Data Governance Best Practice
What is Data Governance?
Simply put, data governance means having a solid data foundation by establishing guides and standards for managing and controlling your data.
However, good data governance is not just good data management but also includes securing, organizing, and accessing your data - as well as ensuring consistent data quality over time.
With a robust data governance framework in place, marketing teams can:
The 6 Building Blocks of Data Governance
Security
Monitoring
Access & Ownership
Transformation
Reconciliation
Classification
Data Classification
In order to ensure robust data governance, it’s a good idea for businesses to develop a data governance framework.
A data governance framework, or data analytics governance framework, is a set of rules and guidelines that help organizations handle their data properly and securely.
It covers the six building blocks of data governance, detailing who can access data, how to protect it, and how to keep it clean and well-organized.
In simpler terms, a data governance framework helps organizations:
Roles & Responsibilities
Good data governance starts with having the right people in the right roles to manage, protect, and ensure data quality.
In smaller businesses, one person might wear multiple hats, handling various data governance tasks. But as your organization grows and data complexity increases, it’s crucial to split these duties among different people.
No matter the size of your company, make sure you know who’s responsible for each critical data governance duty.
Here’s a quick look at the roles and responsibilities that should be clearly assigned, and potentially expanded into full positions as your business scales.
C-Suite
Responsible for setting the overall data strategy, ensuring alignment with the organization's goals, and overseeing data operations at the highest level.Their focus is on leveraging data as a strategic asset, ensuring compliance, and driving data-driven decision-making across the enterprise.
Key roles: Chief Marketing Officer (CMO), Chief Data Officer (CDO)
Data infrastructure
These roles are tasked with designing, building, and maintaining the technical frameworks that support data management. Their work provides the foundation for effective data governance by enabling secure and efficient data flows.
Key roles: Data Governance Manager, Data Architect
Data management
Data management roles are responsible for implementing and enforcing data governance policies and procedures. They ensure that data is accurate, consistent, and compliant with relevant regulations. Their duties include monitoring data quality, managing data access, and ensuring that data governance practices are followed across the organization.
Key roles: Data Steward, Data Quality Manager, Data Privacy Officer, Compliance Officer
Putting in place these building blocks will help establish a solid system for data governance within your organization.
And, with each step, a business will become more mature, able to extract more and more knowledge and intelligence from their data.
This is what is called the “data maturity curve” and most businesses who are serious about the role data plays are somewhere on this trajectory.
As you think about your own data governance, think about where you currently are on this curve, and what steps you can take to reach higher up.
C-Suite
Data Management
Data Infrastructure
End users
6 Key Building Blocks for Effective Data Management
Identify and catalog important data
Put effective security, access, and quality controls in place
Improve the impact of data on the business
Rely on a solid data foundation for current data products and future use cases
5 Benefits of Data Governance: Why Data Governance Matters for Marketers
There are 6 key components of data governance, ranging from foundational building blocks that are concerned with security and access, structural building blocks that transform and harmonize data, and quality-related building blocks that ensure data quality.
[Click on each component to learn more]
Data Governance Frameworks
For marketers, implementing a robust data governance framework allows for a more rigorous and effective approach to managing data. It ensures they have reliable data to inform their strategies, leading to deeper insights into customer behavior and fostering greater trust by safeguarding customer information.
However, for some, developing a data governance framework from scratch can be a little intimidating. If that’s the case, why not download our free data governance framework template.
Following this template should lead you to a more structured approach to your data management that not only enhances the precision and impact of marketing efforts but also contributes to delivering a more positive and secure customer experience.
What is a Data Governance Framework? A Guide for Marketers
Summary
Where are you on the data maturity curve?
Why not take our Data Maturity Quiz?
$12.9 million
The amount that poor data quality costs organizations on average according to IBM.
10x
How much more it costs working with bad data instead of getting it right the first time, according to the Harvard Business Review.
Making sure your marketing data is accurate and up-to-date might sound straightforward, but the reality is that many businesses are still failing to manage their marketing data properly.
In order to get the most value from their data, marketing teams need robust data governance frameworks that ensure that the right teams have the right data to make effective business decisions.
In this guide, we outline the six building blocks of data governance and explain how marketing teams can develop improved data management practices that can enhance campaign accuracy, optimize customer insights, and ultimately drive better business outcomes.
Introduction
Foundational
Ensuring organised, accessible data
Structural
Transforming and harmonizing data
Quality
Creating a data quality feedback cycle
An In-Depth Look at Data Governance Roles and Responsibilities
Maintain accurate data
Protect sensitive information
Stay compliant with laws and regulations
Ensure data is usable and accessible
[Click on each role to learn more]
Learn more about how Adverity can help your data governance efforts
For more information, check out:
What is Data Classification? A Beginner’s Guide for Marketers
End users
Data user roles involve using data to perform specific functions within the organization. They are responsible for using data in a way that aligns with governance policies, ensuring that it is used effectively and securely to drive business insights and decisions.
Key roles: Data Analyst, Marketer
Any good dataset needs to be properly classified. This means sorting it into different types, such as quantitative data, qualitative data, dates, strings, whole numbers, decimals, currencies, percentages, floats, and so on.
Data classification is essentially about converting and organizing various data formats from disparate sources into a single, coherent database. This is crucial because data can vary significantly between platforms and regions. For instance, what Facebook calls "Spend," Google refers to as "Cost," and LinkedIn calls "costinlocalCurrency."
Proper classification helps make sense of these differences, allowing for consistent and accurate data analysis across the board. Depending on the complexity and number of your data sets, data classification can be a very labor-intensive task if performed manually.
To that end, there are a number of useful data classification tools that businesses should consider.
It’s critical to be able to trust your data. Any quality issues, incomplete data sets, and discrepancies between platforms all undermine that trust. Simply put, data reconciliation is the activity of comparing two or more datasets together, identifying discrepancies, and, ultimately, resolving any differences.
It entails the analysis of multiple datasets that have at least one common field or property, for example, a set of comparable metrics such as a cost figure and clicks. Any differences between the data sets are then identified and analyzed to understand the reason behind the discrepancy and how to resolve it.
It's best practice to set up an automated way to monitor discrepancies between platforms that measure the same metric. If those differences get too large, then some difficult questions need to be asked about which platform is counting that event correctly.
Data Reconciliation
For more information, check out:
What is Data Reconciliation? A Guide for Data Analysts and Marketers
Data monitoring is the continuous process of observing, analyzing, and verifying data to ensure its quality, accuracy, and reliability throughout the data lifecycle.
Data monitoring is essential in modern marketing, providing the tools to ensure the quality, accuracy, and reliability of data throughout its lifecycle. As marketers increasingly rely on complex datasets, data monitoring helps maintain data quality, which is crucial for making informed decisions, optimizing campaigns, and delivering better customer experiences.
Data monitoring involves several key processes: data profiling, validation, anomaly detection, error identification, and performance monitoring. These processes ensure that data remains accurate and consistent from its source to its final destination, enabling marketers to trust their insights and act quickly when issues arise.
Despite its importance, data monitoring presents challenges, such as handling large data volumes, dealing with data silos, managing real-time monitoring, and ensuring data consistency. Balancing automation with human oversight is also crucial for interpreting results and making strategic decisions.
To implement effective data monitoring, marketers should establish clear data quality standards, automate monitoring processes, foster a data-driven culture, regularly update monitoring practices, and integrate monitoring across the entire data lifecycle. By doing so, organizations can enhance their marketing performance, comply with data regulations, and build stronger customer relationships.
For more information, check out:
What is Data Monitoring? A Comprehensive Guide for Marketers
Data Monitoring
Data transformation is all about enhancing the quality and value of your data. It involves refining and reorganizing data to extract maximum insights and usefulness.
For example, standardizing naming conventions across campaigns, ad sets, and creative assets helps maintain consistency and organization, much like sorting and labeling files on your computer. This makes it easier to manage and retrieve data efficiently.
Or, suppose you want to calculate your Return on Ad Spend (ROAS). This means you'll need to join two separate datasets; Revenue and Ad Spend. The process of joining datasets not only enriches the data but also simplifies subsequent analyses, making it easier to draw meaningful conclusions and make informed decisions.
Ultimately, data transformation is about making your data more actionable and insightful. By carefully managing and enhancing your data, you ensure that it serves its purpose effectively, providing the critical information needed for strategic planning and decision-making.
For more information, check out:
What is Data Transformation (and Why Should Marketers Care?)
Data Transformation
Heavily connected to data security, it’s crucial to set up the right access privileges. This means not only restricting who has access to certain types of data, but also what people can do with it. Handing over the ability to delete large historical datasets comes with a certain responsibility, so you need to make sure that only the right people have this level of control.
This is important, especially when it comes to Personally Identifiable Information (PII) data, which needs to be ring-fenced and accessed on a need-to-know basis. It’s also worth talking to your designated Information Security officer about how long you can keep hold of that PII data before purging it.
On the flip side, you also need to make sure the people who need access to the right data actually have it. The best way to break down all those doors and get ownership of all your data is to do it systematically, and patiently, with shared documentation that you update as you gain access to each data source, listing the account owner, the type of authorization required, and tracking whether the account owner has been sent a request.
Authorization data needs to be stored safely. If it's managed by third-party software and/or is data that isn’t hosted on your organization’s owned properties, you need to ensure it's encrypted and stored according to ISO and/or SOC2 security standards, as there is a level of vulnerability there.
For more information, check out:
Understanding the Role of Data Ownership in Data Governance
Data Access & Ownership
Data security is at the core of data governance. It includes the physical security of hardware and storage devices, administrative and access controls, and the logical security of software applications and organizational policies and procedures.
While data security arguably used to be mainly an IT team's concern, with the rise in technology and digital marketing, it has become a crucial responsibility for all departments, including marketing. This is especially the case given the role marketing plays in handling personally identifiable information (PII) amid increasingly stringent legislation such as GDPR and CCPA.
Key components of data security:
Access Control: Verifying the identity of users and determining the level of access and permissions based on their roles (see below).
Encryption: Encrypting stored data to protect it from unauthorized access and the use of encryption protocols like TLS/SSL.
Compliance and Regulation: Ensuring compliance with relevant laws and regulations such as GDPR, HIPAA, and CCPA.
Business Continuity and Disaster Recovery: Regularly backing up data and having disaster recovery plans in place to restore data in case of loss.
Third-Party Risk Management: Evaluating the security practices of third-party vendors and partners to ensure they meet your data security standards.
For more information, check out:
What is Data Security? Comprehensive Guide to Protecting Your Marketing Data
Data Security
What next?
To find you where your company is on the Data maturity curve
For more information, check out our blog:
For more information, check out our blog:
For more information on the benefits of data governance, check out our blog:
For more information on data governance for enterprises, check out our blog:
Enterprise Marketing Data Governance: The Key to Effective Data Management
For more information, check out:
What is Data Access? A Guide to Effective Data Governance
For more information, check out:
Data Classification Tools: A Comprehensive Guide for Marketers
For more information, check out:
Data Governance vs Data Compliance: What are Key Differences?
For more information, check out:
Data Quality vs Data Governance: Understanding the Key Differences
Download our Data Governance: Best Practices Checklist
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