Build the right data governance policy for your enterprise

data governance in a company

Last modified on April 23rd, 2024

It can be difficult to implement a new data governance policy. Most people don’t like change. And without clear the benefits set out, it’s hard to get others on board.

Imagine then, that you’ve made it past these hurdles to develop a new strategy for data governance, but it only results in more work, messy data, and risky management practices. With clunky integrations between the different platforms, it ends up creating more barriers.

Building a good data governance framework is important because you only get one chance to implement change. Make sure you do it right by centralizing the data to spot fraud and integrate other platforms – making your work even more secure.

So, what exactly is data governance and how can you build an effective policy for your company? Read on to find out.

At Trustpair, we’re all about data centralization. We help you set up an efficient data governance framework by managing all your third-party data in one place to avoid fraud. Want to learn more? Contact one of our experts.

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Understanding data governance

Data governance refers to the accessibility and integrity of data in your business operations. Your data governance policy outlines company protocols for data treatment, and how you’ll ensure that information remains accurate.

Protecting master data is important for operational resilience. This means that you have the ability to continue daily tasks even in the case of a data breach or security threat. At an enterprise level, customers and partners heavily rely on your services, which means they should continue to run even in the face of adversity.

Thus, stress testing is an underrated, but important part of master data management.

Two of the key concepts in data governance include your:

  • data policy (what are the rules?)
  • data governance operating model (how will the rules be implemented?)

There’s a common misconception that data governance is the same as data management, but it’s important to separate the two. Governance is about building the strategy for data processing, whereas data management follows through with the processes, and interprets the results.

For example, data governance refers to processes like backing up your info and encrypting it to keep it safe. Alternatively, an example of a data management process is data analysis; the interpretation of your numbers to draw a conclusion and impact the business strategically.

What’s the problem with generic data governance policies?

At an enterprise level, almost every company already has an existing data governance policy. Not only is this useful in building a guideline for your employees, but it’s needed to satisfy regulatory requirements (such as the Sarbanes Oxley Act, SOX Law).

At the same time, many of these data governance policies are usually built from generic templates, not using best practices. Without proper optimization or stress testing, you might experience huge inconsistencies from department to department.

For example, if the marketing department uses different customer data than the sales department, they are unlikely to create the most relevant messaging for their target audience… Likewise, the sales department could be leaving money on the table for competitors if they are working with outdated information or data that’s not properly maintained.

In fact, this exact scenario occurred for a Dutch business, SuperOffice, before they decided to overhaul their data governance process between sales and marketing. Once they began sharing trusted data, it resulted in a 34% increase in revenue.

More than inconvenience though, poor data governance also increases your company’s susceptibility to cybersecurity threats and fraud. Without governance policies for data security, much of your customer, supplier, and payment information could be exposed. This is further exacerbated by linking your systems with third parties, which might not have the same standard of accessibility (or rather data-protection standards) as you.

Without the right data governance, you’re exposed to risks like data breaches and fraud. In 2021, Meta was fined $276 million after the information of over 533 million Facebook customers was published online. It happened due to poor data governance; one of their APIs had a weak security design which exposed the entire dataset. Much of the customer data could be found within this API, which is why so many records were leaked in one go.

A lack of control over how the business works with data means a gap in responsibility and accountability. Everyone loses.

Fortunately, business users who work with secure software like Trustpair can ‘lock’ the data around your payments, customers, and suppliers. With secured integrations to SAP, Esker, CGI, and more, your team can experience the benefits of third parties without fearing the threat of a data leak.

The challenges of data governance: Securitas case study

On October 28th, 2021, the Security Detectives (a cybersecurity investigation team) found over 1 million files belonging to Securitas, a multinational security firm. The data contained information and images about employees and equipment at four different airports between Colombia and Peru. It had been left exposed on an open Amazon S3 bucket, with no idea how long the data had been left undetected.

The leak happened due to a misconfiguration of data during an update. The firm didn’t have sufficient visibility over its information, which meant it also lacked control over the data.

Without implementing measures to optimize the data they collected, Securitas put the security and lives of millions of people at risk.

However, the impact of Securitas’ poor data governance goes further: their clients are now at a huge risk of fraud. With airport staff having their IDs made public, they have become ‘politically exposed persons’, at risk for bribery and personal threats.

Moreover, fraudsters can easily replicate these ID cards to gain access to the airports in person. The risk of theft or larger terrorism threats has grown significantly since the breach.

Of course, what happened at Securitas is only one example of poor data governance. But it highlights the significance of developing a data governance policy that meets your organization’s needs.

Implementing software like Trustpair can help you avoid making the same mistakes. With the ability to see all the changes made to your data over time, your team members gain full visibility over its information. Empowering your team to make informed decisions with real-time info ensures you react quickly to threats and leave no room for fraudsters.


3 best practices for tailored data governance: tips and techniques

As we have seen, building a secure data governance policy is not easy and even a small mistake can lead to huge consequences. Here are three best practices when it’s time to implement your own data governance strategy:

  1. Involve the right people
  2. Identify roles and responsibilities
  3. Test and track from the beginning

Involving the right people

Whether you’re working at a small or large scale, it’s important to recognize the disruption caused by making big changes at work. If leadership doesn’t buy into investing in data governance, friction can take over and the process becomes much more demanding.

So it’s important for IT managers to involve all stakeholders in the process from the beginning, in order to drive success. This was the focus of a biotech company that needed to transform its data governance policy:

“The project team was composed of professionals from across the organization. The expectation was communicated that, over time, all functions would be represented to participate in tailoring the framework’s design to address their business needs.”

Getting executives on board and driving real change starts from the top. When successful, it also changes your organization’s culture around data governance, improving the uptake of new policies across the board.

Identifying roles and responsibilities

Setting out who owns which functions of the data governance process is hugely important. It closes the gaps between realms of responsibility, ensuring that you maintain control over your data. In fact, this academic study found that a large organization’s efforts in data governance were hampered by not creating and maintaining clear roles for their staff.

For example, your company can assign a:

  • data owner: responsible for data governance outcomes (how the data is used)
  • data steward: responsible for data elements like cleansing, data lineage, analytics, and data visualization. Usually for just one set of data where they have expertise: their product master data
  • data custodian: maintains the storage and integrity through data migration so that the team works with high-quality, trusted data
  • data manager: supervises the overall data management and governance frameworks including synchronization and makes changes when necessary

These groups each manage different parts of information collection, integrity, and security. Having these defined roles helps each team member know their responsibility and leaves the manual data work to the machines.

Testing and tracking from the beginning

While developing a strategy, we recommend a focus on the metrics you’ll measure and track over time. Deciding on this early is key, as it will allow your team to create a ‘normal’ early on and quickly identify suspicious activity patterns.

Moreover, building a continual habit to measure data quality and risk from the beginning will set the standard across your organization. Coming up with a plan is the easy part, implementing this plan sustainably over the long term is what really counts.

Measuring the success of your data governance strategy

During the strategy phase, it’s important to identify the metrics that will show you how successful the changes are.

Tracking the wrong measures means that your team is wasting precious time on factors that aren’t relevant and aren’t helpful to your efforts. We recommend that you throw the so-called “vanity metrics” out the window.

At the very least, you should find a way to track:

  • Data quality: for example data management company Precisely tracks its quality through accuracy, completeness, timeliness, and accessibility
  • Employee efficacy of new data management standards: employees who struggle to see the value will not engage in new practices
  • The number of risk events: for example, through Trustpair’s anti-fraud platform, our clients have reduced fraud events to zero
  • Costs related to risk events: how much money is your people saving by reducing the risk of fraud and data leaks?

By tracking the right metrics, you can ensure that the strategy you’re implementing is having the targeted effect.

Case study: enterprise data governance done right

Each public school in the district of Paterson had its own system to deal with data. Their teachers and students could log in, but it was clear that multiple students had the same ID numbers, creating learning barriers.

Alongside the autonomous running of each school, it became difficult for leadership to:

  • Standardize across the different schools
  • Compare performance levels
  • Enrich their data hub to read it easily
  • Protect the confidentiality of children’s data
  • Manually treat and process data between multiple systems

By implementing a district-wide data governance solution, a Harvard study found that the stakeholders were able to immediately get more satisfaction from their real-time data.

The chosen program could create automated reports based on graduation rates and which schools had sub-par performances. This allowed for benchmarking and done-for-you color coding, so that leadership could draw a standard across the district. In turn, it led to an easy comparison between individual schools.

Moreover, the staff could automate the removal of duplicate student IDs, ensuring that the teacher and student access to the program was much smoother. The flow of information became more secure without the need for manual data entry and data classification.

Data governance tools to get you there, faster

Data governance is a constantly ongoing process, not one-and-done. This means that choosing the right data governance tools relies on software and systems that evolve as your organization does.

Here are some types of tools worth considering to help with your data governance:

  • Performance or analysis tools: for your people to take insights from the data and make changes to the business
  • Data integrity and security tools: for the team to maintain data quality and protect it from outsiders
  • Clean-up tools: treat the data to make it readable and easily used
  • Integration tools: so that you can work across platforms and collaborate between departments

As the leading anti-fraud platform, Trustpair can take responsibility for your third-party data security. We centralize your data across payments, suppliers, and customers, enabling consolidation into one single dashboard.

In fact, our platform can help you prevent corporate fraud by maintaining data integrity and quality management. Our solution constantly monitors your data, automatically triggering a notification for your team in case of suspicious activity.

Demo Trustpair to learn more about how to secure 100% of your third-party data.

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In summary:

  • You only get one chance to overhaul your data governance practices
  • Generic data governance isn’t good enough to protect your data from fraud
  • When developing your data governance it’s best to involve the right people from the start, identify roles and responsibilities and track metrics early on
  • The importance of a successful data governance strategy shouldn’t be underplayed
  • Using software platforms is integral to good data management, but only if they have a focus on security


Data governance means making sure that your business’ data is accurate and protected. Also known as information governance, it refers to the processes in which your organization collects data, interprets it, and keeps it private.

Without data governance, companies place huge levels of risk on their operations. They are exposed to data mismanagement, mistreatment, and fraudulent practices. Moreover, companies with no data governance practices in place are more likely to cause operational mistakes due to poor data quality.

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