RESEARCH & RESOURCES

TDWI Checklist Report | Seven Tips for Unified MDM with Data Quality and Data Governance

May 13, 2014

Master data management (MDM) can be practiced many different ways, with various user conventions and a broad array of vendor-built technologies. However, this report focuses on a specific practice called unified MDM. Its seven leading characteristics are:

  1. MDM in the context of a unified program for many data management disciplines. Unified data management (UDM) is a best practice for coordinating diverse data management disciplines. UDM enables MDM to leverage competency synergies with related disciplines, such as data quality, data integration, and data governance. 
  2. MDM as one of many solutions built atop a unified vendor framework supporting many functions for data management. By using a vendor’s unified toolset, developers can share development artifacts (for productivity and consistent standards), plus design solutions that incorporate diverse DM functions. The initial investment in a vendor’s unified platform reduces system integration and other costs over time because multiple MDM solutions are built on top of it. A unified platform also accelerates time-to-use for DM projects. 
  3. MDM as a series of easily managed projects. This phased approach avoids risky big-bang projects, and it enables an organization to incrementally grow into multiple MDM solutions that in aggregate amount to enterprise coverage for MDM. 
  4. MDM controlled and guided by data governance and data stewardship. Master and reference data are like all data in that they are subject to the enterprise regulations of governance as well as detailed improvement via data stewardship. A modern, unified platform will provide software functions that automate governance and stewardship tasks. 
  5. MDM continuously improved by multiple data quality functions. Master and reference data benefit strongly from quality measures for standardization, address verification, data enrichment, profiling, monitoring of quality metrics, and so on. 
  6. MDM for business people who act as hands-on stewards, not just technical personnel. A growing number of stewards want and need tool functions designed for them, such as profiling, search, collaboration, and remediation. 
  7. MDM organized and optimized via a hub. Many high-value features of MDM are more broadly disseminated when enabled through a hub, namely collaboration among multiple stake holders, one-stop governance and stewardship, entity resolution, and publish/subscribe methods.

This TDWI Checklist Report examines these characteristics typical of business programs and technical solutions for unified MDM.

Definitions of Data Disciplines
We’ll start with basic definitions for some of the data disciplines discussed in this report: 

Master data management (MDM) is the practice of developing and maintaining consistent definitions of business entities (e.g., customers, products, financials, and partners). MDM’s entity definitions and reference data facilitate the accurate sharing of data across the IT systems of multiple departments and possibly outward to business partners. This way, MDM can improve many data-driven initiatives, such as business intelligence, integrating business units via common data, 360-degree views, supply chain efficiency, the compliant use of data, and customer interactions that span multiple touch points.

Data quality (DQ) is a family of related data-management techniques and business-quality practices, applied repeatedly over time as the state of quality evolves, to assure that data is accurate, up-to-date, and fit for its intended purpose. The most common data quality techniques are name-and-address cleansing and data standardization. Other techniques include verification, profiling, monitoring, matching, merging, householding, postal standards, geocoding, and data enrichment.

Data governance (DG) is the creation and enforcement of policies and procedures for the business use and technical management of data. It is usually the responsibility of an executive-level board, committee, or other organizational structure, although DG is sometimes executed by individuals without a formal organization. Common goals of data governance are to define ownership; improve data’s quality; remediate its inconsistencies; share data broadly; leverage its aggregate for competitive advantage; manage change relative to data usage; and comply with internal and external regulations and standards for data usage. The scope of data governance can vary greatly, from the data of a single application to all the data in an organization.

Data stewardship (DS) is usually performed by a business manager who knows how data affects the performance of his/her business unit or the enterprise. In addition to daily management responsibilities, a steward collaborates with data management specialists and data governors to direct DM work so it supports business goals and priorities. Many stewards use business-friendly tools to explore andprofile data, plus remediate errant or non-compliant data.

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