What is Data Lineage? Business Value of Data Lineage

Data lineage reveals the life cycle of data; it seeks to depict the entire data flow, from beginning to end. Understanding, capturing, and displaying data as it moves from data sources to consumers is the process of data lineage. This covers any modifications made to the data along the process, including how what, and why they were made.

 

Users can use data lineage to verify that their data comes from a reliable source, has through the ‌transformations, and has been placed into the intended location. When sound information is used to support strategic decisions, data lineage is crucial. Data verification becomes nearly impossible, or at the absolute least extremely expensive and time-consuming, if data operations are not properly recorded.

 

By enabling users to explore upstream and downstream, from source to destination, to find abnormalities and fix them, data lineage focuses on evaluating data correctness and consistency.

 

The origin, each stop along the road, and an explanation of how and why the data has migrated through time are all included in the data lineage, which is a map of the data path. 

 

Visual documentation of the data lineage is possible from source to final destination, marking any stops, detours, or modifications along the way. The method makes tracking for operational features like routine use and mistake correction simpler.

 

Also Read | What is Data Validation? Types, Benefits, and Drawbacks

 

What is Data Lineage?

 

Understanding the sources of the data, assessing its quality, and verifying its correctness is crucial for businesses that rely on data to run. Data lineage describes the "line of descent" of the data. 

 

A record of the processes and changes that data underwent as it moved through business systems in order to arrive at a certain location. Data lineage offers a map of the data journey that shows each stage along the route.

 

To guarantee the quality of query results, business reports, business intelligence (BI) dashboards, and training sets, organizations require visibility into how information travels through various workflow processes. When data engineers can monitor who changed something and why, how it was updated, and which method was applied, data quality is improved.

 

There are various benefits to knowing the lineage of data sources:

 

• Assessing the reliability of data depending on its source

 

• Identifying and addressing error causes

 

• Recognizing false data assumptions that might distort the analysis

 

• Giving data governance and regulatory reasons access to audit trails

 

• Ensuring that data transfers remain secure and unaffected by tampering

 

• Recognizing and preventing data duplication to streamline processes and cut costs

 

How To Manage Data Lineage?

 

The management of data lineage is especially important in systems with data lakes. Data lakes are collections of varied datasets in various forms that originate from a wide range of sources. For instance, data lakes may include files in the JSON, CSV, Apache Parquet, or Optimized Row Columnar (ORC) formats, as well as video, log, document, and raw text files. 

 

The datasets in the data lake are always growing, frequently quickly, and several tools may access and analyze this raw data to produce new derivative datasets. When the sheer volume of data is combined with these problems of diversity and velocity, it is difficult for anyone to manually trace the sources and specifics of every data item.

 

Metadata management has to be automated in data lake systems. Metadata management is a major challenge for managing data lakes. Metadata is "the data about the data," as opposed to the data itself that is kept in the data lake. There are many different ‌metadata. 

 

Technical metadata, for instance, may include additional details like the data type, format, and structure (schema). Business items and descriptions may be included in business metadata. Operational metadata frequently includes details about data processing that are essential for tracing data ancestry.

 

Why is Data Lineage important?

 

Data lineage helps you to monitor changes, how they were carried out, and who did them, allowing you to secure the security and integrity of data besides assisting you with difficulties and system migrations.

 

IT teams can view the full data path from beginning to end using data lineage. It facilitates the work of an IT expert and gives business users the assurance to take wise judgments.

 

The position of an individual and the goal of the organization are the main factors that dictate the criteria for a data lineage system. However, data lineage can significantly affect a variety of domains, such as

 

• Data lineage gives business users the ability to see the changes that processed data underwent in order to better comprehend them. This information is essential for enhancing goods and services and running businesses.

 

• Making the most use of both recent and historical information is made possible by data lineage, which lets companies monitor how various datasets evolve as a result of new technologies and gathering methods.

 

• Understanding the location and lifetime of data sources through data lineage enables IT teams to migrate data to a new storage location swiftly, lowering the risk of migration initiatives.

 

• Data lineage aids organizations in managing risks, adhering to industry laws, and carrying out audits since it offers detailed insight over the data lifetime.

 

Also Read | What is a Data Pipeline? Examples and Elements

 

Business Value Of Data Lineage

 

Although having complete access to data throughout its lifespan may seem like an esoteric idea, it may benefit the organization in several ways:

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Business Value of Data Lineage

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1. Boost operational efficiency

 

For practically every judgment, modern businesses rely on BI and decision support systems (DSS). Examples include selecting the most important features to include in new product designs, selecting the best locations for ads, and selecting the best sales and marketing techniques to increase sales, profitability, and customer retention. 

 

In all facets of analytics, "garbage in, garbage out" holds true. Inaccurate data can significantly affect outcomes and hurt corporate success.

 

2. Organize risk and regulatory compliance

 

Businesses in all sectors must cope with a wide range of regulatory regulations. Certain regulatory restrictions only apply to certain sectors. Examples include the Basel Accord, which aims to reduce risk in international banking, and HIPAA, which was created to protect patient information in the healthcare industry. 

 

All industries are impacted by other rules, such as the General Data Protection Regulation (GDPR) of the European Union. For data governance reasons, having metadata that records data lineage decreases risk and the business's expenses associated with compliance. Additionally, it makes complying with any future requirements simpler and more affordable.

 

3. Improved management of changing data sources

 

Systems and data sources are continually changing as company situations change. For instance, a customer behavioral analytics solution that solely relies on the historical point of sale data is very certain to be inaccurate. 

 

Missed e-commerce orders, in-app transactions, and consumers from a range of different sales channels and demographics will all be included in this analytic strategy. Although it may seem obvious, even the most sophisticated businesses are susceptible to data bias and other undiscovered issues with data sources.

 

4. IT cost and risk reduction

 

What all of the aforementioned instances have in common is a reliance on information technology (IT). Businesses may more readily create new apps and solve problems with current applications more quickly and affordably when they have access to datasets and how they are being used. When the sources of data are obvious from their metadata, changing or expanding an analytical programme is more simple and more affordable.

 

Data Lineage Techniques

 

Here are a few typical methods for doing data lineage on important datasets:

 

1. Pattern-Based Lineage

 

Bypassing the code that generated or transformed the data, this method conducts lineage. Metadata for tables, columns, and business reports are evaluated in this process. It studies lineage using this metadata by looking for trends. 

 

For instance, it is quite possible that two datasets containing a column with the same name and identical data values contain the same data at different points of its lifespan. Then, a data lineage chart connects those two columns.

 

The main benefit of pattern-based lineage is that it is technology independent because it just analyses data, not data processing methods. Whatever the database technology, be it Oracle, MySQL, or Spark, it may be utilized in the same way.

 

The drawback is that this approach isn't always precise. When the data processing logic is concealed in the computer code and not readily obvious in human-readable metadata, it can occasionally overlook relationships between datasets.

 

2. Lineage by Data Tagging

 

This method is based on the belief that a transformation engine tags or otherwise marks data. It follows the tag all the way through in order to find lineage. This approach can only be successful if you are familiar with the tool's tagging structure and have a reliable transformation tool that manages all data migration.

 

Even if such a tool exists, it cannot be used to apply lineage via data tagging to any data created or altered without it. So far as executing data lineage on closed data systems is concerned, it is only appropriate.

 

3. Self-Contained Lineage

 

Some businesses have a data environment that includes metadata storage, processing logic, and master data management (MDM). These settings frequently include a data lake where all data is kept throughout its entire lifespan.

 

Lineage may be naturally provided by this kind of self-contained system without the requirement for additional resources. However, just as with the data tagging method, lineage won't be aware of anything that occurs outside of this regulated setting.

 

4. Lineage by Parsing

 

The most effective lineage depends on automatically interpreting the reasoning that is used to process data. This approach performs thorough, end-to-end tracing by reverse engineering data transformation algorithms.

 

Since it must comprehend every programming language and tool used to change and transport the data, this approach is difficult to implement. This might involve XML-based solutions, legacy data formats, SQL-based solutions, JAVA solutions, extract-transform-load (ETL) logic, and so on.

 

5. Manual lineage

 

Talking to individuals and recording the data flow in an organization are both steps in the manual lineage process. Interviews with application owners, data integration experts, data stewards, and other people involved in the data lifecycle are possible. The lineage may then be defined using spreadsheets and straightforward mapping approaches.

 

You could occasionally come across conflicting information or forget to interview someone, which would result in incorrect data lineage. It's a laborious and time-consuming operation to go through the code and manually check tables, compare columns, and other data. Manual data lineage is made more challenging by the complexity and constantly evolving code volume.

 

Despite these difficulties, this method works well for comprehending what is happening in a given setting. When code is absent or inaccessible, manual data lineage also works well.

 

Data lineage enables companies to have fine-grained visibility of data flow across the data lifecycle and supports the management of data governance, impact analysis, and data-driven decision-making.

 

It's important to trace each step that the system takes to alter or process data while developing a data linking system. Each step of the data transformation process requires the mapping of data. In databases and ETL operations, you must maintain track of tables, views, columns, and reports.