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Data Governance and Quality Management in Business Analytics

13 Jun
Review of data for quality assurance

Businesses rely on data to make strategic decisions about everything from new products to customer service. With technology incorporated into almost every aspect of daily life, there’s no shortage of data to analyze. However, not all data is equally valuable or useful.

Analysts depend on data governance policies to manage data availability, usability, integrity and security. Data quality ensures that the data is accurate, complete and reliable. Business analysts follow a data governance framework to manage data assets and use data quality measures to make sure they’re using trustworthy, valuable data. Poor data governance and poor data quality can lead to inaccurate analysis and flawed strategies that undermine business success.1

This article will explore what you’ll learn in a data governance MSBA program and how data quality affects business analytics.

Establishing Data Governance Frameworks

A data governance framework outlines your procedures and policies for handling and using data. For a data governance framework to be effective, every organization will need to tailor it to their business needs and objectives. Data governance is driven by business objectives, which will vary from company to company.2

Components of a Data Governance Framework

Regardless of the specific objectives, there are some components that should be included in every data governance framework, including:3

  • Data quality management: Processes to guarantee data is accurate, complete, reliable, and timely, so it’s effective for strategic decision-making
  • Data security and privacy: Measures to protect data from unauthorized access and breaches, maintaining confidentiality and integrity
  • Data architecture: A blueprint for data management infrastructure, detailing how data is collected, stored, and accessed
  • Metadata management: Processes for managing data about data, ensuring clarity and context for data users

Roles and Responsibilities in Data Governance

A data governance framework should also include clear roles for accountability and effective management, such as:4

  • Data administrators: Leaders who set the strategic direction and oversee the program for the governance of business data
  • Data stewards: Individuals responsible for the quality and management of specific data domains, ensuring adherence to governance policies
  • Data owners: People who have authority over data assets and are accountable for their proper use and compliance
  • Data custodians: IT professionals responsible for the technical environment where data is stored and processed
  • Data users: All stakeholders who interact with data and are required to follow established data governance practices

Data Quality Management Principles

If you’re using data to make high-stakes decisions about business strategy, you want it to be good. Managing data quality provides analysts with accurate data that can drive operational efficiency and sound decisions.5

Characteristics of Valuable Data

High-quality data is:5

  • Accurate: It correctly represents the real-world construct it describes
  • Complete: All required data is present and available for use
  • Consistent: It’s the same across different databases and systems
  • Timely: Data is up to date and available when needed
  • Valid: It conforms to defined formats and standards
  • Unique: Each data entry is distinct, with no unnecessary duplication

Implementing Data Quality Management

A data quality management plan is a systematic approach to implementing policies and procedures that will produce valuable, strategic data. Businesses use the following steps to develop a data quality management plan:6

Define Data Quality Standards

Establish clear data quality standards based on the organization's needs and regulatory compliance requirements. These standards should address data quality mean dimensions, such as accuracy, completeness, and consistency.

Assess and Clean Data

Analyze existing data to identify quality issues, such as duplicates, missing values, or inconsistencies. Correct identified data issues as needed.

Implement Data Governance Policies

Develop and enforce business data governance policies to ensure analytics quality management standards are maintained. This includes defining roles and responsibilities for data stewardship and quality management.

Continuously Monitor and Improve

Regularly monitor all data quality metrics through automated tools and manual reviews. Establish feedback loops to continuously improve data quality management processes based on identified issues and changing business needs.

Integrating Data Governance With Business Analytics

Strong data governance frameworks help maintain data quality. They ensure accurate and reliable data for analytics, leading to more informed and effective decision-making. By establishing data catalogs and metadata management, effective data governance makes it easier for analysts to discover and access relevant data, speeding up the analytics process.7

Governance policies also set consistent data definitions and formats across the organization, for data accuracy and reducing discrepancies in results. Additionally, organizations must implement security measures and compliance protocols that protect sensitive data and improve adherence to applicable regulations.7

Another way data governance in business promotes effective analytics and data usage is by tracking data lineage to provide transparency on data origins and transformations. This information helps analysts understand the context and reliability of the data.7

Challenges in Data Governance and Quality Management

Although there are many advantages to implementing data governance and quality management practices, it can be a complicated process with significant challenges. Some common hurdles businesses have to overcome include:

Data Silos

Fragmented data across different departments and systems can hinder data governance and quality management efforts, leading to inconsistencies and difficulties in data integration. Establishing a centralized framework can break down data silos and promote consistent data management practices across your organization.8

Lack of Awareness and Engagement

Employees may not understand the importance of data quality control and quality management in analytics and may not follow policies and standards. You can promote a data-driven culture by educating employees about the importance of data governance and quality.9

Complexity of Data

Managing diverse data types, sources, and formats can be challenging, especially as data volumes grow. Fortunately, advanced tools and technologies, such as artificial intelligence (AI) and machine learning, can automate data quality monitoring, cleansing and compliance processes.8

Changing Regulatory Requirements

Keeping up with evolving data privacy and security regulations can be difficult and put you at a higher risk of non-compliance. To overcome this challenge, you can implement ongoing compliance programs to stay updated with regulatory changes. Your data governance programs and policies should be updated along with legal requirements.10

Use Your Expertise to Drive Effective Business Strategy

Data science and analytics are driving the future of business. Leaders who master them will have a competitive edge in a crowded hiring environment. The William & Mary Online Master of Science in Business Analytics program will instill the knowledge and skills you need to turn raw data into actionable business strategies for any organization.

Our rigorous curriculum covers business acumen, applied math, computing technology and effective communication. During your program, you’ll learn from top experts, and you can tap into our extensive network of business leaders.

Schedule a call with one of our admissions outreach advisors today to learn more about the value of the Online MSBA.