As 2022 began, most companies are working on their annual business plan or are working on the first actions to start implementing it. And as data becomes one of the most valuable assets companies can have, it’s crucial to also develop a robust action plan for data. 
 
This action plan should be about keeping the data collection, storing, processing and analyzing from all available sources working well and continuously with minimized risks.
 
This means that, if data is part of the annual plan as the number one fuel for the decisions, data quality should be a priority, in order to achieve only the best results. Decisions on where to place the budget, how efforts should be allocated between projects and which tools the company will invest to deal with their data should be mapped on the data plan.

And to prepare for the future with data quality, there’s no better process than data lineage. The ability to track where the data comes from and understand deeply how data goes from one place to another, automated data lineage tracking, is the way to make business plans and processes more efficient and accurate.
 
Not knowing what is happening with all the data the company deals with, from inside the organization from the sources to the consumers, can be the number one mistake to have flawed data that will lead to wrong insights and decisions. Data processing is important but should not be the only focus – a panoramic view of the data flow and each transformation it goes through will minimize the problems and make the company ready for the future next steps.
 

What Is Data Lineage?

Data is always moving and changing and being able to capture and track the changes and the transformations on the data flow is data lineage. From the moment the company captures the data in its source, data lineage will track and follow it until the final process or system. 
 
Data lineage keeps the record of where the data came from, which systems and parts of the company touched the data, who modified it, and all the transformations that occurred during the cycle and why they happened. It’s called the data lifecycle: the steps, process, filtered, jobs, transformations and merges with other data. 
 
The best part of data lineage is that there’s no single unique and “right” definition to it, there are always similarities with other concepts, but the alterations to the data along the cycle is the main focus, along with knowing the source, being able to know who owned the data at each step, having detailed information of the lifecycle and who are the current users of the data.
 

Data Lineage Techniques

There are different techniques for capturing and documenting data lineage. A few of them are:

• Pattern-Based: this technique relies on metadata to determine, so it can look for patterns and classify all the different information from multiple sources. The pattern-based process only requires the data itself and doesn’t need any external system and tools. 
• Tagging: the tagging technique can be applied when the data is stored, processed and managed in one single system that allows the data to flow and move through its lifecycle. 
• Parsing: the most advanced technique, data lineage by parsing understand the logic used to transform the data. It’s the most recommended technique to track the changes when moving data between systems. 

Why Is Data Lineage Important for the Future of Your Company?

To trust your data, you need to understand it first and be sure that quality and accuracy are present. Data is crucial and being able to understand the logic happening on the data flow is even more important than just seeing the data movement happening. 
 
If the company is not tracking the exact data they have flowing between their systems, it becomes almost impossible to explain the current situation they are in, and all the reports and analyzes will be done wrongly, which will for the sure impact the decision-making. 
 
Data lineage does require knowledge from the users owning the data, especially the ones from outside sources, so they can solve any issues, store it correctly and see how it changed along the way. 

• Be Prepared for Compliance and Regulations Changes 

Depending on the market or niche the company is located in, regulatory obligations are one of the number one drives, especially within financial and legal services. And as regulatory compliances keep growing, data governance and management becomes even more essential. 
 
Data lineage is what prepares the data transparency and ensures that the reporting will be accurate. It’s not only about showing the final report, companies now have to use data lineage to prove and show exactly how they got those results. 

• Make Transformational Change Projects Easier 

Data sources should be looked at with care, as they can have major implications for transformational change projects. When taking one, if multiple systems and tools are involved across different departments and multiple users, data lineage will be the main solution to mitigate risks and make a detailed impact analysis. 
 
Just by having access to a data model for the changes being made, the company can make adjustments to avoid unwanted impacts and downstream. Data lineage will reduce all the possible costly mistakes that would take time, effort and money to fix. 

• Manage the Right Data

This topic is aligned with the first one about compliance and regulation, as it can also be considered a type of data governance. Having governance and control of data, keeping track of the accuracy and integrity of all the data sets will show the company what they need to govern with full transparency.
 
Data lineage shows a true reflection of how your company is currently going with full visibility of what is happening, so you can prioritize and put efforts only in what needs to be managed. It’s also the best way to keep the information flowing across areas with better communication. 

• Improve the Way to Future Optimizations 

Adopting data lineage normally breaks down the walls around each department regarding their own processes and creates a collaborative environment with full visibility within the systems and data flows, improving the way to optimize not only the processes but also users, tools and the ways of working.
 

Future of Data Lineage: 2022 and Beyond

Data lineage is not an abstract concept that only a few IT departments use to track data, it’s a process used in the present that will be even stronger in future and has been adapted to make usability better for all business departments. 
 
In the first steps of data lineage, all the data change descriptions were made in a spreadsheet and were usually updated manually by the users. Nowadays, tools and systems keep track of everything automatically, making data a collaborative concept. Data lineage is a real-time process that allows companies to react and make changes moments after something happens.
 
The more automated data lineage becomes, the more efficient the whole data management will become, and even the patterns found on the data can be tracked and described by tools. Those patterns are the trends being created, so leaders can understand where the company and the market are heading and prepare the organization for that.
 
Impact analysis can also be done through an automated data lineage. It’s the way to see which objects will be affected and if any downstream will occur even before the alterations happen. And if any minor disruptions occur, data lineage will help find where they are located so the way to fix it can be decided much faster.
 
A movement that is already happening and will become stronger in the upcoming years is the migration to the cloud. Companies are moving their data out of data centers into the cloud. And to mitigate risks and avoid disruptions in this migration, the data flow needs to be replicated correctly – this means that data lineage needs to be in place and the company must look into their data to identify problems on the sources and possible dead ends. 
 
And as mentioned above, data lineage is also the way of ensuring integrity and accuracy of reports, especially when doing migrations and systematic changes. It’s not only achieving the final report with the right numbers, it is about understanding the logic behind the report.
 
Data lineage allows companies to check upstream and go right where the errors are and which data was affected by it. Any discrepancies can be solved and all the insights taken from the data will be accurate to become concrete evidence to support future decisions.