What is data governance and how to implement it?

“Data is the new oil.” 

Organizations have been pushed to introduce data governance policies because of digital transformation initiatives and the increase in big data. The sheer volume of data that is generated as organizations take advantage of Internet of Things technologies is staggering.

According to Cisco, the daily data production amounts to about 402.74 million terabytes. Organizations have been forced to rethink their approach to data management as they stand to lose in terms of being able to extract value from this deluge and scale their business. 

Which is why traditional approaches no longer suffice and businesses have to adopt data governance. Data governance policies are essential for companies to formally and proactively manage their data data assets throughout the business lifecycle. 

TL;DR

Data governance is critical for organizations to ensure the integrity of their data assets and comply with regulations.

Implementing a data governance process involves defining goals, assessing the current state, establishing a governance structure with clear roles, leveraging automation tools, and continuously monitoring/improving the program.

Foster a culture of transparency, integrity, accountability, and collaboration to leverage data governance to your advantage.

What is Data Governance? 

Data governance is a strategic approach to managing an organization’s data assets throughout its lifecycle. It encompasses checks for data quality, integrity, and availability while adhering to regulatory compliance and business requirements. 

Essentially it is setting internal standards—data policies—that apply to how data is gathered, stored, processed, and disposed of. It governs who can access what kinds of data and what kinds of data are under governance. 

Given the ever increasing volume of data, the importance of having a governance architecture in place, cannot be emphasized enough. 

Why is it important?

 Data governance is important as it helps break down data silos. You need an effective governance architecture to coordinate between departments and units.

Other benefits include, 

1. Data quality: Inaccurate reporting, tainted decision-making, and operational inefficiencies are just a few of the consequences of poor data quality. If these inaccuracies are not fixed, they could affect data analytics. According to a Gartner report, the monetary impact of poor data quality on organizations is estimated to be around $12.9 million per year.
2. Regulatory compliance: organizations must comply with various data-related regulations, such as HIPAA, GDPR, and PCI-DSS. These frameworks define policies and procedures for handling, storing, and retaining this data while ensuring compliance. 
3. Data monetization: Data is invaluable. Data governance allows you to identify and capitalize on data monetization opportunities. According to a Forrester study, organizations that effectively leverage their data assets can increase their revenue by up to 20%.
4. Data analytics: Data governance acts as the bedrock for effective data analytics. It delivers clean data, minimizes compliance risks, and streamlines access.

Every business needs a strategy that caters to its individual needs and requirements. 

How to implement data governance? 

A clear, concise, and accurate data governance strategy is crucial for organizations to effectively manage and derive value from their data assets.

Here are 8 steps you can follow to create a good data governance strategy. 

Step 1: Define your goals and objectives

Whatever your aim, be it improving data quality or leveraging it for monetization, defining it lays the foundation of your governance framework. Establish your scope by identifying critical domains, systems, and processes that will be governed. 

Step 2: Take stock of your current state

Do you have a current system in place? If yes, evaluate your existing system and data lineage to eliminate gaps. 

Answer the following questions to assess your current state 

1. What does your current infrastructure look like? Are you using any procedures or governance tools to help you?
2. Document all the current business processes which involve data collection, processing, storage, and analysis. 
3. Create an inventory of your organization’s data sources, systems, and repositories. 
4. Review your current compliance with relevant data protection laws, regulations, and industry standards. 
5. Consolidate all the findings from your assessment. 
6. If you find gaps, prioritize them based on their impact, risk, and alignment with your business objectives.
7. Develop a roadmap for addressing these gaps based on your priority. 

Step 3: Get the execs involved

Data governance requires strong leadership buy-in. But how do you pitch it to them? 

1. Quantify the benefits: Highlight specific use cases where poor data quality has led to financial losses. For example, according to a Gartner study, poor data quality costs organizations an average of $12.09 million per year.  
2. Competitive edge: Companies like Walmart and Target have leveraged competitive advantage through supply chain management and customer analytics, all thanks to data governance. 
3. Benchmarks: Avail industry specific benchmarks to demonstrate the importance of data governance. 

Step 4: Establish a governance structure

Once you secure leadership buy-in, build a structure. 

1. Appoint a CDO or a Chief Data Officer to head the governance team.
2. Identify data stewards and owners for each functional area. 
3. Recognize everyone who will have access to or use the data.  
4. Assign clear roles and responsibilities to the people appointed.

Step 5: Define policies, standards, and processes

1. Once you have your data inventory completed, classify the data based on sensitivity (e.g., Personally Identifiable Information (PII), financial data) and cruciality.
2. Establish well-defined roles and responsibilities for data stewardship positions. Data stewards are liaisons between data owners and end users. They ensure that data is being handled and striped according to quality standards. 
3. Have a role based access system in place. ‘Responsibility comes with accountability’ ensures that you build a culture of transparency. 
4. Establish quality metrics and standards that you want to adhere to. Like error rates, data validation checks, missing data percentage, data age, etc. 

Step 6: Leverage automation tools 

Automate data asset registration and classification within the data catalog. Use crawlers to find new data sources. Ideally, use a tool that can integrate with your existing data infrastructure. 

Step 7: Training and communication 

Your strategy is only as good as the adoption, and a successful data strategy hinges on it. 

1. Start with foundational training that incorporates all the employees of your organization. Introduce them to data governance, the essentials and the privacy regulations that govern your organization. You can conduct an online training session or send a presentation of it to the employees. 
2. Move on to role specific training. Equip your data owners and stewards with management skills. Educate them on data access procedures, data lineage, lifecycle management and the data governance tools you will be using. Familiarize them with the entire process. You can achieve this through training sessions or on the job mentoring. 
3. Regularly communicate about best practices, procedures or any update in policies with your entire workforce. 

Step 8: Monitor and improve 

Use progress insights to gain a bird’ s-eye view of your governance initiatives. Optimize this process to drive continuous improvement. 

Data governance is an ongoing process and requires regular inputs on various things, like identifying areas for optimization and adapting to changing business needs, regulatory landscapes, and emerging systems or data sources.This framework is merely a starting point. Customize it based on your organization’s size, industry, and regulatory landscape. 

While every business is unique, there are some tried and tested principles that can serve as guideposts. 

Principles of Data Governance: How do you make sure your business adheres? 

Transparency

When your employees embrace the open mindset from the outset, transparency regarding data management and usage becomes ingrained in the organizational fabric and alleviates any potential barriers. Here are a couple do’s and don’ts to promote transparency in your organization:

1. Have a data catalog that explicitly states permission controls and ownership.
2. Perform DIPAs and Data Privacy Impact Assessments prior to implementing new data initiatives.
3. There should be a clear policy for whistleblowers, so fear of recrimination should not exist. 
4. Refusing to compartmentalize information restricts knowledge about using data
5. Keep away from limiting access to data governance resources.

Integrity 

Data integrity is the lifeblood of good decision making. It is a combination of technical controls, policies and cultural practices. 

To ensure integrity, consider following these steps:

1. Put checks in place that confirm accuracy of inputted data.
2. Reduce unauthorized access by utilizing least privilege principle when employing data.
3. Set down clear expectations for procedures involving data.
4. Maintain audit trails for traceability and accountability purposes.
5. Create an atmosphere where issues related to the quality of the information are openly discussed.

Accountability

Everyone; from the C-Suite executives up to lowest level employees must understand their responsibilities as well as consequences of non-compliance. 

Data ownership and stewardship is facilitated by accountability. This makes it possible to have clear oversight and control over data processes. You can build trust with stakeholders and ensure compliance with relevant industry standards. This can enhance your organization’s reputation and drive successful business operations. 

Collaboration 

Here are three ways to increase collaboration: 

1. Have a central place where data is stored such that departmental barriers of ownership can be broken.
2. Bridging the gap between the data owners and users through open communication channels.
3. Provide user-friendly tools for data exploration and analytics, while encouraging team members to collaborate on the insights.

There are various data governance models that translate these principles into actionable frameworks. The type of governance model you chose, will determine the level of autonomy you have.

What is a Data governance model? 

A data governance model is a structured system that outlines work and responsibilities. It addresses data lineage by tracking and documenting data flow through various systems. It gives insights into data maturity, quality issues, compliance risks and lineage. 

It also accounts for data integration and interoperability by enabling data sharing and integration across various systems and platforms. 

Types of Data Governance Models

There are three kinds of data governance models.

The centralized model

This approach involves a central team that oversees and enforces the data policies, standards, and procedures across the entire organization. It relies on an experienced and qualified team, usually chosen by the workforce leadership. 

While this ensures consistency business units may be reluctant to relinquish control over their data assets, fearing a loss of power or decision-making autonomy. You can emphasize that data governance does not strip business units of their data ownership but rather establishes a framework for responsible data management and decision-making.

To make this model work for you, it’s recommended that you work with subject matter experts to establish a standardized business glossary for all the critical data elements. This will mitigate any hindrance caused due to inconsistencies with data definitions and data standards. 

The decentralized model

In a decentralized model, each unit establishes its own processes, policies, and stewardship roles tailored to its specific needs and requirements. Business units can quickly adapt the data governance practices to the changing business requirements. For an industry that’s fast paced and dynamic, a decentralized data model works well. 

Although a decentralized model can potentially lead to fragmented data landscapes, making it difficult to get a holistic view of data assets and lineage in your organization. To mitigate this, establish a central data catalog that integrates data from all business units. 

And to manage metadata, you can create enterprise wide standards.  

The hybrid model 

Hybrid is the best of both worlds wherein an organization establishes a governance committee that designs the overarching strategies and policies and the implementation is carried out by the business units. 

This model can create a lack of accountability due to unclear delineation of responsibilities. To ensure that this does not act as a hurdle, a proper framework with well-defined roles and responsibilities and accountability measures for both the central committee and the departments must be established. 

This model can also create bureaucratic bottlenecks because of the need to communicate with the central committee. Which is why it’s recommended that you implement agile practices and empower the business units with proper decision-making authority. 

Regardless of the model you choose, a data governance model should have certain elements. These help organizations establish an effective framework. 

Features of a good data governance model

A good data governance model should have the following:

Accurate analytics

A good model incorporates lineage tracking capabilities, allowing you to map the data flow from its origin. Understanding data lineage is crucial for accurate analytics. You can pinpoint potential issues that might distort your insights by identifying where your data was manipulated. 

Business Glossary/Nomenclature 

A well-defined business glossary within the model ensures everyone uses the same terminology when referring to data elements. The business glossary should also seamlessly integrate with the data catalog, the central repository for asset information. 

Governance software 

A governance software can assist with automation, especially with repetitive tasks associated with data management. The software can also offer functionalities like data cleansing and profiling. 

It is crucial to have the right features for the data governance program to function effectively but even with these critical features, organizations can face challenges. 

Challenges in Data Governance (and solutions) 

Here are some common challenges in data governance and how to address them 

Data quality 

According to IBM, businesses lose around $3.1 trillion dollars due to poor data quality, alone in the U.S. The number is scary enough for organizations to ensure that they have the highest quality of data. 

To ensure high quality of data you can implement rules, metrics, and processes for data cleansing, profiling, and monitoring to tackle data quality issues. 

Scalability challenges 

Data is growing at an exponential rate. 90% of the entire world’s data was created in the last two years, alone. Which is why you need tools in place to scale effectively. You can use cloud-based data governance solutions as they offer scalability and on-demand resource provisioning to tackle this

To further improve efficiency at scale, you can automate workflows. 

Regulatory requirements

To maintain compliance with regulations like HIPAA, GDPR, CCPA, security measures like data masking, encryption, and access controls need to implemented.

Maintaining these compliances can be difficult, so you need clear and comprehensive documentation and audit trails to demonstrate compliance. 

Resistance to change 

You could face resistance from employees as governance processes demand a cultural shift and changes to the processes that have already been established. Communicate the value of the solution and encourage employees to participate in the process to seek their feedback.

Training, support, and maintaining transparency can help employees overcome the mental hurdle of acceptance. 

Limited resources 

Your organization might not be equipped with the resources required. To supplement the skills and tools required, you can partner with consultants or service providers. 

Organizations can successfully implement the program by proactively addressing these challenges through a holistic and well-planned data governance solution.  

Conclusion 

We are producing, storing and processing data at a rate, never imagined before. It leaves you open to threats, scams and other data related problems. Which is why data governance solutions are no longer an option. Apart from ensuring security, data governance programs enable long term success. 

Data governance is a commitment as it’s an ongoing process. It is not easy but open communication and commitment can significantly enhance the success of your program. Embrace a holistic approach for data governance.

Frequently asked question

1. Importance of data governance

Data governance is important for the following reasons

• Data quality and trustworthiness for reliable analytics and insights
• Consistent data definitions and standards across the organization
• Improved data accessibility and democratization
• Alignment of data initiatives with business outcomes and business decisions. 

2. Goals of data governance

The primary goals of data governance include:

• Ensuring quality of data, integrity, and accuracy. 
• Maximizing the value of data assets and enabling data monetization.
• Facilitating data integration and interoperability. 

3. What are the three pillars of data governance?

The three pillars are: 

• People: Defining roles, responsibilities, and accountabilities (data owners, stewards, governance council)
• Processes: Establishing policies, standards, and procedures for data management and governance
• Technology: Implementing tools and platforms for data integration, quality, security, and governance automation

4. What are the 4 phases of data governance?

The governance journey typically looks like this.

1. Discovery and planning: It begins with you having a clear understanding of your organization’s data landscape. Based on your compliance requirements, align the plan with your business goals. What are you hoping to achieve? Improved quality of data, faster time to insights or a more robust complianc