Home Media SCU Leavey Blog Best Practices for Data Governance and Quality Assurance

Best Practices for Data Governance and Quality Assurance

20 Nov
digital interface of a file manager, folder structure with documents and data scanning

Data has become a hot commodity.1 Many businesses gather enormous quantities of information about their customers, competitors, performance, and operations. However, not all data is created equal. For example, product review datasets may contain spam, while market research data might skew toward younger customers. This poor data quality can cause business leaders to make faulty decisions and miscalculate risk.

Data governance practices can help companies avoid these issues and maintain data quality control. This concept refers to policies and procedures that help ensure data quality, privacy, and compliance. Sound data governance processes increase data legitimacy and allow business analysts to leverage information effectively.1

This guide examines data governance best practices analysts can use to protect their organizations’ digital assets and aid decision-making.

Data Governance and Quality Assurance Strategies

Data governance and data quality assurance go hand-in-hand. Data governance frameworks dictate how every member of an organization handles and safeguards information.1 Data quality assurance ensures that analysts use data that is accurate, relevant, and complete.2 Analysts can use the following strategies to optimize data governance and quality:

Effective Data Governance Frameworks

A data governance framework refers to a set of principles or rules that an organization uses to guide its management and use of data. Robust data governance frameworks include these essential components:3

  • Communication structures: Clear protocols for sharing and discussing data
  • Data requirements: Standards for data collection and maintenance tasks
  • Data request process: Straightforward steps that staff can follow to request access to data
  • Data quality standards: Guidelines to ensure organizations use complete, relevant, accurate data
  • Metadata guidelines: Norms for producing contextual information about data’s origin and purpose
  • Data security policies: Rules about protecting, sharing, and storing confidential data

These frameworks often evolve as laws, technologies, and organizational values change.3 For example, a new law might require businesses to adopt stricter data privacy rules. Analysts could also use new artificial intelligence algorithms that may increase the risk of bias, requiring the creation of new data governance policies.

Data Governance Roles

Effective data governance is a complex and collective process, so organizations often distribute responsibilities across several employees. Three familiar data governance roles are:4

  • Steering committee: This committee consists of executives who develop the overall governance strategy and mission
  • Data owner: This manager oversees specific data domains, gives input on data governance policies, and supervises data quality activities
  • Data steward: This leader oversees data usage in a particular division or department and collaborates with other data stewards to manage data

Data Governance Implementation Steps

Developing data governance programs takes time and effort. These steps can help you get started:4

  • Identify current data governance policies and your organization’s most important data assets
  • Establish data governance policies for the whole data lifecycle, from collection to disposal
  • Gain the support of the company’s chief data officer or another executive leader who can champion data governance and get organizational buy-in
  • Promote a culture of transparency around data-driven decision-making
  • Create clear audit trails for all data governance decisions
  • Avoid building or reinforcing existing data silos

Data Quality Control Framework

A data quality control framework includes policies and procedures for data quality management. These models typically have guidelines for the following processes:5

  • Sourcing data
  • Sampling and profiling data
  • Establishing data quality parameters and standards
  • Creating metadata
  • Mapping data attributes
  • Developing data visualizations

Data Quality Assurance Metrics

The meaning of ‘high-quality data’ can vary by industry and organization, so it’s crucial to define clear data quality metrics and benchmarks. Data quality has several dimensions, including:6

  • Accuracy: Data should accurately reflect the real world
  • Completeness: The dataset should include breadth and depth
  • Consistency: Data should be internally consistent
  • Timeliness: The data should be current and relevant

Business analysts can use many tools to measure data quality, such as Aggregate Profiler, Oracle Enterprise Data Quality, and SAS Data Quality. This software allows users to profile and clean data.6

Data Profiling and Data Quality Management

Data profiling refers to evaluating a dataset to understand data characteristics and identify potential issues. This step involves the creation of metadata, which may include data types, recurring patterns, and missing values. Business analysts can use this crucial information to detect problems and assess the overall quality of the data.6

Improving Data Quality Control Strategies

Business analysts may feel discouraged when they discover they have a low-quality dataset. Fortunately, they can use many data quality control strategies to improve data quality, such as:6

  • Data cleansing: The process of correcting or removing incorrect, missing, or inconsistent data
  • Data quality improvement tools: Software such as OpenRefine and Oracle Enterprise Data Quality enable analysts to clean data, transform it into new formats, and automatically monitor data quality

Compliance and Data Governance Policies

Businesses must often comply with data security regulations, such as the Fair Credit Reporting Act and the Children’s Online Privacy Protection Act.7 As a result, all data governance frameworks must include policies that are consistent with applicable legislation. For instance, data governance policies may include requirements to:8

  • Anonymize data before analyzing it
  • Implement firewalls and other security measures to protect personal data
  • Exercise transparency about data collection

Data Governance and Master Data Management

Master data management (MDM) integrates business and information technology operations to manage pivotal information about an organization. Companies leverage MDM to improve data quality and consistency. This approach can also promote compliance with data privacy regulations and help businesses mitigate risk.9

Data Governance Best Practices

Additional data governance and quality assurance strategies include these directives:10

  • Explain the reasoning behind data governance policies to increase staff buy-in
  • Prioritize a few key performance indicators instead of trying to measure everything
  • Keep the data governance program and frameworks as clear and straightforward as possible
  • Use a peer review system to promote data quality

Advance Your Career With the Most Effective Data Governance Approaches

Develop the skills to build and manage data governance frameworks in the Online Master of Science in Business Analytics (MSBA) program from the Leavey School of Business. Led by industry experts, our innovative curriculum includes courses in database management systems, dashboards, and data visualization. You'll gain hands-on experience through the practicum or capstone project, and the program’s flexible online format empowers you to continue in your current job while strengthening your credentials.

Don’t wait to start enriching your expertise and professional network. Schedule a call with an admissions outreach advisor today.