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Almost every major decision in your business revolves around data.

Data shapes your decisions, guides your strategies, and informs your optimizations.

Yet it’s estimated that 41% of analysts and 30% of marketers don’t fully trust their data. In order to make good decisions, marketers need to ensure data is not just accurate, but secure, and available to the right people —this is why data governance is so important. 

What is data governance?

Data governance in marketing refers to the set of processes, policies, and controls that ensure data quality, integrity, security, and compliance within the realm of marketing activities. It involves managing the data used in marketing campaigns, customer interactions, and analytics in a structured and organized manner.

By establishing rules, frameworks, and processes for data management, you can have complete confidence in your business data. 

But how do you implement effective data governance? What tools are available? What best practices do you need to follow? 

This comprehensive guide will walk you through the major data governance principles and basics, helping you to implement a data governance strategy and leverage its full potential.

We’re going to cover a number of chapters, including: 

  • Data reliability
  • Data consistency
  • Data integrity Vs. data quality
  • Data quality: all best practices
  • How conversion lag can corrupt data quality
  • Empowering data quality control with Adverity
  • Data access
  • Ensuring data access management with Adverity
  • The best data governance tools you should consider
  • How to simplify data governance
  • Empowering your data democratization strategy with Adverity
  • Mastering your data governance with Adverity

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Data reliability

Most business decisions hinge on the careful analysis of data and trends, as they rightly should.

But this approach is dependent on one critical factor — you need to be able to rely on your data for accurate analysis and insight. 

Data reliability can be described as the extent to which your data is complete and accurate, instilling trust among teams for decision-making. This makes data reliability management a pivotal component of effective data governance.

Data reliability management involves establishing a unified view of data, cleaning and validating data, ensuring the recency of data, and ensuring data follows the correct data governance principles. 

Manual processes open data up to human errors. Humans sometimes fall ill and miss deadlines, they accidentally create duplicates or delete files.

Working with an automated data integration platform for data reliability management can help overcome common issues caused by manual processing.

Data consistency

If you’ve ever tried to combine data from multiple sources and found that they were challenging to compare due to different data formats — you’ve experienced issues with data consistency.

The most common example that crops up in marketing data is with date formats such as DD/MM/YY  Vs. MM/DD/YY.

It may seem trivial in the face of things, but issues with data consistency are more than an annoyance — it can actually make it really challenging to gain meaningful insights from your data to inform your decision-making. In fact, if left unchecked, inconsistencies like this can lead marketers to make decisions based on inaccurate data, which might actually end up doing more harm to the campaign than having no data insights at all.

Data consistency is an important part of effective data governance, as it helps ensure consistency in the formatting and structure of values in your data so it’s ready for quick and easy analysis. 

Data consistency issues often stem from the fact that data sources use different data formats, structures, and definitions, leading to inconsistencies when attempting to merge or analyze the data. 

This is where data transformation and data validation can really help. 

Most leading data integration solutions have data transformation and validation capabilities, helping to ensure that data from various sources is not only consistent but also accurate and reliable. For example, a integrated data platform might allow marketers to automatically unify data formats, correct data types, or check for name changes in columns.

Data integrity Vs. data quality

You’ve probably heard the term ‘data-driven culture’ used in the workplace or in marketing circles. 

It’s more than just another marketing buzzword. A data-driven culture is one where teams across the business use data and analytics to optimize their activity and processes to benefit business performance. 

The benefits of embracing a data-driven culture include faster decision-making, greater transparency across the business, and ongoing sustainable improvement in performance. 

However, it’s only achievable if people have trust in their data and a single source of truth, with everyone looking at the same data and using it as a common language to break down silos across teams.

This is where data integrity — the confidence a business has in the accuracy and consistency of its data, becomes important.

A key part of achieving data integrity is ensuring data quality, which involves ensuring it's suitable for its intended purpose.

There’s often confusion amongst marketers when differentiating between data integrity and data quality. To help clarify things, check out the table below — essentially, you can’t have data integrity without data quality: 


Data Quality
Data Integrity


You can read more about both these concepts in detail in our article Data integrity vs data quality: What's the difference?

How conversion lag can corrupt data quality

One of the more complex issues that affects the quality of data is conversion lag. 

If you’ve ever looked at one of your online campaigns and wondered why the most recent CPA stats aren’t as strong as they were 30 days ago — you’ve most likely experienced conversion lag. 

Conversion lag occurs because there’s often a time gap between someone engaging with an ad and actually making a purchase. 

Buyers can take days, weeks, or even many months to decide to purchase after seeing an ad, and most advertising platforms record conversions based on when the ad was viewed, not when the purchase happened. So, while you might see the full spend for your ads immediately, seeing all the resulting conversions takes time.

This causes issues when stakeholders in the business ask to compare last week to the same period last year, for example. The CPA stats from last year have all the conversions accounted for. The stats from last week have conversions yet to come — so the CPA naturally looks much weaker.

Without an understanding of conversion lag, marketing teams might make hasty optimizations to current campaigns, mistakenly thinking they're underperforming. So, simply by having an understanding of this concept, you’re in a better place to avoid making missteps in campaign optimizations.

For a deeper understanding and additional strategies on tackling conversion lag and its impact on data quality, check out our comprehensive article on how conversion lag corrupts data quality (and how to fix it).

Data quality: best practices

We’ve established that data quality is one of the key data governance principles.

But how do you measure — and, more importantly, manage data quality? 

At a top level, data quality is about ensuring your data is accurate, complete, consistent, timely, and relevant.

Common obstacles to achieving data quality include conversion lag, manual data integration mistakes, inconsistent processes, the presence of outdated or duplicate data, and insufficient data validation measures.

However, by following the best practices, you’ll be on the right path to improving data quality across your business. 

Identify the most relevant data
Standardize your data for easy comparison and analysis
Embrace up-to-date data
Use technology to help spot anomalies
Consolidate your disparate data
Enhance your data through transformations
Regularly reconcile your data
Implement a data governance strategy

Empowering data quality control with Adverity

Having established the importance of the 8 data quality best practices, you might be wondering about the practical steps you can take to implement them.

If you think it doesn’t seem feasible to address them all manually, you’d be right. You really need the right data integration platform to help automate the process and monitor the ongoing quality of your data. 

Adverity has a range of features designed to help you improve data quality and maintain data quality control. 

By working with a data integration platform, the process of consolidating your data is fully automated, eliminating the likelihood of manual data errors. This means teams can access their data faster. 

Adverity also has built-in error detection, which alerts data teams to any potential data quality issues and even withholds the upload of poor-quality data to prevent invalid data from entering your data infrastructure.  

You’ll also find a powerful range of out-of-the-box transformations and enrichments that not only automate the standardization and validation of data but also enhance it to provide the value and relevance that your business needs. 

Adverity recently worked with Vodafone to integrate 20 different marketing sources into a centralized data lake, eliminating data silos and providing the business with a single source of truth. The outcome of this project was that Vodafone saw a 20% reduction in the time spent on data integration and an increase of 80% in data quality.

Data access

Have you ever wanted to optimize one of your channels or campaigns, but struggled to find the right data to analyze? Or been overwhelmed by so much data that it’s hard to know where to start?

Or, more concerningly, maybe you’ve stumbled across sensitive business data that should have been out of your reach. 

These scenarios all highlight the importance of data access as part of your data governance strategy. 

While unrestricted access to data presents security and compliance issues, it can be impossible to make effective marketing optimizations without access to the right data.

The first step to improving data access is understanding the optimization goals you want to work towards and the current processes for accessing data. This will help to identify who needs access to which data and where the current situation can be improved.

The next logical step is to centralize the data from your different sources, standardize it, and apply any enrichments that can help to add value to your data. 

With the right technology, you can implement access controls so that the right people can access the right data at the right time, and your data is secure from anyone who shouldn’t have access to it. 

It may sound like a lot, but investing in the right data integration platform can make the process straightforward. 


Ensuring data access management with Adverity

Managing data access management is a key part of any effective data governance strategy, and the right technology can make all the difference. 

Adverity has a suite of features and functionality that can help make the process seamless and secure. 

Role-based filters control who can see which data based on their roles within the business. 

There are also comprehensive user permissions for each of your data streams, where you can dictate who can make changes to the data that makes its way to your internal data environment - be it cloud or on-premise (or both). 

Integrations with each of your data sources are completely secure via protected API connections, data on the move (‘in transit’) or stored data (‘at rest’) all have to encrypted to industry standards — and with Adverity being ISO 27001 certified you have that additional peace of mind.


The best data governance tools you should consider

You’ll most likely appreciate by now that data governance is a broad and complex topic.

There is a wide range of data governance principles, such as data reliability, data quality, data integrity, data consistency, and data access.

Realistically, it’s too much for any business to manage manually. 

That’s why we’ve put together a comprehensive review of the most powerful data governance tools, which includes the following solutions:



Collibra Data Governance

Enhances data governance with tools for classification, cataloging, compliance, and relationship mapping.

SAP Master Data Governance (MDG)

Integrated with SAP's platform, MDG provides a unified data view and standardizes data definitions.


Offers access management, a business glossary, data cataloging, and natural language search.


Promotes simplified data governance using a community-centered, privacy-focused approach.

Apache Atlas

Built for the Hadoop ecosystem, this open-source tool provides metadata management and data lineage.

Precisely: Data360 Govern

Suited for a wide range of organizations, it boasts a data catalog, business glossary, and data quality metrics.

erwin Data Intelligence

Offers a comprehensive Data Catalog, data lineage, quality scoring, and a unique Data Marketplace.

OneTrust Data Governance

Focuses on data security with tools for classification, retention, access management, and compliance setups.


How to simplify data governance

A specific data governance tool may not be needed for many businesses if you choose the right data integration platform. 

Many of the leading data integration solutions are packed with features that can simplify the complex data governance process. 

For example, Adverity comes packed with a wide array of transformations and enrichments that can help you tailor the data in your centralized data infrastructure so it’s just the way you want it. 

There are also powerful features within the platform, like Smart Naming Conventions, which monitors your source data for any mistakes in naming conventions and alerts you about exactly what elements of the convention was incorrect, suggestions on how to correct the error. 

Anomaly Detection is another feature that our clients love —  it helps keep you in the loop about any abnormalities in your data that fall outside the anticipated range, enabling you to take action early to address them. 

Combine this with an Activity Monitor to help you keep on top of the health of your data streams and a robust Authorization Center which allows you to filter the level of access to data, and you might question whether you need a standalone data governance tool at all.

Empowering your data democratization strategy with Adverity

Another data governance principle that we’re yet to touch on is data democratization. 

Have you ever seen departments within a business relying on their own isolated data sources, or interpreting shared data sources differently? 

Perhaps the CX team hoards the CRM data, the marketing team leans heavily on platforms like Meta and Google Ads, and the data team alone accesses business performance data in Power BI. 

This is often called ‘siloed data,’ and it’s not great for collaboration, shared understanding, or business performance. 

Even when different teams across a business use the same sources, they might calculate metrics like revenue or customer acquisition costs differently, leading to discrepancies. Having inconsistent methodologies for data interpretation and calculation can erode trust in data as a shared language and results in inefficiencies, miscommunication, and missed optimization opportunities

Breaking down data silos, promoting data democratization, and establishing a common language for data can lead to better business outcomes and a more cohesive strategy.

Adverity enables businesses to streamline their data integration process, providing all teams with one centralized source of truth that encourages collaboration and enables faster decision-making. 

Mastering your data governance with Adverity

With a robust approach to data governance, you can ensure that your data is accurate, secure, and aligns with data privacy regulations. Additionally, by making data readily available to all teams in your business, they can make more informed decisions, faster.

Without proper data governance guidelines and processes, you increase the risk of errors in your business data and lapses in security, leading to incorrect decision-making (or no decisions being made at all,) and your business falling on the wrong side of data privacy laws. 

Adverity is an ISO 27001 certified, SOC 2 audited data integration platform that complies with all your local data privacy legislations such as GDPR. 

We have features and functionality that help satisfy all the building blocks of good data governance:

Building Block
Features and Functionality
Data Access

Authorization Center and Role-based filters to help ensure the right people can access the right data at the right time. 

Data Unification

More than 600 pre-built connectors and proven integrations, and the ability to load into any data destination. 

Data Classification

Robust data transformation and validation capabilities to ensure data consistency. 

Data Enrichment

A suite of out-of-the-box data transformations to help enhance the value of your data and ensure consistency. 

Data Harmonization

Schema mapping has standard, and advanced functionality like Match and Map to provide additional flexibility. 

Data Reconciliation

Our Activity Monitor and Performance Manager helps you to monitor the performance of your data streams, with alerts if things like resource bottlenecks, time-outs and errors are detected.


So, if you’re looking to improve your data governance strategy, what are you waiting for? 

Take control of your data governance with Adverity.

Find out more about how Adverity can help you today.

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