How to Build a Framework for Data Governance

Good data is the lifeblood of your organization.

As data volume continues to rise, your ability to manage it could increasingly spell the difference between success and failure. Data governance is a framework that helps businesses ensure that they’re managing their data efficiently and effectively.

Data governance encompasses all the people, processes, and technology that an organization uses to manage and protect its data. In this article, we’ll explore the concept of data governance and establish some steps you can follow to implement a strong data governance framework in your own organization.

Key Takeaways:

  • Data governance is a framework for how your organization manages data.
  • A data governance framework has to consider everything from data quality to company priorities to regulatory compliance and combine those factors into a single framework.
  • First Eigen can help you take the next step toward making the most of your data.

Introduction to Data Governance

Since managing data can be a daunting task, data governance helps businesses manage their data to meet their organizational goals. This involves a lot of behind-the-scenes resources coming together to help manage data effectively.

But what exactly does it mean for an organization to “manage data”? Data governance comprises four main goals:

  • Accuracy: Data governance tries to ensure that data is as accurate as possible.
  • Completeness. This involves keeping complete records, which can sometimes include forging new sources of data.
  • Timeliness. Good data too late is no better than bad data – data governance helps ensure up-to-date information is available when needed.
  • Compliance. Data governance helps companies comply with any regulations they’re subject to.

In other words, data governance is a way of thinking about these four aspects of data quality (as well as any other goals of the organization) and using those considerations to create a plan for how your organization will handle its data. Data governance is an effective way for organizations to improve their data quality, mitigate data risks, and increase regulatory compliance by building those things into its data governance framework.

7 Steps to Building a Data Governance Framework

Data governance encompasses various disciplines across different areas of an organization. Follow these steps to build your data governance framework.

1. Define your Objectives

First, you want to identify your objectives. What are your goals? What is the desired outcome of your data governance? The approach that fits your organization will be unique to your needs, but common objectives include improving data accuracy and quality, reducing data-related risks, and increasing compliance with regulations.

Before the metaphorical rubber can meet the metaphorical road, you should create a roadmap for your data governance initiative that takes your unique needs into consideration.

2. Identify Key Stakeholders

In the world of data, context is everything. Any successful data governance framework is the result of collaboration between stakeholders.

This means you need to identify key stakeholders for a successful data governance initiative.

Stakeholders can include business leaders, IT professionals, data stewards, data owners, compliance officers, and legal experts. Each stakeholder has a unique part to play – data governance is an ensemble production, so a successful outcome requires participation from all your key stakeholders.

3. Implement a Framework

With your objectives and stakeholders in mind, establish the governance framework itself. This is the set of all policies, procedures, and guidelines that govern the management of data within your organization.

Implementing this framework includes establishing roles and responsibilities for key stakeholders, creating processes for data management (expanded in step 4), and defining the technology to support this governance (expanded in step 5).

Data governance has to consider data quality, policies, and regulatory compliance to make a data management plan that works for everyone.

4. Develop a Data Management Process

The success of your data governance depends on how effective your process is. Data management spans the gamut of data acquisition, data quality, data integration, data transformation, and data storage.

Design your data management process with the four goals of data governance in mind (accuracy, completeness, timeliness, and compliance) as well as your own unique objectives and priorities. Your data management process should also be scalable, flexible, and easy to use. 

Once you’ve established a data management process, document and communicate your process to all stakeholders.

5. Implement Technology Solutions

The meteoric rise in data volume is mostly due to new technological advancements. These technologies include everything from data governance tools to data quality tools, data integration tools, and data storage solutions.

To keep up with ever-changing technologies, select and implement a data stack that’s compatible with your existing IT systems. These technology tools can help automate as much of your data governance framework as possible, which should improve your data quality, provide novel data-driven insights, and minimize tedious human labor for tasks that software can accomplish faster, cheaper, or better.

6. Measure and Monitor

What does success look like to you? Have you reached it yet?

It’s impossible to answer that question unless you’ve defined metrics that you can monitor over time. By designing metrics to measure the effectiveness of your data governance framework, you give yourself a way to assess your performance and measure success.

The metrics you use should measure how well your data governance framework is meeting its goals: accuracy, completeness, timeliness, and compliance. Over time, use those metrics to assess your performance, identify trends, and make data-driven decisions.

Data quality metrics help you measure and compare your results over time.

7. Establish a Continuous Improvement Process

Building a data governance framework is less like building a porch and more like putting in a swimming pool – it isn’t a weekend project that you can “set and forget.” It’s an ongoing process that requires maintenance, iteration, and continuous improvement.

You want to design a continuous improvement process to identify opportunities for improvement and establish a protocol for making adjustments. This process can include regular audits, stakeholder feedback, and benchmarking against industry best practices. You should document this process and communicate it to your stakeholders to ensure that everybody is on the same page and batting for the same team.

Next Steps with First Eigen

In the digital age, data drives success – companies that are good at using their data tend to outperform those that are not. In the race to make the most of your data, one of the most important steps is to build a framework for data governance.

That is why data governance is such an important topic for decision-makers, and why First Eigen can help make all the difference.

First Eigen uses AI and machine learning to monitor data quality from the source all the way to the end of your data’s lifecycle. As data continues to play a central role in your company’s success, you need to prioritize data governance. By investing in your data, you can maximize your efficiency and effectiveness while minimizing your risk and budget.

To see how First Eigen can help you make the most of your data, schedule a demo today.

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