The 4 Data Governance Principles Demystified

With the ongoing threat of cyber attacks, fast-changing privacy regulations, and siloed data depreciating in value inside silos, businesses have more than a few challenges to overcome in regards to data management. It’s a reality that’s made effective data governance all the more important – providing businesses with the plan, processes, and foresight needed to preserve their data quality, integrity, and accuracy at scale. 

Which is why it’s troubling to hear statistics like, only 10% of financial organizations surveyed believe data governance is valuable (and only 11% consider their own data governance to be strong).

In a world where customer data, trust, and privacy have formed a trifecta for success, how do businesses build a framework for data governance? Below, we tackle this question by focusing on four important principles for effective data governance. 

Understanding the importance of data governance principles

Data isn’t just a by-product of business activities; it’s a valuable asset that drives growth, hones strategies, and lays the groundwork for innovation. When we talk about the principles for data governance, we’re referring to the best practices and guidelines that help shape an effective strategy. 

Data governance itself refers to the processes and policies that dictate how a business manages the collection, security, accuracy, and activation of their data – fairly broad categories in their own right. 

Data governance principles help drill down to the specifics, answering questions like: 

• Which team or stakeholder(s) are directly responsible for this aspect of the data lifecycle? 

• What technologies are we using to help manage our data at scale? What’s our process for vetting and onboarding new tools and systems? 

• How are we complying with applicable privacy regulations? 

• What’s our cadence for QA audits? 

Data governance principles help build out your larger strategy by defining the practicalities of data transparency, security, privacy, stakeholder responsibilities, and more. 

3 key components of effective data governance principles

Data governance takes several pieces working in tandem to create consistent results. Here are six key components to consider when building out your own data governance principles. 

1. Having a clear data strategy

A data strategy provides clarity around what data you collect, where you’re collecting it from, and how it’s being used. This transparency is essential, not only for privacy compliance and security reasons, but also to ensure data isn’t wasting away inside a silo. Here are a few things to consider when putting together your own data strategy:

• Who are the relevant stakeholders? For instance, some companies may assign a Chief Data Officer or a Data Protection Officer.

• Clearly define how the data you’re collecting will drive insights and results for the business overall, like the objectives or KPIs it will relate to. (For example, improving customer support wait times by prioritizing high-value tickets.) 

•  Create a data tracking plan to ensure standardized naming conventions and clarity on what events will be tracked. 

• Identify all the tools in your tech stack that you’ll be using to collect, consolidate,  store, and analyze data. 

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2. Using the right technology

As we highlighted above, there are several tools a business will be using to manage data. Many of these tools can automate the more burdensome parts of data governance, like data classification, transformations, validation, and reporting.

We took a look at how technology stacks tend to evolve, finding themes like new hires tend driving tool adoption, while high-growth companies are more often early adopters. You can read more about all our findings here to make more informed decisions about your tech stack. 

To get buy-in for new tools at your organization, we recommend using a Value-Generation Model that highlights specific use cases, relevant stakeholders, and key performance indicators (KPIs). 

3. Investing in security

This is often the first thing that comes to mind when thinking about data governance, and it’s a crucial one. Businesses should always be invested in protecting their data from outside threats (e.g., malware, ransomware, etc.), but data governance helps plan out your approach to security. 

You can take a look at our own approach to data security to see what we highlighted (like our bug bounty program, certifications, and the rigorous security testing). We’ve also listed out meaningful product security metrics to track to help mitigate risk.

4 data governance principles for ensuring data quality

A key concern for data governance is ensuring data quality. These four principles will help preserve the integrity of your data, especially as you scale. 

1. Accountability

IT departments have traditionally carried the torch for the rest of the organization, but data stewardship must have buy-in from every single department.

By establishing a data governance board or council, you can ensure representation from all stakeholders and have a clear understanding of who is accountable for what.

2. Transparency

Practice good record-keeping with the details of your data governance, including what happens to the most sensitive data (to remain compliant with global privacy regulations). In the event that you experience a data breach or legal conflict, these records can help you quickly piece together a solution.

Not to mention, transparency is a key element in securing user trust. One example of data transparency is with Twilio’s AI Nutrition Labels, which show how data is being used, especially in regards to training LLMs. 

3. Privacy

Data privacy is more than a best practice - it’s the law. As a result, privacy is a key pillar of data governance, outlining everything from which regulations apply to their business and how they’re complying with those regulations at scale. 

Every business should have a clear privacy policy that’s easily accessible on their website, and the right protections in place to ensure the data they collect is properly safeguarded.

4. Integrity

Data integrity refers to how accurate and consistent your data is across your organization. Being focused on data integrity means thinking through potential scenarios, like how you would handle (or better yet, prevent) issues like:

• Errors when transferring data between systems

• Duplicate data

• Accidental data loss or deletion

Actionable steps like creating a company-wide tracking plan, data auditing, and creating a data map to trace the movement of data between tools and systems are all important in ensuring data integrity. 

Protect data integrity at scale with Segment

Segment has made it easy to implement data governance at scale with Protocols, helping businesses to:

• Ensure all data tracked adheres to your tracking plan, and receive notifications for non-conforming events before they enter downstream destinations.

• Make code-free changes to property and event names.

• Apply transformations to data as it flows through Segment to either prevent bad data or customize data for specific destinations.