Understanding The Difference Between Data Management & Data Governance

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Growth, good data stewardship, adaptability – all these business qualities can be traced back to good data management and effective data governance. 

From the lightning-quick evolution of technology to balancing business objectives with regulatory requirements, businesses can’t afford to forgo planning out their policies and processes around how data is collected, managed, and used within their organization. 

But what exactly is the difference between data management and data governance? And how do these two things work in tandem? 

Understanding data governance and management

Both data governance and management are concerned with preserving the quality and integrity of data (e.g., ensuring data is up to date, standardized, properly classified, democratized, compliant with applicable privacy laws). 

In the broadest sense, data governance provides the framework and strategy for handling data. It assigns stakeholders, clear lines of accountability, and sets expectations data validation, tracking, consent management (as just a few examples). 

Data management is the more operational side of this, focusing on how the business will clean data, maintain databases, scale analytics and reporting, and more. 

Now, let’s break this down further. 

What is data governance?

Data governance is an internal set of policies and standards around how your data is collected, used, and protected. 

For instance, data governance may designate who internally has access to highly sensitive data (like a customer’s personally identifiable information), and what protections are in place to ensure this data remains protected. Below, we’ve included a data governance checklist to cover the major points of concern.  

There are also several different kinds of data governance frameworks that you can use when developing your own best practices. You can learn more about these frameworks here. 

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What is data management?

Data management refers to the processes – or practical steps taken – to ensure data is properly collected, cleaned, stored, and used. Good data management turns raw data into a strategic asset for a business, while also ensuring it remains secure and private. Some examples of what would fall under data management include: 

• Collecting and consolidating data from multiple sources (e.g., maintaining integrations, designing databases)

• Data cleaning and/or performing data transformations

• Managing user deletion requests and consent management

• Analyzing data to generate reporting and drive strategies

• And much more!

Why we’re talking about data governance and data management

Data has become businesses' most integral asset. Data is what drives insights and innovation, streamlining operations and powering machine learning models. 

But big data has taken things a step further. While it provides the opportunity for even more in-depth insights, the sheer amount of volume being generated today has made it much more complex to manage. Businesses are now dealing with data from countless different sources and touchpoints in various different formats. The digital landscape has also ushered in new expectations: like around-the-clock availability to consumers, the prevalence of remote workforces, and new regulations around how data is collected and used (which vary by region). 

All this to say, the stakes are incredibly high when it comes to how businesses manage and govern their data. Depending on what industry you’re in, you may be subject to specific laws like HIPAA (for healthcare companies) or the Gramm-Leach-Bliley Act (for Financial Services). 

Failure to protect and secure data at scale can result in hefty fines and a stain on your brand reputation. Failure to process, store, and activate data in real time, while preserving its accuracy, can lead to a lag in product innovation or subpar customer experiences. 

This is where data management and data governance come in. 

Data management vs. data governance

While data management and data governance have similar goals, how they’re implemented looks very different in day-to-day operations. Below, we clarify the distinction between data management and data governance when it comes to business growth, people management, privacy, and analytics. 

Organization and business growth

Using data to drive business growth is a solid strategy. Data can help identify potential churn among customers, automate certain tasks or interactions (e.g. prioritizing high-value customer support tickets), and provide critical insight into business performance with weekly, quarterly, or annual reports. 

But there’s a big caveat to all this: the data being used to make all this happen needs to be accurate and easily accessible across the organization. 

One way data governance would come into play here would be by establishing best practices and policies around ensuring data accuracy. For instance, it could set up a tracking plan to ensure all data adheres to the same naming conventions (to prevent duplicate entries or inconsistencies). Data governance would also dictate why this data is being collected, what business purpose it serves, and what overarching goals and metrics this data would be tied to. 

Data management, then, may be more focused on the nitty-gritty, like running automatic QA checks, reviewing any data violations (i.e., events that don’t adhere to the tracking plan), or reference data management.  

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People management

Data has a vital role in the hiring, training, and managing of your employees. 

Data management often includes the use of an applicant tracking system (ATS) to contact applicants and onboard them efficiently. The use of digital payroll tools and benefits administration all rely on proper data management practices to help HR teams get their jobs done. Even employee performance reviews use data management to make sure feedback from assessment tools is accurate and applicable.

Data governance establishes internal regulations on what information can be collected from applicants according to fair hiring practices and labor laws. It will also lay out the policies for storing and transmitting payroll information. Governance rules determine what sensitive employee information can be shared with which internal stakeholders and what happens to that information when an employee leaves the organization.

Compliance and privacy

From a compliance and privacy perspective, data governance would help define your company’s data privacy policy (you can take a look at ours here). It may also assign a Data Protection Officer to help ensure compliance and conduct independent risk assessments. 

As for data management, that may be more considered with practicalities like: 

• Managing user deletion requests 

• Data masking to protect PII

• Investing in regional infrastructure to comply with data residency laws

Analytics 

Analytics tools are dependent on data – and the more you have, the more specific you can get when it comes to tracking performance, consumer behavior, spotting trends, and so on. 

Data governance policies may set up guidelines around how you choose different vendors, and how they’re integrated into your tech stack. Whereas data management may be more concerned with the orchestration of data, or how it’s consolidated, cleaned, and moved throughout the organization (e.g., reverse ETL for activation, real-time event processing). 

Segment Protocols: Bad data isn’t an option

If there is one point that we’ve made throughout this article, it’s this: bad data isn’t an option. Segment helps businesses effectively manage their data at scale, and automate key aspects of data governance with Protocols. From hundreds of pre-built integrations,to the ability to transform data in transit, here are a few ways that Segment ensures data quality. 

Aligns your teams around a single data dictionary 

Data must exist as a single source of truth. Instead of relying on spreadsheets that can become outdated within a few hours, Protocols offers a living, breathing Tracking Plan. Marketers and analysts can confidently use this single document that offers up-to-the-minute information on data formats and processes.

Leverage automation for speedy data validation

The only way to catch bad data is through frequent and consistent quality assurance checks, which can be tedious and imperfect. Protocols automates QA to pick up any errors in data types or properties before they ruin your next report. Audits take just minutes with the Data Validation tools.

Manages risk and customer privacy compliance

Segment’s Privacy Portal offers a proactive, and automated, approach to privacy and compliance. Segment can automatically classify data by risk level (e.g., credit card information = high risk, whereas someone’s job title = lower risk), and help businesses enforce who in their organization has access to what data. 

Segment also offers an open-source consent manager to systematically inform users about how you collect data (and why) – giving people the opportunity to consent or deny.