Over the past decade, we’ve seen data management go through one sea change after another. The days of driving business insights with historical data. The Big Data years when organizations aggregated mass volumes of data, without really thinking through why they were doing so. And now, the present-day emphasis on AI and machine learning algorithms to predict business outcomes.

All of which is to say that data has grown exponentially complex, diverse, and interconnected over the last several years. So much so that it’s impossible for any one person, no matter how expert or experienced, to manage it manually. And the consequences of this complexity include:

  • Slower delivery of analytical and predictive insights
  • Declining trust in the efficacy of reports, dashboards, and data insights
  • A growing number of data incidents, caused by limited visibility into the end-to-end impact of system changes
  • Increasing risk of non-compliance with regulatory requirements like GDPR and CCPA/CPRA
  • A severe shortage of data engineering and data scientist talent
  • Loss of productivity due to current talent spending disproportionate amounts of time on menial, routine tasks that lead to employee burnout

All of these are reasons why data lineage is poised to become more relevant and business-critical than ever in 2022.

Your Guide to Using – And Benefiting From – Data Lineage in 2022

Data lineage is a way to understand your data’s journey through your data processing systems. It entails mapping the sources the data comes from, where it flows, what happens to it along the way, and all of the direct and indirect dependencies occurring between data entities in the environment.

MANTA’s new Ultimate Guide to Data Lineage in 2022 breaks down exactly how data lineage and the proper analysis of metadata are the keys to getting data complexity under control. It also offers an approach to creating and maintaining these powerful tools so you can overcome blind spots in your own environment.

In our guide, you’ll learn:

  • How data lineage evolved from a simple way to understand data’s journey in your environment to becoming a pivotal tool for mapping and analyzing data pipelines for gleaning actionable and business-driving insights from that data.
  • The advantages and disadvantages of multiple, disparate ways of achieving data lineage and their linked approaches to data discovery.
  • The differences between, and roles of, traditional static metadata and dynamic metadata in controlling data behavior and managing increasingly complex data pipelines.
  • The gamut of business benefits automated data lineage can provide, including data incident prevention that achieves higher productivity and significant cost reductions; data pipeline observability; faster incident resolution; regulatory compliance; faster and more efficient cloud migrations; and how automating routine data tasks, coupled with detailed data lineage maps, are solving the data engineering talent crunch.

Today’s data environments are so complex and chaotic that trying to answer the simplest questions can become a convoluted chore. Without the right tools in place, data will continue to be the tail that wags the dog — causing more problems rather than providing clarity.

This trend will only become more pronounced in 2022, raising the need for advanced data lineage solutions to help tame this chaos, get your data landscape under control, and derive the business-driving insights that your data should be providing for you.

To learn more about the role and benefits of data lineage and metadata in solving these problems, as well as how to create data lineage in your landscape, download The Ultimate Guide to Data Lineage in 2022.

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