an#engagement#model#for# master#data#consumers#€¦ · & 5 human& sales&...

An Engagement Model for Master Data Consumers

2015-‐05-‐21 David Loshin

Knowledge Integrity, Inc. loshin@knowledge-‐integrity.com

© 2015 Knowledge Integrity, Inc loshin@knowledge-‐integrity.com (301) 754-‐6350 1

What is Master Data? •  Master data encompasses the models represenIng the core

business enIty objects used in the different applicaIons across the organizaIon, along with their associated metadata, aOributes, definiIons, semanIcs, roles, connecIons, and taxonomies.

•  Examples include: –  Customers –  Products –  Parts –  Vendors –  Employees –  Suppliers –  LocaIons

•  EnIty concepts are idenIfied in relaIon to the business context

© 2015 Knowledge Integrity, Inc loshin@knowledge-‐integrity.com (301) 754-‐6350

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The Master Data Environment •  A Master Data Environment provides a set of services

enabling data consumers with accessibility to a composite view of uniquely idenIfiable enIIes

•  Requirements are solicited from data producers and data consumers for providing or using master data: –  CollecIng source informaIon about enIIes –  Resolving enIty idenIIes –  Indexing enIty data –  Establishing connecIons among source data records associated with

uniquely idenIfied enIIes –  IngesIng data from the sources into the master data environment –  Project planning for consuming applicaIons to integrate the

consumpIon of master data


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Mistaken Perceptions of Master Data •  Typical pitches for master data management use plaItudes to

moIvate adopIon: –  “Golden record” –  “360° view of the customer” –  “Single source of truth”

•  These concepts are somewhat misleading: –  IntegraIon into a single “golden” record implies transformaIons that

may be inconsistent with operaIonal use –  MDM, by its very nature, cannot be a “source” except under very

constrained circumstances –  “Truth,” from a business process and applicaIon perspecIve, is

malleable –  Dependencies on structure, intent, semanIcs, and context cannot be

ignored © 2015 Knowledge Integrity, Inc loshin@knowledge-‐integrity.com (301) 754-‐6350

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Multi-‐Domain Master Data Use


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Human Resources Sales Customer

Support MarkeIng

Purchasing/Materials

Manufacturing Fulfillment

AccounIng/Finance

R & D

ForecasIng

IT

Business Func=ons

Customers Vendors Suppliers Employees Parts Products

Business En==es

Criteria for Usability


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•  Profiles capturing shared master aOributes •  IdenIfying aOributes selected for indexing Model completeness

• Algorithms for similarity and matching • Methods of implementaIon IdenIty resoluIon

•  Structural consistency •  SemanIc consistency

Conformance consistency

•  List of master data “events” •  Inventory of services Master data services

•  SolicitaIon of interest • Requirements, design, develop, deploy

Process of engagement

•  Provision of master data • ConsumpIon of master data

Methods of integraIon

MDM Engagement Model


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•  Stages in transiIoning an opportunity into a producIon implementaIon for –  Data consumers –  Data producers

•  EvaluaIon will help determine if there is a need for applicaIon data provision or applicaIon integraIon

Produc'onaliza'on, O & M

Tes'ng

Development Plan

Cos'ng Model

Data Governance

Facilitate Requirements Analysis

Ini'al Evalua'on

Iden'fy Opportunity

Initial Evaluation


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•  An iniIal discussion between the business process owners and the MDM team to assess suitability, consider scope, and clarify roles and responsibiliIes

Understand the Business Process

IdenIfy Master Domains

Specify Use Cases

Review Performance

Metrics

IniIal Scope DeterminaIon

•  IntegraIon planning may indicate the business process incorporates data producers, data consumers, or both


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Sample MDM Usage Scenarios Use Context Example

IdenIty resoluIon and registraIon

OperaIonal workflow ValidaIng that a customer aOempIng to enroll in a markeIng promoIon is eligible to parIcipate

IdenIty resoluIon and registraIon

AnalyIc workflow Linking payments to vendors to idenIfy where vendors have been paid more than once for providing the same item

IdenIty management as a service

OperaIonal or AnalyIc Providing a unique enIty idenIfier that links all employee records across benefit applicaIons

RelaIonship and hierarchy linkage

OperaIonal workflow Determining where physicians are associated with mulIple hospital organizaIons

RelaIonship and hierarchy linkage

AnalyIc workflow SupporIng reporIng of rolled-‐up metrics associated with regional sales by customer type

Data quality Informing synchronizaIon and quality within the master catalog

Publishing updated material item data as records are modified

Facilitating Requirements Analysis


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•  Consuming business process owners provide the requirements for using the MDM shared services: –  Data requirements –  FuncIonal requirements –  Performance criteria –  User acceptance criteria –  TesIng plajorm and data

needs –  Accessibility

•  For publishers, the MDM team provides an Interface Control Document (ICD)

•  The MDM team examines whether the exisIng integraIon paOerns are sufficient or if there is a need for customizaIon or addiIonal services

Requirements Analysis

AddiIons to Services Stack

CustomizaIon Needs

Auxiliary Processing

Data IntegraIon

Access paOerns

Business Rules

Performance ExpectaIons

Requirements Analysis: Access Patterns


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•  The Business Process owners evaluate how their applicaIons seek to use the master data

•  The MDM team will share MDM usage paOerns to help drive the analysis process

•  Examples include: –  Direct queries via MDM tool interface (“inspector”) –  Direct queries using SQL –  Federated/Virtualized access –  BI Tools –  Web services –  Extracts –  Real Ime or batch?

Data Governance


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•  System of record –  Have the systems of record been idenIfied? –  Is the master data environment the system of record?

•  Authorship and oversight –  How are new items added to the MDM system?

•  SynchronizaIon –  How frequently is master data entered into the MDM system? –  How frequently is the master data synchronized with the consumer

systems?

•  Stewardship workflows –  What happens when an issue with the source data needs to be

resolved?

•  Responsibility for Data Quality

Costing Model


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•  Enterprise stakeholders subsidize the development of shared services

•  Business data consumers benefit in the use of shared services via their investment

•  Costs include: –  Design and development

staffing –  Dev/Test resources –  AmorIzaIon of services costs –  Chargeback models –  O & M plan

•  Currently: –  Consumer costs require new

money –  Producer costs may be covered

under exisIng budgets

$

Development cosIng

ConsumpIon cosIng

O & M Plan and cosIng

Development & Deployment •  Detailed planning and execuIon of development tasks and

costs •  Details regarding use of data integraIon tools, development,

implementaIon •  Agreement to specificaIon and project plan •  Resourcing & staffing


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Testing


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•  Development and tesIng plajorms that scale with real applicaIon performance sizing

•  Scalable data test plan (ensure accessibility or create data lab or generate test data)

•  Simplify migraIon into producIon

Unit tests Full tests User acceptance ProducIon


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Productionalization and O & M •  ProducIonalize using standard deployment framework:

–  Hardware plajorm –  Database plajorm –  IdenIty resoluIon tool/techniques –  Data integraIon tools –  Data virtualizaIon tools –  Rendering engine –  Business intelligence/reporIng

•  ProducIonalizaIon –  Move to designated producIon environments

•  OperaIons & Maintenance: –  General MDM OperaIons –  General System Maintenance –  Periodic Review and Revision of Business Rules


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Performance Criteria and Measures Criterion Descrip'on of Measure

Persistence Volume The amount of data and number of enIIes that can be managed within the profile repository and index.

TransacIon Volume The number of master data transacIons per Ime period.

Index size The extent to which the index can grow.

User Load The number of simultaneous interacIve and system users.

Query Response Time The speed at which queries are saIsfied

Refresh Cadence The Ime period in which the master index is updated.

Ease of deployment How simple it is for users to adopt the use of the master data environment.

Time to value How quickly a prospecIve consumer can adopt the use of the master data environment.

Batch size The number of batch transacIons that must be saIsfied in a Imely manner.

Result set size The maximum number of records to be returned from a search.


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Developing the Roadmap and Plan •  Clearly explain the engagement cycle with the prospecIve

customer •  Devise common deployment paOerns ahead of Ime to speed

Ime to value •  Specify how funcIonal requirements address performance

expectaIons •  Provide template cost model, interoperability model, and

integraIon plans for the prospecIve consumer


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Developing the Roadmap and Plan •  Clearly explain the engagement cycle with the prospecIve

customer •  Devise common deployment paOerns ahead of Ime to speed

Ime to value •  Specify how funcIonal requirements address performance

expectaIons •  Provide template cost model, interoperability model, and

integraIon plans for the prospecIve consumer

Questions & Suggestions •  www.knowledge-‐integrity.com •  www.dataqualitybook.com •  www.decisionworx.com •  If you have quesIons, comments,

or suggesIons, please contact me David Loshin 301-‐754-‐6350 loshin@knowledge-‐integrity.com


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an#engagement#model#for# master#data#consumers#€¦ · & 5 human& sales&...

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