Unlocking the Power of Data Lineage for Data Governance
Today, it’s hard to find a business leader that isn’t thinking or talking about their organization’s data. Collecting, storing, leveraging, and protecting data have become core functions in successful businesses, and having a robust data governance strategy is a must.
Data governance is the process of crafting policies, procedures, and standards for managing data within a business. This system requires defining roles and responsibilities for data access and management, establishing standards regarding data quality, and setting guidelines for appropriate data usage. The goal of a robust data governance strategy is to ensure that the data is secure and protected — therefore meeting regulatory and customer requests — but also used appropriately to further the business’s impact and potential.
While having a data governance strategy in place is becoming more and more commonplace for companies of all sizes and industries, there are a number of common challenges that can get in the way of executing it. Below, we’ll explore some of these challenges and how implementing a data lineage solution can help solve some of them.
Common Data Governance Challenges
Some of the challenges that act as obstacles to data governance include:
Data is more voluminous and more complex: Whether it’s through their products, customer interactions, or operational processes, companies are collecting increasing amounts of data that’s more complex than it ever has been. This means that businesses need the right data management policies, tools, and systems to understand and use that data.
Various data silos: Data is stored in multiple locations that aren’t integrated with each other. This usually happens when there are multiple operational systems, or when the business is distributed due to past mergers or various product verticals. This impedes visibility and collaboration, and can ultimately reduce an organization’s ability to make strategic decisions for the entire business.
An inconsistent approach to storing and sorting data: To use data effectively, companies need to have a process for categorizing and formatting it, so that it can be easily leveraged. This approach needs to be consistent across the business so that data can be analyzed, measured, and compared at a universal level. Systems like reference data, data lineage, and data hierarchies can help here.
An overall lack of visibility: Due to silos, inconsistencies in data records, and a lack of collaboration and communication, organizations tend to have gaps in visibility when it comes to their data. To be truly effective in their decision-making, teams need a 360-degree view of their organizational data.
Increasing needs for data privacy and security: As companies gather increasing amounts of complex data, they need to ensure that they have a robust framework for protecting that data. When it comes to data governance, teams will need to prioritize data privacy and security while also finding innovative ways of leveraging the information available to them.
Evolving regulatory implications: Businesses are under more pressure than ever to abide by regulatory requirements when it comes to their data. As such, their data governance efforts need to include capabilities such as logging and monitoring, data quality reviews, up-to-date standards, and risk management.
The prevalence of remote and hybrid work: With many employees working from home or on remote networks, companies have had to strengthen their approach to tracking and managing data in a way that’s secure but doesn’t compromise the employee’s ability to work. Solutions like data lineage are important to understand where data goes, how it’s manipulated, and how it’s being used, shared, and accessed.
As businesses put together their data governance strategy, they need to account for these potential barriers. One approach that can help address a number of these issues is a data lineage solution.
How Does Data Lineage Support Data Governance Efforts?
Data lineage, also known as data tracing or data provenance, is the process of tracking data as it travels within an organization’s systems. It’s primarily used to get clarity on where data is from, how it’s been processed or modified (and by whom), how it’s being used currently, and who uses it.
In other words, data lineage helps teams get insight into a piece of data’s lifecycle and allows security teams to have all the information they need to implement their own policies and protect data from insider threats and cybercriminals.
When it comes to data governance, data lineage has an important role to play as it:
- Increases visibility into the organization’s data and reduces important gaps
- Provides more context around every data point
- Speeds up root cause analysis to address any issues
- Reduces unknowns
- Improves data quality
In addition, data lineage can also better position companies to make strategic decisions, as they can better trust the data they’re using. With all these benefits, a modern data lineage solution can ultimately enhance data governance efforts, future-proofing the organization’s path to success.
Ali Cameron is a content marketer that specializes in the cybersecurity and B2B SaaS space. Besides writing for Tripwire’s State of Security blog, she’s also written for brands including Okta, Salesforce, and Microsoft. Taking an unusual route into the world of content, Ali started her career as a management consultant at PwC where she sparked her interest in making complex concepts easy to understand. She blends this interest with a passion for storytelling, a combination that’s well suited for writing in the cybersecurity space. She is also a regular writer for Bora.
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