Gideon du Toit, Midlands Health Network, Focus Day, Presentation at Chief Data & Analytics Officer...

Practical Data Governance & Data Quality Strategies for Success

Gideon S. du Toit

https://www.pinnacle.co.nz/

And when I am not the CDO and CIO…

Let’s Network

Gideon S. du Toit [email protected]

Obstacle IdentificationFor Improved Data Quality

Sometimes it’s easy to see where the obstacle is.Sometimes we must dive deeper…

The AGILE Obstacle Identification Process

An obstacle is any behaviour, physical arrangement, procedure or checkpoint that makes getting work done slower without adding any actual contribution to the work.

Activities that do add value to our work may be slowed down by obstacles, but are not obstacles in and of themselves.

FROM -http://www.agileadvice.com/2006/03/10/referenceinformation/the-art-of-obstacle-removal/

AGILE Obstacles

Physical Environment

Knowledge

Personal

OrganisationalCultural

Dis-Unity

Common Obstacles

Analysis Paralysis

Format of Data

Culture

Lack of Data

Politics

Available Hardware

Available Skilled Staff

Budget

Time

9

Less common Obstacles

Urgent vs. Important

Systems / Consolidations

Changing data

Metadata: missing, corrupt, dated

No defined processNo measurement of quality

Legacy systems

Poor data quality

“EVERYTHING IS FINE”

No CE buy in

Incentives don’t exist

ROI is not clear

Linking marketing metrics to business metrics

Less common Obstacles



Changing data

Metadata: missing, corrupt, dated

No defined processNo measurement of quality

Legacy systems

Poor data quality


No CE buy in


ROI is not clear

Linking marketing metrics to business metrics

SECURITY & PRIVACY

Lack of budget/resources/time

Legacy systems


Metadata does not exist, is corrupt, is old and out dated


No measurement

Lacking skillsRapidly changing

nature of data


Lack of defined process

No CE buy inNot knowing that there is

a problem

ROI is not clear

Budget

Linking marketing performance metrics to business metrics.

Lack of analytical and/or data management skills

Lack of IT systems and tools for data collection and analysis

Poor data quality

Lack of integrated data/centralized databaseChanging data

Volume of data is too MUCH

Pareto Principle

Removing Obstacles the AGILE Way

Direct - Deal with the obstacle directly without involving other people.

Command and Control - Identify the obstacle and give precise instructions for its removal to a person who will directly

perform the removal. The overall approach of “command and control” is not recommended for Agile environments since it is

disempowering.


Influence - Identify the obstacle and suggest means to deal with it to a person who has the authority or influence to get others to deal with it. This indirect method of obstacle removal can be slow and frustrating. However it usually has better long-term effects than command and control.

Support - Offer to assist and encourage the removal of obstacles that have been identified by other people. In many respects this is a very effective method. It can assist with team-building and learning by example. People are usually grateful for assistance.


Coaching - Train others on the art of obstacle removal including obstacle identification, types of obstacles and strategies for dealing with obstacles. Observe people’s attempts to remove obstacles and give them feedback on their actions.

Creating a Culture of Obstacle Removal - Encourage and measure obstacle removal at all organizational levels until it becomes habitual.

Creating a FrameworkFor Estimating & Capturing Benefits of

Data Quality Projects

Based on work of:Martin J. Eppler University of Lugano (USI) School of Communication Sciences Switzerland

Costs resulting from

low quality data

and

assuring data quality

Cost examplesMaintenance

Excess labor

Higher search

Assessment

Data re-input

Viewing irrelevant informationLoss of revenue

Loss of exisiting customers

Loss of new customers

Higher retrieval

Higher data administration

Injury

Lawsuits

Process failure

Rework Acceptance testingIncreased time of delivery

Information quality assessment or inspection costs

Information quality process improvement and defect prevention costs

Preventing, Detecting and Repairing low quality data

Investment costs of improving data infrastructures

Investment costs of improving data processes

Training costs of improving data quality know-how

Management and administrative costs associated with ensuring data quality

Now categorize costs in terms of their originIncorrect capture or entryIncorrect processingIncorrect distribution or communicationIncorrect re-capture/re-entriesInadequate aggregationInadequate deletion

Now categorize costs in terms of their effectLost customersScrap and re-work in productionIdentifying bad data in operationsRe-entry at data capture pointScreening at data use pointsTracking mistakesProcessing customer data complaints

Costs by Information Quality AttributeInaccurate information

Decreasing data

Unreliable information

Increasing data

Marginal data

Inconsistent information

Fixed dataVariable dataExponential data

Direct vs. indirectAvoidable vs. unavoidableImpact: major, minor, etc.

One-time vs. ongoingVariable vs. fixed

Occurring vs. dormantShort term vs. long run

Controllable vs. uncontrollableQuantifiable vs. non quantifiable

Cluster these examples into cost groups based on shared criteriaWhere the costs originateWho bears the costsHow the costs can be measuredWhich DQ attributes they affectEtc.

that are mutually exclusive and collectively exhaustive

Reduce the cost groups into major cost categories

Relate the various cost categories to one another in an instructive waye.g. through an information lifecycle perspective

Information Lifecycle

Fit the costs you listed into the information lifecycle

Through this process of refinement, the concept of data quality cost is iteratively sharpened, but also

viewed from various perspectives.

Closing the knowledge vs. action gap

The People Factor

Identify the correct peopleStart at the top or start at the bottom?

Skills vs. Will

The right people are required to support, sponsor, steward, operationalize, and ultimately deliver a

positive return on data assets.

Executive steering committee

Executive sponsorship

Grassroots efforts

An effective data governance program should include all of these rolesExecutive sponsorData steward/data quality stewardData governance leaderA program or project management office

Technical vs. Creative Skills

Leading by Example

Creating and Activating a FrameworkFor Information Management in Your

Organisation

Recap:We have identified the obstacles to improving Data Quality

We know now what the benefits are of doing these data quality projects

We have identified the correct people and empowered them

Now we need to put it all together into an information management framework and activate that framework

10 Fundamentals necessary to build effective information management competency within an organization:

VisionBusiness easePeopleToolsPoliciesMeasurementWhole of managementChange managementProject managementGovernance

Create a Vision StatementWhy are we doing this

Who is this aimed at

What do we want to do for them

What factors will determine success

What roles will people play in this across the organisation

What time frame do you have to create this in

What are the tools that you are going to use?“In house” transactional/operational applications“In house” analytical applicationsCloud-based applications and platforms, social data…Data dictionaries / Metadata / Business rules glossaryMeasuring toolsSecurity and legal compliancePolicies

PoliciesAgreed upon

By EVERYBODY!!

Documented

And kept ALIVE!!

Complied with

and this is the

HARDpart

Policies Include:Data accountability and ownershipOrganizational roles and responsibilitiesData capture and validation standardsInformation security and data privacy guidelinesData access and usageData retentionData maskingArchiving policies

* Whole of System * Change Management * Measurement * Project Management * Governance

Completed Framework

Embedding Your Data Governance Processes

Across the Business

Based on the work of:

THE NETWORK FOR BUSINESS SUSTAINABILITY

http://nbs.net/

PlanEnvision Engage in a process to make sense of a set of future scenarios

Prioritize Identify a set of material economic, environmental and social issues

Strategy Incorporate consideration of information environment limits and data quality foundations into the organization’s core

ImproveReview Revisit the organization’s efforts and progress towards its goals

Root Causes Proactively seek to learn from prior incidents

Ask Employees Proactively seek employees’ opinions and ideas

Listen Foster a culture that is receptive to employees’ opinions

InnovateImprove Operations

Improve Products and Operations

Think Systematically

Internal Knowledge

Explore

Pilot

Connect OutwardsScan Make use of systems or processes

Benchmark Compare with the performance of other organizations

Standards Comply with a recognized set of external standardsFeedback Solicit input from those outside the organization

External Knowledge Exchange knowledge with other organizations

Engage LeadersFrame Communicate the importance of data quality

Ask Proactively seek management’s opinions

Prime Prepare future organizational leaders

Cultivate Champions Identify influential individuals and support them

Build ReadinessTrigger Disrupt the status quo

Explain Explain how data quality connects to an employee’s everyday experiences

Look Beyond Raise the level of awareness of data quality through information that challenges existing assumptions, profiles new technologies or presents alternate visions of the future

Shape IdentityBrand Employ characteristics that call to mind data quality

Share Stories Build a capacity for and acceptance of the sharing of data

Mission, Vision, Values Integrate data quality into the organization’s mission, vision and values

SignalSelf-Regulate

Commit

Inform

Follow Up

Recognise

Model

DemonstrateInvest resources to ensure that your organization’s commitment to data quality aligns with its on-the-ground actions

AssignClearly establish accountability for delivering on your organization’s data quality vision

IntegrateGovernance

Risk

Procedures

Business Planning

Business Processes and Systems

Policies

Assess ProgressMeasure

Information Systems

Report

Verify

Analytics

Questions?

So long, and thanks for all the fish

Gideon du Toit, Midlands Health Network, Focus Day, Presentation at Chief Data & Analytics Officer...

Data & Analytics

Transcript of Gideon du Toit, Midlands Health Network, Focus Day, Presentation at Chief Data & Analytics Officer...