Within any organization, several stakeholders may be interested in data lineage. Previously, only the IT department knew about data lineage and its importance. Business stakeholders have started to acknowledge the need for improved data management. Every business area has been, in this day and age, affected by the explosion of data.
Understanding where your data came from and how it evolved provides measurable benefits. Even the most basic data lineage aids in developing better working practices.
Data lineage is critical to your company’s ability to govern and manage data.
So What Is Data Lineage?
In a nutshell, data lineage is the flow of data within your company and data flowing into and out of your business. Date lineage also includes the systems and applications used to move data. Additionally, data lineage addresses alterations made to the data as it progresses.
“Data lineage,” “Data chain,” and “Data flow” are terms in use to describe data movement and transformation.
Why Is Data Lineage Improvement Important?
“Lost time is never found again,” said Benjamin Franklin. According to Erwin, about 70% of data professionals spend 10 hours or more each week on data-related activities. The majority of that time is spent searching for and preparing data.
Getting to know your companies data lineage will allow you to:
- Improve adherence to data legislation
- Enhance the quality of data flow and processes
- Assist with improving software systems
Building knowledge, using the Alation Data Catalog, of how using this will positively affect revenue.
Data governance improves analytics and identifies what is and isn’t available. Knowing how it is utilized also helps your company follow data protection standards and regulations.
Top Benefits of End-to-End Data Lineage
Here are some of the benefits of implementing data lineage in your company.
Ability to Audit Data
We have already touched on data governance, but there is another significant benefit – audibility!
A data audit examines data to determine its quality or value. Unlike auditing finances, data auditing looks at key indicators to conclude a data set’s quality.
Improving Business Data Practices
Data preparation is a hot topic for both business and IT.
The most significant difficulty that nontechnical users face is the same one that data scientists have faced: slow, complicated, and time-consuming data preparation. Focusing on data lineage will move closer toward solving this challenge.
Non-IT users like business and data analysts want smarter self-service solutions that simplify and speed up data preparation. IT wants solutions to help companies prepare data faster, be more productive, and serve users better.
Enterprise metadata management (EMM) is defined as the business discipline responsible for managing the metadata associated with an organization’s information assets.
Metadata is “information that describes various aspects of an information asset to maximize its usability over its life.”
Metadata management is a bonus that comes with a better grasp of data lineage.
Metadata describes and provides information about other sets of data. For example, when monitoring data, movements make information searchable and accessible.
Data-driven analytics and reporting require collaboration among several corporate units and departments.
Data lineage visualization can assist business users in identifying the relationships in the data. This results in increased transparency and audibility.
Seeing data pipelines and information flows aids compliance efforts even more.
Data Quality Improvements
Data quality is influenced by how data is moved, transformed, interpreted, and selected by people, processes, and technology.
The first step in improving data quality is identifying the source of the problem. The cause of an issue can be established once a data steward determines where a data defect was introduced.
Suppose you need to modify a field in the ETL; you will need to know what would happen upstream if you do so. Everything could be OK — or it could all go horribly wrong.
Data lineage is the only method to understand the impact of this modification. However, manually mapping it out could take anywhere from hours to weeks, depending on how complicated your BI landscape is. On the other hand, automation can plan it out in a matter of seconds.
By its very nature, manually tracing data lineage is highly time-consuming and tedious, which is why automation is such a helpful tool.
Consider this: you are evaluating a change to a software system. With up-to-date data lineage, you will assess the change quicker and more accurately. If you only have a manually updated view of your data, considering modifications will be a headache for sure.
Data Lineage Tools
Tapping into multiple, sometimes hundreds, data sources can be a nightmare.
Enterprises must take data from its source, convert it (clean and transform it), and then load it into a business intelligence platform. From there, it is served to data scientists for analysis.
Data engineers need specialist knowledge to set up and maintain ETL (extract, transform, load). Engineers and analysts alike used data lineage software to make this easier.
Enterprises would be flying blind if they didn’t have data lineage tools. These tools illuminate the data flows across the complex ecology of interconnected data flows.
Examples of Data Lineage
You have two spreadsheets, Employees1.xlsx and Staff2.xlsx. The employees’ sheet has Name, Surname, and Mobile Number. The staff sheet has Name, Surname, Employment Number, Department, and Allergies. You plan a function for the staff, don’t break any pandemic protocols, and send a text confirming allergies. Using staff lineage techniques such as vlookup, merge, or others, you can bring both sets of data into one view very easily.
This may seem a simple example; imagine if you have 100’s or even 1000’s employees!
Doing It Right
BI teams may rethink their data lineage and take control of the processes. Using the correct automated metadata management technology is critical.
It’s not simply about securing your business’s future or saving a few hours of labor. It’s about having standardized, repeatable processes that ensure correctness and produce constant value.
Consider deploying an automated data lineage solution the next time you or your BI team are looking for a needle in a haystack.
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