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Master Data Management (MDM) in the Public Sector

Don Hoag Manager

Emerging Technologies Work Group

Agenda

What is MDM?

What does MDM attempt to accomplish?

What are the approaches to MDM?

• Operational • Analytical

Questions

What is Master Data?

“A process that spans an organization’s business processes and application systems, enabling the ability to create, store, maintain, exchange, and synchronize a consistent, accurate, and timely ‘system of record’ for core business. Addresses the harmonization and integrity of enterprise data which is vital to ensuring a consistent and complete view of business entities across the enterprise.”

– Department of Public Welfare, Pennsylvania The characteristics of master data are: • Shared across systems • Fundamental to the proper execution of processes • Owned and governed by functional groups • Uniquely identified entities While the above definition of master data may be acceptable, there are many different interpretations of master data Master Data elements

Point of View

Data elements that form the foundation of an organization’s processes that are in its enterprise systems

Enterprise Applications (SAP, Oracle)

Reference data that is referred to by transactions and the system configuration

MDM Vendors

Data fields that are infrequently modified and shared throughout the enterprise Common fields across all definitions Examples of differences Customer name; program, service, and provider; customer Social Security Number, parent or legal guardian; service location’s address Language code of user interface, flag to determine system feature enablement System of origin description, time tag of field that was updated Emerging Technologies Work Group Copyright © 2009 Deloitte Development LLC. All rights reserved.

Master Data — A Subset of Structured Data

Types of Structured Data *

     

Metadata

— Structure, meaning, and relationships of data. (column cusname stands for customer name and has a size of VARCHAR(50)).

Reference Data —

Codes describing state and behavior of organization entities and transactions. (list of States, address types, etc.)

Enterprise Structure Data

(organization hierarchy)

—

Hierarchies within the enterprise

Transaction Structure Data —

Organization entities in which transactions act upon (customer data, provider data)

Transaction Activity Data

welfare worker)

—

Operational transactions used in applications (case entries for a child

Transaction Audit Data —

String of transactions executed to bring about a process flow (transaction logs showing execution of driver license creation)

More Semantics

* Source: BeyeNetwork, Malcolm Chrisholm

Less Less Volume and Volatility More

Identifying Master Data Attributes

The differing definitions of master data make it challenging for governance organizations to determine what data elements qualify for management. The scoring system can be used to assist in answering the question of whether data in question is master data

Identifying Master Data Criteria Shared Value Volatility Description

Is the data used by more than one business process/system?

The element is fundamental to a business process, subject area, or business system. Data modification behavior

Rating

0 – Data used in a single system/process 1 – Data used by two systems/processes 2 – Data used by more than two systems/processes 0 – Data is useful to individuals only 1 – Data is critical to a single business process 2 – Data is critical to multiple business processes 0 – Transaction data 1 – Reference data 2 – Data added to or modified frequently, but the data is not transaction data

Total Results

0-2 Attribute is not master data (or any criteria is rated 0) 3-4 If any criteria is rated 0, attribute is not considered master data. Otherwise, attribute minimally meets criteria for master data and further investigation should be considered >4 Attribute is master data

Type of Attribute

There are also different categories of master data attributes. Identifier — ID, Alternate IDs, Cross-reference. Core — Core fields shared across many processes Extended — Business process specific Most MDM solutions manage identifier, core, and a subset of extended attributes Emerging Technologies Work Group Copyright © 2009 Deloitte Development LLC. All rights reserved.

MDM

MDM is accomplished through the implementation of an overarching governance structure, business processes, data organization, data architecture, and enabling technology.

What Is MDM ?

• A maintainable “system of record” for core business entities • The single source for core business entities for the enterprise

Vendor Enterprise Master Data Employee Customer Provider Location Programs Efficiencies From MDM

• Improved data management resulting in better performance • Increased efficiencies resulting from reduced data error

Decision Support Benefits From MDM

• Increased confidence in decisions resulting from better understanding of data • Reduced risk

Operational and Transactional Processes Child Welfare Medicaid Motor Vehicles Insurance Child Support Financial Business Intelligence

Approaches to MDM Given the nature of MDM and the various groups promoting its efficacy, there are several approaches to its implementation:

• Operational: An application- or system-based approach that attempts to centralize and standardize the collection of subject area data into a single solution, to which other applications publish and subscribe.

• Analytical: A logical or physical data structure approach that attempts to centralize and standardize the view of subject area data into a single solution, with which users may see data that spans across applications or programs.

Operational MDM

An application- or system-based approach that attempts to centralize and standardize the collection of subject area data into a single solution, to which other applications publish and subscribe.

People: Enterprise Architects (e.g., Architecture, Data, Software) Process: • Gathering information about current data standards and usage in an organization from documentation, application owners.

• Defining hierarchy of applications and their priority on updates • Addressing anomalies and constraints – Lends to data governance discussions and data quality discussions Technology: • New application development associated with primary subject areas (e.g., customer and provider) • Modification of existing systems to publish and subscribe Emerging Technologies Work Group Copyright © 2009 Deloitte Development LLC. All rights reserved.

MDM Architecture — Hub and Spoke

The Hub and Spoke integration approach is the most effective high level architecture approach used in MDM solutions today. Data management, stewardship, and governance processes Data Management

Portals, extranets, Web services, and knowledge systems

Services interface

MDM

  Centralized repository for customer, provider, program, and employee “master” data Standard data entities, attributes, and business rules Data interface

Transaction systems, legacy repositories, and applications

Reporting interface Business intelligence and reporting • All or some of the master data is maintained centrally in a hub architecture • System of record is put in place with respect to master data entities being maintained • MDM related communication is channeled via the hub • Allows for more effective policies around data standardization and deduplication can be put in place • Foundations for governance of master data and associated processes become a reality Emerging Technologies Work Group Copyright © 2009 Deloitte Development LLC. All rights reserved.

Analytical MDM

A logical or physical data structure approach that attempts to centralize and standardize the view of subject area data into a single solution, with which users may see data that spans across applications or programs.

People: Data Architects, Report Developers Process: • Top-Down Approach for definition of Subject Areas • Bottom-Up Approach for definition and conformance of Dimensions Technology: • Unified modeling • Data Dictionary/Metadata • Extraction, Transformation and Loading (ETL) tools Emerging Technologies Work Group Copyright © 2009 Deloitte Development LLC. All rights reserved.

Analytical Unified Modeling

Integrating core data in a logical and physical environment for data analysis provides for a singular customer view across programs. 1 2 3 4 6 MCI MCI # First / Last Name DOB Citizenship Gender DPW Sys Code HCSIS Consumer MCI # First / Last Name DOB Race Service Coord ELN Client MCI # PA Secure ID School District Primary Language Grade Level PACSES Client Client ID First / Last Name DOB Sex Case Worker MCI_Landing MCI # First / Last Name DOB Citizen ship Gender DPW Sys Code HCSIS_Consumer_Landing MCI # First / Last Name DOB Race Service Coord ELN_Client_Landing MCI # PA Secure ID School District Primary Language Grade Level PACSES_Client_Landing Clien t ID First / Last Name DOB Sex Case Wo rker HCSIS_Case Recipient ID ELN_Enrollment Recipient ID PACSES_Case Recipient ID 5 Operational Integrated Recipient ID MCI # PA Secure ID Grade Level ELN_Recipient 1 1 Recipient ID Source Sys ID Source Sys Code First Name Last Name DOB Citizenship Gender Language Source 1 Flag Source 2 Flag Source 3 Flag Source Counter 1 1 Recipient ID MCI # Service Coord HCSIS_Recipient ODS_Recipient 1 1 Recipient ID Client ID Case Worker PACSES_Recipient Emerging Technologies Work Group Copyright © 2009 Deloitte Development LLC. All rights reserved.

Lessons Learned

Our experiences with large master data management programs have provided us with many lessons learned.

Business value, leadership, and team Scope, communications, and governance Process and architecture

Contact Information

Don Hoag Deloitte [email protected]

412-402-5292