Data Governance Best Practice

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.

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.
• 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.

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.

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.

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.

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.