Designing Valuable Data Governance Frameworks

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As organizations continue to rely on data to make key business decisions, they must establish a robust data management plan to ensure that data across their organization is handled consistently and not misused. Businesses that successfully define who, where, what, when, why, and how of their data can derive valuable outcomes and improve business performance. 

This process of managing the availability, integrity, and security of enterprise data is termed data governance. Effective data governance also helps businesses address data privacy and security issues that are increasingly taking center stage with rising data breaches over the past several years. 

Let’s explore how businesses can build a robust data governance framework and follow best practices to maintain compliance across shifting data privacy laws and regulations.

Table of Contents

Data Privacy Regulations and Data Governance

Several data privacy laws are already in place across different countries that restrict businesses from using sensitive customer data. As new threats emerge, these laws will continue to evolve, and more new regulations will likely come into force. 

While adhering to the new laws may seem straightforward from the outside, the reality is that most organizations lack historical knowledge about their data assets. Moreover, for many, the data assets are spread out across different technological landscapes. In addition, there are some businesses that generate large amounts of data every day, making it difficult for them to efficiently fulfill the requests from regulators. 

For example, Article 16 of the EU’s General Data Protection Regulation (GDPR) mandates businesses to correct inaccurate or incomplete customer data without any delay. However, many organizations overlook the importance of this data validation. Similarly, in the US, the recently introduced California Consumer Privacy Act (CCPA) imposes substantial responsibilities on organizations collecting personal information from a California resident. The privacy law mandates that organizations store personally identifiable information (PII) only as long as it’s needed for a specified purpose. If customers no longer want the enterprise to store their data, the enterprise must delete it permanently.

However, such a task is challenging since personal data may reside across multiple sources. In addition, there can be proximity data, such as IP addresses and cookie settings, that may require cataloging and categorization to identify the data’s availability, usage, and context.

Despite the complexity in tracking down the customer data scattered across siloed systems, regulators hold businesses accountable and require them to modify customer data as and when needed. Without end-to-end visibility, organizations cannot guarantee data quality or compliance. What businesses need is a unified view of customer data that they can trust for decision-making and quickly respond to evolving regulatory requirements.

Enterprises can overcome these challenges by developing a mature data governance framework that helps them stay compliant with these laws, and at the same time, enable them to leverage data for better business outcomes.

Also read: Why You Should Hire a Chief Data Officer

Building a Data Governance Framework for Regulatory Compliance

The definition of a data governance framework is to establish a set of rules and policies to manage, utilize, and protect enterprise data effectively. However, in the context of evolving regulatory requirements, a data governance framework must also support the demands of regulators and facilitate faster response to imminent regulatory and privacy threats.

Regardless of your industry, if you’re looking to implement a data governance framework for your organization, here are some of the essential steps you should follow.

Define Data Governance Goals

The first step to building a data governance framework for your enterprise is to establish clear goals. Why do you need governance in the first place? When you have clarity from the beginning, it becomes easier to get through the rest of the implementation process. 

There can be several potential goals you can consider:

  • Complying with data privacy laws
  • Unifying data from disparate sources
  • Offering customers more control over their data
  • Establishing data distribution policies across the organization
  • Providing high-quality data to employees for effective decision making
  • Securing sensitive business/customer data and preventing unauthorized access
  • Exploring data monetization opportunities
  • Reduce data management costs

While this list isn’t exhaustive, you can consider goals that matter the most to your business and start small instead of applying everything at once. Once you succeed with smaller goals, it gets easier to scale from there.

Identify Data Governance Roles

Figuring out the authorities that will deal with your sensitive business data is an essential part of building a practical data governance framework. Define roles and responsibilities clearly to understand how each role will interact and who will be accountable for what. Some of the major roles related to data governance include:

  • Chief Data Officer (CDO): A senior executive who leads and oversees data governance initiatives. 
  • Data Governance Manager: If there’s no CDO, the manager leads the data governance team.
  • Data Governance Committee: Made of key business executives and decision makers, this committee makes policies and defines standards that the data governance manager and the team enforces.
  • Data Stewards: They handle the day-to-day tasks of managing data across departments and maintaining data quality at each level.
  • Data Governance Team: The data governance team ultimately implements the data governance framework across the organization. It is typically comprised of data analysts, data architects, data strategists, and data compliance specialists.

Also read: Steps to Improving Your Data Architecture

Apply End-to-End Data Governance

A successful data governance framework must encapsulate your entire data landscape — from your data warehouse to your business intelligence solutions. If your enterprise data is not thoroughly governed, it will be difficult to gain control over it later down the line. With end-to-end data governance, you can track all the data pipelines and trace the data lineage. Additionally, this process will also ensure that quality data is securely delivered to end users.

Leverage Automation through AI and ML

Manually identifying all personal information from different data systems is a challenging, time-consuming task. This manual process can also introduce errors and duplication. For a faster and more efficient approach, you can use data catalog tools that leverage AI and machine learning algorithms to proactively identify hidden personal data from multiple sources. These tools can also help you develop repetitive and efficient processes to handle data requests, approvals, and other permissions.

Follow Data Governance Best Practices

Once you’ve established your data governance framework, there are a few best practices that you can follow to ensure smooth implementation. Some of these include:

  • Standardize your data into set formats and leverage technology to enforce those standards when processing data
  • Ensure classifying your data and always tag everything with metadata
  • Define and track KPIs to monitor how your data is being used
  • Remember unstructured data living in your archives
  • Regularly measure the progress of your data governance initiative
  • Communicate frequently, celebrate small wins, and acknowledge setbacks.

Stay Compliant and Gain Customer Trust 

Developing a data governance framework helps to avoid financial penalties and reinforced adherence to regulatory privacy standards. Effective data governance not only puts the business into full compliance, but also builds customer trust. A solid framework also ensures that insights derived from the data hold the value to generate better business outcomes.

Read next: Top Data Science Tools 2021

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