New Year’s Resolutions for your Data Management Strategy

1© 2019 IDERA, Inc. All rights reserved.
NEW YEAR'S RESOLUTIONS FOR YOUR DATA MANAGEMENT STRATEGY
JANUARY 15, 2019
Ron Huizenga
Senior Product Manager, Enterprise Architecture & Modeling
@DataAviator

2© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 2© 2019 IDERA, Inc. All rights reserved.
AGENDA
▪ About those resolutions…
▪ Implementing lasting change…
▪ Data management strategy…
▪ Data management challenges…
▪ The path forward…
▪ Final Thoughts.

RESOLUTIONS (AND WHY THEY DON’T WORK)
▪ Well intended, poorly executed
• 80% of resolutions fail by the first week of February
• Then we repeat the painful cycle the next year
▪ Why do so many resolutions fail?
• Way too ambitious
• Some are too ambiguous
• Can’t measure progress or success
• Attempting several major changes at once
• Increases anxiety and burnout

▪DATA MANAGEMENT
▪ Some Things that Need to Change

DATA GOVERNANCE
▪ Don’t try to buy it. You can’t!
▪ So stop trying to!
▪ Governance requires lots of hard
work and commitment throughout
the organization
• People
• Process
• Technology
• Culture Data
Governance
Solution
A - Z

DON’T TAKE “EXPERT” REPORTS & OPINIONS AT FACE VALUE
▪ Industry analyst reports are opinions, not industry wide
consensus
• They may be biased
• Be aware that many “industry rankings” are “pay to play”
• Don’t bet your company’s future on them
• Read critically for informative purposes only
• Just because it is expensive, that doesn’t make it valuable
• Do your own homework!
• Make your own decisions based on requirements and fit for your
organization
Industry Analysis
Report & Ranking

JUST … DON’T
▪ Just because someone else is doing it,
that doesn’t mean that you need to
• Make decisions based upon business
strategy and requirements
▪ The new technology or trend is NOT the
solution to everything
• Beware of anything touted as a
replacement for all of your existing
technology
• Silver bullets apply only to werewolves
▪ And stop using the meaningless
buzzwords:
• Big Data
• Digital Transformation
An ineffective
strategy known as:
“Management by in-
flight magazine”

WHAT WORKS TO IMPLEMENT LASTING CHANGE?
▪ Have a defined target
• Break down into small, sustainable changes
• Plan, then execute
• Incorporate contingencies
• Without a plan, the chance of success is virtually ZERO
• “Hope is not a strategy”
▪ Concrete
▪ Measurable
▪ Continuous improvement approach
• Evaluate, measure, adjust
• Rinse & repeat
• Add additional changes in small increments
▪ Now, let’s apply this to data management strategy!

DATA MANAGEMENT STRATEGY IS VITAL
“Organizations that do not understand the
overwhelming importance of managing data and
information as tangible assets in the new
economy will not survive.”
- Tom Peters, 2001

INFORMATION CAPABILITY STUDY – HOW ARE WE DOING?
▪ Very few organizations utilize information to its full potential
▪ Deficiencies in technical capability, skills, lacking data culture
▪ Lack of investment in value-driven information strategies
▪ Very few understand how to derive maximum value from information
• This will erode corporate value if not corrected
* Based on 2015 PwC/Iron Mountain study: Seizing the Information Advantage

INFORMATION MANAGEMENT DISPARITY
▪ Misguided Majority – 76%
• Informed but constrained
• Uninformed and ill-equipped
▪ Data seen as a byproduct, or taken
for granted
• Low comprehension of commercial
benefits that can be gained
▪ Constrained by legacy approaches,
regulations
▪ Weak analytic capability, or
• strong analytic capability, lacking
value focus
• Low analytical capacity
▪ Can be overwhelmed by data volume
▪ Data is domain of data architects
▪ IT led rather than business led
▪ “Spreadsheet hell”
▪ Information Elite – 4%
▪ Proactive Action
• Diversify business models
• Improve operating efficiency
• Identify / implement new market
opportunities
▪ Tangible data value
• Linked to organizational KPIs
▪ Exploit data for competitive advantage
▪ Balanced approach between security
and value extraction
▪ Holistic approach
• Governance is part of normal business
▪ Well defined information strategy
• Reflects business objectives
▪ Often in following sectors
• Healthcare, Manufacturing & Engineering

Technology &
Infrastructure
Information &
Strategic Business
Enablement
HIGH LOW
LOW HIGHValue Generation
Primary IT Focus
Risk
Level 0 1 2 3 4 5
Description None Initial Managed Standardized Advanced Optimized
Data Governance None Project Level Program Level Division Level Cross Divisional Enterprise Wide
Master Data Management
no formal master
data clasification
Non-integrated
master data
Integrated, shared
master data
repository
Data Management Services
Master data stewards
established
Data stewardship
council
Data Integration
ad-hoc, point to
point
Reactive, point-to-
point interfaces,
some common tools,
lack of standards
common integration
platform, design
patterns
Middleware utilization:
service bus, canonical
model, business rules,
repository
Data Excellence
Centre (education
and training)
Data Excellence
embedded in
corporate culture
Data Quality
Silos, scattered data,
inconsistencies
accepted
Recognition of
inconsistecies but no
management plan to
address
Data cleansing at
consumption in
order to attempt
data quality
improvement
Data Quality KPI's and
conformance visibility,
some cleansing at source.
Prevention approach
to data quality
Full data quality
management
practice
Behaviour
Unaware /
Denial
Chaotic Reactive Stable Proactive Predictive
Data Maturity
Introduction Expansion Transformation

DATA STRATEGY & BUSINESS ALIGNMENT
Vision
Statement
•How will you change the
world?
•Compelling, exciting
•Some day …
Mission
Statement
•What to do to accomplish the
dream?
•Every day
Business
Strategy
•Specific goals and objectives
•Implement the mission
Data Strategy
•Align data management practices
to achieve the business strategy
Data
Management
•Deliver, control, protect and enhance
data value
•Plans, policies, programs, practices

VISION VS. MISSION
▪ Vision Statement
• The dream: how does your organization wish to change the world?
• “Some day …”
• Should be big, exciting, compelling
▪ Mission Statement
• What are you going to do to accomplish the dream?
• WHAT you do!
• WHO benefits from it?
• HOW you do it!
• “Every Day !”
• Should NOT be stated in financial terms
▪ Business Strategy
• Supporting Goals & Objectives
• Quantifiable
• Measurable
“Mission statements that express the
purpose of the enterprise in financial
terms fail inevitably to create the
cohesion, the dedication, the vision of the
people who have to do the work so as to
realize the enterprise’s goal.”
“The mission statement has to express
the contribution the enterprise plans to
make to society, to economy, to the
customer.”
Peter Drucker

Actionable: easy to understand. It is clear when chart your
performance over time which direction is good and which direction is
bad, so that one knows when to take action.
Measurable: Need to be able to collect data that is accurate and
complete.
Specific: Metrics must be specific and target the area that is
being measured.
Relevant: There is a common trap of trying to measure everything.
Only measure what is relevant. Ignore the noise from irrelevant data.
Timely: Need to be able to get data when it is needed (as near to
real time as possible). If data is received too late, it may no longer be
actionable.

DATA STRATEGY OBJECTIVES
▪ Information governance oversight body comprised of all key functional areas
• Supported by senior leadership
• Owned by the business – NOT owned by IT
▪ Culture of evidence based decision making
• Information is a valuable asset
▪ Protect sensitive and valuable information
• Secure access to those who need it
▪ Fit for purpose data analysis, interpretation, visualization
▪ Sound data architecture & enterprise architecture
• Data modeling – understanding the data
• Business process modeling – how data is created and used

DATA MANAGEMENT & GOVERNANCE STRUCTURE

DATA LAKE
What’s
in your
data
lake
swamp?

INFORMATION REFINERY

Logical Data Lake
CHALLENGE: MULTI-HYBRID DATA ECOSYSTEM
RDBMS
DataIngestion
Approved
Raw Data
Sandboxes
(Data Science)
Raw Transient
Data
Refinery Refined
Data
Trusted
Data
MDM
Store
Self-serve
Analytics &
Reporting

MAPPING THE DATA
▪ Data Models
• Conceptual
• Logical
• Physical
• Dimensional
• Enterprise/Canonical
▪ Visual Data Lineage
▪ Enterprise Data
Dictionaries
• Naming Standards
• Attachments
▪ Metadata Repository
• Business Glossaries

DATA MODEL UTILIZATION
Documentation
and/or Physical
Database
Generation
(project focused)
Conceptual,
Logical,
Physical
(Design)
Enterprise
including
canonical,
lineage,
governance
metadata
Full governance
metadata,
business
glossary
integration,
lifecycle, value-
chain
Fully integrated
modeling,
glossaries,
metadata, self
serve analytics
DataMaturity
Evolution

GOVERNANCE CONSIDERATIONS
▪ Data Classification
• Master, Reference, Transaction
• Prioritize
• Divide and conquer
▪ Data Quality
• Data characteristics
• Critical data elements
▪ Regulations
• Security
• Privacy
Data
Governance
Data
Architecture
Data Modeling
& Design
Data Storage
& Operations
Data Security
Data
Integration &
Interoperability
Documents &
Content
Reference &
Master Data
Data
Warehousing
& Business
Intelligence
MetaData
Data Quality

SOME QUESTIONS MODELING CAN ANSWER
▪ To understand organizational data
• What’s important?
• Where is it? (can be may places)
• Where did it come from?
• How is it used (business processes)?
• What is the chain of custody?
• What are the business rules?
▪ Governance
• How do I identify private information?
• How long should I keep the information?
• Master Data Management classification
• Data quality
• Is it fit for purpose?
• What changed and why?

CLASSIFICATION

BUSINESS GLOSSARIES & TERMS

POLICY STATEMENT EXAMPLES (GDPR)

POLICIES RELATED TO A SPECIFIC DATA OBJECT

STANDALONE METADATA REPOSITORIES DON’T MAKE THE CUT!
▪ Metadata Repository only
• Metadata import
• Metadata Catalog (without visual
models)
• Text search & lookup
• Like the “Flat Earth Society”
▪ Fully integrated metadata and
visual models (ER/Studio)
• Global perspective & focal point for:
• Data Models, Business Process
Models
• Visual Data Lineage
• Metadata, Policies, Reference Data

FINAL THOUGHTS
▪ Replace annual resolutions with continuous, lasting change
▪ Stop the bad habits
• Management by in-flight magazine, etc.
▪ Roll up your sleeves and reap the rewards of honest, hard work
▪ Align data strategy with corporate vision, mission, goals
▪ Conduct a data maturity assessment
• Be realistic and honest about your starting point
▪ SMART metrics to measure and assess progress
▪ Data management based on enterprise architecture & modeling
• Data modeling in particular
• Your models are your maps for the journey!
• Metadata repositories without integrated modeling don’t make the cut.
▪ Don’t take on too much at once
• Start small and grow - pilot project(s) to demonstrate value
• Focus on business areas with the best returns
• Grow from there
▪ Celebrate success!
▪ Rinse & repeat.
▪ Pursue a hobby or interest that has nothing to do with your work
• Refreshes your perspective
• Makes you a more interesting person in general.

THANKS!
Any questions?
You can find me at:
ron.huizenga@idera.com
@DataAviator

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