Why Your Data Management Strategy Isn't Working (and How to Fix It)

1© 2019 IDERA, Inc. All rights reserved.
WHY YOUR DATA MANAGEMENT STRATEGY ISN'T WORKING
(AND HOW TO FIX IT)
MAY 7, 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.
PRE-FLIGHT BRIEFING
▪ Information capability
• Industry studies
• Data maturity
▪ Data management misconceptions
▪ Implementing lasting change
• The people problem
• Strategic alignment
• Architecture and governance
▪ Post Flight De-brief

▪INFORMATION CAPABILITY

COMPANIES ARE FAILING IN THEIR EFFORTS TO BECOME DATA DRIVEN
▪ The percentage of firms identifying themselves as being data-driven has
declined in each of the past 3 years
• 37.1% in 2017, 32.4% in 2018, 31.0% this year
• in spite of increasing investment in big data and AI initiatives
• Source: Harvard Business Review, Feb 5, 2019 (Randy Bean and Thomas Davenport)
▪ Survey of industry leading, large corporations
• 72% of survey participants report that they have yet to forge a data culture
• 69% report that they have not created a data-driven organization
• 53% state that they are not yet treating data as a business asset
• 52% admit that they are not competing on data and analytics
• 93% of respondents identify people and process issues as the obstacle
• The difficulty of cultural change has been dramatically underestimated
− 40.3% identify lack of organization alignment
− 24% cite cultural resistance as the leading factors contributing to this lack of business
adoption.
• Firms must become much more serious and creative about addressing the
human side of data if they truly expect to derive meaningful business benefits
• Source: 2019 Big Data and AI Executive Survey (NewVantage Partners)

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

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

▪MISCONCEPTIONS
▪ That can sabotage your Data Management Strategy

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

CLOUDED JUDGEMENT
▪ “The cloud” is not magic
• “Cloud” just means it's running on someone else's computer(s)
• AND it's exposed to the internet!
• YOU are still responsible for managing it.
▪ Cloud deployments are on the rise
• So is mismanagement of those deployments
▪ In fact, just last week *
• Cloud Database Deleted After 80 Million Accounts Details Leaked
• The 80 million households impacted make up over half of all households in the US
• The online cloud database required no password to access.
• “Put simply, many organizations don’t have the expertise to secure the data they keep on
internet-connected servers. This lapse creates opportunities for exposure of sensitive
data.”
* https://www.cloudwedge.com/news/cloud-database-deleted-after-80-million-accounts-details-leaked/

DATA MODELING AND DATA SCIENCE ARE NOT THE SAME
▪ Data Modeling
• Metadata
• Entity-Relationship modeling
• Conceptual
• Logical
• Physical
• Data Flow and Lineage
▪ Data Science
• Data Content
• Statistical modeling & analysis
• Correlation, regression, patterns
• Trends and algorithms
• Data Visualization
• Major focus on data cleansing

MACHINE LEARNING IS NOT A SUBSTITUTE FOR DATA MANAGEMENT
▪ Definition:
• Machine learning, a branch of artificial
intelligence, can be described as
systems that learn from data
• in order to make predictions, or
• to act autonomously (or semi-
autonomously) in response to what it
has learned.
• Can eliminate the need for someone
to continuously code or analyze data
themselves to solve a problem
▪ “If your data is bad, machine learning
tools are useless.”
• Thomas Redman (Harvard Business Review)
▪ “If your data is bad, machine learning
accelerates garbage-in, garbage-out
(GIGO). You simply achieve disaster
faster.”
• Ron Huizenga

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
▪ “Expert” demystified (Urban Dictionary)
• “Ex” = has been, “spurt” = drip under pressure
• Someone with a blog
• A contract liar
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”

▪OVERCOMING THE MISCONCEPTIONS
▪ Changing Behavior

FIRST AND FOREMOST, IT’S A PEOPLE PROBLEM
▪ We need to overcome human nature
• 92% of the 17 million people that try
to quit smoking each year fail.
• 95% of people who lose weight fail to
keep it off long term.
• 88% of people who set New Year’s
resolutions fail at their attempt.
• Only 10% of the population has
specific, well-defined goals, but even
then,
• seven out of the ten of those people
reach their goals only 50% of the time
▪ Two forces that motivate people:
• Avoid pain
• Gain pleasure
▪ This causes the ‘yo-yo’ pattern in some
people
• they go back and forth between taking
action to create change and losing their
drive to take any action at all.
▪ Change is never a matter of ability, it’s
a matter of motivation.
▪ Change can not be a “should”, it is a
“MUST”
Organizational change management is critical!
Some wisdom from Tony Robbins:

HOW DO WE 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

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 & ENTERPRISE ARCHITECTURE
▪ The foundation for data management & governance

DATA MANAGEMENT & GOVERNANCE STRUCTURE

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 governance
library (ER/Studio)
• Visual models and linked metadata:
• Data Models, Process Models
• Visual Data Lineage
• Metadata
• Glossaries, Policies, Reference Data

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

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?

DATA MODEL: GOVERNANCE METADATA

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

PROCESS MODELING PROVIDES CONTEXT

Corporate
Accounting
Customer
Service
Human
Resources
Marketing Sales
Supply
Chain
Governance
policies
GDPR
HIPAA
PCI
PIPEDA
SOX
MDM
catalog
Reference
Data
Master Data
DATA GOVERNANCE LIBRARY

GLOSSARIES & TERMS

GOVERNANCE POLICY CATALOG

REFERENCE DATA SET CATALOG

FINAL APPROACH
▪ Information capability in organizations is poor (and declining!)
• despite investment in AI, big data initiatives (shiny ball)
▪ We must address the human side of the equation, rather than
chasing technology
• Address people and process issues
• Organizational change management
▪ Overcome the misconceptions and face the realities
• Data governance is hard work
• Cloud deployment doesn't push the management responsibility to
someone else
• Don't confuse data architecture & modeling with data science
• Machine learning is not a substitute for data management
• Don't take the "expert" rankings and recommendations at face
value
• Don't follow the crowd
• Stop chasing the shiny ball of technology
• Eliminate the ambiguous buzzwords

POST FLIGHT DE-BRIEF
▪ Implement lasting change
• Align data strategy with corporate vision, mission, goals
• Quantifiable metrics to measure success
▪ Data architecture/enterprise architecture foundation
• Data governance library
• Integrated models/metadata/glossaries, policies
▪ Roll up your sleeves, but don’t take on too much at once
• Be realistic and honest about your starting point
• 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.

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

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