data-ed webinar: data quality success stories

Dr. Peter Aiken, Founder, [email protected] Akens, Data Consultant, [email protected]

Data Quality Success StoriesDataversity Webinar 7-12-2016

Copyright 2016 by Data Blueprint

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Peter Intro Slide


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Karen Akens, CDMP

Data management and solution development experience for numerous government and commercial clients

Connector between Business & IT based on practical experience in both arenas

Focus on Data Quality, Data Governance & Stewardship, and Business Intelligence

Speaker at EDW, DGIQ, various DAMA chapters

Board member of DAMA-Central Virginia.


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Information transparency Analytics Business Intelligence Increasing efficiencies Decreasing costs Driving holistic decision-

making across the organization

High Quality Data is Critical


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Getting Started with Data Quality

Our approach begins with discovering The data that is most impactful to your

business needs Your organizational capabilities to manage

data as an asset (foundational practices) The state of your technical environment

(technical practices)

and then laying out the path forward in a roadmap That is achievable and matches your

organizations abilities to deliver That builds momentum with specific, short-

term win projects That outlines a long-term vision and

implementation milestones


Clients Data Landscape

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Growth through acquisition No data accountability Fractured Technology Landscape Need to Align with Global Education Strategy

Challenge

No Comprehensive BI Capability Lack of Unified Product and Portfolio

Management Poor Data Quality & Unreliable Reporting Increasing Costs Due to Poor Data Mgmt.

Business Impact

Centralized Data Governance Program Formalize Data Stewardship Become Proactive vs. Reactive Increase Transparency and Decrease Cost

Opportunity


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Case Study - Supplier Master

Business Value Achievements: 1) Consolidated number of suppliers getting better terms and conditions

2) Reduced suppliers with immediate payment terms, increasing cash flow3) Removed duplicate supplier, increasing ability to track spending/reduce risk dup/payments4) Increase in email addresses, (order email/remittance email) faster communication vendors, reduce cost of remittances via post.

5) Improving quality contact information risk of missed payments and supplier relationships

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0200400600800

1000120014001600

20-Oct-142-Dec-14

Data Governance Board 12/14/2014


Challenges from a Lack of Data Quality

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Its a ticking time bomb waiting to explode

No Account Creation Controls

No Standard Product

HierarchyNo

Universal Product Model

Visible Inactive Records

Labor Intensive Manual

Data Clean-up

Inconsistent Use of

Business Terms

Duplicate Accounts

Missing Remittance

Info

InaccurateReports

Missing Data

Who owns the data?

Who fixes?


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Selling the

Message

Share60-second Elevator Speech

Use Current Inconsistencies

that Impact Reporting

Obtain Senior Level

SponsorshipQuantify Value

of Data

Perform Data Quality Pilot

Demonstrate Stewardship

Success Story

And ask for help before you think you need it.


If you want to avoid situations like this

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One US system had 11,500 active cost centers, increasing risk mis-posting &

mapping effort

One S. African system missing electronic remittance info in 88% of

cases, payments sent by post, increasing cost & lag time

No Standard Product HierarchyCant determine product profitability

90% of suppliers in one US system on immediate payment terms, impact to cash flow

Lost revenue of $2 million annually, not utilizing rights previously granted

No processes for deactivating vendors, 222,000 obsolete vendors removed from one of two US

systems, many systems still contain ROT

Supply Chain 320 hours every year end tracking missing 1099 data to avoid tax

penalties

Data issues not fixed at the source;never ending battle - financial resources spend 35%

time reconciling data

Data that Matters


you need to have this

Enterprise Data

Strategy

Data Governance & Stewardship Framework

which articulates

roles of data owners and

data stewards

Senior level sponsorship & organizational

culture that treats data as a strategic asset Data

Governance Board with a mandate to drive data

quality enterprise wide

Master Data Management

solution

Data quality principles that are embedded in process &

system designacross the enterprise

Standard Business

Glossary with an authoring

and publishing process

but not all at once!


Where to Start When Developing a Data Quality Framework

No Accountability or Responsibility for Data

Many resources create, review or manage data No formal data stewardship roles and responsibilitiesDifficult to determine who is accountable & responsible for data

Establish Data Ownership & Increase Data Accountability

Define clear data ownership & stewardship roles, accountability & responsibility of data.

Define a vetting & onboarding process ensuring resource capacity

Establish decision rightsMaintain a master list of all Data Stewards and their related data domains.

Inconsistent Master Data

Fire drill to fix data issues in isolationLittle standardization across Lines of Business and Geographies

Difficult to report on a global level at needed level of detailNo formal master data change control process

Consistent Master Data Management

Develop master data standardsEstablish change controlDefine consistent data models Ongoing governance and stewardship of master data

Inconsistent Data DefinitionsPoor Data Quality

Business Terms definitions differ by groupData monitored in silos Fragmented use of a variety of toolsFocus on find and fix instead of root cause analysisNo standard reporting/tracking metrics

Term Authoring & PublishingIncrease Data Quality

Establish and implement process to define business accredited terms & publish for consumption enterprise wide

Stewards define business rules used to structure & profile data

Develop and implement DQ standards Ongoing Score carding & DQ metric reporting


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Every work stream has a part to play if organization is to move from a reactive to proactive approach to improving data quality

Principle Implications1. Capture data right, first

timeWherever possible all data is captured once, at source, and validated on input

2. Engineer-in positive impacts on data quality

Wherever possible data quality improvement is automated, proactive and on-goingSystems, processes and products are inherently designed to improve data quality. e.g.

The possibility of errors when data is entered or changed is engineered out

Processes are designed to enter and maintain accurate data

Data entry is quick and intuitive for users

3. Integrate data quality into business processes

Data quality standards and rules are defined and integrated into day-to-day operations e.g. instances of non-compliance are fixed at root causeThere is clear accountability throughout the organization for promoting & sustaining good quality data


Discovery - Identify potential data quality issues.Profile Data - Review sample data and existing data creation and usage process to provide context for business rule discussion with Data Owners and Business Data Stewards.Develop Business Rules - Work with Data Owners and Business Data Stewards to review documented business rules and capture undocumented rules. Define Metrics - Define metrics and acceptable thresholds against which to measure levels of quality.Evaluate Data with Metrics - Execute business rules against production data and evaluate results. Utilize acceptable thresholds set by the Data Governance Board to evaluate the data. Findings Review - Review the Findings with the Data Owners and Business Data Stewards.Remediate Anomalies - Implement and execute remediation process to fix problems with production data.Monitor Health - Define and implement a continuous monitoring/remediation plan to prevent and/or fix data quality problems in the future.

Repeatable Process


Profile Data

Develop Business Rules

Define Metrics

Evaluate Data with Metrics

Remediate Anomalies

Monitor Health

Discovery

FindingsReview

Findings Review

Discovery


Identifying Business Need & Resources

Discovery process not solely the responsibility of business, IT, or Data Governance/Data Quality organizations. Requires collaboration.

Business need or problem definitions can be influenced by a variety of sources such as:

Migrating to One ERP and One CRM

Master Data Management Processes

Suspected data quality deficiencies impacting BV & regulatory requirements

Data Governance Board initiatives

Needs of data-centric business strategies and opportunities

Directives from executive sponsorship team


Identifying Business Need & Resources Identify Key Resources

BusinessData Quality

Center of Excellence

IT

Data Quality Analyst

IT Data Steward

BusinessData

Steward

DataOwner


Identifying Business Need & Resources Refine Problem & Develop Initial Business Case

Data quality team refines original problem statement to ensure that the defined project objectives are achievable and in alignment with enterprise strategy.

Refinement of Problem

Statement

Begin a list of potential business impacts related to degraded quality of data within the project scope. Human capital expense for manual correction Revenue lost due to inaccurate information Regulatory fines from compliance violations Damage to corporate reputation

Initial Development of Business Case


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Profile Data


Define Metrics


Remediate Anomalies

Monitor Health

Discovery

FindingsReview

Findings Review


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What to Include?

Data Quality team should work to define the specific data elements and their encompassing source systems which will be included in the analysis.

Focus on Data that Answers Questions

Confirm that the data available in the defined data sources is capable of answering the questions posed by the project problem statement.

Identifying and Requesting Data


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Allows for a query against live data that can be re-utilized in a repeatable process.

Preferred for access to current data. Provides greater flexibility of data import options. Requires effort from IT team members and may have an

associated cost.

Build a Direct Database

Connection

Useful when direct connection is not available. Requires knowledgeable analyst for identifying correct

format and uploading. Each data load requires a new data extraction effort

Extract Data into Flat Files

Identifying and Requesting Data

Consider - Staging Area for data preparation

Two Options


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An initial profile should be run against the data without any business rules to confirm a successful data import.

This profile serves two purposes It is a sense check, allowing the analyst an overview of the

data to ensure the data was loaded properly. It provides an overview against which initial observations can

be made.

Initial Data Profiling and Discovery


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Initial Data Profile Output

Uniqueness

Percentages Counts Key Fields

Nulls

Percentages Counts Key Fields

Min/Max

Unexpected Values

Values outside domain

Data Review at a Glance


Profile Data


Define Metrics


Remediate Anomalies

Monitor Health

Discovery

FindingsReview

Findings Review


Data owners Business data stewards IT data stewards

Conduct a data profiling debrief session

Purpose of the data profiling exercise Scope of the data included in the profile Expectations of them to assist in the development and

application of business rules to future profiles.

Communicate to the data owners

Initial Data Profiling and DiscoveryReport Findings to Data Owners and Stewards


It may be advisable to extract information from reporting tool results into another format which can be shared with all members of the data quality team. Excel PDF

Peculiarities of the data profile should be highlighted for review with the data owners.

Any inferences about potential business rules, as well as questions about patterns in the data, should be noted.

Initial Data Profiling and DiscoveryCollect and Report Information from Profile


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Next StepsInitial profiling is just the beginning of the Data Quality Process

The real benefit is in developing business rules that can be applied to data in order to continue the repeatable process and develop actionable insights.


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Profile Data


Define Metrics


Remediate Anomalies

Monitor Health

Discovery

FindingsReview

Findings Review


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Defining Business Rules and MetricsSourcing Business Rules

Possible sources of business rules

Master Data Standards documents

Subject matter expert interviews Data Stewards, Owners, and

Consumers Desktop procedures documents Process and system

documentation

What to look for

Allowable values Required fields Links between fields Fields that link between data

domains Potential duplicate records Insights into patterns that might

be found in the data profile


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Defining Business Rules and MetricsExample Business Rules Business Rule Related Business Action Data Quality Check

Tax Identifier is required for all non-employee vendors.

A W-9 is required before entering a new vendor into the vendor management system.

Rule is violated if Vendor Type Employee and Tax ID is Null

Tax Identifiers should be entered in the valid format for type of identification number.

Consistent formatting of tax identification numbers allows for higher confidence in searching and validation.

Rule is violated if tax ID is not in a valid format for the type, i.e. SSNs should be 999-99-9999; FEINs should be 99-9999999

Entities (companies, employees, products, etc.) should be unique and duplicates should not be entered.

Entity names entered into the system should be entered in a consistent format to assist with presentation and elimination of duplicates.

Rule is violated if entity names are duplicated.

E-mail addresses must be entered in valid formats.

Complete e-mail addresses should be entered into the system in order to ensure valid contact information.

Rule is violated if email address field is not a valid format (e.g. [email protected])

mailto:[email protected]


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Defining Business Rules and MetricsWhat Makes Good Metrics?

Meaningful to the Business the score should relate to improved business performance

Measurable must be able to be quantified within a discrete range

Controllable some action can be taken to change the data and improve the score

Reportable should provide enough information to the data steward to take action

Traceable must be able to be tracked over time to show improvement efforts


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Defining Business Rules and MetricsExamples of Metrics for Various Dimensions

Does each value fall within an allowed set of values? Does each value conform to the defined level of precision?Accuracy

Is data present in required fields?Completeness

Is the data used the same way across the enterprise?Consistency

Is the data up to date?Currency

Are identifying data elements unique?Integrity

Are data elements stored as assigned data types, e.g. is text stored in a telephone number field?Conformity

Do duplicate records exist?Duplication


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Profile Data


Define Metrics


Remediate Anomalies

Monitor Health

Discovery

FindingsReview

Findings Review


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Evaluating Data & Reporting FindingsRe-profile Data with Business Rules and Report Findings

Definition, refinement, and application of business rules should be repeated iteratively and reviewed until the data owners are satisfied with the accuracy and completeness of the business rule implementation.

Present all findings to the data owners and stewards for review.

The goal of this step is to finalize the data quality assessment definition such that an ongoing monitoring process can be modeled from the activity.


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Profile Data


Define Metrics


Remediate Anomalies

Monitor Health

Discovery

FindingsReview

Findings Review


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Two Routes

Find-and-Fix Process Change

Remediating AnomaliesCorrective Actions

Leverage the continuous monitoring of data quality reports to confirm that the data cleansing procedures are effective

BestPractice


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The costs of poor data quality include: human capital expense for manual correction revenue lost due to inaccurate information regulatory fines from compliance violations damage to corporate reputation

Data Stewardship Training - Session 2 37

Business Value from Data Quality


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Business Value Calculations# Errors Identified

Potential Cost Avoidance

Business Rule: Customer Address Invalid 84367 92,952.42$

Calculation Description:Manual effot to research and correct an invalid Customer Address

Average Salary for worker engaged in correcting address 25,000.00$ Average Salary including benefits 34,375.00$ Salary per hour 16.53$ Salary per minute 0.28$ # minutes to correct an invalid address 4Cost of manual effort to research and correct one address: 1.10$

Sheet1

# Errors IdentifiedPotential Cost Avoidance

Business Rule: Customer Address Invalid84367$ 92,952.42

Calculation Description:Manual effot to research and correct an invalid Customer Address

Average Salary for worker engaged in correcting address$ 25,000.00

Average Salary including benefits$ 34,375.00

Salary per hour$ 16.53

Salary per minute$ 0.28

# minutes to correct an invalid address4

Cost of manual effort to research and correct one address:$ 1.10


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State the issue (e.g. duplicate vendor records are causing issues with payments) Ask Why? five times

Remediating AnomaliesFive Whys for Root Cause (Danette McGilvray)

New master records are created instead of using existing ones.

Why are there duplicate records?

The reps dont want to search for existing records.

Why do they create new duplicate records?

Search takes too long.Why dont they want to

search for existing records?

Reps have not been trained in proper search techniques, system performance is poor.

Why is the search time too long?

Reps are measured by how quickly they can create a new master record and they dont see the implications of duplicate data downstream.

Why is long search time a problem?


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Profile Data


Define Metrics


Remediate Anomalies

Monitor Health

Discovery

FindingsReview

Findings Review


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MonitoringAt the Enterprise Level

Customer Product Supplier

Open Deferred Remediated Total Issues Open Deferred Remediated Total Issues Open Deferred Remediated Total Issues

156

48

97

11

225

140

19

66

145

43

90

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Data Quality Issues by Domain as of 1-31-2015

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 105 110 115# Open Issues

Customer

Product

Supplier

26 Critical Issues

26.80% of Open

31 Critical Issues

46.97% of Open

5 Critical Issues

11.63% of Open

Critical Data Quality Issues

Total Data Quality Issues open more than 30 days: 92

Total Data Quality Issues open more than 60 days: 31

Total Data Quality Issue open more than 90 days: 17

OpenDeferredRemediatedTotal Issues


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Establish Process to Consume Artifacts from

Data Profiling

Take corrective measures to improve the

data quality

Verify through monitoring that

improvements were implemented by either data cleansing, controls at

the root cause, or a combination of both.

The data stewards should understand how to

interpret the metrics, including what is being measured and why.

Monitoring can be costly so it should focus

primarily on those processes that are

essential to the business.

MonitoringMonitoring by Data Stewards


Data Governance & Stewardship Maturity Model

Define

Control

Measure

Expand

Optimize

Business Glossary &

Roles

Data Standards

DQ Dashboards

Data Sprints

Continuous Improvement

Identify & catalog data assets, map to owners & stewards Stewards are identifying, defining critical data, publishing

business accredited terms for consumption

Define authorities, control changes Data Standards enforced by Stewards & Owners Harmonize definitions across functions, Lines of Business,

Geographies

Measuring data quality (DQ) Monitor ongoing stewardship operations & data use Data Standards implemented for new system

Repeatable data management processes in place Expand scope & breath of stewardship program Increase volume & efficiency of data it supports.

Iteratively enhance data quality & stewardship performance Continuously prioritize & act upon enhancement opportunities

from monitoring & expansion activities.


Parts of organization unaware of DG/Stewardship and do their own thing; inconsistent with DG standard

Business units may be unaware of benefits and added value

Risk: Awareness

Business units refuse to adopt standards put forth System constraints make it difficult to implement new standards Business units do not engage the Global Data Services

team on projects

Risk: Adoption

Funding model that aligns with governance and organizational structure (i.e. building data connections to sources with DQ tool)

Cost of building and establishing Global Data Services

Risk: Funding

Stewardship skills are hard to maintain Build and sustain capability across a large world-wide

organization

Risk: Training

Data Governance and Stewardship is a long-term program, not a one-time project

Risk: Time to Build

Strong communication plan that is meshed into overall corporate communications

Corporate governance and strong sponsorship of DG/Stewardship

Mitigation: Awareness

Accountability and approval process by Data Owners and DG Enterprise Steering Committee

Document exceptions and work-arounds Corporate governance and Architecture Review Board to

align projects with DG/Stewardship

Mitigation: Adoption

DG & Stewardship funding established Cost allocation aligned with DG & Stewardship model Project specific costs

Mitigation: Funding

Partner with Data Architecture, Global Change & Process Excellence unit to provide a training curriculum

Define staffing models and career paths that outline training and align with DG/Stewardship

Mitigation: Training

Leverage parallel opportunities to accelerate build and implementation (Master Data, Global KPI reporting, One ERP road map, One CRM)

Pilot projects to quickly show tangible benefits

Mitigation: Time to Build


QUESTIONS??

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Data Quality Success StoriesDataversity Webinar 7-12-2016Peter Intro SlideKaren Akens, CDMPSlide Number 4Slide Number 5Clients Data LandscapeCase Study - Supplier MasterChallenges from a Lack of Data QualitySlide Number 9If you want to avoid situations like thisyou need to have thisSlide Number 12Slide Number 13Repeatable ProcessSlide Number 15Slide Number 16Slide Number 17Slide Number 18Slide Number 19Slide Number 20Slide Number 21Slide Number 22Slide Number 23Slide Number 24Slide Number 25Slide Number 26Next StepsSlide Number 28Slide Number 29Slide Number 30Slide Number 31Slide Number 32Slide Number 33Slide Number 34Slide Number 35Slide Number 36Business Value from Data QualityBusiness Value CalculationsSlide Number 39Slide Number 40Slide Number 41Slide Number 42Slide Number 43Slide Number 44QUESTIONS??