The author of this article, Anurag Sinha is the Co-Founder and Managing Director of Wissen Technology.
Product developers, for example, depend on data consistency to perform correct transactions or for applications to retrieve accurate records. Data scientists are also consumers of data and require the same for developing machine learning models. Citizen data scientists, too, need clean data to build reliable data visualizations.
The state of data is open to change, and if data scientists or developers need to query a SQL or NoSQL database to learn about the state of data in the past, they need to look at database snapshots or proprietary features for this view.
While these snapshots can compare older data sets, they are inadequate for tracking how the data changed. This is where data lineage comes into play.
What Is Data Lineage?
While we need access to data, knowing how the systems and people modify that data is equally essential. For example, questions could be which business process or technology tool changed the data, or did the data change by an algorithm, an API call, or a data flow, or did it change when someone entered the data into a form?
What were the changes to documents, records, field nodes, or attributes, and what were those changes, and in which context they were made? These and many such questions become relevant as data becomes the lifeblood of enterprises of all sizes.
Data lineage becomes even more essential today as data travels from one place to another across cloud-based and on-premise infrastructures and through databases, data lakes, reporting systems, ETLs, and data warehouses.
Data lineage is the story that takes enterprises through the journey that the data makes through the system. It provides a stepwise record of where the data comes from and tracks the journey the data makes to reach its existing state. This includes the transformations made by data and its journey across different business systems. Data lineage maps the entire data flow to understand, document, and visualize the data in all its stages.
The Importance of Data Lineage for Product Businesses
Data lineage has become extremely important for product businesses today because it ensures that the data comes from the right source, has been transformed correctly, and is loaded to the specified location. It becomes important, especially when making strategic decisions that require accurate information. However, improper tracking of data processes makes it hard to verify the reliability of data and makes this process time-consuming and effort intensive.
Data lineage is also becoming essential for businesses because of the increase in data products. Without accurate lineage, there is no proof that these products are what they claim to be. Similarly, it becomes hard to optimize data-backed processes, carry out error resolution, comprehend process changes, and facilitate system migrations and updates without data lineage.
Knowing how the data has changed, what changes have been made to it, and how it has been updated and processed contributes to better data quality. It provides greater assurance of data integrity and data confidentiality.
Key Benefits of Data Lineage
1. Improve Regulatory Compliance
Enterprises need data lineage to navigate regulatory compliance with ease. Data lineage essentially tracks all the components that are vital for business compliance and holds the records of events and accounts.
With this granular clarity, enterprises can improve risk management scenarios, standardize data handling, and ensure that data processes follow company policies, compliance, and regulatory demands. This becomes especially important in the banking and finance sector, where important metrics and figures in reports must be backed up with data.
2. Generate Data Trust
There is an increasing push for businesses to become more data-driven, make data-driven products, and build data-driven business processes. However, users are less likely to work with data if they do not trust the data they are expected to work with. This impacts organizational efforts to transform digitally and leverage the benefits of data.
Data lineage uncovers the life cycle of data and includes all transformations the data underwent along the way from its source to its current state to the time of access. This clarity brings greater transparency which builds confidence in the quality of data and empowers users to accept or reject the data based on their needs.
3. Improve Data-Dependent Decision-Making and Impact Analysis
All units of the modern-day enterprise rely on data for making strategic decisions. Data lineage impacts all aspects of business growth, including product and service development, and helps businesses gain clear insights. It provides transparency on business rules and changes and helps enterprises set clear priorities and define reasonable goals with greater confidence and knowledge.
Organizations can also improve their impact analysis capabilities with data lineage as it improves the speed of impact analysis. Data lineage allows enterprises to identify the data assets that have been impacted by modifications and helps them quickly mitigate inadvertent disruption to data assets.
Since data is not static either in components or methods of collection, data lineage makes it easier for organizations to reconcile old and new data sets, combine and recombine them with confidence, and extract actionable insights.
4. Reduce Risks During Process Changes
Organizations must identify clear ways to optimize processes to improve productivity and efficiency and drive better organizational outcomes. Data lineage helps in identifying errors in the data and where these originated. Granular clarity on where the errors occur and what impact new process changes will have downstream help organizations identify risks, create mitigation plans and implement process changes with greater confidence.
5. Enable Easier Data Migration
Since organizations collect a vast volume and variety of data, determining how the data will be stored, which storage method works across platforms, geographies, and time zones, and who gets access become complex tasks to navigate. The data lineage process removes all ambiguity from this mix and helps organizations ensure that data remains platform agnostic. This ensures smoother, easier, faster, and low-risk system migrations.
It also makes it easier for IT departments when moving data to new servers or software. This becomes an important capability as data migration from one storage system to another is now an inevitable part of enterprise life in the face of constant technological evolution.
Why Does Data Lineage Matter for Product Companies?
Today, the volume of data collected, speed of processing, and data legislation continue to increase exponentially. In this world riddled with VUCA and driven by data, establishing robust data lineage tracking processes become inevitable for those organizations who want to thrive in this complex market.
Without data lineage, organizations are leaving themselves vulnerable to errors and fines and, worse, the loss of customer confidence. Solidifying the data foundations with data lineage as such is now critical for organizational resilience, competitiveness, and profitability.
About the Author:
Anurag Sinha is a technology leader with an ability to build and lead delivery-focused high-performance global teams. He has over 17 years of rich experience in building solutions for financial services. Before joining Wissen, Anurag worked at MSCI as the head of application development group at Mumbai and led the development of key analytical capabilities for BarraOne platform.
Anurag has also worked as an Executive Director at Morgan Stanley for over 8 years from their Mumbai and New York offices. Anurag holds a B.Tech and Master of Management in Finance from IIT Bombay.
