A career in data management is never boring. Over the last decade, we have seen the explosion of data volumes, combined with an ever-increasing desire by leading businesses to deliver insights. The data itself is not the problem, however, the increasing variety, complexity, and volumes of data are exacerbating the challenges businesses have always faced when it comes to managing and using data effectively. One of the key challenges is maintaining data lineage – the ability to track the origin, movement, and transformation of data throughout its lifecycle.

Over time, we have constructed enormous data pipelines that amalgamate various components like batch, real-time, streams, microservices, cloud, noSQL, AI/ML, all to extract valuable insights for our business. We must ensure that we operate in an ethical and responsible way and can demonstrate this through proof. This includes building new pipelines, as well as scrutinizing and examining existing ones.

In this article, we’ll explore why the need for data lineage will be more urgent than ever in 2023 and how it can help businesses overcome the challenges of the information age.

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Missing the tools to manage data complexity

However, despite our years of experience, we lack the tools to comprehend and oversee this complexity fully. It always feels like something is missing, and we are left with blind spots. The repercussions are significant:

• The delivery of new analytical or predictive insights decelerates, as we grapple with comprehending a complicated environment and lose the ability to modify existing systems promptly in response to current business requirements, while still adhering to regulatory mandates (particularly around privacy) and other necessities (including ethics and sustainability).

• Confidence in reports, dashboards, and analytical insights diminishes as we fail to explain adequately how the presented data is computed, its origin, and its associated data quality or privacy attributes.

• The number of data incidents surges as we are unable to determine the full extent of new business requirements or technical changes that must be executed in our data systems.

• We waste the time of data engineers, who spend up to 50% of their time manually running impact analysis, assessments, or root-cause analysis, which can be incredibly frustrating.

• The risk of non-compliance and regulatory penalties, especially those related to data privacy (PoPIA, GDPR, etc.) and data quality (BASEL, SOX, AML, etc.), increases.

All of these factors inevitably result in missed opportunities (failing to derive more business value from data), wasted investments (billions spent on creating uncontrollable data infrastructures), and frustration for both business and technology teams.

What is Data Lineage?

Data lineage is the process of tracking the movement of data throughout its lifecycle. This includes understanding where the data came from, how it was transformed and processed, and where it is currently located. By establishing data lineage, businesses can gain a complete view of their data and ensure that it is accurate, trustworthy, and compliant with regulations.

The Growing Importance of Data Lineage

As data volumes continue to grow and new regulations come into play, the need for effective data lineage is becoming more urgent than ever. In particular, the following factors are driving the importance of data lineage in 2023:

• Regulatory compliance: With regulations like PoPIA, GDPR, CCPA, and other data privacy laws, businesses must be able to demonstrate that their data is being collected, processed, and used in compliance with these regulations.
• Data quality: Maintaining accurate and high-quality data is crucial for making informed business decisions. Data lineage helps businesses identify data quality issues and fix them before they impact business operations.
• Data integration: As businesses collect data from multiple sources and store it in various locations, it’s essential to understand how this data is being integrated and transformed to deliver reliable data pipelines.
• Data security: As cyber threats become more sophisticated, businesses must have a complete view of their data to identify potential security risks and take action to protect their data.
• Business Agility: Businesses need to make decisions more quickly, based on data that they can trust. Data lineage provides both data engineers and data consumers with visibility into where data comes from, and whether it can be trusted.

Benefits of Data Lineage

Establishing effective data lineage provides full visibility into your organization’s data. Cutting out data blindspots can provide numerous benefits for businesses, including:

• Improved data quality and accuracy
• Increased trust in data for decision-making
• Enhanced regulatory compliance
• Faster and more effective data integration
• Improved data security
• Quicker delivery of BI reports

How to Establish Effective Data Lineage

Establishing effective data lineage requires a structured and consistent approach. The following steps can help businesses establish effective data lineage:

• Define data lineage requirements
• Identify data sources
• Map data lineage
• Monitor and update data lineage
• Test data lineage

Written down as a list of bullet points it sounds pretty simple. Yet, in practice, the volume and complexity of most environments mean that lineage is very seldom available, or has been built laboriously for compliance pipelines only.

Automating data lineage is essential because it enables organizations to keep track of the origin, movement, and transformation of their data throughout the entire data lifecycle. By automating this process, you can efficiently and accurately map the flow of data, including its lineage, quality, and privacy attributes, which are critical for ensuring regulatory compliance and maintaining stakeholder trust.

Manual data lineage is often a time-consuming and error-prone process that can lead to missed connections and incomplete documentation. Automating data lineage provides a complete and accurate view of how data moves through your systems, which can help identify potential issues and streamline data management and analysis.

Automated data lineage also makes it easier to troubleshoot data-related issues, such as identifying the root cause of data quality problems or pinpointing the origin of data privacy violations. This can save time and resources and prevent costly penalties and reputational damage.

In summary, automating data lineage is crucial to effectively manage and protect your data, ensure compliance with regulatory requirements, and gain actionable insights from your data.

Factors to consider when selecting a data lineage tool

When selecting a data lineage tool, factors to consider include:

1. Data Source Support: The tool should support the data sources you use.
2. Data Lineage Scope: The tool should be able to provide data lineage for your entire data ecosystem.
3. Data Lineage Depth: The tool should be able to provide a detailed view of data lineage, including transformations and dependencies.
4. Data Lineage Visualization: The tool should have easy-to-use, customizable data lineage visualization capabilities.
5. Data Lineage Automation: The tool should be able to automate data lineage discovery and maintenance.
6. Collaboration: The tool should have collaboration features that enable sharing and communication of lineage information.
7. Cost: The tool should be cost-effective and provide value for the investment.
8. Security: The tool should provide security measures that ensure the privacy and confidentiality of your data.

Conclusion

In a data-driven world where the volume and complexity of data continue to grow, data lineage is not just a nice-to-have; it’s a necessity. Businesses that can effectively establish and automate data lineage will be better equipped to navigate the challenges of the information age. They will make decisions based on trustworthy data, ensure compliance with regulations, and ultimately derive more value from their data assets. As we step into 2023, the need for data lineage has never been more urgent, and those who embrace it will lead the way in the age of big data.

Understand How Data Lineage Can Help Your Business Intelligence Efforts by reading our post.

Explore additional Data Lineage use cases with this podcast from MANTA SVP of Products, Ernie Ostic