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Despite good intentions, data management strategy and practices are coming up short in the majority of organizations. This is often due to many factors that are attributable to low data maturity and misalignment with business objectives. IDERA’s Ron Huizenga will discuss a number of misconceptions and behaviors that limit effectiveness, as well as how to overcome them. This will enable you to identify and implement the necessary changes to achieve business alignment and derive maximum value from your data management strategy and practices.
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WHY YOUR DATA MANAGEMENT STRATEGY ISN'T WORKING
(AND HOW TO FIX IT)
MAY 7, 2019
Senior Product Manager, Enterprise Architecture & Modeling
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▪ Information capability
• Industry studies
• Data maturity
▪ Data management misconceptions
▪ Implementing lasting change
• The people problem
• Strategic alignment
• Architecture and governance
▪ Post Flight De-brief
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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
• 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)
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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
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INFORMATION MANAGEMENT DISPARITY
▪ Misguided Majority – 76%
• Informed but constrained
• Uninformed and ill-equipped
▪ Data seen as a byproduct, or taken
• Low comprehension of commercial
benefits that can be gained
▪ Constrained by legacy approaches,
▪ Weak analytic capability, or
• strong analytic capability, lacking
• 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
▪ 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
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LOW HIGHValue Generation
Primary IT Focus
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 Management Services
Master data stewards
ad-hoc, point to
some common tools,
lack of standards
service bus, canonical
model, business rules,
Silos, scattered data,
inconsistecies but no
management plan to
Data cleansing at
order to attempt
Data Quality KPI's and
some cleansing at source.
to data quality
Full data quality
Chaotic Reactive Stable Proactive Predictive
Introduction Expansion Transformation
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▪ That can sabotage your Data Management Strategy
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▪ Don’t try to buy it. You can’t!
▪ So stop trying to!
▪ Governance requires lots of hard
work and commitment throughout
• Culture Data
A - Z
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▪ “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
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DATA MODELING AND DATA SCIENCE ARE NOT THE SAME
▪ Data Modeling
• Entity-Relationship modeling
• Data Flow and Lineage
▪ Data Science
• Data Content
• Statistical modeling & analysis
• Correlation, regression, patterns
• Trends and algorithms
• Data Visualization
• Major focus on data cleansing
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MACHINE LEARNING IS NOT A SUBSTITUTE FOR DATA MANAGEMENT
• 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
• 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
• Ron Huizenga
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DON’T TAKE “EXPERT” REPORTS & OPINIONS AT FACE VALUE
▪ Industry analyst reports are opinions, not industry wide
• 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
▪ “Expert” demystified (Urban Dictionary)
• “Ex” = has been, “spurt” = drip under pressure
• Someone with a blog
• A contract liar
Report & Ranking
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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
• Silver bullets apply only to werewolves
▪ And stop using the meaningless
• Big Data
• Digital Transformation
strategy known as:
“Management by in-
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▪OVERCOMING THE MISCONCEPTIONS
▪ Changing Behavior
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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
• 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
• 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
Organizational change management is critical!
Some wisdom from Tony Robbins:
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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”
▪ Continuous improvement approach
• Evaluate, measure, adjust
• Rinse & repeat
• Add additional changes in small increments
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DATA STRATEGY & BUSINESS ALIGNMENT
•How will you change the
•Some day …
•What to do to accomplish the
•Specific goals and objectives
•Implement the mission
•Align data management practices
to achieve the business strategy
•Deliver, control, protect and enhance
•Plans, policies, programs, practices
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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
“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
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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
Specific: Metrics must be specific and target the area that is
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
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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
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▪DATA & ENTERPRISE ARCHITECTURE
▪ The foundation for data management & governance
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DATA MANAGEMENT & GOVERNANCE STRUCTURE
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STANDALONE METADATA REPOSITORIES DON’T MAKE THE CUT!
▪ Metadata Repository only
• Metadata import
• Metadata Catalog (without visual
• Text search & lookup
• Like the “Flat Earth Society”
▪ Fully integrated governance
• Visual models and linked metadata:
• Data Models, Process Models
• Visual Data Lineage
• Glossaries, Policies, Reference Data
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DATA ECOSYSTEM COMPLEXITY
▪ Data Models
▪ Visual Data Lineage
▪ Enterprise Data
• Naming Standards
▪ Metadata Repository
• Business Glossaries
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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?
• 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?
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DATA MODEL: GOVERNANCE METADATA
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DATA MODEL UTILIZATION
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PROCESS MODELING PROVIDES CONTEXT
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DATA GOVERNANCE LIBRARY
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GLOSSARIES & TERMS
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GOVERNANCE POLICY CATALOG
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REFERENCE DATA SET CATALOG
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▪ 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
• 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
• 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
• Don't follow the crowd
• Stop chasing the shiny ball of technology
• Eliminate the ambiguous buzzwords
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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.
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