Become a Power Data Steward Operate with greater power and efficiency with your own Data Steward Health Plan

IDQ Conference, November 4-7, 2013 Tutorial – 12:45pm-4:00pm, Monday Nov. 4 University of Arkansas at Little Rock

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Speaker bio: Tina McCoppin

Tina McCoppin, Partner at Ajilitee • Data Governance Strategist • Information Management delivery programs Engagement / Program

Manager • Former Engagement & Project Manager for Fortune 1000 companies:

HP, Knightsbridge (“Big Data”), Forte, Seer, Pansophic, Accenture • 25+ years of IT integration experience

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TIME TO PUT ON YOUR SNEAKERS!

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It’s time to GET FIT

To go from THIS To THIS

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Or for Data Stewards, this means

To go from THIS To THIS

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The wake up call

• Data quality issues remain a top barrier of effective Business Intelligence and Analytics

• Poor data quality can cost organizations $8M-$20M+ annually • The average B2B company has critical data errors in 10-25% of its

records • Companies with high data quality can earn 66% more revenue

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Let’s start! Our topic today

A Data Steward Health Plan is the key to transforming data governance into a sustainable program which brings real business value – and doesn’t wear you out!

Agenda 1. What are the responsibilities of a Data Steward? 2. How and where to “trim the fat” 3. The Data Steward Health Plan 4. Defining a Data Policy 5. Benchmarking & measuring 6. Communication 7. Practical tips and tools

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Exercise: Class Profile

• _____ Finance (Banking, Investment) • _____ Insurance (P&C, Life) • _____ Healthcare / Hospital / Pharmaceutical • _____ Energy • _____ Education • _____ Government • _____ Manufacturing • _____ Food Services • _____ Retail • _____ Telecommunications • _____ Transportation • _____ Leisure & Accommodations (hotels, resorts) • _____ Non-profit / Charitable / Religious • _____ Technology (Soft/Hardware, Tools, Vendors) • _____ Consulting Services • _____ Other

• _____ No Data Governance • _____ In first year • _____ 1-2 Years • _____ 2-5 years • _____ 5-10 years • _____ 10+ years

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STEWARD RESPONSIBILITIES

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DATA QUALITY

DW, BI AND

DATA INTEGRATION

BUSINESS KNOWLEDGE &

EXPERIENCE

PERSONAL TRAITS

OPERATIONAL SYSTEM RESPONSIBILITIES

• Persuasion / Negotiation • Reputation / acknowledgement • Facilitation

Expectations for Data Stewards

Skills demonstrated:

• Analytical • Technical

prowess • Strategic /

impact & implications

Skills demonstrated:

• Management • Process

Improvement • Subject matter • People /

Communication

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Description The role of the Data Steward is to manage, investigate, and resolve data quality issues in enterprise applications, while safeguarding against data loss. Data stewards also guide decision makers in determining where to place specific data while considering business purposes and how the location of certain data will incur particular risks. This individual is also expected to take a lead in preventing data quality issues by identifying frequent user errors, and working with business units to strengthen user competence. This role may be for a specified data domain or multiple domains. Responsibilities Strategy & Planning Facilitate implementation of a data management strategy, including user policies and training materials, identifying refresh cycles, and data quality statistical reporting for achieving and maintaining high data quality. Work alongside both IT and business unit staff and Senior Management to: • Coordinate the data placement/location in line with business strategies; • Develop and maintain a data integration strategy; and • Develop and maintain a data security strategy. • Ensure that project management and software development methodologies include the steps,

activities, and deliverables required to achieve high quality data for their specified domain. Acquisition & Deployment • Ensure that new systems, applications, and data integration measures adhere to existing data

management practices, policies, and procedures Operational Management Ø Identify and ensure the resolution of data quality issues, such as uniqueness, integrity, accuracy,

consistency, and completeness in a cost-effective and timely manner Ø Execute audits periodically to ensure that data is being properly managed in On-Premise and that legal

or security requirements are consistently being met Ø Review data profiling and data quality statistics on a regular basis. The results of these audits should be

communicated to data trustees and tied into service level agreements (SLAs) between data entry personnel and the appropriate business units

Ø Devise, coordinate, and/or participate in mass data-cleansing initiatives for the purpose of purging and eliminating corrupt or redundant information from corporate databases

Ø Identify causes of poor data quality, implement solutions and communicate findings to employees, management, and stakeholders

Ø Develop and enforce methods and validation mechanisms for ensuring data quality and accuracy at the point of entry

Ø Work collaboratively with the system architects to develop methods for synchronizing data entering company systems from multiple points and within infrastructure On-Premise and third parties

Ø Make recommendations on protocols and standards that will support the data management strategy

Position Requirements Formal Education & Certification College diploma or university degree in the field of information management/knowledge management and/or 2-3years equivalent work experience. Knowledge & Experience • Familiarity with database concepts • Previous exposure to data integration and management • Specific knowledge of master data or claims platform(s) is desired • Strong understanding of data entry/update best practices • Working technical knowledge of SQL is extremely desired

Personal Attributes • Strong customer service orientation • Proven analytical and problem-solving abilities • Ability to effectively prioritize and execute tasks in a high-pressure environment • Good written, oral, and interpersonal communication skills • Ability to conduct research into data issues and as required • Ability to present ideas in business-friendly and user-friendly language to all levels

of staff – including C-level executives • Highly self motivated and directed • Keen attention to detail • Team-oriented and skilled in working within a collaborative environment

To ensure organization responsiveness, the following metrics have been developed: • Erroneous data will be either corrected, addressed, or elevated to the higher

decision/management level as appropriate within X business days • Meta-data will be reviewed on an annual basis to ensure its accuracy and

relevancy • Participation in resolving business definitions and ensuring common definitions

across the enterprise will occur on an as needed • Validation and approval (or rejection) of lists of values for critical data elements

will be performed within 30 (X??) business days from the submission date • Operational training will be carried out on an as needed basis.

Data Steward – example position posting

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Essentially, you’re on a “yo-yo” diet

• Expected to hold a “day job” yet still address data quality, metadata, data profiling, etc., etc., etc.

Or… • The Steward role becomes equated with (or solely focused on) Data

Quality Or… • Management does not see evidence of value, so budget is cut or the

role or entire data governance group is disbanded Just plain trying to juggle too much at once

Operational system upgrades, analytic reporting, and other important programs reduce the time and attention spent on data governance

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• Too much time spent on… ◦ Endless and/or repetitive meetings ◦ Not knowing where to focus ◦ Never having time for important (but non-critical) items ◦ “Spinning” on a single or a few issues <OR CONVERSELY> ◦ Spread thin trying to go after ‘too many’ and not resolving ‘any’

Problem summary

• Too little time spent… ◦ Mapping repeatable and automated processes, workflows and

communication plans ◦ Communicating to the business community ◦ Seen as a recognized DG SME and trustee of the data ◦ Facilitating resolution or remediation activities across lines of

business ◦ Providing insight to the DG Council so that policies can be identified

and published

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TRIM THE FAT: FOCUS ON STEWARD ESSENTIAL TASKS

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Categorizing Activities

Categories we will look at: • Data Governance

◦ Policies ◦ Data Quality and Process ◦ Glossary or Dictionary ◦ DG Justification ◦ Communication

• Best Practices ◦ Data model ◦ Standards ◦ Authorizing data access

• Project-related work ◦ Subject Matter Expert ◦ Reviewer / Approver

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Categorizing Activities: Healthy activities for Data Governance

• Identify and develop policies and procedures • Identify Pain Points, options and remediation

o Data quality issue pain points o Process issue pain points

• Describe (or understand) process flows and / or data flows • Create use cases for pain points • Review data profiling results of pain points • Identify Critical or Governed Data Elements (GDE) • Define business definitions for data elements • Develop ROI for DG • Establish data quality metrics • Develop / track performance measurements for DG Program • Communicated & Sell Data Governance

o Prepare & present to DG Council o Present (describe / sell) DG at Department staff meetings

• Develop & give formal Data Steward training courses • Update DG website

Policies

DQ & Process

Glossary

Communication

Justification

RED is my own personal “top priority” Data Steward activities. Yours might be different – but not much!

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Categorizing Activities: Healthy activities for Best Practices

• Subject Matter Expert (SME) for modelers of: Entities, Attributes, Relationships & definitions

• Create Use Cases to validate Logical Data Model • Review source system mappings to Logical Model • Review data standards • Provide valid values for reference data • Responsible for single, conformed values • Establish standard length for a data attribute • Establish standard data type for a data attribute • Provide business definitions of entities and attributes • Provide business glossary / metadata (data model, sources,

etc.) • Identify security classifications on data attributes • Identify data access authority to data (in DW or MDM) à

Business Usage matrix

Data model

Standards

Authorizing

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Categorizing Activities: Healthy activities for Project-related Steward Contributions (e.g., BI/DW, MDM)

• Review (approve as appropriate) Requirements / BRD • Review of Requirements Traceability Matrix (RTM) • Review of source system mapping to common record format or

targets • Identify or review business transformations • Track Future Phase enhancements: Request for new metrics,

reports • Develop (or review) Test cases • Review and approve System Integration Test (SIT) • Participate in User Acceptance Testing (UAT) • Establish Audit, Balance & Control (ABC) metrics and thresholds

◦ Identify points where reports will be provided ◦ Identify information captured on report ◦ Identify defect resolution approach

• Participate in Lessons Learned • Operational activities – identify defects / bugs • Operational activities -- handle steward-related questions • Conduct Metadata review -- of data lineage, best source • Provide trust scores (for MDM) • Provide Match / Merge rules: Automated and manual (for MDM)

SME Reviewer Approver

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Exercise: Current Efforts

• Utilize the following pages as a “Data Steward Activity Guide Checklist”

• If you are the Data Steward: ◦ Identify the activities for which you are responsible or are involved in

• If someone else is the Data Steward: ◦ Identify the activities which you believe he/she is spending time on

• If you do not have a Data Steward: ◦ Identify the activities which you believe the steward should spend time on

• Place an X in each of the boxes in which you (or your company’s) Data Stewards

are involved

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Exercise Handout: Data Governance Healthy Activities

____ Total number of checked boxes

q Identify and develop policies and procedures q Identify Pain Points, options and remediation

q Data quality issue pain points q Process issue pain points

q Describe (or understand) process flows and / or data flows q Create use cases for pain points q Review data profiling results of pain points q Identify Critical or Governed Data Elements (GDE) q Define business definitions for data elements q Develop ROI for DG q Establish data quality metrics q Develop / track performance measurements for DG Program q Communicated & Sell Data Governance

q Prepare & present to DG Council q Present (describe / sell) DG at Department staff meetings

q Develop & give formal Data Steward training courses q Update DG website

____ Total number of RED boxes

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Exercise Handout: Data Standards Healthy Activities

q Subject Matter Expert for modelers of: Entities, Attributes, Relationships & definitions q Create Use Cases to validate Logical Data Model q Review source system mappings to Logical Model q Review data standards q Provide valid values for reference data q Responsible for single, conformed values q Establish standard length for a data attribute q Establish standard data type for a data attribute q Identify security classifications on data attributes q Identify data access authority to data (in DW or MDM) à Business Usage matrix q Provide business definitions of entities and attributes q Provide business glossary / metadata (data model, sources, etc.)

____ Total number of checked boxes

____ Total number of RED boxes

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Exercise Handout: Project-related Steward Healthy Activities (DW, MDM)

q Review and approve Requirements / BRD q Review of Requirements Traceability Matrix (RTM) q Review of source system mapping to common record format or targets q Identify business transformations q Support Future Phase enhancements: Request for new metrics, reports q Develop Test cases q Review and approve System Integration Test (SIT) q Participate in User Acceptance Testing (UAT) q Establish Audit, Balance & Control (ABC) metrics and thresholds

q Identify points where reports will be provided q Identify information captured on report q Identify defect resolution approach

q Develop Lessons Learned q Operational activities – identify defects / bugs q Operational activities -- handle steward-related questions q Conduct Metadata review -- of data lineage, best source q Provide trust scores (for MDM) q Provide Match / Merge rules: Automated and manual (for MDM) ____ Total number of checked boxes

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Exercise Handout: Data Governance Unhealthy Activities

q Large and time-consuming number of DG or Steward meetings each week q Critical policies and procedures are ignored by Business Areas or IT areas q Continually reviewing same Pain Points with little progress -- still open after 1 year q Data profiling results of pain points reveal issues but no action for improvement

occurs q No Critical/Governed Data Elements (CDE and GDE); or CDEs are not prioritized

higher than other elements; or everything is a CDE q No agreement on business definitions for data elements across Business Areas q Re. Communicate & Sell Data Governance – You never ask for actions of DG

Council after you present to them q You developed Steward training materials but no one is taking Data Governance

and/or Steward training courses q No one is visiting or we do not track DG website activity

____ Total number of checked boxes

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Exercise Handout: Data Standards or Project Unhealthy Activities

q Excessive or beyond planned review hours (LDM, mappings, etc.) q You sit in on IT meetings but you don’t understand half of the topics (or topics do not

apply to you)

____ Total number of checked boxes

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Exercise Handout: Count

____ Total number of checked boxes Data Governance (p11) ____ Total number of checked boxes Data Standards (p12) ____ Total number of checked boxes Project (p13) ____ Total number of checked HEALTHY boxes ____ Total number of checked UNHEALTHY boxes

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STEWARD HEALTH PLAN: PRIMARY ACTIVITIES

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Introducing the Data Steward Health Plan

Strike the right balance • CARDIO

o Lower intensity, energy-generating activities ₋ Standards and conformity ₋ Metadata ₋ Enterprise glossaries and

data dictionaries

• WEIGHT TRAINING o Strengthening and developing

the “skeletal muscles” of your organization ₋ Enterprise data

integration ₋ Data quality ₋ MDM ₋ DW

+

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But…first choose your Steward “Body type”

Start with the end in mind: • What DG “muscles” do you want to develop? • Just like athletes, we work on different things based on our aim • Work to build those muscles that suit your organization goals and

your stewardship goals

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One workout routine does not fit all!

• Assess your own organization: key is deciding which governance functions will be included ◦ Different mix for each institution ◦ Mix will define “protect and manage data as a corporate asset” ◦ Do not over commingle responsibilities ◦ Prioritize the implementation of the functions ◦ Determine implementation sequence

• Important functions to make sure are covered: ◦ Data quality / certification ◦ Data ownership / stewardship ◦ Meta, reference and master data

• Important stewardship growth must include: ◦ Subject matter expertise in data domain(s) ◦ Subject matter expertise in data management best practices ◦ Tools & technologies competencies ◦ Leadership competency

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Set reasonable goals

To improve your health To improve your data governance

Drink more water and less soda, caffeine and alcohol

Spend more time on nearer-term business value activities & less on “never-ending” issues

Reduce sugar and sodium Reduce time (to reach decisions / consensus) in meetings in which little is accomplished

Do 20 min. cardio daily: walk, run, jump rope, aerobics…

Track to a plan in which you prioritize and allot time to DQ, policies, communications…

Weight training for major muscle groups weekly

Work on major organization project – ensure adherence to best practices and standards

Lose 4-8 lbs per week

Measure the data quality improvements: %s, counts, ROI. <E.g., Returned mail, pends, etc.>

Get a trainer Get a supportive business & IT advocate – and C-level sponsor. And look for a DG Mentor

But still --push yourself Be BOLD! Be willing to push boundaries

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How to create your own Health Plan

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Governance activities – list and prioritize

Responses Data Definitions & Rules

Data Quality

Corporate Asset

Active ownership & management of information across the businessResponsibil ity or ownership of business rulesManagement of the l ifecycle of data (creation, architecture, retrieval)Process, definitions & controls regarding data definitionsCorporate process on how data is managed - the rulesRules (business process framework) and Infrastructure (organization, Enabler for data quality (regulatory compliance)Total management of data as an assetCustodian of data (business rules)Rules and processes around data - accountabil ityHarmonize data into a worldwide view (master data)Accountabil ity for data (who, how, & why)Administration of data flowsData consistency through organizationOverheadPeople, process & master dataDefinitions & management of reference dataCleansing of data (cross reference & data mapping)Identifi ing and managing changes in dataEnabling a single version of the truthOwnership & accountabil ity for data

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Have a visible DG Health Plan calendar

Apr’12 May’’12 Jun’12 Ju’l12 Aug’12 Sep’12 Oct’12 Nov’12 Dec’12 Jan’13 Feb’13 Mar’13 Apr’13

► Identify supporting stewards

► Provide training to stewards

► Conduct dry-runs of process flow

► Conduct / facilitate Quarterly Data Steward meetings

► Send emails / communiques

Data Steward Leadership ► Assign / distribute data

steward inputs across stewardship team

► Track progress on DG Maturity Model

DG Council ► Intakes: manage & control ► Institute / refine process flow

► Propose policies ► Create funding requests

DW, MDM & ODS, etc. ► Demonstrate ability to allocate steward work (e.g., artifact reviews) across other data stewards ► Involve others in development

Align to activities of EDW, MDM, DG Council, Data Stewards, and applicable high priority projects

► Take additional courses

► Instruct / train selected courses

DG / MDM / EDM / DQ Training Modules

► Create additional (special topic) courses

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• Just like a workout routine, be aware of how long you spend on an activity. This helps prioritize your work.

• Recognize the different Steward activities and “divide and conquer.” Really, can one person do it all? Assign tasks.

• Get training on Persuasion and Leadership! Lots of time lost due to inability to influence.

• Incorporate tools for transparency and visibility.

Top tips for practical time management

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• Know which activities need lots of ‘reps’ (repetitions) ◦ Weekly meeting on data quality issues list with a team who

can help resolve ◦ DG Council meetings where Steward presents items that

need to become policies or need the weight of C-levels to implement

• Know which activities are ‘sprints’ of activity ◦ Data model validation ◦ Approval of standard values

• Know which activities are endurance ◦ Major data change – e.g, being at the forefront of SSN

Randomization ◦ Identifying and tracking security of data (in lockstep with

Corporate Security) ◦ Getting conformance on a business definition for “Market

Segment” or “Product Hierarchy” or “Revenue”

Top tips for practical time management

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Exercise: Time Management Assessment

1. Do you keep a To-Do list? 2. Do you have a prioritized To-Do list? 3. Do you differentiate URGENT from IMPORTANT? 4. Are <20% on your list HIGH priority? 5. Do you block out time in your calendar? 6. Do you have personal goals? (This year? 3 years) 7. Are you aware of and work to reduce distractions? 8. Are you aware of if/when you procrastinate? 9. Are you attuned to not taking on too much? (Have

you ever seen yourself as a micromanager)? 10. Are you attuned to being a person who thrives on

being “busy”? 11. Do you make sure to take breaks during the day? (5

minutes every hour or two – a walk, coffee, chat with a colleague)

1. ___Y___ / ___N___ 2. ___Y___ / ___N___ 3. ___Y___ / ___N___ 4. ___Y___ / ___N___ 5. ___Y___ / ___N___ 6. ___Y___ / ___N___ 7. ___Y___ / ___N___ 8. ___Y___ / ___N___ 9. ___Y___ / ___N___

10. ___Y___ / ___N___

11. ___Y___ / ___N___

____ Total number of “Y”

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Exercise: Time Management Assessment

12. Do you rarely complete tasks at the last minute or rarely ask for extension?

13. Do you know how much time you spend on various jobs you do?

14. Do you leave contingency time in your schedule to deal with “the unexpected”?

15. Are you attuned to not letting distractions keep you from working on critical tasks?

16. Do you rarely have to take work home? 17. Do you regularly confirm your priorities with your

boss? 18. Before you take on a task, do you check that the

results will be worth the time put in?

12. ___Y___ / ___N___

13. ___Y___ / ___N___

14. ___Y___ / ___N___

15. ___Y___ / ___N___ 16. ___Y___ / ___N___ 17. ___Y___ / ___N___

18. ___Y___ / ___N___

____ Total number of “Y” Add the count from the previous page with this page

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DEFINING A DATA GOVERNANCE POLICY

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Creating the DG Policy

A typical measurement of maturity for a Data Governance program is the ability to establish and propagate policies related to the control and management of data. So what constitutes a “Data Governance policy?” How is it enforced so that it becomes a part of everyday business? • Creating the DG policy

◦ Who identifies and articulates? ◦ What are the data policy components? ◦ Example of a data policy

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Before the “Policy”

• Symptoms of data quality issues are well-known at the operational level. But root cause can be difficult to discern as it is often tied to how data is captured or handled (people and processes)

• Humans look for the “path of least resistance” • Just because a policy exists doesn’t mean people will obey

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DG Policy

• Purpose: To establish official guidelines for data standardization, quality, usage and integrity ◦ Should follow and align with corporate goals ◦ Help ensure compliance with state and federal regulations ◦ Set clear standards of behavior to avoid confusion for acceptable business practices

• Definition:* ◦ Consistent, repeatable processes that implement the agreed upon guiding principles ◦ Quality and governance is part of Process, not reactive audits ◦ Integrated with system development processes

* John Ladley – DAMA Phoenix conference, Nov 2012

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Who identifies and articulates the policy?

• Identifiers: ◦ Can be almost anyone – and DG COE (i.e., Stewards and IT, et al dedicated to DG) needs

to encourage input across the enterprise

• Articulators ◦ Data Stewards, Subject Matter Experts ◦ DG Council ◦ DG COE

Communicate

Stakeholders

Notify DG Council

Post to Sharepoint

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Request

Data Profiling Requirements Logs

Stewards, SME, IT, Stakeholders Project Teams, Architects

Provide Input

Requestor (Stakeholder, PM)

Data Steward

Create 1

2

Discover, log issue

Root cause, extent, impact, costs

• How? That’s up to your organization, but one scenario

DG Core Team

Determine if warrants issuing a policy

Approve

Propose Corporate DG policy

Data Stewards Stakeholders Solution Architects 3

4 5

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What are the components of a DG policy?

SAMPLE COMPONENT DESCRIPTION

Policy Name

Name or title (keep brief, no more than 5-6 words)

Description

Description of the policy: State the purpose, the data elements included, industry standards which are being applied, an example of the reason the policy is needed

Data Policy Categorization For sorting purposes, you might wish to categorize the policy: e.g., Enterprise (applies across the entire organization) or Department (list the specific Line of Business to which the policy applies)

Data Classification For sorting purposes, you might wish to categorize the policy: e.g., Standard, Legal, Data Quality, Data Security

Procedures Describe the processes that will track or enforce the policy: Who is involved, what they will do, how they will report

Expected Business Benefits This can be a Cost-Benefit analysis, or ROI, or qualitative and quantitative expectations

Measures, Metrics & Reporting

List the measurements that are used to track the policy

Stakeholders List of the business and IT stakeholders – any groups to whom the policy applies or who are impacted by the policy. This can include vendors and partners with whom you do business

Contacts (Stewards, DG COE, Data Trustee)

List the supporting data governance team, especially the stewards responsible

History (Effective Date, Revised Date)

List the effective date of the policy (and if any updates or modification are later made, capture the Revised Date as well)

Training

Designate if training on the policy is mandatory . Describe which training course contains a review of the policy

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You might wish to tag, categorize or type your Policies

Policy Category Type Comment Standardize Address Enterprise Standardization Conform to USPS

Restrict availability of Sensitive or Personal Data Elements

Enterprise Data Security Data authorization and access will be granted on a “needed for role” basis

New Customer Type Department Standardization Notify Data Steward of any new requirements

Exclude SSN as a Data Key Enterprise Legal Federal Mandate #US500-091

All new Level 1 (top priority) projects >$1MM will budget and plan for inclusion of DQ metrics and Audit / Balance / Control thresholds

Enterprise Data Quality DG COE will participate during project initiation on defining metrics

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Policies need metrics, benchmarks, dashboards

Policy Metrics Benchmark Standardize Address # letters mailed

# and % returned # and % return by category (e.g, Moved-no forward address, Undeliverable as Addressed, etc.) # of newly entered bad addresses # & % fixed each month Trend analysis: -- % comparison each mailing -- year over year

2013 goals: Reduce return mail from current return rate of 12% to > 5% Reduce by 80% Increase by 15%

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Estimate costs and value for implementing Policies

The US Postal Service estimates that “Undeliverable As Addressed (UAA)” mail costs them approximately $2 billion each year. They developed a set of “best practices” to improve address quality. And…they demonstrated them graphically: Low v High Impact; Low v High cost

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DG POLICY EXAMPLE: Policy for Address Standardization

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Data Policy: Address Standardization

Policy name Address Standardization Policy statement This policy states that the organization shall follow the USPS standard for US

mailing addresses To maintain consistency with address types that can exist across different lines of business, different systems and repositories and to enable the organization to appropriately and accurately correspond with its customer, members and Customers

Description of why the policy was identified

There are multiple systems in which address information is created. Currently these systems do not follow the same standard for “Mailing Address” • Excessive Mail Return

• Large number of returned claim reimbursements • Returned reimbursements result in significant claims adjustments, lost

postage, lost investments in resources, paper, and printing, etc. • Address Information Mismatched

• Customer Address data across systems do not match the Claims system • Customer Address data in the core systems

• Remedying the Address using Multiple Systems • Multiple (and disparate) systems and code “side fixes” are used in an

attempt to standardize at point locations

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Data Policy: Address Standardization

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Stakeholders Address Data Steward Marketing Business Sponsor Sales Manager stakeholder Customer Relationship Management Mailroom Manager

Data Classification Choose all that apply: · Security · Legal obligation Ö Data quality Ö Standards

Measures, Metrics, Reporting

· Monthly “Difference Report” between systems with Address (Customer System vs Claims System)

Expected Business Benefit

Tangible: Mailing Cost savings = $1.5M per year Intangible: Customer satisfaction = Retention

History · Effective date · Revised date

Effective – March 1, 2013

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Data Policy: Address Standardization

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Glossary of Terms / Acronyms

• Mailing Address: The postal address where a mail can be addressed to a person or organization

• USPS: The United States Postal Service (also known as the Post Office or U.S. Mail) has established a standard for ADDRESS (address line 1, address line 2, standard set of zip codes, etc.)

Procedures Processes: See Process_Flow_Address_Standard.vsd (list the Sharepoint link or drive) Technology New projects will utilize the “USPS validation module” – contact is Mark Myers, Data Architecture Team (312-455-2600 x451)

Training 1. New Employee Training – In “Section 4 Data Governance – Review of Policies” page 20

2. Computer-based training – “Module 4B – Data Governance Policies”

Compliance Not applicable

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• Make sure DG Policies are published and available to the enterprise • At the very least have a Shared Drive • For more sophisticated avenues, you can Google “ECM “ or “ CMS“ or “ Portal“

software • Sharepoint, Plumtree, Documentum, OpenText, Interwoven, BEA, Websphere

and other ECM or CMS or Portal software

• Have a log of all DG policies

• Have the details available

• Publish the metrics and measurements that track the Policy

— Should have the benchmark — Ongoing updates to the metrics — Trending analysis — Display the figures (spreadsheet-style to start). Develop Dashboards as your DG

matures

Creating a home for Policies

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Key Take-Aways: Creating the DG Policy

• Establish the process for how a DG Policy is created: roles, responsibilities, who needs to buy-in and provide approval, decision points

• Define your organization’s Data Policy components – and who is responsible for capturing or defining the components

• Determine where the policy is defined and made publicly available

Process

Components

Publication

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BENCHMARKING

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Measuring success

Metrics show if your efforts are working. The quality of data can be measured and scored –

• With a quality process, metrics can be captured and constructed so data quality can be measured and “evaluated” for its intended use

• By capturing quality metrics, Service Level Agreements can be developed and defined

• Defined “quality metrics” are captured at four stages: ◦ Source Files metrics ◦ Data Profiling metrics ◦ End-to-End Checks ◦ Business Checks BI

tool

Product

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Measure / Track / Score – example

Category Low Medium High Essential•Accommodate possible merger integration X•Incorporate more sources/feeds X•Support more functionality, users, and analysis X•Open data access 23x7 X•Improve regulatory & compliance response time X•Enhance decision support & planning X•Widen access to & analysis of performance metrics X•Align needs, investment with strategic direction X•Reduce redundancy of data and effort X•Lower information delivery costs X•Optimize investment across initiatives X•Focus resources on analysis, not data collection X•Institute rigorous data quality & certification X•Support “much to many” data & reporting needs X•Reduce manual or automated fixes & workarounds X

Relative Cost Low Low Moderate HighImplementation Risk N/A Low Moderate HighTime to Implement / Schedule N/A Low Moderate LongMatch to Business Drivers No Limited Yes Yes

Overall Feasibility Not an Option

Not Sustainable

Acceptable Risk;

Managed Evolution

High Risk; High Cost

Scoring

Business Satisfaction

IT Dept Goal

Revenue / Cost Focus

Data Quality Focus

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Data Governance success -- Operational Metrics

Metric Description Notes

Number of DG council sessions Number of monthly meetings held

Easy to calculate

Participation level

Attendance is kept of Council member attendance

Easy to calculate

Number of policies established Number of Data Issues or Opportunities that result in a policy. Not all will result in a policy.

Easy to calculate

Number of data issues or opportunities remediated or moved to point of “completion”

Number of Data Issues or Opportunities

Easy to calculate

Number of Data Steward resources onboarded in a timely fashion

Number of Data stewards brought on board per the proposed Data Steward Operating Model

Easy to calculate

Are the Stewards running a repeatable process?

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Data Governance success -- Strategic Metrics

Metric Description Notes

Number of systems retired as a result of DG applications or policy decisions made

Number of system or large modules of systems that were removed from operational use based on the presence of Data Governance applications. This can includes MDM, CRM, EDW, etc.

This can be directionally estimated.

Number of interfaces rationalized

Number of interfaces that are created, and created once for new or existing projects. Note this is related to the above metric.

This, like the previous metric, can be difficult to quantify.

Number of processes for which Data Governance is a checkpoint

Number of places in the organization we can clearly point to and say that Data Governance is a checkpoint, of some sort, in their process. For example, in SDLC , Change Mgt or Arch Review process

Can be measured. There are 2-3 primary checkpoints to be inserted into other processes.

$ saved by implementing Data Governance recommendations

This overlaps with most of the other strategic metrics.

Not clear this is directly measurable but can be directionally estimated.

Once a quarter, ask the DG Council: Are Stewards making a difference?

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COMMUNICATION

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How do we get them to listen?

Your DG message is being pitted against your audience’s job responsibilities, emails and voice mails, company notices, legal mandates, team meetings, and crises of the day.

dg policies

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• Collateral and Communications — Tailored PowerPoint Presentations — DG Intranet Site / SharePoint site — DG Newsletter — Cafeteria Tent Cards — E-mails & Internal Communications — Elevator Sheets — TV

• Team and Enterprise Events — Lunch n’ Learn — Roadshows or Townhalls — Monthly Stewards or Stakeholder

Meetings

• Program/Project Meetings — Data domain or LOB Program Kick Off

Meeting

• Employee & Consultant Training — Day 1 – New Employee Training — Formal Training Course — Computer-based Training (like PHI) – required — Utilize quizzes and certifications

• DG incorporated into SDLC and Chg Mgt — PMO project initiation list — PMO Gate Reviews — Risk Meetings — IT Audits — Architecture Review Boards

• Executive Meetings — DG Council Meetings — IT Senior Leadership Meetings — Strategy Sessions with Directors

Avenues for communication

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PRACTICAL TIPS AND TOOLS

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1. COTS: ◦ Data profiling tools ◦ Data quality tools ◦ Metadata repositories ◦ Master data management tools with automated match/merge

capabilities ◦ Workflow and/or Business Process Management (BPM) tools

2. BYO (Build Your Own) ◦ Audit, Balance and Control reports ◦ Data steward workgroups ◦ Lunch-n-learns ◦ Sharepoint or shared site for training, information exchange

Tools

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Key takeaways and conclusion

• Keep the prime objective in mind: ◦ To protect and manage data as a corporate asset!

• As you consider your own data governance path: 1. Seek a common definition of just what data governance means to your

institution 2. Identify the governance functions that are required 3. Implement an initial governance function that can evolve with the institution

• A Data Steward Health Plan should… ◦ Identify what body type you want and what muscles to work on à

₋ Make a checklist and prioritize it against a timeline ◦ Track your activities and time consumed by each against the prioritization ◦ Establish metrics ◦ Make sure to make the metrics visible – and be ready to defend why they

justify the cost

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Are you ready? Can you handle the truth?

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Thank you

TINA MCCOPPIN PARTNER AND FOUNDER, Ajilitee

tmccoppin@ajilitee.com

847.840.0858

www.linkedin.com/company/ajilitee

www.facebook.com/ajilitee

www.ajilitee.com