Data Maturity - A Balanced Approach

1© 2019 IDERA, Inc. All rights reserved.
DATA MATURITY – A BALANCED APPROACH
JULY 16, 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
▪ Data Ecosystem Complexity
▪ Information capability
▪ Maturity standards
▪ Maturity indicators
• Data Maturity
• Process Maturity
▪ Supporting considerations
• Data value chain
• Data lifecycle
▪ Enterprise architecture & governance
▪ Modeling specifics
▪ Summary

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

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)
▪ 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 know how to derive maximum value from information
• Source: 2015 PwC/Iron Mountain study: Seizing the Information Advantage

MATURITY STANDARDS AND BOKS!
▪ CMM: Capability Maturity Model (1988)
• Sponsored by US Department of Defense
• Carnegie Mellon University, Software Engineering Institute
• Used as the basis to derive many other standards
▪ Other Standards
• DMM: Data Maturity Model
• BPMM: Business Process Maturity Model - Object Management Group
• COBIT: Control OBjectives for Information and Technology 2000
• ITIL: Information Technology Infrastructure Library 2002
• TQM: Total Quality Management
• SPC: Statistical Process Control
▪ The Bodies of Knowledge
• DMBOK: Data Management Body of Knowledge
• BABOK: Business Analysis Body of Knowledge
• PMBOK: Project Management Body of Knowledge

WHAT IS MATURITY?
▪ Mature
• “Having reached an advanced stage of development”
▪ Organizational maturity requires:
• Data Maturity
• One cannot be achieved without the other!
▪ They are fundamental to:
• Enterprise architecture
• Governance Data
Process

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 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

Low Productivity High
Low Quality High
High Risk Low
High Waste Low
Cost cutting Efficiency Value generation
Chaos Management Leadership
Level 1 2 3 4 5
Description Initial Managed Standardized Advanced Optimized
Focus Individual: people rely on personal
methods to accomplish work
Proactive: management take
responsibility for work unit
operations and performance
Integrated: standard processes
based on best practices in work units
Stable: variation reduced - re-use,
mentoring, statistical management
Systematic: improvements
evaluated and deployed using
organizational change management
Work management Inconsistent: little or no preparation
for managing a work unit
Managed: balance commitments
with resources
Adaptable: standard processes
tailored for best use in different
circumstances
Empowered: staff have the process
data to evaluate and manage their
own work
Continual: individuals and
workgroups continuously improve
capabilities
Efficiency Inefficient: few measures for
analyzing effectiveness
Repeatable: work units use
procedures that have proven to be
effective
Leveraged: common measures and
processes. Promote organization
wide learning.
Multi-functional: advance from
functional processes to role based
business processes. (ownership)
Aligned: performance aligned across
the organization to attain strategic
objectives
Culture Stagnant: no identifiable foundation
for commitment and improvement
Responsible: work units manage
capability to meeting their own
commitments. (Silos)
Professional: organizational culture
emerges from common practices
across work units
Predictable: metrics in place to
predict capability & performance
Preventative: Systematic
elimination of defects and problem
causes
Business Process Few activities explicitly defined.
Processes lack current state
documentation.
Basic management processes and
controls established to track
progress. Processes planned,
documented, tactically performed.
Process is documented and
standardized. Cross functionality
understood.
Detailed measures of process and
output quality. Processes managed,
controlled and forecasted using
quantitative techniques (and
statistical algorithms)
Continuous process improvement
enabled by quantitative feedback.
Processes fully integrated, fluid,
highly predictable
Decision making Tribal Knowledge, gut-feel
decisions, hierarchical structure.
Functional process orientation, data
driven decisions, quality by
inspection
Integrated processes, performance
metrics, data driven decisions
Self service dashboards & analytics,
exception management
Competitive advantage through best
practice innovation.
Architecture Disparate IT systems Random services adoption Full service adoption Service Oriented Architecture (SOA) Process driven enterprise
Process Maturity

PROCESS MODEL UTILIZATION
Documentation
Business Process
Management (BPM)
Process
Improvement
Process Design
Fully Mature
(Lean, Six Sigma)
ProcessMaturity
Evolution

SUMMARY DESCRIPTORS
1 2 3 4 5
Initial Managed Standardized Advanced Optimized
Individual initiative, heroics Reactive Integrated Stable Systematic
Inconsistent Repeatable Adaptable Empowered Continuous improvement
Inefficient Responsible common processes Multi-functional Strategically aligned
Stagnant Tactical processes Documented and standardized Predictable Preventative
Lacking current state documentation Functional process orientation Integrated processes Detailed quantitative measures Quantitative feedback
Tribal knowledge Quality by inspection Performance metrics Self service analytics Fully Integrated
Gut-feel decisions Random services adoption Data driven Exception management Competitive advantage
Disparate IT systems Basic controls Full service adoption Service Oriented Architecture (SOA) Best practice innovation
Cost cutting Project monitoring Efficiency Capability management Value generation
Ad hoc Reduced rework Management Automated tactical process steps Leadership
Low alignment Typically meet schedules Automated exception reporting Flexible Full governance
Unpedictable No business architecture Business silos still exist Measured Business rules
Reactive Immature or no data architecture Data management services Controlled Organizational change management
Point-to-point interfaces Integrated master data repository Middleware (service bus) Master data stewards Optimizing
Non-integrated master data Common integration platform Canonical model Data Excellence center Data Stewardship council
Lack of standards Recognize data quality problems Model/metadata repository Prevention approch to data quality Data culture
Inconsistencies recognized Data cleansing at consumption Data quality KPI's Proactive Full data quality management
Chaotic Design patterns Some data cleansing at source Confident forecasts Predictive
Project data governance Program data governance Divisional data governance Cross divisional data governance Enterprise data governance
Organizational Maturity

ORGANIZATIONAL MATURITY JOURNEY
Not Feasible
Not Feasible

ENTERPRISE ARCHITECTURE
Enterprise Enablement
ApplicationArchitecture
BusinessArchitecture
TechnicalArchitecture
Data Architecture
Governance

ADDRESSING GOVERNANCE THROUGH MODELS
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

DATA VALUE CHAIN
Data
Data is the representation of
facts as text, numbers,
graphics, images sound or
video
Information
Definition
Format
Timeframe
Relevance
=+
Information is Data in
context. Without context,
data is meaningless.
Knowledge
Patterns & Trends
Relationships
Assumptions
=+
Knowledge is information in
perspective, integrated into
a viewpoint based upon the
recognition and
interpretation of patterns
(i.e. trends) formed with
other information and
experience.

HOW DO WE GET FROM DATA TO KNOWLEDGE
•Identify data stores
•Reverse engineer
Where is the data?
•Naming standards
•Universal mappings to link entity instances
What is it?
•Visual data lineage
•Business process models
Where did it come from and how is it used?
•Data dictionary
•Business Glossary
What does it mean?
•Reference & master data management
•Data Classification
•Security classifications
•Regulatory policies
How do I govern it?

DATA - LIFECYCLE
▪ Describes how a data element is created,
read, updated, deleted (CRUD)
▪ Many factors come into play
• Business rules
• Business processes
• Applications
▪ There may be more than 1 way a particular
data element is created
▪ Need to model:
• Business process
• Data lineage
• Data flow
• Integration
• Include Extract Transform and Load (ETL) for
data warehouse/data marts and staging areas
Create/Collect
Classify
Store
Use/ModifyShare
Retain/Archive
Destroy

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

DATA MODELING CONTEXT
Implementation Models
Categories
CustomerCustomerDemo
CustomerDemographicsCustomers
Employees
EmployeeTerritories
Order Details
Orders
Products
Region
Shippers
Suppliers
Territories
Data Warehouse
Enterprise Model(s)
Enterprise Data Dictionaries
ConceptualModels

LINK ENTITY INSTANCES (WHAT AND WHERE)
Enterprise Data Model
Implementation Model 1 Implementation Model 2
Purchase Order Header
Purchase Item
Customer Address
Address
Sales Order Header
Manufacture Item
City
Country
Sales Order Line
Supplier Address
Purchase Order Line
Vendor
Supplier
SuppliersProduct
Item
Part
Customer
Client CustomersProvince State
State / Province
Universal Mappings - Repository

HIGH LEVEL PROCESS CONTEXT

BASIC PROCESS

HOW IS THE DATA USED IN BUSINESS PROCESSES?

EXPANDED PROCESS DETAIL (NEXT LEVEL)

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

INTEGRATED MODELING, ENTERPRISE ARCHITECTURE, GOVERNANCE COLLABORATION PLATFORM
Enterprise Data
Dictionaries
Logical & Physical Data Models
Dimensional Models
Visual Data Lineage
Conceptual Data Models
Business Process Models
Goals &
Strategies
Applications
Business
Units
Business
Rules
Stewards
Business
Glossaries
Business
Concepts
Reference
Data Sets
Policies
Alerts &
Notifications
Security
Follow
Capability
Discussion
Threads
Data
Sources

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

POST FLIGHT DE-BRIEF
▪ Organizational maturity requires:
• Data Maturity
▪ Modeling is essential!
• Data modeling
• What, where
• Process modeling
• How, context
• Data lineage
• Lifecycle
• More knowledge from
• Metadata
• Business glossaries
▪ Core to enterprise architecture and governance
▪ Approach
• Continuous, incremental improvement
• Celebrate success!
• Repeat

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

Data Maturity - A Balanced Approach

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