Today's technology landscape is data-driven. Understanding and managing data is a core function of any enterprise. All this data can unlock capabilities like real-time, geo-targeted advertising delivered to a consumer's mobile device – but the promise of this data can't be fully realized until your organization has addressed its data governance strategy.

What is Data Governance?

Data governance is the practice of managing, controlling, and auditing data to ensure it remains secure, private, accurate, and usable throughout its lifecycle. This requires establishing clear rules, standards, and processes for data handling. It’s critically important in an era when data privacy ethics are top of mind for consumers, and regulations like GDPR and CCPA carry hefty fines for violations.

The promise of Customer Data Platforms (CDPs) is to provide a single source of truth for customer data, but to do that data governance needs to be a priority. Customer Data Infrastructure (CDI) platforms like MetaRouter play a pivotal role in this ecosystem by offering businesses with data management solutions that are in lock-step with internal and external data governance policies. 

Data governance is an all-encompassing process that requires coordination from prioritization in the modern enterprise. Here are just a few ways data governance plays an important role in the data lifecycle:

• Data governance is a continuous process that requires monitoring, evaluation, and improvement over time. This ongoing process requires an internal team to run effectively
• Data governance needs to align with your business strategy. For example, if you are a media company collecting data and using identity resolution, it's critical that you also have a consent framework in place.
• Data literacy across the organization is a critical component of the data governance process. All teams arguably play a role in data security, data collection, and data usage. Educating your team on data handling, interpretation, and usage is key to maximizing the value of your data.
• Staying up to date with technological developments and ensuring the ethical use of those tools is essential for data governance strategies. Tools like artificial intelligence are transforming the landscape, but addressing challenges like machine learning biases can greatly enhance the effectiveness of your data.
• Data governance metrics are essential for ensuring the success of your process and identifying areas for improvement. Tracking metrics like data quality, data adoption,  and data latency are all part of the data governance process.
• Data governance policies outline how you handle data from the point of collection to how you share that data with various downstream vendors. Just because data is collected doesn't mean it's authorized to be shared with third parties and carefully auditing data collection and data sharing processes is integral to a compliant policy. 
• Defining and setting up mechanisms to enforce consent based on user preferences. If a user opt's in to Facebook but opt's out of tiktok, you need to have a procedure to ensure consent is enforced throughout your data ecosystem.

Why Data Governance is Essential

An effective data governance strategy ensures that your data is accurate, consistent, and can be used to benefit your business. Too many organizations are sitting on a goldmine of rich data insights and struggle to use their data effectively. A well-thought out data governance strategy offers solutions to four modern data challenges: 

• Complying with regulatory requirements
• Maintaining customer and internal trust
• Improving data quality
• Scaling data with growth

Complying with regulatory requirements

Strict regulations like GDPR and CCPA have made data governance an absolute priority in modern sales and marketing. The data you collect must comply with international regulations, elsewise you run the risk of hefty penalties and alienating your customers. Data governance practices need to account for the data collection process; a modern solution is to enforce compliance at the moment of collection with a Customer Data Infrastructure (CDI) platform like MetaRouter.

Maintaining customer and internal trust

Modern consumers have two expectations:

1. They expect businesses to handle their personal data responsibly and ethically.
2. They want personalized experiences that enhance their buying experience.

To balance these expectations, your data governance strategy must put customer trust first. This means carefully managing and respecting throughout the data lifecycle: from acquiring consent to collect data in the first place to transparently and openly disclosing how personal data is used. For example, using identity resolution technology to display real-time, geographically relevant advertising to consumers unlocks powerful capabilities, but without the all-important consent mechanism, your efforts could result in an erosion of trust.

The other side of the trust coin is internal trust of your data. No one wants to be responsible for violating customer trust or consent. If your team is suspicious of how data was obtained or doesn't trust the chain of custody with data, it can dramatically weaken the effectiveness of your data. This is where technology like consent enforcement can have a significant impact – you can trust that the user's consent choices are respected and enforced throughout your data pipeline.

Improving data quality

Data quality is a core mandate for your data governance strategy. High-quality, reliable data is one of the most valuable business assets you have; unfortunately, many businesses without a data governance strategy in place are swimming in the waters of so-called "dirty data." A carefully managed data governance strategy ensures all teams operate off the same data regardless of the collection mechanism or the platform. For example, ensuring data in your Customer Data Platform (CDP) aligns with the data in e-commerce system or marketing automation platform prevents costly, embarrassing errors in customer messaging.

Scaling data with growth

As businesses scale, so does the volume of data collected and managed. Data governance processes and systems need to be designed to handle higher scale to ensure all downstream workflows remain effective. You need to map out your data dependencies and account for how you'll add new systems or software to your data stack. If you're adding a new marketing software, how does the existing data flows work with this tool? How does data collected from this new tool impact your overall data quality and governance? The answer to these (and many other) questions will help to guide your business as you scale up.

Data Governance vs. Technical Governance

Data governance and technical governance are closely related but distinct functions. Data governance deals with the policies and processes for managing data, while technical governance focuses on the technical infrastructure and software systems supporting these processes.

Data governance processes must result from collaboration across multiple departments like IT, marketing, and legal. The scope of data governance will typically include:

• Defining data standards and policies
• Maintaining data quality
• Ensuring access to data and the security protocols surrounding that access
• Creating centers of data ownership within an organization
• Setting up checks to ensure compliance with data and privacy regulations

Technical governance or IT governance is generally broader in scope and focused primarily on the handling of data through the management of IT resources. Its scope may include:

• Defining IT strategy to align with business objectives
• Managing IT investments and resources, such as software and human resources
• Security practices and risk management protocols
• Establishing IT policies and standards

A Collaborative Approach

The best practice in modern data governance is to define a collaborative approach that combines both data governance and technical governance. This means establishing a clear line of communication between data and technical governance teams. The policies defined by the data governance team are enforced by the IT team, and the IT teams processes can help the data governance team define standards that are grounded in the realities of data collection and management in your organization.

Both teams have the ability to create headaches for the other team. For example, data governance policies ought to align with IT capabilities—it’s easy to draft an ideal data management scenario, but IT may need resources, time, and investment to implement these policies. Likewise, IT projects need to cross-reference data governance strategies to align with data governance policies from the outset.

In an ideal world, your business sets up a collaborative, cross-functional team that keeps lines of communication open. These interconnected teams can set up joint metrics that help guide the implementation of policies and measure their success.

Key Components of a Data Governance Framework

A strong data governance framework is essential for ensuring your organization’s data is accurate, secure, and used responsibly. Let's take a closer look at some of the key components of a data governance strategy:

• Data Stewardship and Ownership
• Data Policies and Standards
• Data Quality Management
• Compliance and Legal Alignment
• Data Security and Privacy
• Data Lifecycle Management
• Data Governance Metrics and Monitoring
• Data Governance Technology and Tools

Data Stewardship and Ownership

Who owns the data governance strategy? It's critical that your organization has clearly defined roles for data owners who are accountable for specific data sets. Data stewards can also be assigned to handle data on a day-to-day basis. This accountability piece is critical to give your team the ability to trace the origin of any data inconsistencies or breaches.

Data Policies and Standards

What are your standards and policies for data handling? Do you clearly set out practices for handling data? For example, can data from your CDP be used in your advertising campaigns? Establishing these types of policies and standards is critical. Your policies should define how data should be collected, stored, accessed, and shared within the organization. Policies should outline how you handle sensitive data to ensure full compliance with regulations like GDPR and CCPA. 

Key elements of your data policy include:

• Data Classification: Assigning a sensitivity level to data (such as public, internal, and confidential) and establishing clear protocols for each category.
• Data Retention: Determine how long data should be stored and when it should be deleted according to various privacy laws.
• Data Access Control: Strict controls need to be in place to determine who can access different levels of data, usually assigned through role-based access systems.

Data Quality Management

Maintaining high-quality data for your organization is an essential part of your data governance strategy. This helps everyone make the most of your data through data-driven decision-making and maintaining customer trust. Including mechanisms for data quality control means having data that is accurate, consistent, complete, and up-to-date across all systems.

Examples of processes for managing data quality include:

• Data Validation: Checking the data for errors, inconsistencies, or gaps.
• Data Cleansing: Making sure to correct inaccuracies and inconsistencies through either manual or automated processes.
• Data Reconciliation: Taking steps to ensure the data used across different systems is consistent and accurate.

Compliance and Legal Alignment

Adhering to compliance standards and data privacy regulations is a key mandate of any data governance strategy. Your framework needs to account for consent management, define your data audit processes, and outline your protocols in case of a data breach. Your framework needs to account for consent management, define your data audit processes, and outline your protocols in case of a data breach

Data Security and Privacy

How do you secure your organization's data? This component of your data governance strategy needs to cover elements like:

• Encryption: Sensitive data needs to be encrypted both in transit and at rest
• Access Controls: Stringent controls to regulate who can access, view, or modify data.
• Regular Security Audits: Performing regular audits to identify vulnerabilities before they can be exploited

Data Lifecycle Management

Data has a lifecycle which needs to be carefully monitored. It starts when the data is collected and ends when the data is archived or deleted. Defining how data is handled at each stage of its lifecycle is critical. How do you collect data? How do you store data? And what is your process for archiving or deleting data

Data Governance Metrics and Monitoring

It makes good sense to implement a measurement plan for your data governance strategy. The types of metrics you may use will help to monitor data quality, security risks, and data utilization. A few metrics to consider tracking are:

• Data Accuracy: Measure how correct your data is by comparing your data against known correct values.
• Data Completeness: Measure how complete your data is, for example, comparing the percentage of completed fields for customer records.
• Data Access Control Violations: Measure how often unauthorized individuals attempt or succeed in accessing restricted data.
• Consent Management Compliance: Measure how well your organization captures and stores user consent for data collection. Include measures like the percentage of records with valid consent and how quickly you can produce consent records if needed.
• Data Downtime: Measure the availability of data for business operations and aim for high availability to enable everyone in your organization.
• Training Completion Rate: Measure the percentage of employees who have completed data governance training.

Data Governance Technology and Tools

As we discussed in the Data Governance vs Technical Governance section, the tools and software used by various departments in your organization play a key role in data governance. Modern CDI platforms like MetaRouter can play a pivotal role in enforcing compliance at the moment of data collection, alleviating concerns about whether consent for data obtained was received. These tools give fine-grain control over how data is collected, distributed, and managed.

The Future of Data Governance

As we move forward in an increasingly data-centric, privacy-focused world, the future of data governance will be shaped by emerging technologies, privacy regulations, and consumer preferences. Data governance technology has a big role to play in helping organizations thread the needle between all these forces. Maintaining customer trust and ensuring compliance while unlocking the full potential of data assets is a unique challenge modern businesses face.

The rise of AI and machine learning in business operations brings its own opportunities and challenges. Data quality is paramount to ensuring that machine learning models are trained on accurate, unbiased data sets. Addressing these algorithmic biases must be part of your data governance strategy because the quality of data going into the model determines the quality of the output. The promise is that you can use this data to achieve real-time personalization to enhance customer experience and improve conversion rates.

The trend in the regulatory environment is likely to continue, with a tightening of privacy and data protection laws. More countries are enacting laws similar to GDPR and CCPA requiring businesses to proceed carefully when collecting consumer data. It’s likely that the default for data collection will be strict adherence to acquiring consumer consent prior to collecting data, and the sooner your data governance strategy accounts for this, the better.

To meet the market pressures around data governance and privacy-first data collection, tools like MetaRouter are changing the landscape. As a server-side data management tool, MetaRouter enables you to enforce consent at the moment of data collection. This means that whenever you collect data, it won’t be distributed unless it passes the required compliance checks. Automating data compliance will be a key pillar in future-proof data governance strategies.

Meeting the demands of a rapidly changing privacy and data landscape is the challenge of modern data governance strategies. A combination of internal policies, technological tools, and legal checks is critical to meet this challenge. By addressing these concerns, your organization will be well positioned to take advantage of emergent technologies like AI and machine learning to utilize the full potential of your data.