Data Governance Society

December 13, 2011

Thank you to our Sponsor!

www.liaison.com

Agenda

• November Workshop Readout

– Survey Results

– Challenges of Data Governance

• Speaker: Mary Levins, Business Information Architect• Speaker: Mary Levins, Business Information Architect

Alcon, a Novartis Company

– “5 Steps to Master Data Integration:

A Data Governance Perspective”

• 2011 Wrap Up & 2012 Preview

Challenges

• Talent acquisition

• Organizational structure(s)

• Buy-in & support (upward & horizontally)

• Return on investment (ROI)

• Change management (change leadership)

• KPI’s for measuring DG success

• Measures

• Technology

Business User

12%

Data Custodian

6%

Respondent Demographics

Consultant

35%

Data Governor

29%

Data Architect

18%

Undiscplined

14%Proactive

22%

Governed

0%

Respondant Maturity Disposition

Undiscplined

14%Proactive

22%

Governed

0%

Respondent Program Maturity

Reactive

64%

Reactive

64%

Organization Structure

18%

Buy-in and support Technology

Talent Acquisition

8%

Measures

8%

Other

6%

Challenges

Buy-in and support

(upward and

horizontally)

16%

ROI

13%Change

13%

KPIs

9%

Technology

9%

Addressing the Challenges

• Definition– A concise description of the challenge

• Symptoms– How does the challenge manifest itself?

– How do you know that you have this challenge?– How do you know that you have this challenge?

• Impact– What is the affect on the business associated with

this challenge?

• Approach– A recommended model for addressing the challenge

Organizational Structure(s)

• Definition– Keeping it from becoming an IT init.

– How to make it a COE

– Councils, boards, etc…

– Accountability (with authority) and Responsibility

• Symptoms– DG program perceived as part of the org it’s in (not corporate effort)– DG program perceived as part of the org it’s in (not corporate effort)

– Lack of resource dedication, “part-timers”

– Lack of formal councils, boards, that meet regularly

– Loss of momentum, with surges and spikes

– IT get’s blamed – business owns the data but not the problems with the data

• Impact– Funding of the DG program affected by alignment, silos etc…

– Programs fail, things don’t get done (lack of execution)

– Bad decisions are being made (decisions with bad data)

• Approach– More collaborative environment

– More strategic approach to DG

Change Leadership

• Definition– Getting people to have one definition of DG

– Manages expectations from the program

– Crafting a transformational vision that people understand and can get behind

– “Creating a shared vision”• Commonly understood and supported

• Symptoms– Push-back

– Having to re-explain yourself repeatedly– Having to re-explain yourself repeatedly

– Lack of buy-in

– We’re stuck in the day-to-day (tactical and reactive)

– People are aware of what they’re told but they are not changing their behaviors

• Impact– Appropriate people are unaware of new policies, process

– Lack of community input

– Behaviors don’t change => lack of lasting impact or value of the change (doesn’t stick or reverts back)

• Approach– People should feel as if they are asking for the DG program as opposed to having it forced upon them

– Behaviors change permanently and continue to change as required over time

– A well understood vision

5 Steps to Master Data Integration:

A Data Governance Perspective

Mary Levins

Business Information Architect

Alcon, A Novartis Company

• Business Problem: Why Integrate Data ?– Company Overview and Situation

– Business Drivers

• The 5 Steps – Challenge, Purpose and Approach

Agenda: 5 Steps to Master Data Integration

• The 5 Steps – Challenge, Purpose and Approach– Step 1 – Extract

– Step 2 – Standardize

– Step 3 – Match

– Step 4 – Group

– Step 5 – Maintain

• Lessons Learned

12

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Continued Innovation: Leveraging Core Competencies

Lightstream Process Technology

Lens Wetting Technology

Stronger Together: Merger with

Novartis in April 2011

• Leader in ophthalmic surgical products

• Comprehensive portfolio of pharmaceutical

• Ophthalmic pharmaceutical prescription drugs (excluding Lucentis)

• Comprehensive portfolio of contact lenses and lens care products

We are now the second largest division of Novartis,one of the most successful and respected healthcare companies worldwide

of pharmaceutical products for chronic and acute diseases of the eye

• Leading multi-purpose contact lens disinfecting solution

The New Alcon: World Leader in Eye

CareSurgical Pharmaceutical Vision Care

� Most complete line of ophthalmic surgical products

• Cataract• Vitreoretinal• Refractive

� Products for chronic and acute diseases of the eye

• Glaucoma• Allergy• Anti-infective / Anti-

inflammatory• Dry eye

� Alcon Multi-purpose disinfecting solutions

� CIBA VISION portfolio of

� Novartis Ophthalmic pharmaceutical prescription and over-the-counter drugs (excluding Lucentis)

� CIBA VISION portfolio of contact lenses and lens care products

• Silicone hydrogel• Daily disposable• Color• Hydrogen peroxide

= $3.2 bn = $3.5 bn = $2.7 bn2010 pro-forma sales

Master Data:

Data that is a critical company asset

used by multiple businesses, functions,

and users across one or many systems.

Customer Data Integration:

Wikipedia definition: “customer

data integration (CDI) combines the

technology, processes and services

Finance/ Credit

CRMWeb Services

Definitions: What is Master Data and

Customer Data Integration?

technology, processes and services

needed to set up and maintain an

accurate, timely, complete and

comprehensive representation of a

customer across multiple channels,

business-lines, and enterprises —

typically from multiple sources of

associated data in multiple

application systems and databases”.

Single version of the Truth

Customer

Master Data

BI/ Reporting

ERP Transactions

Marketing

16

Primary Business Driver to Integrate

Master Data

A single view of the

customer was needed

to support

Novartis

- PharmaOphtha

1. Sales Force

Realignment

2. Supply Chain and

Distribution Changes

17

CIBA VISION

-Ophthalmologists,

Optometrist, Opticians; Retail

stores, other

Alcon

– Surgeons, Ophthalmologists,

Optometrist, Opticians, Retail Stores, other

Challenge: Create a Single View of the

Customer

Sold-to Party:

Dr. Joe Brown

Customer Master

Address

Banking DataPartner Functions

CIBA VISION

SAP

Customer

(Front Office)

“F” Accounts

Sales Calls

Sales Objectives

Sales Sample Drops

Customer-to-Commercial Account

CRM (Siebel)

Alcon

( Address

( Partner

Functions

( Banking Data

Commercial

(Back Office)

JDE Accounts

CTI Screen Pops

CS SRs

Commercial

(Back Office)

JDE Accounts

CPaks ($$)

Commercial

(Back Office)

JDE Accounts

IOLs ($$)

Commercial

(Back Office)

JDE Accounts

Equipment ($$)

Commercial

(Back Office)

JDE Accounts

Shipments

TS SRs

TS Service

Agreements

CARS Contracts

Customer-to-Commercial Account

Affiliations

ERP (JDE)

Business Value: Minimize customer impact by ensuring customer data is

available to support integrated business functions.

– Step 1: Extract

– Step 2: Standardize

– Step 3: Match

– Step 4: Group

The 5 Steps to Customer Data

Integration

– Step 4: Group

– Step 5: Maintain

19

Challenge: Define what data to integrate

Purpose: Minimize effort to meet requirements

Approach:

• Identify all Source Systems

• Understand Data Model across source systems

– Data Attributes

Step 1 – Extract Data

A Data

Governance

Perspective:

Ensure Business – Data Attributes

– System of Record

• Define Criteria

– Account type

– Partner Function (Bill to/ ship to)

– Definition of ‘Active’

• Complete Data Profile and Measures

• Manage Extract Date

20“Devil is in the details”

Ensure Business

and IT

alignment with

definitions

Challenge: How do we ensure we keep the right

record?

Purpose: To support the matching process

Approach:

• Identify a common industry reference

Step 2 - Standardize

A Data

Governance

Perspective:

Clean data in

source system • Identify a common industry reference

• Standardize address data in extract file against the

same source

21

“Data is dirtier than you think”

source system

against

standards

Challenge: How do we get to a single customer record?

Purpose: Define a cross reference across systems

Approach:

• Initial Match

– Standardized records

– Source to Target

Step 3 - Match

A Data

Governance

Perspective:

Define a strategy

with IT and

Business – Source to Target

• Detailed Match

– Custom Tool for Business to compare initial matches

– Define more detailed business rules

• Automated Match

– Using verified business rules

22

“One to one vs one to many”

Business

alignment

Challenge: How do we support the business

integration needs first?

Purpose: Categorize the work to prioritize

Approach:

• Define Categories

Step 4 - Group

A Data

Governance

Perspective: • Define Categories

– High Match

– Low Match

– No Match

• Define Action for each category

23

“Get Quick wins”

Perspective:

Measure

Challenge: How do we ensure customer master data administration

across multiple systems?

Purpose: Minimize the risk while on separate ERP systems

Approach:

• Define a new system of record moving forward

Step 5 - Maintain

A Data

Governance • Define a new system of record moving forward

• Define an on going maintenance process

– New customer accounts

– Changes to existing customers

24

“Centralize Data Administration”

Governance

Perspective:

Define process

ownership and

accountability

No data quality issues.Higher level of

customers called on;

increase in business.

100% seamless and

transparent to the

customer.

Exceed customer

expectations.

Customers easily

identified Best day

dream

DataSales ForceCustomersOrder

Processes

How is success defined?

Impact

Scenario

Wrong accounts

matched, not available,

data quality degrades,

duplicates created, CDA

process overwhelmed

Customers not called on

by Sales Rep resulting in

loss of sales.

Can’t meet customer

expectations and lose

customers and business

Wrong customer

account or not available,

ship to wrong customer.

High resource impact

Worst

nightmare

Majority of customer

data available in Alcon

and stable.

Sales Reps are calling on

the right account.

Can meet customer

expectations.Able to take orders

and ship. As planned

expectations.

25

Customer Master Data Integration – High Level Process

Extract Files

Experian QAS Load Cross

Reference

Address

Standardization

and

Auto Match

Proposed Good

Matches (> 86%)

Proposed

Low Match

(50%<X<

87%)

Maintain

Apply Rules to

improve

one to one

match

IT - Alcon

IT – CIBA VISION

IT - Alcon

IT – CIBA VISION

IT – Alcon

Alcon CDA

Alcon CDA

CDA ToolManual Verification

One to One match

Verify results

match

CIBA VISION

Yes

No Create as

New Alcon

Customer

One to One

Match?

Steps (high level)

1. Extract

2. Standardize

3. Match

4. Group

5. Maintain

• Ensure Business and IT alignment with definitions

• Define “success” up-front to ensure the project is aligned with business drivers

• Focus on “quick wins” first

Key Lessons Learned

• Focus on “quick wins” first

• Define ongoing process ownership and accountability

• Communicate to the organization– New Master Data Management processes and

procedures

– New business rules

27

2011 Wrap Up & Preview

Niki Rabren

Executive Director, 3sage Consulting

Data Governance Society Board Member

Data Governance Society VisionA corporate America driven by value-added processes and a solid

foundation in 100% reliable information upon which executives can base

decisions and steer their organizations.

Data Governance Society MissionOur mission is to raise awareness of Data Governance as a

transformational business function and to foster a collaborative non-

competitive environment for Data Governance professionals to share

their experiences and showcase their successes.

Milestones for 2011

• Strong Atlanta Data Governance Community

• Large sponsorship community

• DGS Established Organizational Structure to

operate and recruit volunteers

• Designed 2012 meeting schedule to further

DGS mission

Now Calling Volunteers!

John Eisenhauer

Founder

Board Chair

John Eisenhauer

Strategic Alliances &

Sponsorships

David Keating Volunteer 1

Niki Rabren

Marketing

Volunteer 2 Volunteer 3

Holly StarlingOperations

Johannes Dorsche

Volunteer 4

Rick Young and Steve Strout

Content

Mario Brenner Volunteer 5

Email info@datagovernancesociety.org

2012 Calendar

January OFF July OFF

February Organizational Structure August KPIs & Measures

March OFF Sept OFF

Subject to change based on availability of speakers

April Buy-In / Change

LeadershipOctober Technology

May OFF November OFF

June Return on Investment Dec Talent Acquisition

Now calling Speakers!

Email info@datagovernancesociety.org

Additional Challenges

Technology

• Definition– Too much noise (“buzz”)

– Silver bullets (battling marketing vs reality)

– Tech skills (matching tech to skills rather than skills to tech)

– Tools don’t necessarily address “real” problems

– Difficult to find holistic solutions/technologies/services

– Explaining that technology is NOT the solution to DG but without it makes things much more difficult

• Symptoms– Shelf-ware

– Continual assessments

– New tool ever 6 months– New tool ever 6 months

– Complicated application landscape

– Lack of tool integration

• Impact– Constituents don’t know which tool to use or don’t have tools required to solve their problem

– Excessive costs (which counter our ROI efforts) (“Didn’t we already buy something for that?”)

– Duplication of data, efforts, technologies => very confusing (complex0

– Decreases effectiveness of DQ and Data Integration (DI) programs

• Approach– It’s a poor craftsman that blames her tools

– Keep it simple

– Business is enabled and is “happy” with the affect that technology has had on their operations

Talent Acquisition

• Definition– There is a shortage broadly-skilled DG resources available for hire.

• Symptoms– Resources that are applying for DG roles, are not experienced in

more than one aspect (security, MDM, BI, etc…)

• Impact• Impact– Creating a tactical DG Office that can also bring strategic value to an

organization is more difficult.

– Increased costs associated with having to hire more people to fills specialized roles rather than hiring “switch-hitters.”

• Approach– To Be Determined

– The Data Governance Society will be conducting a Talent Census project to quantify and asses this challenge.

Buy-in & Support

• Definition– Explaining (education) value to get buy-in (from all of those that are required to buy-in)

• Creating a message that is brief and concisely to the point that executives need to hear and understand\

– Tying the program to critical business problems/strategies

• Symptoms– Push-back

– Lack of funding

– Lack of an organization for DG

– Disorganized approach to DG (ad-hoc, immature)– Disorganized approach to DG (ad-hoc, immature)

• Impact– Authority

– Inability to move forward – stays tactical (never gets strategic)

– Loss of talent, focus and dedication

• Approach– Sponsorship (funded)

– Defined/declared authorities and responsibilities

– Formal org. (dedicated or collateral)

– Part of peoples performance objectives

Return on Investment (ROI)

• Definition– Quantifying the costs associated with NOT “doing DG”

– Lack of metrics to measure the efforts that are part of DG

– Tying DG to revenue

• Symptoms– ROI numbers often appear to be soft (there are more soft – ROI numbers often appear to be soft (there are more soft

returns than hard returns)

– Constantly have to justify existence

– No one wants to give up the money

• Impact– Lack of funding => no program, no support, etc…

• Approach