{"id":2361,"date":"2022-07-31T10:57:36","date_gmt":"2022-07-31T15:57:36","guid":{"rendered":"https:\/\/www.dpriver.com\/blog\/?p=2361"},"modified":"2022-07-31T10:57:38","modified_gmt":"2022-07-31T15:57:38","slug":"data-lineage","status":"publish","type":"post","link":"https:\/\/www.dpriver.com\/blog\/data-lineage\/","title":{"rendered":"Data Lineage \u2013 The Key To Understanding Your Data Landscape"},"content":{"rendered":"\n

In this article, we’ll take a closer look at data lineage<\/strong>, the key to understanding your data landscape. Nowadays, most organizations face the complexity of data jumbled on servers from various vendors that may support different platforms. These diverse big data ecosystems can work harmoniously together, but often the linkages between the systems are poorly documented. Most organizations are likely to figure out exactly where their data resides and how it interacts with upstream and downstream applications in a pinch.<\/p>\n\n\n

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\"Data
Data Lineage<\/figcaption><\/figure><\/div>\n\n\n

What really happened to your data?<\/strong><\/p>\n\n\n\n

Understanding the data lineage and data relationships of the environment is the key to grasping the reality of the data. Data lineage is similar to the data life cycle and can help us track the process of data from source to destination. It details the flow of data and its dependencies.<\/p>\n\n\n\n

The information captured from data lineage makes it possible to trace data back to its origin, which also explains the data usage process, which would be time-consuming without an automated data lineage solution<\/a><\/strong>. In short, data lineage will answer questions such as “Where did this data come from?” or “How did you arrive at this reported number?”.<\/p>\n\n\n\n

Knowledge of Data Relationships Plays a Key Role in Assessing the Impact of Changes on Other Systems<\/strong><\/p>\n\n\n\n

This knowledge is useful for better data governance<\/a><\/strong>, improved data quality<\/strong><\/a> and integrity processes, “hidden” data management, and overall metadata management<\/strong><\/a>.<\/p>\n\n\n\n

Map Data to Establish Benchmarks<\/strong><\/p>\n\n\n\n

One of the fundamental benefits of mapping data flow and data lineage is that it establishes a baseline. Mapping data graphically helps to better visualize various data elements and their relationships. These techniques are very useful in identifying potential pitfalls at different stages, and help data managers proactively take necessary corrective actions.<\/p>\n\n\n\n

Data lineage can help provide a more comprehensive view of data, which facilitates better data compliance and easier diagnosis of business rule discrepancies. The starting point for capturing and representing complete data lineage is access to metadata, which most databases typically already have. Knowing this information, this is the easy part, the real work begins with discovering and learning about the “hidden” undocumented data in the data environment.<\/p>\n\n\n\n

The Challenge of “Hidden” Data<\/strong><\/p>\n\n\n\n

“Hidden data” is very common in older legacy and siloed systems, where complete documentation is often missing or lacking. If an enterprise uses only 20% of its visible (” known “) data for data management and analysis at the raw database metadata level, discovering and tracking all data elements and data relationships is a huge problem and cannot effectively leverage the other 80% of its “hidden” data assets. Addressing this issue requires a lot of effort, resulting in time-to-market delays and\/or deployment with substandard products or misinformation, which puts the enterprise at a significant competitive disadvantage compared to other data-savvy companies.<\/p>\n\n\n\n

Data Lineage Through Data Transparency<\/strong><\/p>\n\n\n\n

To create a good data lineage solution, data transparency must be ensured, and as a simple case study in the financial sector, regulators want a comprehensive understanding of how banks derive their risk assessment numbers, such as capital liquidity ratios. <\/p>\n\n\n\n

To do this, financial institutions must be able to explain to regulators in a timely manner how they arrived at the reported numbers, including all the raw data used to calculate the numbers. On a technical level, this requires banks to search their corporate databases to identify data items and track database data relationships between and within the database. Banks must respond promptly (usually within 5 business days) to auditors’ requests to inquire about the source of the figures and how they sourced the data. The problem is that this is often highly manual and tedious.<\/p>\n\n\n\n

Required Solution<\/strong><\/p>\n\n\n\n

Many business plans require you to understand the data environment, unless you know the current data assets, otherwise it is difficult to determine what content need to access or change to meet new business requirements and the lack of understanding of the company’s data assets or unable to understand the relationship between work and data flow leads to waste and the conclusion is not correct, so the database benchmarking is a basic activity, can help the CDO, CTO, Application Architect, and Data Architect to:<\/p>\n\n\n\n

Understand and Leverage Organizational Data and Limit Data Burden<\/strong><\/p>\n\n\n\n

Many business initiatives require you to understand the data environment, and unless you know your current data assets, it can be difficult to determine what needs to be accessed or changed to meet new business requirements. A lack of knowledge of your company’s data assets or an inability to understand relationships and data flows can lead to wasted work and incorrect conclusions. Database benchmarking is therefore a fundamental activity that helps Cdos, Ctos, application architects, and data architects:<\/p>\n\n\n\n