Creating a Data Governance Office

ORGANIZATION

XTIVIA’s client is a leading car rental company which operates in more than 10,800 corporate and licensee locations throughout 145 countries in North America, Europe, Latin America, Asia, Australia, Africa, the Middle East and New Zealand. The company is the largest worldwide airport general use car rental company with more than 1,700 airport locations in the U.S. and more than 1,300 airport locations internationally. In addition to car rental, the company also owns the vehicle leasing and fleet management company.

CHALLENGE

As part of a significant transformation initiative, our client needed to define a consistent future state that will be the foundation for the requirements for the technology teams implementing the future state systems. However, the customer did not have a working body or a decision body that will help define the future state. This challenge was compounded by the global footprint with same business terms interpreted differently and different business terms used to mean the same thing, inconsistencies in data interpretations and use, and multiple copies and versions of data resulting in poor data quality across the Enterprise. The inconsistent use and interpretation of data and poor data quality also created legal and financial compliance challenges resulting in high-risk exposure. This challenge included but not limited to General Data Protection Regulation (GDPR) compliance. The customer needed a Governance Organization that will help establish the standardized business glossary across the enterprise and establish processes and procedures for resolution of inconsistencies in the data, business rules, and resolving data quality issues.

“As part of a significant technology transformation initiative, we needed a technology partner that had deep expertise and the capacity to understand our situation as well as the ability to implement the solution. XTIVIA was that partner. As a result, we integrated hundreds of mission-critical applications in a short time frame.” – Global VP of Architecture

SOLUTION

1. Executive Sponsor
2. Data Governance Council of global Business Leaders – mostly Senior Vice Presidents from different lines of business
3. Multiple Working Steering Committees – mostly Subject Matter Experts in each domain
4. Data Owners and Data Stewards
5. Data Quality Center of Excellence

• Established DG processes and change control procedures.
• Designed the artifacts needed for data gates and implemented gate reviews.
• Established data management maturity (current state) baseline.
• Set cadence to measure the data management maturity every 6 months.
• Created DG KPI Dashboard. This dashboard will measure the maturity model, data standards, data quality and data program projects.
• Documented and communicated the Global Business Glossary of Terms. Identified main data sources for creation and analysis of business terms. Established Business Glossary repository.
• Created Data Policies, standards related to the policies and system processes that will implement policies as well as metrics for monitoring the policy enforcement.
• Established Data Classification that was applied by the data owners to help in persistence, transmission, and retention policies for their data.
• Developed Data Models of critical legacy systems data: Identified impacted Systems Data Models. Reverse engineered systems set for decommissioning and create CDE (Critical Data Elements) data dictionaries.

BENEFITS

• The customer had an empowered organization and processes in DGO to resolve any data and business process inconsistencies across the Enterprise.
• The steering committees removed any inconsistencies in business terms, processes, and future state requirements. This resulted in several person-year savings in the implementation of future state systems and standardized the business processes globally.
• Reduced the multi-million dollars compliance risk across the enterprise.
• Improved the go-live data quality and established an organization and processes to maintain continuous improvement in data quality.

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