aviana dg-dq webinar 2010-06-16 v7

Aviana Global – Overview

Incorporated in 1994 Focused exclusively on Data

Integration, Data Warehouse & Business Intelligence solutions for 15 years

LA Development Center since 1990

Consultants average 7 years of Business Intelligence experience

Business focused with partnerships across leading technologies

Flexible service offerings

History of successful delivery Proven methodologies

streamline implementations and provide a roadmap for success

Focused management and delivery teams provide lower costs and better managed projects

Cross-trained with multiple Information Management skill sets

Leverage technology to achieve business goals

Commitment to client success

Experience Client Benefit

1 May 11, 2010

Steven Strutz

Founded Master Data Management Centers of Excellence incorporating Data Governance and Stewardship programs consisting of both business and IT personnel with responsibility for data consistency, standardization, and quality across business and technology areas.

Authoring textbook on “Enterprise Architecture as a Strategy for Managing Information”.

Provides leadership, management, and direction on cross-organization engagements in analysis, design, and development efforts focused on fulfillment of business needs in areas such as Customer Relationship Management (CRM), Supply Chain Automation, Customer Service, and Web Enablement (B2B and B2C).

Lead programs and projects for Fortune 1000 clients to identify business improvement opportunities though streamlining business processes, IT systems, and strategic use of data across the enterprise.

Data Governance Webinar Series: Deliver Trusted Information with a Data Quality Initiative

June 16, 2010

Presented by Aviana Global Technologies

3 © 2010 Aviana Global Technologies/Steven Strutz. All rights reserved.

Deliver Trusted Information with a Data Quality Initiative

High quality data is vital to ensuring informed business decisions.

With rapid changes in sources of information and an ever increasing demand for access to this information, organizations are inundated with risks associated to poor data quality.

Allowing these challenges to go unmet can adversely affect revenue, cost, and overall customer satisfaction.

During this webinar, learn how to identify, measure, and address data quality problems in your organization and provide the trusted information necessary to stay competitive in today's marketplace.


Understanding the consequences of poor data quality

Business-driven poor data qualities are caused by end users inaccurately creating data or defining data. Examples include:

• Inaccurate Data: By inaccurately creating a record for “Ms. Anthony Jones”, rather than “Mr. Anthony Jones”, bad data quality is created. Inaccurate data is also demonstrated by the “duplicate data” phenomenon. For example an organization has a customer record for both “Anthony Jones” and Tony Jones”, both the same person.

• Inconsistent Definitions: By having disparate views on what the definition of bad data quality is, perceived bad quality is created.

Technology-driven poor data qualities are caused by not applying technology constraints either data base or data integration. These types include:

• Invalid Data: By not applying constraints, alphanumeric data is allowed in a numeric data field (or column)

• Missing Data: By not applying key constraints in the database, a not null field has been left null.


Existing data quality efforts are undermined by the lack of executive focus and a cross-enterprise support organization

Identifying data risk points and high value targets – Where to Start…

Data is a specific, identifiable asset of the company – Companies make significant investments to develop data assets

• Data is distinctive to business operations and processes – providing value to Clients

• Knowledge and data are unique and viable assets of the business • Requires effective oversight and management

– Raw data forms the building blocks for information • Data once collected, organized, examined, and studied becomes information • Reliable, accurate, and correct data, put into context by business processes,

transforms data into information – Information is the basis for meaningful, actionable business decision-

making • Reliable, accurate, complete, and timely information augments company’s

competitive advantages • Knowledge and insight is key to being a marketplace leader

– The ability to use data as a “strategic asset” is a significant business differentiator


Knowledge is Power!

Identifying data risk points and high value targets – Master Data Management

Master Data Management is the set of programs, disciplines, processes, and governance necessary to manage data – Single version of the truth

• Trusted Source – Ensures consistency, currency, meaning, integrity and quality of data

used within or across multiple business areas or business processes • Enables high quality business intelligence • Enables business process measurement and optimization

– Enables simplified, clearly articulated, and effective communication across business areas and business processes

– Enables coordination, integration, and reconciliation of historical and evolutionary data lifecycles

– Enables regulatory compliance – Enables enterprise-wide collaborations


Identifying data risk points and high value targets – Master Data Defined

Core set of data elements – with their associated hierarchies, attributes, properties, perspectives, and dimensions that span the enterprise and drive the business

– Examples: People, Places and Things such as organization, company, product, legal entity, employee, geographic location, etc.

Company's set of "control" data that enables the understanding of the meaning and context of each piece of data

Not transaction data – transaction data is information that is generated and captured by operational systems using Master Data to describe activities of the business

Core elements of the business that are applied to multiple transactions and are used to categorize, aggregate, and analyze transactional data

Creates a single version of the truth about these objects across the enterprise


Identifying data risk points and high value targets – Master Data Candidates Organization

– Internal • Financial Organization

– Chart of Accounts – Cost Center

• Organization Structure – Business Units – Departments – HR Organizations

– External • Company

– Customer – Account – Partner – Resellers – Supplier – Vendor

Person – Employee

• Doctor • Nurse • Lab Technician

– Patient – Other

• Contractor • Contact • User

Product – Software

• Bill-of-Material (BOM) – Developed component – Developed Module – Package – OEM – Item

• Catalog Item – Professional Services – Education – Support – Maintenance

Contract – Sales – Professional Services – Support – Maintenance – Education – Procurement – Supplier\Service – Employment

Location – Site – Geography – Territory


Active, Committed Champions

Reusable Component-driven Development Process

Cost Savings

Faster Development Time

Accurate Reporting

Data Stewardship Organization

Business Processes Information Technology Processes

Benefits

Enterprise Information Management Organization

Data Governance

Defining a repeatable process – Data Governance

Master Data Management mandates Data Governance

Data Governance – Exert authority and control over data, business

processes, and business decisions that might lead to loss of data quality

– Framework within which to collaborate • Establishes the “Rules of Engagement”

– Specifies decision making rights and processes – Encompasses People, Process, and Enabling

Technologies

– Organization that provides oversight • Establishes accountabilities for people and systems

charged with the integrity of information • Identifies data providers and consumers

– Quantifies successes by monitoring performance with appropriate metrics and measurements


Defining a repeatable process – Data Governance

Data Governance is the orchestration of people, process, and technology to enable an organization to leverage data as an enterprise asset.

Data Governance is how an enterprise manages its data assets. Defining business rules, policies, procedures, roles and responsibilities for

ongoing governance to ensure that data is accurate, consistent, complete, available, and secure.

Building governance infrastructure, technology and supporting organization Developing common and standard data domain definitions Developing architecture practices and standards Monitoring and improving data quality


Data Governance is the process by which an organization will behave or act to ensure the appropriate execution of its mandate, and typically, protect and maximize the benefits inherent in its data assets.

Defining a repeatable process – Data Governance Principles and Key Considerations

Stewardship – Lead activities and programs – Provide ongoing “care and feeding” of Master Data assets – Engage organizational sponsors and stakeholders

Business Rules Management – Orchestrate processes in support of a robust Master Data

Management effort – Effectively manage Integration Points with all data constituents – Oversee lifecycle management of Master Data (e.g., origination,

consumption, archiving, and retirement) – Facilitate cross-functional teams preparing for business

transformations


Defining a repeatable process – Data Governance Principles and Key Considerations (continued)

Integrity – Leverages consistency, standardization and reuse of data assets – Focus on the conformance of data and its proper usage

• Metadata Management

Security – Appropriately protect corporate information assets

Risk Mitigation – As an asset, high value data requires inventory and control – Establish clear lines of responsibilities – Secure proper sponsorship from senior management – Identify issues earlier in the process to avoid cleanup activities later


Defining a repeatable process – Effective Data Governance

Effective Data Governance is determined by several factors: – Business structure or model – Organizational culture – Organization of “functions” (i.e. shared services, alignment by geo or

LOB, outsourced functions) – Degree of centralization or decentralization – Relative maturity of Information Management within the

organization – Specific issues or pain points within the organization


Looking at ways to measure data quality Effective data quality consists of two types of practices - Preventative and Detective

Preventative data quality best practices focus on the development of new data sources and integration processes

Detective data quality best practices focus on identification and remediation of poor data quality

When these practices are undertaken as part of an overall data governance strategy with executive support and enterprise implementation data quality efforts result in sustained success

15

On-going data quality monitoring and reporting is a key component of sustaining the results of preventative and detective data quality efforts and is an investment in data quality

© 2010 Aviana Global Technologies/Steven Strutz. All rights reserved.

The Data Quality Management approaches the creation and implementation of data quality metrics and measures via an iterative, Proactive/Reactive approach;

Enhance and EvolveEnhance and Evolve

Developcapability

and capacity

Designorganization

and governance

Lead, communicateand engage

Alignindividuals and teamsManage

performance

Transform culture Benefits delivered

Case for change; including change

trigger

Design and

Implement

Plan, Align and Mobilize

Focus onoutcomes

and benefits

Vision and

Commit Developcapability

and capacity

Developcapability

and capacity

Designorganization

and governance

Designorganization

and governance



Alignindividuals and teams

Alignindividuals and teamsManage

performanceManage

performance

Transform culture

Transform culture Benefits delivered

Case for change; including change

trigger

Design and

Implement

Design and

Implement

Plan, Align and MobilizePlan, Align

and Mobilize

Focus onoutcomes

and benefits

Vision and

Commit

Implement actionable solutions based on these priorities

Prioritize efforts to correct the source/cause of the data quality failure

Step Four

Develop actionable solutions to address these failures

Analyze known and discovered data quality problems to uncover the source cause(s) of

the failure

Step Three

Develop exception reports and scorecards to qualify and/or quantify the problems.

Measure conformance to existing data quality standards, business rules,

processes

Step Two

Create appropriate metrics to enable accurate measurements based on data needs/uses and existing enterprise data uses and processes

Define and understand the enterprise and specific user needs/uses of data. Identify and classify into appropriate dimension

Step One

Implement actionable solutions based on these priorities

Prioritize efforts to correct the source/cause of the data quality failure

Step Four

Develop actionable solutions to address these failures

Analyze known and discovered data quality problems to uncover the source cause(s) of

the failure

Step Three

Develop exception reports and scorecards to qualify and/or quantify the problems.

Measure conformance to existing data quality standards, business rules,

processes

Step Two

Create appropriate metrics to enable accurate measurements based on data needs/uses and existing enterprise data uses and processes

Define and understand the enterprise and specific user needs/uses of data. Identify and classify into appropriate dimension

Step One

PRO

ACTIVE –

INITIAL PASS

REAC

TIVE -ITERATIVE

Looking at ways to measure data quality – Data Quality Management – Implementation


Defining a repeatable process – Data Governance Functional Model


Data Governance Working Group • Virtual staff • Non-Invasive Approach • Specific task teams to

address issues identified by Data Governance Team

Defining a repeatable process – Tools for Data Governance

Master Data Dictionary – Enterprise definitions for data elements and attributes – Data Models

Business Function Master Data Element Matrix – Business Process Models – RACI Modes – Where Used Matrices and Models – Create, Read, Update & Delete attributes by business function

Master Data to Corporate Application Matrix – Master Data element usage in application portfolios – Spider Diagrams

Master Data Management website


Defining a repeatable process – Business Process Model - High Level Process Flow


Defining a repeatable process – Data Models Identify and Depict Data Needed In Subject Areas Supporting Business Processes and Operations


Defining a repeatable process – Data Requirements, Conceptual Model – Major Data Subject Areas


Defining a repeatable process – Data Requirements of Business Functions and Processes


Defining a repeatable process – Indentifying Data Ownership by MDM Element

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Master Data Elements

Business Functional Area

Enterprise Data Steward

Functional Data Steward

Organization

Location

Chart of Accounts Set of B

ooks Person

Resource Party

Project Inventory Item

Item

Catalog

Bill of M

aterial Sales Territory

Accounting CFO CAO RA RA RA RA I I C RA C I C C

Customer Operations and Data Management SVP Senior Director I I I I I I RA I I I I R

Human Resources SVP RA RA C I RA RA C I I I I I

Procurement C R I I I I R I R R C I Product Operations R R I I I I I I R RA R I Professional Services R C I I C R R R C C C C R&D Prod1 Operations

R R I I C R I R RA C RA I R&D Prod2 Operations Sales C I I I C R R I R C C RA

IS&T I I I I I I I I I I I I

RACI is a model used to identify roles & responsibilities during an Organization Change Process

A = to whom "R" is Accountable - who must sign off (Approve) on work before it is effective C = to be Consulted - has information and/or capability necessary to complete the work I = to be Informed - Must be notified of results, but need not be consulted

R = Responsible - owns the problem/project


Defining a repeatable process – Indentifying Data Relationships by MDM Element (Person)

24

Business Functional Area

Person

Proposed Framework

Accounting I

• Accountable for primary Data Governance

• Accountable for data definition • Establish framework

Customer Operations and Data Quality I • Notified of framework

Human Resources RA

• Accountable for primary Data Governance

• Accountable for data definition • Establish framework

Procurement I • Provides input in framework

Product Operations I • Documents framework • Works within framework

Professional Services C • Documents framework • Works within framework

Research and Development C • Documents framework

• Works within framework

Sales C • Provides input in framework

IS&T I • Notified of framework C-Create R-Read U-Update D-Delete

Phase 2 Phase 3 Phase 4

GL

AP

AR

FA

PO

TAX

PM

HR

iExp

OTL

OIC

INV

PIM

Cust

Hub

CON

OM

PRI

CE

BOM

ENG

IB

SVC

Person R

R

R

R

R

R

R

CR

UD

R

R

R

R

R

R

R

R

R

R

R

R

R

R = Responsible - owns the problem/project

A = to whom "R" is Accountable - who must sign off (Approve) on work before it is effective

C = to be Consulted - has information and/or capability necessary to complete the work I = to be Informed - Must be notified of results, but need not be consulted

Person

Purchasing creates buyer

Sales creates resource to author

and manage contracts

Person creates training plan

Sales creates resource as sales

representaive

Operations creates resource as

support agent

Purchasing assigns approval

limits to requestors

Fidelity updates employee deduction

information

HR assigns Person to

organization

Sales creates resource as call

center agent

HR createsPerson

Create

Person

Sales creates resource to access customer data hub

Operations creates laborers for

manufacturing

Finance assigns AR Clerk approval

limits

Finance assigns AP Clerk approval

limits

AP pays person expense report

Finance assigns assets to person

Operations creates workers for cycle

counts and physical invetory

Person enters expense reports

Person enters time sheets

Operations creates resource to access product data hub

Credit Mgmt creates resource

to process collections

Professional Services creates

resource for project assignment

Sales creates resource for inside

sales

Person creates travel

arrangements

ReadUpdate

Update

ReadRead Read Read

Read

Read

Read

Read

Read

ReadReadReadReadReadReadRead

Read

Read

Read

Read

Promote to Submission

Process

Establish Baseline

Metrics and Anticipated

ROI

Conduct Impact

Analysis to Other

Applications

Research issue with Impacted

Parties and Applications

Investment?

Yes

No


Setting goals and defining success criteria – Benefits of Master Data Management Programs

Supports business simplification goals by engaging teams across the enterprise to steward corporate data assets

– Reduce transaction processing costs – especially where they concern automation

– Ensure regulatory compliance – Improve customer relationships

Collaborative environment within which to voice issues that affect each business area involving Master Data quality, policies, and procedures

– Lines of Business understand the benefits of promoting, monitoring, maintaining and communicating information about Master Data

Master Data considered “trusted sources” – Ensure integrity of corporate knowledge assets – Relied on to make informed decisions

• Mitigate Risks associated with bad or inaccurate data • More accurate reporting and business analytics, with fewer workarounds and

re-work efforts


Setting goals and defining success criteria – Goals and Objectives

To – Prepare necessary plans, artifacts, programs, processes, disciplines, and governance required for

enterprise-wide Master Data Management In a way that

– Clearly bounds the scope of the effort – Accounts for all Master Data

• Including that not systematically managed • Including tribal knowledge

– Promotes proper information organization and management – Demonstrates a traceable pathway from business goals through to technology components and

services – Aligns with, and contributes to, the set of officially maintained models within the Master Data

Management framework So that

– Future decision-making at both the strategic and tactical levels has access to relevant information – Master Data Management fulfills Business Requirements for ?????? – Master Data Management efforts are more clearly justified, sequenced, sized, prioritized, and

executed – Master Data is up-to-date and available for use in the proper place, in the proper format, with

consistent meaning and application throughout the business


Aviana Global Technologies Customers

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/ ASP


http://www.toshiba.com/tai-new/index.jsp

http://www.tetratech.com/tthmpg.asp

http://www.kaiserpermanente.org/splashpage.do

Next Steps – Lunch and Learn Workshop

Lunch and Learn Workshop at your location – 11:30AM – 1:00PM

Grab your colleagues for lunch to discuss Data Governance initiatives and how to get started at implementation

Delivery of custom presentation based on your infrastructure and environment

To receive a copy of this presentation

To set this up please email Terry Peddlesden ([email protected]) or call 714-782-7532

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Any Questions?


Thank You for Attending!