Data Lineage: Everything you need to know

The term “data lineage” has been thrown around a lot over the last few years. What started as an idea of connecting datasets quickly became a very confusing term that now gets misused often. It’s time to put order to the chaos and dig deep into what it is. Because the answer matters quite a lot. And getting it right matters even more to data organizations.

What is Data Lineage?

In the entire process of data generation, processing and integration, circulation, and final demise, a relationship will naturally form between data. This relationship between data is expressed by drawing on a similar relationship in human society, which is called the data lineage relationship. It is one of the components of metadata.

It can be used to analyze the lineage path of the table and fields from the data source to the current table, whether the relationship between the lineage fields is satisfied, the data consistency of the concern, and the rationality of the table design. It can be used to analyze the impact of changes in upstream data on downstream data and trace the source of upstream problems when changes in downstream data occur.

data lineage

How does it work?

Metadata allows users of data lineage tools to fully understand how data flows through the data pipeline. Metadata is the “data about the data”, which includes various information about the data assets, such as the type, format, structure, author, date created, date modified, and file size. Data lineage tools provide a full picture of the metadata to guide users as they determine how useful the data will be to them.

In recent years, how we store and leverage data has evolved with the evolution of big data. Companies are investing more in data science to drive decision-making and business outcomes. However, for them to construct a well-formed analysis, they’ll need to utilize data lineage tools and data catalogs for data discovery and data mapping exercises. While data lineage tools show the evolution of data over time via metadata, a data catalog uses the same information to create a searchable inventory of all data assets in an organization. Together, they enable data citizens to understand the importance of different data elements to a given outcome, which is foundational in the development of any machine learning algorithms.

Why is Data Lineage important?

Just knowing the source of a particular data set is not always enough to understand its importance, perform error resolution, understand process changes, and perform system migrations and updates.

Knowing who made the change, how it was updated, and the process used, improves data quality. It allows data custodians to ensure the integrity and confidentiality of data are protected throughout its lifecycle.

It can have a large impact in the following areas:

  • Strategic reliance on data – good data keeps businesses running. All departments, including marketing, manufacturing, management, and sales, rely on data. Information gathered from research, from the field, and operational systems help optimize organizational systems and improve products and services. Detailed information provided through data lineage helps better understand the meaning and validity of this data.
  • Data in flux – data changes over time. New methods of collecting and accumulating data must be combined and analyzed, and used by management to create business value. Data lineage provides tracking capabilities that make it possible to reconcile and make the best use of old and new datasets.
  • Data migrations – when IT needs to move data to new storage equipment or new software systems, they need to understand the location and lifecycle of data sources. Data lineage provides this information quickly and easily, making migration projects easier and less risky.
  • Data governance – the details tracked in data lineage are a good way to provide compliance auditing, improve risk management, and ensure data is stored and processed in line with organizational policies and regulatory standards.

Some benefits

Your organization is likely flooded by large and complex datasets from many sources – financial systems, web analytics, ad platforms, CRM systems, marketing automation, partner data, and maybe even real-time sources and IoT. So, knowing where your data is coming from and knowing you can trust it can be a major challenge.

The primary benefits of a robust data lineage process are that it allows you to do the following:

  • Discover, track, and correct data process anomalies.
  • Confidently migrate systems.
  • Lower the cost of new IT development and application maintenance.
  • Combine new datasets and existing datasets with agile data infrastructure.
  • Meet data governance goals and lower the cost of regulatory compliance.
  • Increase trust and reliance on data across your organization.
  • Improve data analysis and thereby business performance.

data lineage

Types of Data Lineage

There are two different types of data lineage – business lineage and technical lineage. Rudimentary data lineage solutions only have business lineage; more advanced data lineage tools have both business and technical lineage. Business lineage provides only a summary view. It shows an interactive map that traces data flows from source to report.

Business lineage is an important tool for business analysts who want to see where their data is coming from to ensure they are using data from a reliable source, but do not want to be bogged down by every alteration in the data.

In contrast, detailed technical lineage allows IT and data architects to view transformations, drill down into the table, column, and query-level lineage, and navigate through their data pipelines. Together, business lineage and technical lineage provide a holistic view of an organization’s data so that data citizens in all departments and roles can use data to make accurate business decisions.

The cloud and the future of Data Lineage

Data simplifies the role of gathering information in some ways and complicates the role of its management in others. The internet, cloud computing, mobile devices, and the Internet of Things (IoT), have made mass amounts of data accessible to every business.

The cloud makes data governance, the collection of processes, roles, policies, standards, and metrics that ensure effective and efficient use of information, imperative for helping businesses to succeed. Data lineage helps sort and organize all that data, giving businesses a clear window to their data for fact-checking and rapid access.

As the cloud continues to grow and evolve, data lineage will become increasingly important for governance issues. While data governance efforts protect data, they can also slow down or limit access. Trustworthy data that isn’t delivered to the right resource at the right time can have a negative effect on time to market.

Is your organization ready to manage data input from the cloud so that you can make more informed decisions at the moment?

Data lineage plays an important role in this rapidly changing system. Tracking data’s origin, and its path through your business, including transformations and targets, is the only way to tackle errors head-on and make governance issues a thing of the past through transparency.

The sheer volume of data at any given moment becomes unmanageable without the proper software tools and solutions. Getting behind the times, and losing track of the data streaming in is simply not an option. A cloud solution offers scalability and reduced cost, as well as de-duplication, data quality, simple data exchange, and multiple source collection and storage. The data governance afforded by a data lineage solution is the key to a smooth ride in the cloud.

data lineage


Data lineage isn’t a new concept, but it is one that’s often misunderstood. However, as data becomes more critical to more areas of business, getting it right is increasingly important.

It requires an understanding of exactly what data lineage is and why it’s so important. Additionally, it requires a thoughtful approach to addressing data lineage that matches the needs of a modern data organization – which means true end-to-end data lineage. And finally, it requires the right tool to support this end-to-end lineage in an automated way.


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