Enterprise Data World Webinar: Mastering & Referencing Data for the Enterprise

Mastering & Referencing Data for the
Enterprise
20 January 2015
Michael Jennings
©2015 Walgreen Co. All rights reserved. Confidential and proprietary information.

Walgreens Company Overview
Walgreens is the nation’s largest drugstore chain,
with fiscal 2013 sales of $72.2 billion. The Company
has 240,000 employees.
• Walgreens filled 821 million prescriptions in fiscal 2013.
• Walgreens serves 6.3 millions customers daily.
• Walgreens has more than 8,582 locations, including our
stores, worksite health centers, infusion and respiratory
service facilities, specialty pharmacy, and mail service
facilities.
• Fast Company ranked Walgreens ranked as the 7th most
innovative health care company for its leadership in
redefining the role of the pharmacy. The magazine cited new
mobile app features that help patients manage their
medications as well as the company's partnerships with the
CDC and the Department of Health and Human Services to
expand in-store HIV testing, which created a new standard
for the patient-pharmacy relationship.
2

Key Component of Enterprise Data Management
©2015 Walgreen Co. All rights reserved. Confidential and proprietary information. 3

An Unclear Term?
“Master Data”
Master Data Reference
Data
4

Master and Reference Data
Organizations have different business areas, processes, and applications that
need the same data across the enterprise. These enterprise core data types
provide the context to transactional data throughout the organization.
Inconsistency with the consumption of this enterprise data has a dramatically
negative impact on business.
Master Data Management is the process of:
• Identification, control and publishing of core data domains with the most accurate and timely
data available (a ‘golden version’) to improve data consistency and quality across the
enterprise.
̶ Examples include, Customer, Product, Location.
Reference Data Management is the process of:
• Managing and publishing of controlled sets of defined data values (e.g., vocabularies,
codes, lookup values, classifications, taxonomies) for use either within a business domain or
at an enterprise level.
̶ Examples include, industry codes, type codes, others.

Master Data Benefits
As Master Data is the key to binding business transactions, there are several
benefits that can be derived by mastering and controlling the data fidelity
Improved Data Quality and decision making
• Better and consistent interaction with customers across all channels.
• 'Data Governed' environment with data domain teams responsible for master data domains.
• Reduced errors in business process execution (e.g., product list).
• Elevating siloed data across the enterprise to one single version of data.
• Reduced reconciliation in downstream data warehouse systems.
Lower Cost and faster time to market
• Faster and consistent view of data across the enterprise containing the latest set of key
attributes for the domain.
• Adoption of Industry standard data structures allows ease of data sharing within the
organization.
• Complete, timely, and consistent data lowers data quality issues and resulting cost.

Customer LocationChannel
ServiceVendorProduct
Common Subject Areas of the MDM
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Enterprise Data Model Subject Area Examples

Types of Master Data
Core Entity Data
• Parties and Roles -- Individuals, internal and
external organizations, households, customers,
accounts, agents, employees, suppliers,
distributors, regulators, clubs, subscribers,
members
• Locations and Addresses
• Products, Raw Materials and Other Inventoried
Assets
• Contracts and Other Agreements
• “Things with Serial Numbers”
Reference Data Sets
• Vocabularies – Codes, classifications,
taxonomies
• External Vocabularies – Industry codes,
geopolitical areas, diagnostic codes, units
of measure, currencies
• Internal Vocabularies -- Status codes, type
codes, catalog codes, territories, G/L
accounts, cost centers
• “Controlled sets of Defined Data Values”
9

Managing Hierarchies
Parties and Roles
• Organizations
• Membership
• Addresses
Products
• Bill of Materials
Reference Data Taxonomies
• Classes / Types (Codes)
• Processes
• Territories / Regions
10

MDM Depends Upon …
Data Governance and Stewardship
• To build consensus on data values -- names, codes, business definitions,
usage guidelines
• To define valid formats and ranges
• To identify systems of record
• To define cleansing and match/merge rules
• To review and approve/reject reference data changes
• To define usage rights
• To define policy and monitor compliance
• To identify, track and resolve issues
11

Reference Data Standard Examples
International Organization for Standardization:
• ISO/IEC 639, Codes for the representation of languages
• ISO/IEC 3166, Codes for the representation of names of countries and their
subdivisions
• ISO/IEC 4217, Currency and funds name and code elements
• ISO/IEC 5218, Codes for the representation of human sexes
Vocabulary Standards
• SNOMED CT  ‐ Systematized Nomenclature of Medicine – Clinical Terms
» Clinical medicine nomenclature created  by the College of American
Pathologists.  Will be used to describe a condition or diagnosis.
• LOINC ‐ Logical Observation Identifiers Names and Codes
» Designated standards for use in U.S. Federal Government systems for the
electronic exchange of  lab results.

Leveraged National & Industry Standards
13©2015 Walgreen Co. All rights reserved. Confidential and proprietary information. For internal use only.

MDM Depends Upon …
Enterprise Data Models
• To consistently define master data entities and attributes
Data Integration Architecture
• To identify data sources and system consumers
• To map / align sources and consumers (traceability) and
define transformations
• To design consistent and scalable data services for validation,
cleansing, and consolidation
• To design consistent and scalable data services for inquiry,
subscription, and replication
14

Examples of Master Data
Examples of
Transactional Data
Master Data like Store / Location
• How many locations do we have?
• Which location or group of locations has
the best or worst performance?
Hierarchy data like Product Hierarchy
• What is the revenue for potato chips sold
across all stores?
Lookup data like Transaction Types
• What percent of customers are loyalty
program members?
1234
15

Different Systems - Different Views
App 1
App 2
App 3
App 4
16

Approach to Mastering a Data Domain

Master Data Management (MDM) Architecture
18

MDM ODS at Global Outsourcing Firm
MDM Approach
• MDM ODS used as integration and
distribution point for master data.
• MDM domains include Employee,
Contact, Location.
• All data consumers of master data
retrieve from the MDM ODS.
• Data Integration accomplished
through message broker utilizing
standard and custom data adaptors.
• Uses a defined set of common master
data attributes.
19

Finding that initial program opportunity
Identifying Business Opportunities
• What area is business strategy being focused?
• What is planned in the program/project portfolio for the upcoming FY?
• What are the Tier 1 & 2 programs/projects for the company (e.g., Project
Portfolio)?
• What are the major business applications being implemented (e.g., ERP,
Claims, Billing, CDI, PIM, other)?
• What are your potential executive sponsors focus areas?
• What business areas and/or applications have known data management
challenges?
20

Finding that initial program opportunity
Identifying Business Opportunities
• What business strategy areas and/or applications have existing or
upcoming regulatory, government mandated, or other compliance
challenges (e.g., 5010, ICD‐10)?
• If Risk Management exist, what business areas and/or applications are
considered exposed from a data management perspective?
• Target new critical business initiatives (e.g., customer loyalty program).
21

MDM Planning – Some Lessons Learned
• Create a practical process and criteria for evaluating master data
candidates.
• Effective evaluation of master data candidates requires a
minimum level of meta data specification.
• Business name, meaning, usage, business benefit and risk.
• Key planning issues to address:
• How do you narrow the list of candidates to the vital few?
• Where and when is it essential?
• Who decides what to master?
• Who approves the business case?
22
All part of an MDM Strategy

Master Data Candidate Evaluation Criteria
• Is this data duplicated in multiple systems?
• Is there real benefit in using identical data?
• Is there a trusted source for this data?
• Is there consensus on rules for mastering?
• Can the rules be automated and enforced?
• What will it cost to provide consistent data?
23

Guidelines for MDM Success
• Plan Ahead
• Define the Problem: the Business Impact and Benefits
• Define the Strategy: the MDM Program and Components
• Define the Architecture: MDM & Data Integration Tools
• Define the Roadmap: Incremental Implementation Plan
• Gain MDM Program Commitment
• Manage Scope – Business Prioritize
• Which Master Data?
• Which Reference Data?
• Iterative and Incremental Delivery
• Integrated EIM Approach
24

Michael Jennings
Senior Director, Enterprise Data Architecture
Walgreens
1419 Lake Cook Road, MS: L497
Deerfield, IL 60015 USA
Mike.Jennings@Walgreens.com
www.linkedin.com/in/micahelfjennings
Michael Jennings is a recognized industry expert in enterprise architecture and information
management with more than twenty-five years of experience in various industries. Mike speaks
frequently on enterprise architecture and information management concepts and practices at major
industry conferences.
He is a co-author of the book "Universal Meta Data Models" (2004) and a contributing author to the
books "Building and Managing the Meta Data Repository" (2000) and “The DAMA Guide to the Data
Management Body of Knowledge - DMBOK” (2009).
Mike was recognized with the 2015 DAMA International Professional Achievement Award and as
one of Information Management Magazine’s 25 Top Information Managers for 2012.
He currently serves as VP of Programs for the Wisconsin DAMA Chapter and as VP of Operations
for DAMA International.
Bio
26

Enterprise Data World Webinar: Mastering & Referencing Data for the Enterprise

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