Data Governance Services

Data Governance Services

Data here, data there, data everywhere: identify, protect, and leverage your assets through an established data management strategy.

Governance Program

To allow for data to be leveraged as a true organizational asset, Asureti incorporates the data program components of people, process, and technology.

Appropriate data governance can provide:

  • Improved analytics for operational decision-making including more accurate planning and forecasting.
  • Increased visibility into performance.
  • Potential for cost reduction or operational efficiencies.
  • Competitive advantage opportunities including product/service or market-based understanding.
  • Enhanced ability to identify opportunities and threats.

Case Study: Data Management Program Stand-up and Roadmap

Asureti performed a current state review to assess processes, people, and technology within the organization as compared to desired program structure. From this assessment, initial program components were designed. Program build focused on:

  • Defining data strategy (including vision statement) for the organization.
  • Defining data management objectives and priorities.
  • Defining data program structure, including roles & responsibilities, cross-functional dependencies, key operational components, regulatory compliance requirements, and monitoring structures.
  • Defining data governance structures, including definition of data domains (e.g. business areas, product types, etc.), key data types, and master sources.
  • Identifying of master data components and related recommendations from data mapping efforts.
  • Baseline policy standards for data management.
  • Baseline data management definitions and taxonomy structures.

Asureti’s Data Governance program roadmap provides guidance and recommendations related to operational, organizational, technical, and timing considerations enabling alignment with the organization’s defined objectives.

Case Study: Data Exposure Risk Assessment

Asureti supported a global financial services entity in its transformative data governance journey by performing a data-related risk exposure assessment. The outcome was intended to help the organization identify key risk points and achieve its data strategy objectives. This assessment included following key data governance principles:

  • Data Compliance (alignment of data governance structures with legal and regulatory obligations)
  • Data Protection (security controls and standards)
  • Data Retention & Deletion (standards for managing business records)
  • Data Quality (standards for creation/intake, integrity and completeness of processing, and availability)
  • Data Model (standards for structures and use)
  • Data Exchange (structures for managing data exchanges and sharing within and outside the entity)
  • Data Stewardship (standards for ownership, usage structures, and accountability)

Assessment results provided a roadmap for recommended data governance program enhancements, and incorporated key data management principles into cloud migration efforts. 

Case Study: Data Governance Program Build

Asureti designed and implemented a comprehensive data governance program for an insurance industry technology provider. Core build components included:

  • Data inventory across corporate data and customer data domains, and in third-party vendor solutions
  • Corporate-wide data governance program, including implementation and training for program requirements
  • Recommendations for long-term data management and data protection

Program design enabled development of privacy compliance operations, enhanced data protection structures, and enabled transparency of risk reporting related to the organization’s data assets.

Case Study: Information Lifecycle Management Implementation

Project efforts included design and implementation of data management standards, operational processes, and supporting technologies for a global legal services firm. This increased revenue through invoicing for client data storage. It also decreased costs due to improved retention rates and deletion processes. Efforts included documentation of data life cycles and identification of duplicative/extraneous data sets for deletion (or new invoicing).

Reduction of extraneous data improved data processing structures and aligned data management operations with regulatory and data protection standards. Data management structures also enhanced reporting and operational management insights for an upgraded end-client service delivery.

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