at a high level, but also facilitates much deeper analysis of data assets, systems and their relationships.
For example, data quality analysts of a manufacturer needed to know which data assets and reports used Standard Industrial Classification (SIC) to know where to focus their data quality checks. Using Alex’s lineage, all data assets associated with specific business terms like ‘SIC’ were traced and visually displayed, heavily increasing the efficiency and effectiveness of the analysts.
In another case, a data architect was tasked with simplifying their organization’s data landscape. They used Alex to capture the whole data landscape into one place and enabled reporting on assets under a certain usage threshold. Then, they used Alex lineage tools to identify pathways with high degrees of complexity, orphan assets and duplicated assets. The architect had the ability to view all assets in the landscape, filter and find what they needed, navigate through the assets to gain further understanding, and then use that information to determine which assets could be decommissioned to simplify the data landscape as a whole.