Data-Ed Slides: Best Practices in Data Stewardship (Technical)

Best Practices in Data Stewardship
Copyright 2016 by Data Blueprint Slide #
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In order to find value in your organization’s data assets,
heroic data stewards are tasked with saving the day-
every single day! These heroes adhere to a data
governance framework and work to ensure that data is:
captured right the first time, validated through
automated means, and integrated into business
processes. Whether its data profiling or in depth root
cause analysis, data stewards can be counted on to
ensure the organization’s mission critical data is
reliable. In this webinar we will approach this
framework, and punctuate important facets of a data
steward’s role. 
 
Date: June 14, 2016
Time: 2:00 PM ET/11:00 AM PT
Presenter: Peter Aiken, Ph.D. & Mike Ogilvie

Executive Editor at DATAVERSITY.net
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Shannon Kempe

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• 30+ years in data management
• Repeated international recognition
• Founder, Data Blueprint (datablueprint.com)
• Associate Professor of IS (vcu.edu)
• DAMA International (dama.org)
• 9 books and dozens of articles
• Experienced w/ 500+ data
management practices
• Multi-year immersions:
– US DoD (DISA/Army/Marines/DLA)
– Nokia
– Deutsche Bank
– Wells Fargo
– Walmart
– …
Peter Aiken, Ph.D.
• DAMA International President 2009-2013
• DAMA International Achievement Award 2001 (with
Dr. E. F. "Ted" Codd
• DAMA International Community Award 2005
PETER AIKEN WITH JUANITA BILLINGS
FOREWORD BY JOHN BOTTEGA
MONETIZING
DATA MANAGEMENT
Unlocking the Value in Your Organization’s
Most Important Asset.
The Case for the
Chief Data Officer
Recasting the C-Suite to Leverage
Your MostValuable Asset
Peter Aiken and
Michael Gorman
5

Mike Ogilvie
• 15+ years experience in data-centric solutions
• Architecture/Design experience in:
• Data Warehouse
• Data Integration
• Data Quality
• Solutions/Data/Consulting experience for
numerous government and commercial clients
• B.S. Physics - James Madison University
• Focus on Data Governance, Data
Stewardship, Data Quality, and requirements
consulting
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Presented by Peter Aiken, PhD & Mike Ogilvie
With Great Data Comes Great Responsibility

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1. Data Management Overview
2. Business needs for Data Stewardship
3. Data Stewardship Principles
4. Opportunities to foster a data-driven culture
5. Take Aways, References & Q&A
Tweeting now:
#dataed

9Copyright 2016 by Data Blueprint Slide #
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#dataed

UsesUsesReuses
What is data management?
10
Sources
 
Data
Engineering
 
Data  
Delivery
 
Data 
Storage
Specialized Team Skills
Data Governance
Understanding the current
and future data needs of an
enterprise and making that
data effective and efficient in
supporting  
business activities  
Aiken, P, Allen, M. D., Parker, B., Mattia, A.,  
"Measuring Data Management's Maturity:  
A Community's Self-Assessment"  
IEEE Computer (research feature April 2007)
Data management practices connect
data sources and uses in an
organized and efficient manner
• Engineering
• Storage
• Delivery
• Governance
When executed,  
engineering, storage, and  
delivery implement governance
Note: does not well-depict data reuse

What is data management?
11
Sources
 
Data
Engineering
 
Data  
Delivery
 
Data 
Storage
 
Resources 
(optimized for reuse) 
Data Governance
AnalyticInsight

You can accomplish
Advanced Data Practices
without becoming proficient
in the Foundational Data
Practices however  
this will:
• Take longer
• Cost more
• Deliver less
• Present  
greater 
risk 
(with thanks to  
Tom DeMarco)
Data Management Practices Hierarchy
Advanced  
Data  
Practices
• MDM
• Mining
• Big Data
• Analytics
• Warehousing
• SOA
Foundational Data Practices
Data Platform/Architecture
Data Governance Data Quality
Data Operations
Data Management Strategy
Technologies
Capabilities
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Data$Management$
Strategy
Data Management Goals
Corporate Culture
Data Management Funding
Data Requirements Lifecycle
Data
Governance
Governance Management
Business Glossary
Metadata Management
Data
Quality
Data Quality Framework
Data Quality Assurance
Data
Operations
Standards and Procedures
Data Sourcing
Platform$&$
Architecture
Architectural Framework
Platforms & Integration
Supporting$
Processes
Measurement & Analysis
Process Management
Process Quality Assurance
Risk Management
Configuration Management
Component Process$Areas
DMM℠ Structure of  
5 Integrated  
DM Practice Areas
Data architecture
implementation
Data  
Governance
Data  
Management 
Strategy
Data  
Operations
Platform 
Architecture
Supporting 
Processes
Maintain fit-for-purpose data,
efficiently and effectively
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Manage data coherently
Manage data assets professionally
Data life cycle
management
Organizational support
Data  
Quality

Data Management BoK
The DAMA Guide
to the Data
Management Body
of Knowledge
Published by DAMA
International
• The professional
association for Data
Managers
• 40 chapters worldwide
DMBoK organized
around
• Primary data
management functions
focused around data
delivery to the
organization
• Organized around
several environmental
elements

Data Governance from the DMBOK
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

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The Challenge of Managing Data
Current state ! isolated
databases and personal
definitions
• Stunted ability to
communicate
• Lost documentation
• Inconsistency across
departments
Ideal state ! unified
organizational outlook
• Regimented asset
ownership
• Consistent names &
definitions
• Meaningful data &
metadata
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The REALITY of Managing Data:
*But it can’t fulfill its
potential without careful
maintenance of its source
and storage…
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DATA  
IS  
AN
ASSET*

The Solution: Data Governance
• What is it?
• DAMA
– The exercise of authority, control, and shared decision-making (planning,
monitoring, and enforcement) over the management of data assets
• Robert Seiner (TDAN and KII Consulting)
– The exercise and enforcement of decision-making authority over the
management of data assets and the performance of data functions”
• Steven Adler (IBM)
– Coordinating communication to achieve collective goals through collaboration
• What is it, really?
– A custody battle over data instead of kids
– Critical to organizational success
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What is Data Governance?
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Managing
Data with
Guidance

In Practical Terms…
Managing Data with Guidance
Data governance decides who is
responsible for the stewardship of
data and metadata, and how they
collectively represent the
organization
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•Data & metadata capture & use
• Data quality -> measurement,
optimization, and improvement
• Data policies & procedures
• Constant management and
process evolution

Correct 
implementations
Correct 
functionality
Correct designs
Correct  
specifications
Implementations  
based on  
erroneous design
Implementations  
based on  
erroneous specs
Incorrect  
implementations
Uncorrectable 
errors
Hidden 
errors
Correctable 
functionality
(Adapted from [Mizuno 1983] as reproduced by Davis 1990.)
Design
Implementation
Requirements
Testing
imperfect program products
Data Stewardship ! Data Quality
Erroneous  
designs
Erroneous  
specifications
the "real" problem
Designs based on  
erroneous specs
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Relative Cost/Effort to Repair System in Relation to Development Stage
(Adapted from [Davis 1990.)
$0.00
$2.00
$4.00
$6.00
$8.00
$10.00
$12.00
$14.00
$16.00
$18.00
$20.00
Coding Unit
Test
Acceptance
Test
Maintenance
Nearly 50% of problems
are detected only after
completion of
acceptance tests
Requirements
Design
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Operational Modes
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Legend
Decentralization
Centralization
Interaction
Organization
Design
Organization &
Roles
Processes &
Procedures
Maintenance &
Usage
Policy &
Standards
“Totally Centralized”“Totally Decentralized” “Federated” “Centralized-Hybrid”
Degree of CentralizationNone Total
???Preliminary Recommendation
of Target State???
Centralized Data Governance
structure involves data
stewardship councils organized
into functional domains and
managed centrally by the CDO
organization
Federated Data Governance structure.
Involves each functional data domain to
be managed independently by the
respective business lines. Aligned to a
central data governance working group
broken down by the core governance
functions.
A hybrid centralized / federated model
allows each LoB to manage the respective
stewardship councils while being
accountable to the central governance
organization. The central CDO
organization will manage enterprise-wide
level data domains.
Metrics &
Reporting
Decentralized Data Governance
Structure. Involves each functional
data domain to be managed
independently by the respective
business lines for all core
governance functions. Limited
alignment to a central governing
body. 6
Current State

In Practical Terms (Again)…
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All Ships Need Data Stewards
• Data stewardship
– the operational aspect of data governance
• Formalized accountability for data  
management
• Facilitates day-to-day actions & aspects of a data governance
program
• Data stewards can extract knowledge from and make decisions
regarding data assets and the people who develop & utilize them
• Formalization of unformalized tasks
– for which specific training is still required
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A recent job posting Mid Career (2+ years of experience)
• The Data Governance Analyst IV will support the development and
implementation of SCUSA’s data governance management and
functional system administration across the organization.
• The person in this role will be a SME and the primary data steward
for a particular domain within the Enterprise Data Governance
organization and will support key initiatives especially the Risk
Data Aggregation, Risk Data Reporting, FRY14 and Capital Plan in
addition to firm-wide strategic projects with data impacts.
• The right candidate will have an opportunity to join a growing team
and contribute towards shaping the operating policies and
procedures.
• Master’s degree or MBA a plus.
• Eight (8) to ten (10) years of experience querying data,
identifying anomalies, gaps and issues preferred.
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Essential Functions:
• Become a SME in the assigned area and assist Data Quality team with establishing data quality rules, thresholds and
quality reports and dashboards.
• Serve as the primary data steward for a particular area / domain across the auto finance and unsecured lending business
lines.
• Provide analytical support to Business partners to identify critical data elements for the projects in scope, perform initial
analysis and partner with MIS and Business teams to define and capture metadata, lineage, business rules and
transformation logic.
• Own end-to-end governance process for all identified domains across the domain, plan and drive group / 1:1 discussions.
Represent the domain in data governance working meetings and operating committee.
• Manage risk exposure by analyzing root cause for issues identified and engage the right parties across the teams with
recommended strategies for timely resolution.
• Input and maintain the metadata into Informatica metadata tool and work with IT to establish processes for on-going
maintenance and setting up exception reporting.
• Develop key relationships with SME’s and data owners and drive discussions to standardize / rationalize attributes &
metrics and assist with establishing clear ownership of data elements.
• Collaborate with reporting analysis and data strategy functions to represent needs of the domain, assess and identify
impacts across multiple complex projects.
• Contribute to the development of training materials and deliver / coach 1:1 or small groups on the new processes across
the data governance lifecycle.
• Escalate project timeline and quality issues appropriately to ensure overall program success.
• Supports the development and implementation of new data policies.
• Supports the creation of program business definitions and data management goals and principles for execution.
• Performs data analysis for various enterprise wide data quality initiatives.
• Positions business areas for successful audits and regulatory exams by supporting the implementation of industry
guidelines and ‘best practice’ frameworks.
• Coordinates the analysis of data gaps by collaborating with appropriate data owners and business partners.
• May lead or direct the work of junior analysts.
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Requirements:
• Bachelor’s degree required in BA/BS Computer Science / Information System /
MIS/ Mathematics/ Statistics / Operations Research/ Economics; or equivalent
combination of education and experience, required.
• Eight (8) to ten (10) years of related professional experience working with MIS,
Databases, process management areas.
• Advanced understanding of a Business-Technology function, based on prior
experience working across Business and IT areas.
• Innovative sense of business processes and demonstrated ability to link to the
datasets and vice-versa.
• Advanced knowledge of Excel, Access, Visio, Powerpoint and SharePoint.
• Demonstrated ability to turn ideas into visual representation for conveying complex
data issues to senior business partners.
• Demonstrated ability to thrive in a demanding environment and excellent
collaborative skills to quickly establish partnerships across various stakeholders.
• Advanced interpersonal, negotiation and collaboration skills.
• Time management skills with strong attention to details.
• Ability to work independently and manage multiple task assignments, with guidance
in only the most complex situations.
• Ability to maintain confidentiality.
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Working Conditions:
• Extended working hours may be required by management and
business needs.
• Travel to multiple facilities may be required.
• May be required to lift, push, or pull materials weighing up to
twenty (20) pounds.
• May be required to sit and review information on a computer
screen for long periods of time.
• May require repetitive motions of the hands and wrist related to
writing and typing at an electronic keyboard.
• Corporate / satellite office role.
• This job description does not list all the duties of the job. You may
be asked by your supervisors or managers to perform other
duties. You will be evaluated in part based upon your performance
of the tasks listed in this job description.
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1. Specific background in data is not required
2. Describes a relationship to data–not necessarily a
position
3. A data steward is ideally dedicate to that role
4. Does not need to have the title
5. Public or Industry Data Steward Certification is maturing
6. Multiple steward types are possible for maturing
organizations/operations
7. Training should be focus on formalizing accountability.
• adapted from http://tdan.com/seiners-rules-for-becoming-a-data-steward/16867
Becoming a Data Steward
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Data Steward Types: Basic
• Business data stewards
– Manage from the perspective of
business elements (i.e. business
definitions, data quality)
• Technical data stewards
– Focus is on use of data by
systems and models (i.e. code
operation)
• Project data stewards
– Gather definition, data quality
rules, and project issues for
referral to business and technical
data stewards 
(Definitions adapted from Plotkin: Data Stewardship: An Actionable
Guide to Effective Data Management and Data Governance)
35

Data Steward Types: Ancillary
• Domain data stewards
– Manage steward data required across multiple business areas (i.e. customer
data) and metadata documentation
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• Operational data stewards
– Responsible for directly
inputting data or instructing
those who do; provide
assistance to business data
stewards in spotting data
issues and identifying their root
causes 
(Definitions adapted from Plotkin: Data Stewardship: An
Actionable Guide to Effective Data Management and Data
Governance)

Data Steward Responsibilities
37

Data Stewards Must Be…
Accountable
• Focus on meaning and
quality
• Identify needs for “official”
process
• Operate as single point of
contact for data owned by a
business function
38

Authoritative
• Answer questions
about steward’s
data
• Assign and oversee
data-related work
• Enforce decisions
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Organized
• Need to minimize
negative effects of:
– Changes to business
processes
– Transformations from
system to system 
 
 
 
 
 
 
 
 
 
– Source data problems
– Definition uncertainty
– Changes to technical
platforms and processes
• Lack of accountability +
failure to communicate =
data surprises ! lack of
trust in data & results
40

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Data Stewardship Implementation Options
Minimally Intrusive
• Identifying people into roles rather than assigning them
• Leveraging existing responsibility
Command and Control
• Assigning people into roles
• Giving people new responsibilities
“2 x 4”
• Data governance is “not optional”
• People will have to make time
43

What does a data steward do?
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OPERATIONALIZE

Proactive approach to unearthing good stewards
• Build governance into what the data
stewards do
• Governance Activity Matrix
• Governance Processes RACI Chart
• System Development Life Cycle
(SDLC)
• Project Planning
• Avoid referring to them as “data
governance processes” 
(Adapted from http://tdan.com/seiners-rules-for-becoming-a-data-steward/16867)
45

Reactive Approach to Identifying Data Stewards
Follow a data quality methodology
1. Qualify & prioritize data issue
2. Identify affected data domain &
stewards
3. Conduct data systems &
resource discovery, root cause
analysis, and cost-benefit
analysis
4. Analyze & recommend
resolution
5. Gain approval, funding, and
resources
6. Resolve data issue 
(Adapted from http://tdan.com/seiners-rules-for-becoming-a-data-steward/
16867)
46

Data Stewardship Drives Data Culture
47

Data Stewardship Drives Data Culture
48
The
Basis

Data$Management$
Strategy
Data Management Goals
Corporate Culture
Data Management Funding
Data Requirements Lifecycle
Data
Governance
Governance Management
Business Glossary
Metadata Management
Data
Quality
Data Quality Framework
Data Quality Assurance
Data
Operations
Standards and Procedures
Data Sourcing
Platform$&$
Architecture
Architectural Framework
Platforms & Integration
Supporting$
Processes
Measurement & Analysis
Process Management
Process Quality Assurance
Risk Management
Configuration Management
Component Process$Areas
DMM℠ Structure of  
5 Integrated  
DM Practice Areas
Data architecture
implementation
Data  
Governance
Data  
Management 
Strategy
Data  
Operations
Platform 
Architecture
Supporting 
Processes
Maintain fit-for-purpose data,
efficiently and effectively
49
Manage data coherently
Manage data assets professionally
Data life cycle
management
Organizational support
Data  
Quality

One concept for process
improvement, others include:
• Norton Stage Theory
• TQM
• TQdM
• TDQM
• ISO 9000 
and focus on understanding
current processes and
determining where to make
improvements.
DMM Capability Maturity Model Levels
Our DM practices are informal and ad hoc, dependent
upon "heroes" and heroic efforts
Performed
(1)
Managed
(2)
Our DM practices are defined and
documented processes performed at the
business unit level
Our DM efforts remain aligned with
business strategy using standardized and
consistently implemented practices Defined
(3)
Measured
(4)
We manage our data as a asset using advantageous data
governance practices/structures
 
Optimized
(5) 
DM is strategic organizational capability, most
importantly we have a process for improving
our DM capabilities

Assessment Components
Data Management Practice Areas
Data Management
Strategy
DM is practiced as a
coherent and
coordinated set of
activities
Data Quality
Delivery of data is
support of
organizational
objectives – the
currency of DM
Data  
Governance
Designating specific
individuals caretakers
for certain data
Data Platform/
Architecture
Efficient delivery of
data via appropriate
channels
Data Operations
Ensuring reliable
access to data
Capability
Maturity
Model Levels
Examples of practice maturity
1 – Performed
Our DM practices are ad hoc
and dependent upon "heroes"
and heroic efforts
2 – Managed
We have DM experience and
have the ability to implement
disciplined processes
3 – Defined
We have standardized DM
practices so that all in the
organization can perform it with
uniform quality
4 – Measured
We manage our DM processes
so that the whole organization
can follow our standard DM
guidance
5 – Optimized
We have a process for
improving our DM capabilities

Data Program Coordination
Organizational Data Integration
Data Stewardship
Data Development
Data Support Operations
Data Management Maturity Measurement
Focus:
Implementation and
Access
Focus:
Guidance and
Facilitation
Optimizing(V) 
Managed(IV) 
Documented(III) 
Repeatable(II) 
Initial(I)
• CMU's Software  
Engineering Institute (SEI) Collaboration
• Results from hundreds organizations in
various industries including:
– Public Companies
– State Government Agencies
– Federal Government
– International Organizations
• Defined industry standard
• Steps toward defining data management
"state of the practice"

A Data Stewardship Maturity Model
53

54

How It Works
Top-down
• Stemming from
executive vision &
direction
Bottom-up
• Initiated within
execution teams
and then adopted
by C-level
55

the Data Doctrine(.com)
We are uncovering better ways of developing 
IT systems by doing it and helping others do it. 
Through this work we have come to value: 
Data programmes preceding software development
Stable data structures preceding stable code
Shared data preceding completed software
Data reuse preceding reusable code
 
That is, while there is value in the items on 
the right, we value the items on the left more.

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Benefits of a Stewardship Program
• Programs are ongoing; projects end
• Programs are tied to the financial calendar
• Program management is governance-
intensive
• Programs have greater scope of financial
management
• Program change management is an
executive leadership capability
58

Questions?
It’s your turn!
Use the chat feature or Twitter (#dataed) to submit
your questions to Peter & Mike now!
+ =

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