Become a Power Data Stewardoperate with Greater Power

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

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

2 TIME TO PUT ON YOUR SNEAKERS!

3 It’s time to GET FIT

To go from THIS To THIS

4 Or for Data Stewards, this means

To go from THIS To THIS

5 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

6 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

7 Exercise: Class Profile

• _____ Finance (Banking, Investment) • _____ No Data Governance • _____ Insurance (P&C, Life) • _____ In first year • _____ Healthcare / Hospital / Pharmaceutical • _____ 1-2 Years • _____ Energy • _____ 2-5 years • _____ Education • _____ 5-10 years • _____ Government • _____ 10+ years • _____ Manufacturing • _____ Food Services • _____ Retail • _____ Telecommunications • _____ Transportation • _____ Leisure & Accommodations (hotels, resorts) • _____ Non-profit / Charitable / Religious • _____ Technology (Soft/Hardware, Tools, Vendors) • _____ Consulting Services • _____ Other

8 STEWARD RESPONSIBILITIES

Skills Skills demonstrated:DATA DW,demonstrated: BI AND • ManagementQUALITY DATA• Analytical INTEGRATION • Process • Technical BUSINESSImprovement prowessOPERATIONAL SYSTEM KNOWLEDGE• Subject & matter • StrategicRESPONSIBILITIES / EXPERIENCE• People / impact & CommunicationPERSONAL implications TRAITS

• Persuasion / Negotiation • Reputation / acknowledgement • Facilitation 10 Data Steward – example position posting

Description Position Requirements The role of the Data Steward is to manage, investigate, and resolve data quality issues in enterprise Formal Education & Certification 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 College diploma or university degree in the field of information incur particular risks. management/knowledge management and/or 2-3years equivalent work experience. 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. Knowledge & Experience This role may be for a specified data domain or multiple domains. • Familiarity with database concepts • Previous exposure to data integration and management Responsibilities • Specific knowledge of master data or claims platform(s) is desired Strategy & Planning • Strong understanding of data entry/update best practices Facilitate implementation of a data management strategy, including user policies and training materials, • Working technical knowledge of SQL is extremely desired identifying refresh cycles, and data quality statistical reporting for achieving and maintaining high data quality. Personal Attributes Work alongside both IT and business unit staff and Senior Management to: • Strong customer service orientation • Coordinate the data placement/location in line with business strategies; • Proven analytical and problem-solving abilities • Develop and maintain a data integration strategy; and • Ability to effectively prioritize and execute tasks in a high-pressure environment • Develop and maintain a data security strategy. • Good written, oral, and interpersonal communication skills • Ensure that project management and software development methodologies include the steps, • Ability to conduct research into data issues and as required activities, and deliverables required to achieve high quality data for their specified domain. • Ability to present ideas in business-friendly and user-friendly language to all levels Acquisition & Deployment of staff – including C-level executives • Ensure that new systems, applications, and data integration measures adhere to existing data • Highly self motivated and directed management practices, policies, and procedures • Keen attention to detail Operational Management • Team-oriented and skilled in working within a collaborative environment 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 To ensure organization responsiveness, the following metrics have been developed: 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 • Erroneous data will be either corrected, addressed, or elevated to the higher decision/management level as appropriate within X business days 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 • Meta-data will be reviewed on an annual basis to ensure its accuracy and personnel and the appropriate business units relevancy Ø Devise, coordinate, and/or participate in mass data-cleansing initiatives for the purpose of purging and • Participation in resolving business definitions and ensuring common definitions eliminating corrupt or redundant information from corporate databases across the enterprise will occur on an as needed Ø Identify causes of poor data quality, implement solutions and communicate findings to employees, • Validation and approval (or rejection) of lists of values for critical data elements management, and stakeholders will be performed within 30 (X??) business days from the submission date Ø Develop and enforce methods and validation mechanisms for ensuring data quality and accuracy at the • Operational training will be carried out on an as needed basis. 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 11 Ø Make recommendations on protocols and standards that will support the data management strategy 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

12 Problem summary

• 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 ◦ Spread thin trying to go after ‘too many’ and not resolving ‘any’ • 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

13 TRIM THE FAT: FOCUS ON STEWARD ESSENTIAL TASKS

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

15 Categorizing Activities: Healthy activities for Data Governance

• Identify and develop policies and procedures Policies • 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 DQ & Process • Create use cases for pain points • Review data profiling results of pain points Identify Critical or Governed Data Elements (GDE) • Glossary • Define business definitions for data elements • Develop ROI for DG • Establish data quality metrics Justification • 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 Communication • Develop & give formal Data Steward training courses • Update DG website

RED is my own personal “top priority” Data Steward activities. Yours might be different – but not much! 16 Categorizing Activities: Healthy activities for Best Practices

• Subject Matter Expert (SME) for modelers of: Entities, Attributes, Relationships & definitions Data model • 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 Standards • 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 Authorizing Business Usage matrix

17 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) SME • Participate in User Acceptance Testing (UAT) • Establish Audit, Balance & Control (ABC) metrics and thresholds Reviewer ◦ Identify points where reports will be provided ◦ Identify information captured on report Approver ◦ 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) 18 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

19 Exercise Handout: Data Governance Healthy Activities

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 checked boxes 20 ____ Total number of RED boxes 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 21 ____ Total number of RED boxes 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 22 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 23 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 24 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

25 STEWARD HEALTH PLAN: PRIMARY ACTIVITIES

Strike the right balance • CARDIO + • WEIGHT TRAINING o Lower intensity, energy- o Strengthening and developing generating activities the “skeletal muscles” of your ₋ Standards and conformity organization ₋ Metadata ₋ Enterprise data ₋ Enterprise glossaries and integration data dictionaries ₋ Data quality ₋ MDM ₋ DW

27 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

28 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

29 Set reasonable goals

To improve your health To improve your data governance

Drink more water and less soda, Spend more time on nearer-term business value caffeine and alcohol 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, Track to a plan in which you prioritize and allot run, jump rope, aerobics… time to DQ, policies, communications… Weight training for major muscle Work on major organization project – ensure groups weekly adherence to best practices and standards Lose 4-8 lbs per week Measure the data quality improvements: %s, counts, ROI. 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

30 How to create your own Health Plan

31 Governance activities – list and prioritize

Responses Data Definitions Data Corporate & Rules Quality Asset Active ownership & management of information across the business Responsibility or ownership of business rules Management of the lifecycle of data (creation, architecture, retrieval) Process, definitions & controls regarding data definitions Corporate process on how data is managed - the rules Rules (business process framework) and Infrastructure (organization, Enabler for data quality (regulatory compliance) Total management of data as an asset Custodian of data (business rules) Rules and processes around data - accountability Harmonize data into a worldwide view (master data) Accountability for data (who, how, & why) Administration of data flows Data consistency through organization Overhead People, process & master data Definitions & management of reference data Cleansing of data (cross reference & data mapping) Identifiing and managing changes in data Enabling a single version of the truth Ownership & accountability for data

32 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

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

Council, Data DG Council Propose policies Stewards, and ► Intakes: manage & control ► ► Institute / refine process flow ► Create funding requests applicable high priority projects Data Steward Leadership

► Identify supporting ► Conduct / facilitate ► Assign / distribute data stewards Quarterly Data Steward meetings steward inputs across ► Provide training to ► Send emails / communiques stewardship team stewards ► Track progress on DG ► Conduct dry-runs of Maturity Model process flow

DG / MDM / EDM / DQ Training Modules

► Take additional ► Instruct / train selected courses ► Create additional courses (special topic) courses 33 Top tips for practical time management

• 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.

34 Top tips for practical time management

• 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” 35 Exercise: Time Management Assessment

1. Do you keep a To-Do list? 1. ___Y___ / ___N___ 2. Do you have a prioritized To-Do list? 2. ___Y___ / ___N___ 3. Do you differentiate URGENT from IMPORTANT? 3. ___Y___ / ___N___ 4. Are <20% on your list HIGH priority? 4. ___Y___ / ___N___ 5. Do you block out time in your calendar? 5. ___Y___ / ___N___ 6. Do you have personal goals? (This year? 3 years) 6. ___Y___ / ___N___ 7. Are you aware of and work to reduce distractions? 7. ___Y___ / ___N___ 8. Are you aware of if/when you procrastinate? 8. ___Y___ / ___N___ 9. Are you attuned to not taking on too much? (Have 9. ___Y___ / ___N___ you ever seen yourself as a micromanager)? 10. Are you attuned to being a person who thrives on 10. ___Y___ / ___N___ being “busy”? 11. Do you make sure to take breaks during the day? (5 11. ___Y___ / ___N___ minutes every hour or two – a walk, coffee, chat

with a colleague) ____ Total number of “Y”

Exercise: Time Management Assessment

12. Do you rarely complete tasks at the last minute or 12. ___Y___ / ___N___ rarely ask for extension? 13. Do you know how much time you spend on various 13. ___Y___ / ___N___ jobs you do? 14. Do you leave contingency time in your schedule to 14. ___Y___ / ___N___ deal with “the unexpected”?

15. Are you attuned to not letting distractions keep you from working on critical tasks? 15. ___Y___ / ___N___ 16. Do you rarely have to take work home? 16. ___Y___ / ___N___ 17. Do you regularly confirm your priorities with your 17. ___Y___ / ___N___ boss? 18. Before you take on a task, do you check that the 18. ___Y___ / ___N___ results will be worth the time put in?

Add the count from the previous page with this page ____ Total number of “Y” 37 DEFINING A DATA GOVERNANCE 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

39 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 40 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 41 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 • How? That’s up to your organization, but one scenario Determine if warrants issuing a policy Discover, log issue Data Stewards Stakeholders 1 Create 3 Solution Architects Propose Corporate DG policy 5 4 DG Core Team

Request Approve Data Steward Requestor (Stakeholder, PM) 2 Provide Input Post to Root cause, extent, Sharepoint Data Profiling impact, costs DG Council Requirements Notify Logs

Stewards, SME, IT, Stakeholders Project Teams, Architects Communicate 42 6 Stakeholders 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 43 You might wish to tag, categorize or type your Policies

Policy Category Type Comment Standardize Address Enterprise Standardization Conform to USPS Restrict availability of Enterprise Data Security Data authorization Sensitive or Personal Data and access will be Elements 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) Enterprise Data Quality DG COE will projects >$1MM will budget participate during and plan for inclusion of DQ project initiation on metrics and Audit / Balance / defining metrics Control thresholds

44 Policies need metrics, benchmarks, dashboards

Policy Metrics Benchmark Standardize Address # letters mailed 2013 goals: # and % returned # and % return by category Reduce return mail from (e.g, Moved-no forward current return rate of 12% to > address, Undeliverable as 5% Addressed, etc.)

# of newly entered bad Reduce by 80% addresses

# & % fixed each month Increase by 15%

Trend analysis: -- % comparison each mailing -- year over year

45 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

46 Policy for Address Standardization DG POLICY EXAMPLE:

47 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 There are multiple systems in which address information is created. Currently policy was identified 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

48 Data Policy: Address Standardization

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, · Monthly “Difference Report” between systems with Address (Customer Reporting System vs Claims System) Expected Business Tangible: Benefit Mailing Cost savings = $1.5M per year Intangible: Customer satisfaction = Retention History · Effective date Effective – March 1, 2013 · Revised date

49 49 Data Policy: Address Standardization

Glossary of Terms / • Mailing Address: The postal address where a mail can be addressed to a Acronyms 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

50 50 Creating a home for Policies

• 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 51 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

52 BENCHMARKING

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 Product ◦ End-to-End Checks ◦ Business Checks tool BI

54 Measure / Track / Score – example

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

Relative Cost Low Low Moderate High Implementation Risk N/A Low Moderate High Time to Implement / Schedule N/A Low Moderate Long Scoring Match to Business Drivers No Limited Yes Yes Acceptable Not an Not Risk; High Ris k ; Overall Feasibility Option Sustainable Managed High Cost Evolution

55 Data Governance success -- Operational Metrics

Are the Stewards running a repeatable process?

Metric Description Notes

Number of DG council sessions Number of monthly meetings Easy to calculate held Participation level Attendance is kept of Council Easy to calculate member attendance Number of policies established Number of Data Issues or Easy to calculate Opportunities that result in a policy. Not all will result in a policy. Number of data issues or Number of Data Issues or Easy to calculate opportunities remediated or Opportunities moved to point of “completion” Number of Data Steward Number of Data stewards Easy to calculate resources onboarded in a timely brought on board per the fashion proposed Data Steward Operating Model

56 Data Governance success -- Strategic Metrics

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

Metric Description Notes

Number of systems retired as a Number of system or large modules of This can be directionally estimated. result of DG applications or systems that were removed from policy decisions made operational use based on the presence of Data Governance applications. This can includes MDM, CRM, EDW, etc.

Number of interfaces Number of interfaces that are created, This, like the previous metric, can be rationalized and created once for new or existing difficult to quantify. projects. Note this is related to the above metric. Number of processes for which Number of places in the organization we Can be measured. There are 2-3 Data Governance is a checkpoint can clearly point to and say that Data primary checkpoints to be inserted Governance is a checkpoint, of some into other processes. sort, in their process. For example, in SDLC , Change Mgt or Arch Review process $ saved by implementing Data This overlaps with most of the other Not clear this is directly measurable Governance recommendations strategic metrics. but can be directionally estimated.

57 COMMUNICATION

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

59 Avenues for communication

• Collateral and Communications • Employee & Consultant Training — Tailored PowerPoint Presentations — Day 1 – New Employee Training — DG Intranet Site / SharePoint site — Formal Training Course — DG Newsletter — Computer-based Training (like PHI) – required — Cafeteria Tent Cards — Utilize quizzes and certifications — E-mails & Internal Communications • DG incorporated into SDLC and Chg Mgt — Elevator Sheets — PMO project initiation list — TV — PMO Gate Reviews • Team and Enterprise Events — Risk Meetings — Lunch n’ Learn — IT Audits — Roadshows or Townhalls — Architecture Review Boards — Monthly Stewards or Stakeholder • Executive Meetings Meetings — DG Council Meetings • Program/Project Meetings — IT Senior Leadership Meetings — Data domain or LOB Program Kick Off — Strategy Sessions with Directors Meeting

60 PRACTICAL TIPS AND TOOLS

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

62 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

63 Are you ready? Can you handle the truth?

TINA MCCOPPIN PARTNER AND FOUNDER, Ajilitee

[email protected]

847.840.0858