Top 6 Data Governance Case Studies with Real-life Examples

Data governance is an effective strategy for developing internal data standards and policies that govern who has access to data, and how data is utilized in business operations and analytics applications.

Data governance programs frequently include data quality improvement projects and master data management (MDM) ones. An effective program can help organizations ensure that data is consistent, reliable, and accessible and that its use adheres to data privacy laws.

Below are the top 6 real-life data governance case studies from diverse industries, including consumer goods, financial services, aerospace, and hospitality:

Industry overview

Case study insights

1.  Procter & Gamble (P&G): Master data management

P&G’s master data management (MDM) and metadata were out of sync with the operational reporting systems for global and regional users, and the company experienced significant data leakages within the organization. 

Challenges

• Complex data systems: P&G’s data systems were complicated, with 48 SAP instances in various downstream application servers, making identifying and reconciling data errors time-consuming.
• Lack of central data control: P&G had several separate business units that started using their own data management processes, the company needed a centralized data quality platform and toolset.

Solutions and outcomes

P&G’s data governance team deployed a data quality software to improve the quality and control of their master data, which included over 32 unique SAP instances and billions of records.

Additionally, a data quality assurance and control (DQA/DQC) plan was created to phase out some existing third-party technology.

• Improved productivity: Before implementation, analysts would download all data offline once a week, integrate it from numerous sources, and manually resolve discrepancies and variances. The automated processes saved time for data stewards and reduced manual errors.
• Reduced operational risks: Minimized data leakage and duplication, lowering compliance and financial risks.
• Actionable insights via dashboards: Management had timely access to health reports and performance metrics.

Read more: AI data governance, open-source data governance tools.

2. Unilever: Streamlining vendor onboarding

Unilever operates in nearly 190 countries, with over 400 brands, thousands of suppliers and customers, and over one billion global consumers. They needed a master data management strategy that could handle complexity and scalability.

Challenges

• Complex global operations: Managing master data across 190 countries, over 400 brands, thousands of suppliers and customers, and more than a billion consumers.
• Separated data management: The lack of a unified master data management (MDM) system leads to inefficiencies and potential data inconsistencies.
• Need for efficient vendor onboarding: The company required a streamlined HR onboarding process for vendors to maintain operational efficiency across geographies.

Solutions and outcomes

Unilever partnered with a master data management solutions management company to foster the digitization of its management operations across vendor, customer, and product channels. Thus, the company implemented a new vendor data management process in almost 40% of the countries.

The following outcomes have been achieved:

• Centralized and documented data points: Data points from diverse categories and locations were consolidated throughout its record systems and back-end applications, resulting in increased efficiency, data quality, and speed.
• No-code data management: With the new deployment Unilever used low-code capabilities to enable more control over its master data.
• Streamlined HR operations: Onboarding time for vendors decreased to hours instead of days.

3. A large financial services company: Launching a new data registry platform

A large financial services company partnered with an asset management and data analytics team to establish a new data platform registry.

Challenges

• Lack of accountability: The financial service company couldn’t allow permissions depending on contractual (information barrier) and disclosure requirements.
• No traceability: The company couldn’t integrate registry data with file activity from drop zones, shares, APIs, and other sources.
• Lack of reporting and visualizations: The reporting and visualization capabilities were insufficient, making it difficult to assist analytics in managing, monitoring, and driving insights into data usage.

Solutions and outcomes

The outsourced team established a 3-phase solution to build a vendor data registry platform to consolidate inventory of 22,000 feeds, mapping 127 services across 97 vendors,  and help improve the capacity to track, monitor, and govern data usage.

Phase 1: Vendor data registry design and prototyping

• Gap analysis and current state assessment: Evaluated the existing processes, artifacts, and market data usage to identify improvement opportunities.
• Data model: Designed a vendor registry data model and metadata framework for improved governance and usability.

Phase 2: Platform improvements

• Cloud-based integrations: Leveraged cloud services to build a platform integrated with the client’s existing ecosystem.
• API Integration: Integrated the registry platform with firmwide APIs for enhanced connectivity and automation.

Phase 3: Reporting and governance

• Comprehensive analytics and reporting: Built capabilities for detailed reporting, feed lineage tracking, and analytics on market data consumption.
• Unified acquisition platform: Developed a streamlined framework for vendor onboarding and market data acquisition.

4. GE Aviation: Enabling data democratization

GE Aviation division is a branch of General Electric’s global network, one of the world’s largest aircraft engine manufacturers. The company faced data governance challenges related to inconsistent data usage and siloed systems.

Challenges

• Lack of operational efficiency: The traditional systems lacked operational efficiency, causing supply chain bottlenecks. For example, it was challenging to transform sensor data into actionable analytics for customers.
• Data access: Operations teams faced difficulties obtaining data compared to other departments. 
• Data complexity: Engineers struggled with analyzing large, complex datasets using traditional tools like Excel and Minitab.
• Manual and time-consuming processes: Finance teams relied on Excel for complex reporting, requiring significant effort.

Solutions and outcomes

GE Aviation established a centralized, cross-functional team to manage the data governance initiatives of its 1,800+ users globally. The company launched a data governance program that includes:

• A self-service data governance framework where employees can easily access and utilize data for insights without heavy reliance on IT teams.
• A self-service data analysis and model-building platform Dataiku to allow users to discover, and govern data across the organization.

The following outcomes have been achieved:

• Improved automation: The self-service data program streamlined processes, enabling quicker access to data and automating trivial tasks.
• Robust governance: Data products were governed using a data intelligence platform for proper documentation, data ownership, and workflow approvals.
• Self-service data usage: The program helped employees leverage self-service data initiatives for tool administration or process automation.

  For example, employees gained autonomy using ‘Daasboard’ for data monitoring, and Starfish for process automation.

5. KPMG LLP: Optimizing data for digital transformation

KPMG LLP is the U.S. branch of the KPMG global network of independent professional services businesses that provide audit, tax, and advisory services. KPMG operates in 143 countries and territories, with over 273,000 employees

Challenges

• Lack of central data management: KPMG LLC needed to consolidate data sources into a single platform.
• Data quality and transparency issues: KPMG LLC lacked high-quality, transparent customer data for digital capabilities.

Solutions and outcomes

KPMG aimed to create a centralized MDM program to analyze data across member firms. The goal was to eliminate duplicate and inconsistent customer and financial data and standardize it.

Here’s a summary of the key outcomes of KPMG’s implementation of the MDM program :

Operational efficiency

• Enhances data reliability: Consolidation of disparate data sources into a unified, trusted data supply chain ensures consistency and reliability across member firms.
• Streamlined reporting: Management reporting is now supported with minimal manual intervention, leveraging high-quality reference data.
• Improved data quality: The use of data governance tools helps ensure that data is cleaned based on

Strategic benefits

6. Holiday Inn Club Vacations: Improving data visibility for each customer

Holiday Inn Club Vacations (HICV) is a family-focused travel brand operating over 30 resorts across the U.S. and Mexico. 

Challenges

• High expectations for personalization: Travellers expect highly personalized experiences, such as unique recommendations and tailored vacation ideas, and a seamless experience across both online and offline channels.
• Operational shifts: The company leadership demanded a framework that focuses on post-resort marketing strategies to nurture and build lifetime loyalty. 

Meeting these changing needs necessitates a customer-centric culture supported by high-quality data.

Solutions and outcome

Holiday Inn Club Vacations used cloud master data management, data governance, and data quality solutions to unify its customer data with increased data visibility for each member.

Streamlined data integration:

• Consolidated data from 7 main systems into a cloud-based platform.
• Leveraged low/no-code capabilities to integrate 350,000+ customer profiles from the main systems.

Secure data management:

• Cloud-native architecture enabled scaling of data ingestion.
• Verified member profiles were seamlessly integrated into data pipelines, reducing compliance risks.

Automation:

• Used metadata intelligence, data lineage, and natural language processing to reduce manual tasks.

What is data governance?

Data governance is the data management discipline involved with the quality, security, and availability of your organization’s data. It ensures data security by developing and executing policies, and processes for data collection, ownership, storage, and usage. 

What is data governance used for?

• Data stewardship: Data governance often means delegating accountability and duty for both the data itself and the mechanisms that assure its proper use to “data stewards.”
• Data quality: Data governance is also used to assure data quality, which refers to any activities or processes that ensure data is appropriate for usage. Data quality is often assessed across six dimensions: data accuracy, completeness, consistency, timeliness, validity, and uniqueness.
• Data management: This is a broad concept that encompasses all aspects of managing data as an institution’s asset, from collection and storage to usage and monitoring, ensuring that it is used securely, and cost-effectively.

Benefits of data governance

Having an effective data governance framework can help companies meet several benefits:

• Gain more value from enterprise data.
• Establish a single source of truth (SSOT).
• Help to ensure data privacy, security, and compliance.
• Securely utilize data for AI efforts.
• Facilitate more accurate data analytics.

Gain more value from enterprise data

Data governance may assist in ensuring data integrity, accuracy, completeness, and consistency by establishing a framework that promotes robust data stewardship.

For example, data lineage technologies can assist data owners track data throughout its lifecycle, including any changes that the data takes during extract, transform, and load (ETL).

Establish a single source of truth (SSOT)

A correctly managed data system can provide a single source of truth throughout an organization. When all parties use the same data sets, data driven decision making becomes simpler.

Combining data definitions and metadata into a single data catalog can help eliminate misinformation and inefficiencies. Thus data governance frameworks can serve as the foundation for self-service solutions that ensure uniform data and access across the enterprise. 

Help to ensure data privacy, security, and compliance

Data governance policies frequently include operations that make it easier to comply with government regulations governing sensitive data and privacy, such as:

• EU’s General Data Protection Regulation (GDPR),
• United States Health Insurance Portability and Accountability Act (HIPAA)
• Payment Card Industry Data Security Standards (PCI DSS).

Data governance frameworks contribute to the development of data systems that are clear, explainable, fair, and inclusive. As a result, these data systems foster privacy and security.

Securely utilize data for AI efforts

According to an IDC survey, only 45% of respondents reported having “rules, policies, and processes to enforce their responsible AI principles” to defend against security breaches and regulatory risk.

Data governance entails comprehending the origin, sensitivity, and lifecycle of the data that an organization uses. This is the core of any AI governance practice and is critical to managing various organizational risks.

Data governance can enable enterprises to provide high-quality data to AI and ML initiatives while securing the data and adhering to appropriate norms and regulations. Governance mechanisms, for example, can assist in guaranteeing that sensitive personal data is not accidentally fed to an AI system.

Facilitate more accurate data analytics

Having the correct data is essential for data analytics. Carefully managed data enables beneficial initiatives such as business intelligence reporting and more complicated predictive machine learning (ML) projects.

For example, properly profiling data—reviewing and cleansing data to better comprehend its structure—can help make sense of the connection between various data sets and sources.

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