2013-Aug-21 Essentials to Starting an Enterprise ...€¦ · • Master Data & Reference Data Management – manages the acquisition, integration, and usage of the core business data,

Essentials to Starting an Enterprise Information ProgramAugust 21, 2013

Michael JenningsWalgreens

©2013 Walgreen Co. All rights reserved. Confidential and proprietary information. For internal use only.

Agenda

• Enterprise Information Management Framework Data Governance Metadata Master & Reference Data Data Modeling Data Quality

• EIM Program Initiation & Reality• Lessons Learned

©2013 Walgreen Co. All rights reserved. Confidential and proprietary information. For internal use only. 2

Enterprise Information Management Framework

3©2013 Walgreen Co. All rights reserved. Confidential and proprietary information. For internal use only.

Why do we need a Framework?

Frameworks provide a context for structuring a program

Frameworks offer a way to organize the various concepts of the program that suit the organization optimally

Frameworks allow the organization to adopt and incorporate best practices from the various disciplines into a coherent and organized approach


Enterprise Information Management FrameworkStarting EIM Definition


1) Reduce overall business cost

2) Improve business capability:

a) Consistent data experience for customers, clients, partners, and vendors

b) Privacy and compliance

c) Mergers and Acquisitions

d) Customer loyalty

e) Efficient business transactions with external parties

f) Customer safety

g) Others?

EIM Program ObjectivesSample business objectives


EIM Goals

1) To understand the information needs of the enterprise and all relevant stakeholders.

2) To reduce costs through efficient management of data and information.

3) To capture, store, protect and ensure the integrity of the data and information needed, across all business functions and applications.

4) To improve the quality and availability of data and information throughout the enterprise.

5) To promote consistent understanding of the meaning and context of data across the enterprise.

6) To prevent inappropriate use of data and information.

7) To support effective business processes and informed decision making through accurate data.


Enterprise Information Management FrameworkContext with the DMBOK v2 Framework


Enterprise Information Management FrameworkAlternative EIM Framework


Metad

ata Man

agem

ent

Data Con

text

Data M

odel/ClassificationData Structure and Fram

ework

Structured Data

Management

UnstructuredData

Management

Master Data &Reference DataManagement

Business Intelligence &

Data Warehousing

Data Quality Management

Data Security Management

DataIntegrationManagement

Data DeliveryManagement

Data GovernancePolicies, Processes, Standards, Organization, and Stewardship

Enterprise Information Management FrameworkA Framework of Disciplines (continue)

• Data Governance - establishes the policies, processes, standards, solutions, education, and organizational structure for managing data. Data stewards, with subject matter experts & advisors, support and implement the direction set by data governance by providing guidance to data management programs and projects in the organization and by raising issues and feedback.

• Metadata Management – provides context to the data in the enterprise for business and IT to make informed decisions.

• Data Model/Classification Management – provides the business and IT alignment framework to clearly understand the data, structure and content, an organization is managing.

• Structured Data Management – establishes and manages the policies, procedures, standards, guidelines, data models, meta data, business rules, organizational oversight & change management, and technology for an organizations’ structured data. Includes management of business, back office, and support applications.

• Data Content Management – establishes and manages the policies, procedures, standards, guidelines, taxonomies, metadata, business rules/schedules, organizational oversight & change management, and technology for an organizations’ data content (unstructured).

• Master Data & Reference Data Management – manages the acquisition, integration, and usage of the core business data, both entities and data values in the enterprise. Master Data consolidates disparate core data entities while Reference Data ensures the quality and use of controlled data values and relationships


Enterprise Information Management FrameworkA Framework of Disciplines

• Data Warehouse / Business Intelligence – acquires, integrates, and manages data, structured and content data, from across the enterprise for use in trending, statistical and historical analysis, data mining, data visualization, and other analytic purposes for business and IT.

• Data Quality Management – establishes the criteria, specifications, and standards to be used to measure, monitor, and address data quality issues with business significant elements in an organization.

• Data Security Management – ensures data sensitivity, privacy, and security is established and maintained through definition of business roles, rules and procedures for the enterprise.

• Data Integration Management – establishes and manages the procedures, standards, guidelines, meta data, organizational oversight & change management, and technology for integration of data (interfaces, ETL, services, other methods) throughout the enterprise.

• Data Delivery Management – establishes and manages the procedures, standards, guidelines, meta data, organizational oversight & change management, and technology for delivery of data (reports, dashboards, OLAP, other methods) throughout the enterprise.


Key EIM DisciplinesEIM Discipline Focus


Enterprise Information Management FrameworkChallenges for EIM Programs

• No two EIM programs are the same.

• Differences include:

» Organizational, political, and cultural differences in each organization.

» Level of business and executive support.

» Level of enterprise realization, support and seed funding.

• Everyone wants enterprise until they have to pay for it.

» Data management maturity level.

• Adopt the best practices to your organization, while aligning to your unique data management state, business strategy and goals.

• Maintain a long-term enterprise focus while implementing incrementally and iteratively.


Data Governance


Walgreens Data Governance Definition


Provides the organizational oversight and methods to effectively manage data as an asset across the

organization

Data Governance Program’s Mission/Vision


Establish a data governance program comprised of the people, policies, standards, processes, metrics, education, and

technologies required to fully manage our corporate data as an enterprise asset

Mission/Vision

As our business continues to evolve, the ability to fully leverage the vastdata available across Walgreens effectively becomes increasinglyparamount for continued growth and profitability.

Walgreens recognizes the need to develop an Enterprise Data GovernanceProgram to manage the quality, consistency, usability, security,accessibility, and availability of enterprise data in the organization.

EIM / Data GovernanceHow to Meet the Objectives


1) Establishment of enterprise data oversight

2) Improve data quality

3) Establishment of enterprise master and reference data

4) Establish enterprise data policies, standards, and processes

5) Documented enterprise business definitions and business rules

6) Enable impact analysis and data lineage

7) Engage with projects and SDLC to utilize data governance practices

8) Measure and monitor metrics for business impact

9) Communication, socialization, and education

Data GovernanceIs Not…


1) Governing all data in the enterprise (only prioritized data supporting key business processes and decisions)

2) Interpreting compliance, privacy, HIPAA, and other laws and regulations

3) Establishing protection methods for data assets

4) Legal interpretations around data management

5) Impacting existing applications near‐term to meet EDG policies and standards (applied to new applications or major revisions to legacy applications)

6) Business or technical areas outside the data management lifecycle

Data GovernanceInteraction Groups and Processes

Resource Allocation Committee

(RAC)

Service Governance

Technology Steering

Committee

HIPAA Security Steering

Committee

Records & Information Governance Committee

(RIGC)

Project Architecture

Reviews (PAR)

Enterprise Software

Development Life Cycle

(SDLC)


Engagement Points

©2013 Walgreen Co. All rights reserved. Confidential and proprietary information. For internal use only.20

Enterprise Data Governance Executive Committee (EDGEC)

Enterprise Data Governance Executive Committee (EDGEC) – provides strategic data management direction to the overall EDG program based on business strategy, direction, and prioritization. Approves enterprise data governance policies, standards, and processes. Communicates and promotes the Enterprise Data Governance program throughout the organization.

Data Governance Committee (DGC) – provides operational data management direction for issues, request, and questions that cross data domains or subject areas to ensure approaches and methods are done consistently. Venue for proposing new or modified data management policies, standards, and processes for endorsement by the DGC and then to the EDGEC for approval. Data Domain Leads serve on this committee besides their own Data Domain Teams.

Data Domain Team (DDT) – provides direction for operational data management issues and questions for a single data domain or subject area level including development of logical master records.

Data Governance Office – provides data management education, communication, and collaboration throughout the enterprise. Supports major application programs and initiatives that have potential impact on enterprise data management direction.

Data Governance Committee

(DGC)

Data Domain Team (DDT)

Data Governance

OfficeOperational Data Steward

(Information Owner)

Program Organization Structure


Data Governance Steward Types


22

Policies and StandardsDefinitions


Definition: Data Policies are the overall business rules and processes that an enterprise utilizes to provide guidance for data management. Policies might include adherence of data to business rules, providing guidance for protection of data, defining enterprise data management functions, and others.

Examples:• Outbound Data Sharing Policy• Comprehensive Information Security Policy • HIPAA Privacy Policies

Policy

Definition: Data Standards are the precise criteria, specifications, and rules for the definition, creation, storage and usage of data within an enterprise. Data Standards include basic context items like naming conventions, number of characters, and value ranges, and can also include classification. Data Domain Teams may also dictate specific quality measures, retention rules, and backup frequency.

Examples:• Data Classification Standard• Name and Address Standard • National and Industry Standards (HL7, GS1)

Standard

Enterprise Data Standards

Establishing data standards and data models based on industry standards and best practices.

NameLanguage

Race & EthnicityEmail AddressTelephoneAddress

These standards can be applied at an enterprise level. Business rules may vary at an application level based on business requirements.

Adoption of this standard can be on a go‐forward basis. Retro fitting for legacy systems will be done on a case by case opportunity basis.


Leveraged National & Industry Standards


26

Policy Sub-Committee

Permanent Policy Sub-Committee established for policy development and implementation.

The Policy Sub-committee includes representatives from Legal, Privacy and Compliance, Risk Management, Vendor Collaboration, and Information Security.

The committee is empowered to make recommendations.


Enterprise Clinical Terminology Sub-committee


A governing group established to provide direction and guidance across Walgreens related to clinical terminology code sets, crosswalks, and local codes data standards as a means to data interoperability to improve healthcare data quality and health record processing efficiency.

Data GovernanceIssue & Request Flow


Email Address

EA/EDG Wiki

Data Governance Office

Data ManagementIssue / Request

EDGEC

DGC

Policy Sub‐Committee

DDTs

Metadata


What is “New York”?

• Is it a state?

• Is it a city?

• Is it a sports franchise?

• Is it a destination or organization point?


Metadata

Metadata, like any other enterprise asset, requires careful management in order to realize its full value and opportunity to our organization. Metadata is all the physical data and knowledge about

the business and technical processes used by an organization.• Metadata shows business and technical users where to find information in data stores.

• Metadata also provides details on:• Where the data came from• How is got there• How it was transformed• Its level of quality• Provides assistance with what it really means and how to interpret it

• Metadata includes information about he physical data, technical and business processes, rules and constraints of the data and structures of the data used by an organization.


Metadata is knowledge about your organization

What is Metadata?


METADATA

DATA

Plan Table

Metadata

Metadata is the data context that explains the definition, control, usage and treatment of data content within a system, application or environment throughout the enterprise.

Metadata provides the fabric that interconnects all of the other enterprise information management disciplines (e.g., data governance, structured & unstructured data, data models, master & reference data, data quality, data integration, data warehousing & business intelligence, data security).

Meta Data Categories

Business

Technical

Stewards from Data Governance, in conjunctions with SMEs, develop and define metadata.

Ideally, a metadata repository product, built or purchased, is used to integrate, collect, organize, and delivery metadata to the organization.


Metadata Questions

• Who will use the data?

• What is the purpose of the data? Business or technology?

• What is the business value and prioritization of the data?

• How will the data be used (e.g., lookup, calculation, flag, hierarchy, business rule, other)?

• Is the meaning of the data static or does it change over time?

• What relationships does the data have to other data?

• Does the data originate from a industry or national data standard?

• Does the data have regulatory or compliance requirements?

• Does the data have classification implications (e.g., competitive, PI, HIPAA, PCI, Legal, other)?

• Others?


Business Metadata

• Business metadata is metadata about the business terms, business processes and business rules.

• Business metadata provides the semantic layer between your systems and their business users.

• It provides users a roadmap for navigating all the data in the enterprise by documenting what information is available and, when accessed, provides a context for interpreting the data.

Invaluable for making sound business decisions.


Technical Metadata

• Technical metadata is metadata describing technical aspects of IT systems, which designers and developers use to build and maintain them.

• Examples of technical metadata include descriptions of database tables, data attributes, sizes, data types, database key attributes and indices and technical data transformation rules.


Metadata Model for Data Warehousing


Enterprise Metadata Model


message queue

exchange messages

software

rule violation

quality evidence

contains

Legend

Data Package & StructureData Package IdData Group IdData Element Id

Domain Id (FK)

Data TransformationTransformation Package Id

Transformation AuditTransformation Package Datetime

Transformation Package Id (FK)

DomainDomain Id

Data QualityData Quality Metric ID

Data Package Id (FK)Data Group Id (FK)Data Element Id (FK)

SystemSystem ID

Organization ID (FK)

OrganizationOrganization ID

PersonPerson Id

Organization ID (FK)

ProjectProject Id

Business RuleBusiness Rule ID

Subject AreaSubject Area Id

Data StewardData Steward ID

HardwareHardware Unit Id

SoftwareSoftware Module Id

Process Id (FK)Person Id (FK)System ID (FK)

EnvironmentEnvironment Id

XOR

XML Schema (DTD)XML Doc IdData Package Id (FK)Data Group Id (FK)Data Element Id (FK)

XSLTXML Doc IdTransformation Package Id (FK)

Business TransactionProcess Id

Transform Processes

Enterprise SystemsIT Portfolio Management

*

XML, Messaging & Business TransactionsBusiness Rules, Business Meta Data & Data Stew ards

Managed Metadata Environment


Meta DataSourcing

LayerMeta DataIntegration

Layer

Meta DataRepository

Meta Data Management Layer

Meta DataMart

Meta DataDelivery

Layer

Meta Data Direct Adapter Extracts

Meta DataExtracts

Meta Data Aligned

Meta Data Meta Data Reports

Meta Data Direct Searches and Reviews

Update Schedules

Update Mappings

Update Extends,Security

Update Statistics,Metrics

Update Reports

Update Content

Metadata Tool Data Lineage Example


Master & Reference Data


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.• Reduces 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.


Guiding Principles for Master Data Management

Master data can be defined as either Enterprise level or Business Area level

Enterprise Master Data is used by majority of business areas hence the need for data accuracy, consistency, and better quality.

• Examples: Customer Name, Customer Address & Contact Preferences, Organization Name & Corporate Address, Employee, Location details

Business Area Master Data is master data unique at least one business areas• Examples: Provider Name, Provider Address, Provider NPI & DEA, Item UPC, Drug NDC

• Business area master data must be linked to an enterprise master data instance and should not be managed independently

• A framework consisting of master data domains like Customer, Provider, Product, tools, processes and services will be referred to as the MDM Hub

• MDM Hub is a logical collection of master data domains• Applications will interact with MDM Hub for enterprise master data• Enterprise and Business area master data is managed through the MDM Hub• Services to interact with master data will be managed centrally• Applications will refer to master data via lookup keys to minimize replication• Data matching engine is considered part of the MDM Hub• All data reconciliation is performed in the MDM hub• Applications, including data warehouse and decision systems, will receive master data from the MDM Hub


Approach to Mastering a Data Domain


Golden View of Data Domain


What is the “Golden Record” for Data Domain? Will be the enterprise view of the Data Domain Will define enterprise data fields

Potential Sources/Projects for the Golden Record: Legacy applications New applications or initiatives 3rd Party/External Others

Address the definition of the Golden Record through Master Data Management methodology and processes

Identify and establish Data Domain Identify Source(s) of Record Gather high level business requirements for data domain Identify initial set of data fields that make up the golden record for master data

based on business need including standards (e.g., ARTS)

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.


Data Modeling


Enterprise Data Model

An enterprise data model (EDM) is an integrated subject‐oriented data model defining the business relevant data produced and consumed across an entire organization.

• Business Relevant ‐ data critical to the effective operation and decision‐making of the organization.

• Integrated ‐ all of the entities, attributes and rules in the model are defined once, without redundancy. The concepts in the model fit together as the business sees the enterprise. It does not reflect organizational boundaries or silos.

One version of the Customer entity, one Order entity, etc.

Every data element also has a single name and definition.

• Subject‐oriented ‐model is divided into commonly recognized subject areas or data domains that span across multiple business processes and application systems.

Subject area/Data Domain ‐ High‐level classification of data used to associate a set of data attributes/objects into a specific business grouping.


Other Benefits of Enterprise Data Model

• Improve overall organization communication between business and IT.

• Provides a framework for information systems.

• Provides a starting point for identifying master data candidates.

• Improves standardization (names, definitions, data characteristics for key data attributes) across the enterprise.

• Improved data and application consistency.

• Improve interoperability / data exchange.

• Enable faster application development (reduced duplication of analysis, less rework, standardization, project scoping, etc).


Enterprise Data Model

The Enterprise Data Model (EDM) is comprised typically of 3 models:

Subject Area Model (SAM) ‐ highest level enterprise model to start decomposing the information of interest to your organization. Aids in semantic resolution of key data, aids in data stewardship / governance, downstream model organization. Typically 12 – 20 key data subjects.

Enterprise Conceptual Data Model (ECDM) ‐ identifies significant business entities and their relationships, which uncovers critical business rules. Provides framework for information systems – but still a business model.

Enterprise Logical Data Model (ELDM) ‐ lowest level enterprise data model where business attributes are added to data entities, data entities may be consolidated, starts to resemble a model which could be leveraged for implementation.


Enterprise Data Model Layers


52

Logical ViewsWith business attributes

An effective Enterprise Data model starts with alignment to overall business view of data and builds to subsequent levels of detail based on that view.

Conceptual ViewsWith relationships

SubjectArea Model

Subject Areasdecompose

into lowerlevels ofdetail

Logical

Conceptual

Subject Area• 12-20 business subject areas or data domains• One diagram

• 100 to 250 business significant entities• No attributes• Developed iteratively

• Business prioritized attributes

• Developed iteratively


An incremental approach to enterprise data model development is typically recommended. A high-level conceptual model (SAM) is developed first to

provide an immediate data model reference.

Using a phased approach means that subsequent implementation phases, to develop lower levels of EDM detail ,are defined and developed by subject area as

prioritized by Data Governance based on business need.

Leverage Data Governance committees, teams, and SMEs

Leverage existing data models. Leverage industry and national

data model standards (e.g., ARTS)

Data Model Design

Conduct a top‐down final validation

Resolve any remaining discrepancies

Obtain appropriate approval/endorsement from Data Governance

Data GovernanceApproval

Communicate data model to organization through stewards and other available means

Update key processes for management of EDM (e.g., SDLC, others)

Develop a deployment plan for EDM use.

Data ModelDeployment

Data ModelingProviding a Foundation

Customer Location

OrderProduct

Subject Areas / Data Domains


Example of an EDM


Example of a HR Conceptual Level Data Model


Data Quality


Common Data Quality Issues

• Customer demographic issues.

• Inconsistent definitions of business data (e.g., profitability, customer, etc.)

• Duplicate client records.

• Lack of standardization in reference/lookup codes (e.g., language, race, gender, many others).

• Inconsistent and corrupted addresses and phone numbers.

• Data integration issues creating challenges in reconciling a Data Warehouse to sources.

• Manually maintained data, with no data quality controls (e.g., client list, product list, vendor list, etc.).

• Inconsistent controls on data at the source of input.

• Pervasive data redundancy.

• Others….


Data Quality

Data Quality is a measure of the degree to which data satisfies the information needs of its consumers, reflects the nature and state of the real world concepts to which it relates, is coherent within itself, and provides value in the decision-

making processes for which it is to be utilized.

• Data is the basis for your information, and its quality directly affects your ability to leverage information as a strategic asset.

• The quality of your data supports your ability to provide meaningful information to your customers and organization.

• Accurate and comprehensive data directly supports our ability to serve your customers and manage our business effectively.


Data Quality means data usefulness

Accuracy - The degree of data reflects the nature and state of real world objects or concepts they represent. Data represents reality correctly and must have a standard measure.

Completeness - The extent data provides a comprehensive reflection of the nature and state of the real world objects it is intended to represent and how those objects or concepts relate to each other. Data gaps are minimized and data subjects are covered adequately.

Timeliness - Data is stored in systems within an acceptable time from the business event.

Consistency - Data is defined and reported with the same meaning and values across the enterprise. A summary of the validity, accuracy, usability, and integrity of related data between applications, repositories, and across the enterprise.

Compliance - Data content and context meets applicable compliance, regulatory, and legal requirements.

Metadata - Available context for data is sufficient to meet business and project requirements, especially for data that the business has identified as high priority.


Data Quality Dimensions

Business Value of Addressing Data Quality

Establishing an enterprise data quality management program will provide an organization with data quality oversight, processes and tools which can be used

to improve the quality and consistency of enterprise data.

The results will be: • Enterprise understanding of and engagement with data quality.

• Common language, terms, and metadata, understood and accepted by the enterprise.

• Tools and processes to expedite data quality issue analysis and resolution.

• Increasingly consistent, standardized data.

• Reduced number of data issues in production.

• Project, process, and application efficiencies.

• Increased confidence in data by the business.



Data Quality Assessment Matrix

Data Quality Profiling

A set of methods and tools used to process data for assessment purposes, which allows for the analyses of the actual data values in the source databases. Identify noncompliant data (data type, length, domain, value, etc.).

Recommend key structures (primary & foreign) based on content.

Recognize data entry patterns in order to determine requirements for additional edits or application formatting.

Identify data errors and anomalies based on business rules.

The results merely suggest business rules, the rules must be validated by stewards. Example, data profiling will discover the actual range of values for a specific field. Data steward

must validate the range and add values that are possible, but do not occur in the source.

The results will suggest anomalies in the data which require steward review. Example, data profiling may point out a range of values which cover a high percent of the total

occurrences. Values with a very small occurrence (< .1%) are suspect of being invalid.



Data Quality Profiling Example

Attribute: SOL_BILL_HNDLG_CODE

• Solicitation Handling Type Code

• Indicates whether payroll solicitation and/or billing require special manual handling

The valid range of values appears to be 0-3 and blank

A value of blank suggests the field

is optional

Very low occurrence of 4,

is it valid?

• Addressing data quality is not an isolated activity, but involves every phase from data capture through processing, to usage.

• Data quality may not need to be absolute, but rather sufficient to support planned usage, comply with applicable regulations, and minimize unnecessary costs.

• Data quality issues can have a varying level of criticality based on the impact to the business.

• Refine data quality assessment ranking approach for your organization and business need.

• Identify candidate attributes from sources based on business criticality and suspected with low quality based on feedback.

• Data Governance should be used to appropriately address data quality for the enterprise.


Data Quality Approach

EIM Program Initiation & Reality


Finding that initial program opportunity (continue)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?


Finding that initial program opportunityIdentifying Business Opportunities

What business areas and/or applications have existing or upcoming regulatory, government mandated, or other compliance challenges (e.g., 5010, ICD‐10)?

If Risk Management exits, what business areas and/or applications are considered exposed from a data management perspective?

What groups or projects are considered to be at an enterprise level in the organization?

Target new critical business initiatives (e.g., customer loyalty program).


Talking to BusinessBusiness Sponsorship & Involvement

Research & understand business initiatives for

business area

Research & understand real or potential data

challenges in business area

Do not talk about need for information

management or data management

Discuss how sponsorship and

participation will enable their business initiative

or business challenge to be successful

Clearly identify time and resource commitment.

Identify cost savings and/or increased

business capabilities or process efficiencies through sponsorship

Understand and speak the language of the

business area

Has a similar business initiative been tried and

failed before?

As sponsor membership grows, leverage current

participants for new sponsors


Cultural & Political Realities

Business Initiatives? Sponsors?


Met

71

Lessons Learned


Methods for Achieving EIM Objectives (continue)

1) Identify and lobby business executive sponsors.

2) Identify pilot or POC opportunities.

3) Drive organizational adoption through an enterprise level data governance program.

4) Drive efforts to improve data quality across the organization.

5) Establishment of enterprise master and reference data initiatives. Prioritize subject areas based on business need.

6) Establish enterprise data policies, standards, and processes.

7) Establish an EDM as a reference data model.


8) Use enterprise data definitions and structures to organize your data and information areas.

9) Engage with tier 1 & 2 projects and SDLC to utilize data management practices.

10) Measure and monitor data management metrics for business impact.

11) Perform continuous communication, socialization, and education efforts.

12) Provide business value early on, as prioritized by the business.

13) Pick and choose your areas of focus, concentrating on areas of strategic business significance.


Methods for Achieving EIM Objectives

Questions


Thank You

75

Appendix


Enterprise Data Governance Executive Committee (EDGEC)


Data Governance Committee (DGC)


Enterprise Data Governance Organization



EIM Organization Example

EIM Organization Example


Planning

Business SolutionsData Governance & Stewardship

Records Information Management

Enterprise Content (Unstructured)

Strategy

Data OperationsInformation

Architecture& Management

CustomSoftware

Development

Data Movement

Modeling(Conceptual &

Logical)

Extract Management(Development,

Fulfillment)

Metadata Mgmt

Legacy Report Rationalization(Application)

Quality Assurance

Extract Management(Business)

Internal Informatics

Legacy Report and Extract Management

(External Rpting)

Communication and Program

Socialization

Client Reporting

Enterprise Information Management & Strategy

Michael JenningsDirector, Data Governance – Enterprise ArchitectureWalgreens1419 Lake Cook Road, MS: L497Deerfield, IL 60015 USA847 964 [email protected]/in/micahelfjennings

Michael Jennings is a recognized industry expert in enterprise informationmanagement. He has more than twenty-five years of information managementexperience in various industries. Mike speaks frequently on enterprise informationmanagement concepts and practices at major industry conferences. He is a co-authorof 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 theData Management Body of Knowledge - DMBOK” (2009). Mike was recognized with the2013 DAMA International Professional Achievement Award and as one of InformationManagement Magazine’s 25 Top Information Managers for 2012.

Bio


Documents

2013-Aug-21 Essentials to Starting an Enterprise ...€¦ · • Master Data & Reference Data Management – manages the acquisition, integration, and usage of the core business data,