DataEd Slides: Approaching Data Management Technologies

Peter Aiken, Ph.D.
Approaching
Data
Management
Technologies
Copyright 2019 by Data Blueprint Slide # !1
Unlocking Business Value
Peter Aiken, PhD
• DAMA International President 2009-2013 / 2018
• DAMA International Achievement Award 2001  
(with Dr. E. F. "Ted" Codd
• DAMA International Community Award 2005
Peter Aiken, Ph.D.
!2Copyright 2019 by Data Blueprint Slide #
• I've been doing this a long time
• My work is recognized as useful
• Associate Professor of IS (vcu.edu)
• Founder, Data Blueprint (datablueprint.com)
• DAMA International (dama.org)
• 10 books and dozens of articles
• Experienced w/ 500+ data
management practices worldwide
• Multi-year immersions
– US DoD (DISA/Army/Marines/DLA)
– Nokia
– Deutsche Bank
– Wells Fargo
– Walmart
– …
PETER AIKEN WITH JUANITA BILLINGS
FOREWORD BY JOHN BOTTEGA
MONETIZING
DATA MANAGEMENT
Unlocking the Value in Your Organization’s
Most Important Asset.

Unified Data Orchestration for the Cloud
Dipti Borkar |Vice President, Product | Alluxio
dipti@alluxio.com | @dborkar

4 big trends driving the need for a new architecture
Separation of
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Hybrid – Multi
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1Infogix Confidential Copyright 2019
• Innovating data solutions since 1982
• Headquartered in Chicago
• Large and mid‐size customers world‐wide:
• Organizations rely on Infogix so they can trust
their data
• Average customer tenure > 18 years
Infogix
“Industries that thrive on data”

By the end of this session, you should have a better
understanding of data management technologies in
terms of:
• Technology Considerations
• Data Technology Architecture
• CASE Tools
• Repositories
• Profiling/Discovery Tools
• Data Quality Engineering Tools
• Data Life Cycle
• Other Technologies:
– Servers, EII Technologies, Portals, Conversion Tools
Approaching Data Management Technologies
Transform
4
Problems with forklifting
1. no basis for decisions
made
2. no inclusion of
architecture/engineering
concepts
3. no idea that these
concepts are  
missing from  
the process
4. 80% of organizational
data is ROT
Less
Cleaner
More shareable  
... data
Making Cloud Successful
Copyright 2019 by Data Blueprint

Gartner Strategic Planning Assumptions
• By 2021
– Strategy using data hubs, lakes and warehouses will support 30%
more use cases (capabilities) than competitors.
• By 2022
– 50% of cloud decisions based on data assets provided rather than
on the product capabilities.
– Active metadata will reduce time to data delivery by 30%.
• By 2023
– AI-enabled automation will reduce  
the need for IT specialists by 20%.
– 75% of all databases will be cloud, reducing the DBMS vendor
landscape and increasing complexity for data governance and
integration.
https://www.gartner.com/document/3894971?ref=solrAll&refval=219836558&qid=de595a5685b6f86db0ec6
Gartner Cloud Vendor Offerings
CSP-specific data assets may be of interest when combined
with easy access, becoming a key differentiator.  
For example:
• Google:
– Google Search data
– YouTube data
– Google Ads data
– Retailers.
• Azure
– LinkedIn
– Office 365 data
– Sales and customer-relationship-focused analytics

Core Data Management Capabilities
 
 
 
UsesUsesReuses
What is data management?
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

Standard data
Data supply
Data literacy
Making a Better Data Sandwich
Data literacy
Standard data
Data supply
Standard data
Data supply
Data literacy

Standard data
Data supply
Data literacy
This cannot happen without engineering and architecture!
Quality engineering/ 
architecture work products  
do not happen accidentally!
Technologies by themselves, are a One Legged Stool

Success Requires a 3-Legged Stool
People
Process
Technology
People
Process
Technology

• as opposed to mobile
device management
• MDM is a discipline or
strategy
– "… where the business
and the IT organization  
work together to ensure
the uniformity, accuracy,  
semantic persistence,
stewardship and
accountability  
of the enterprise's official,
shared master data."
• Sold as technology-
based solution
Definitions

Master Data Architecture
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
• Technology-first
approaches often de-
emphasize the  
people and process
components
• Successful MDM also
requires
– Governance/quality
– Process architecture
Tools and Methods 
Are Required!

Growth of Data vs. Growth of Data Analysts
• Stored data accumulating at  
28% annual growth rate
• Data analysts in workforce  
growing at 5.7% growth rate
Supply/demand for data talent
https://www.logianalytics.com/bi-trends/3-keys-understanding-data/
R. Buckminster Fuller

https://en.wikipedia.org/wiki/Moore%27s_law#/media/File:Moore%27s_Law_Transistor_Count_1971-2016.png
Postpone technology investments  
as long as possible
The hardest part of
requirements is not
doing design
Vendor Hype
• CIOs/CDOs feel pressure
• Vendor/project promise auditing
• No understanding of hype curve

Who wrote this … ?
• In considering any new subject,
• there is frequently a tendency
first to overrate what we find to
be already interesting or
remarkable, and
• secondly - by a sort of natural
reaction - to undervalue the true
state of the case.
– Lady Augusta Ada King,
(1815 – 1852) 
Countess of Lovelace
– (aka) Ada Lovelace,  
daughter of Lord Byron
– Publisher of the first  
computing program

Gartner Five-phase Hype Cycle
http://www.gartner.com/technology/research/methodologies/hype-cycle.jsp
Technology Trigger: A potential technology breakthrough kicks things off. Early proof-of-concept stories and media interest
trigger significant publicity. Often no usable products exist and commercial viability is unproven.
Trough of Disillusionment: Interest wanes as experiments and implementations fail to deliver. Producers of the
technology shake out or fail. Investments continue only if the surviving providers improve their products to the
satisfaction of early adopters.
Peak of Inflated Expectations: Early publicity produces a number of
success stories—often accompanied by scores of failures. Some
companies take action; many do not.
Slope of Enlightenment: More instances of how the technology can benefit the
enterprise start to crystallize and become more widely understood. Second- and third-
generation products appear from technology providers. More enterprises fund pilots;
conservative companies remain cautious.
Plateau of Productivity: Mainstream adoption starts to
take off. Criteria for assessing provider viability are more
clearly defined. The technology’s broad market
applicability and relevance are clearly paying off.
Hype Cycle for Data Management

Hype Cycle for Information Governance and Master Data Management
Hype Cycle for Analytics and Business Intelligence

terms of:
• CASE Tools
• Repositories
• Data Life Cycle
Data Management Technologies
• Managing data technology should follow the same
principles and standards for managing any
technology
• Leading reference model for technology
management is the Information Technology
Infrastructure Library (ITIL):
http://www.itil-officialsite.com/home/home.asp

Understanding Data Technology Requirements
Need to understand:
• How the technology works
• How it provides value in the  
context of a particular business
• Requirements of a data technology before determining
what technical solution to choose for a particular situation
Suggested questions:
• What problem does this data technology mean to solve?
• What sets this data technology apart from others?
• Are there specific hardware/software/operating systems/
storage/network/connectivity requirements?
• Does this technology include data security functionality?
Defining Data Technology Architecture
• Data technology is part of the overall technology
architecture
• It is also often considered part of the enterprise’s data
architecture
• Data technology architecture addresses 3 questions:
1. What technologies are  
standard/required/preferred/acceptable?
2. Which technologies apply to which  
purposes and circumstances?
3. In a distributed environment, which  
technologies exist where, and  
how does data move from one node to another?

terms of:
• CASE Tools
• Repositories
• Data Life Cycle
Computer-aided software engineering (CASE)
is the scientific application of a set of tools and
methods to a software system which is meant
to result in high-quality, defect-free, and
maintainable software products. It also refers
to methods for the development of information
systems together with automated tools that
can be used in the software development
process.
CASE Tools
Computer Aided Software/ 
Systems Engineering Tools
• Scientific application of a set of tools and
methods to a software system which is
meant to result in high-quality, defect free,
and maintainable software products
• Refers to methods for the development of
information systems together with
automated tools that can be used in the
software development process
• CASE functions include analysis, design,
and programming
Source: http://en.wikipedia.org/wiki/

CASE-based Support
http://www.visible.com
CASE-based Support

CASE-based Support
CASE Tool Evolution
• Microsoft
– Excel
– Powerpoint
– Visio
• ERwin ER/Studio
• Rational Rose
• Open source
– It is never free. Even open-sourced  
technology requires care and feeding
• List of CASE Tools
– http://www.unl.csi.cuny.edu/faqs/software-enginering/tools.html

Figure 18.2 Sample
budget for
implementing a $2500/
seat CASE technology
can be $2.5 million
over a 5-year period
[adapted from Huff "Elements of a Realistic
CASE Tool Adoption Budget" © 1992
Communications of the ACM]
$187K =
$2500/seat
× 75 seats
$360K = training
$500K = workstations
$150K= assessment costs
$910K = total initial investment
$150K = in-house support
$ 55K = hardware and software maintenance
$ 60K = ongoing training and misc.
$265K = annual additional investment
× 5 years
$1325K investment over 5 years
CASE Tool: "Taxonomy"
This includes
• Senders
– flows from the CASE effort that
can inform the re-architecting
effort.
• Receivers
– flows from the project that can
inform the CASE effort.
• Senders and receivers
– some elements, such as
restructuring and reengineering,
are both senders and receivers.

A variety of
CASE-based
methods and
technologies can
access and
update the
metadata
metadata
Integration
Additional metadata uses  
accessible via: web; portal;  
XML; RDBMS
Everything must "fit" into one
CASE technology
Changing Model of CASE Tool Usage
Limited access
from outside
the CASE
technology
environment
CASE  
tool-specific  
methods  
and  
technologies
Limited additional 
metadata use
terms of:
• CASE Tools
• Repositories
• Data Life Cycle

The Biggest Challenges to Data Management Practice
One Eighth of the Data Management Spend
• Metadata management
is still a nascent
discipline that only
represents 12% of the
time spent in data
management
88%
12%
Metadata

Repositories have been difficult to "sell"
21 September 1999
Michael Blechar, Lisa Wallace
Management Summary
Most executive and IS managers view an IT metadata repository as an
esoteric technology that is not directly related to the business.
However, as will be seen, an IT metadata repository can substantially
help IS organizations support the applications, which in turn support
the business. An IT metadata repository is a pre-built system and
reference database where the IS organizations can track and manage
the information about the applications and databases they build and
maintain; think of it as the inventory and change impact reporting
system for IS. These repositories track metadata such as the
descriptions of jobs, programs, modules, screens, data and
databases, and the interrelationships between them. Metadata differs
from the actual data being described. Metadata is information about
data. For example, the metadata descriptions in the repository tell one
that the field "customer number" appears in Databases A, B and F ...
[From gartner.com]
What tools do you use?
45%
23%
13%
9%
7%
2%
1% 1% 1% 1%
None HomeGrown Other CA Platinum Rochade Universal
Repository
DesignBank DWGuide InfoManager Interface
Metadata
Tool
• Almost one in four organizations
(23%) is building their own
Repository Technologies in Use
Number Responding=181
• Almost one in two organizations
(45%) doesn't use
• The "traditional" players are 16%

Metadata Repositories 2004
"However, due to
cost (these tools
start at about
$150,000, but
frequently exceed
$1 million) and
being slow to
market in terms of
support for new
service-oriented
architectures
(SOAs), CA and
ASG have opened
the door to smaller
competitors"
Magic Quadrant for Metadata Management Solutions

IBM's AD/Cycle Information Model
https://wiscorp.com/kwf_diagram.html

Implementing Metadata Repository Functionality
• "The repository" does not have to be an integrated
solution
– it must be an easily integrateable solution
• Repository functionality (does not equal a) repository
– metadata must easily evolve to repository solution
• Multiple repositories are not necessarily bad
– as interim solutions, Excel has been working quite well
• Minimal functionality includes
• ability to create, read, update, delete, and evolve metadata items
• Remember the 1st law of data management
– In order to manage metadata, you need metadata repository
functions
terms of:
• CASE Tools
• Repositories
• Data Life Cycle

Time Spent by Data Management Teams Across Disciplines
Data Discovery Technologies
• Data analysis software technologies deliver up to 10X
productivity over manual approaches
• Based on a powerful computing technology that allows data
engineers to quickly form candidate hypotheses with respect to
the existing data structures
• Hypotheses are then presented to the SMEs (both business and
technical) who confirm, refine, or deny them
• Allows existing data structures to be inferred at rate that is an
order of magnitude more effective than previous manual
approaches
• Pioneers include Evoke->CSI, Metagenix->Ascential->IBM,
Sypherlink
Profiling 
Discovery  
Analysis

How has this been done in the past?
Old
• Manually
• Brute force
• Repository
dependent
• Quality
indifferent
• Not repeatable
New
• Semi-automated
• Engineered
• Repository
independent
• Integrated quality
• Repeatable
• Currency
• Accuracy
Select an Attribute to
get a list of values
Double-click a value to
see rows with that value

Reactive  
 
 
 
 
 
 
Proactive
Comparing Weekly Progress
Monday
Morning: 
Model 
preparation
Afternoon: 
Model refinement/
validation session
Tuesday
Morning: 
Model refinement/
validation session
Afternoon: 
Model refinement/
validation session
Wednesday
Morning: 
Model 
preparation
Afternoon: 
Model refinement/
validation session
Thursday
Morning: 
Model refinement/
validation session
Afternoon: 
Model refinement/
validation session
Friday
Morning: 
Model 
preparation
Afternoon: 
Model refinement/
validation session
Monday
Morning: 
Model  
preparation
Afternoon: 
Model  
preparation
Tuesday
Morning: 
Model  
preparation
Afternoon: 
Model refinement/
validation session
Wednesday
Morning: 
Model  
preparation
Afternoon: 
Model  
preparation
Thursday
Morning: 
Model  
preparation
Afternoon: 
Model refinement/
validation session
Friday
Morning: 
Model  
preparation
Afternoon: 
Model  
preparation
57
 
Baseline 
 
Relative
Condition
& 
Amount
of 
Evidence
[ ]
Confounding
characteristics 
 
Data Handling,
Operating
Environment  
&
Language
Factor 
 
(Factor => 1)
[ ][
Beneficial
characteristics
 
 
Key End User
Participation &
Net Automation
Impact 
 
(Impact =<1)
]
Historical
organizational
reverse
engineering
performance data
[ ]
=
Project
characteristics
"The purpose of the Preliminary System Survey is to determine how long and how  
many resources will be required to reverse engineer the selected system components."
[ ]Project
characteristics
=
Project
Estimate
Preliminary System Survey (PSS)
58

terms of:
• CASE Tools
• Repositories
• Data Life Cycle
Data Quality Engineering Tools
• 4 categories of activities:
1. Analysis
2. Cleansing
3. Enhancement
4. Monitoring 
 
 
 
 
 
 
 
 
• Principal tools:
1. Data Profiling
2. Parsing and
Standardization
3. Data Transformation
4. Identity Resolution and
Matching
5. Enhancement
6. Reporting

DQ Tools
1. Data Profiling
– Need to be able to distinguish between good and bad data before
making any improvements
– Data profiling is a set of algorithms for 2 purposes:
• Statistical analysis and assessment of the data quality values within a data set
• Exploring relationships that exist between value collections within and across data
sets
DQ Tools
2. Parsing & Standardization
– Data parsing tools enable the definition of patterns that feed into a
rules engine used to distinguish between valid and invalid data values
– Actions are triggered upon matching a specific pattern
– When an invalid pattern is recognized, the application may attempt to
transform the invalid value into one that meets expectations
https://www.youtube.com/watch?v=r9UhJxFT5rk

DQ Tools
3. Data Transformation
– Upon identification of data errors, trigger data rules to transform the flawed
data
– Perform standardization and guide rule-based transformations by mapping
data values in their original formats and patterns into a target
representation
– Parsed components of a pattern are subjected to rearrangement,
corrections, or any changes as directed by the rules in the knowledge base
DQ Tools
4. Identify Resolution &
Matching
– Basic approaches to matching:
– Deterministic
• Relies on defined patterns and rules for
assigning weights and scores to
determine similarity
– Predictable
• Only as good as anticipations of the rules
developers
– Probabilistic
• Uses statistical techniques to assess
probabilities that pairs of records
represent the same entity
– Not reliant on rules
• Refined based on experience -> matchers
can improve precision as more data is
analyzed

DQ Tools
5. Enhancement
– A method for adding value to information by accumulating additional
information about a base set of entities and then merging all the
sets of information to provide a focused view
Examples:
– Time/date stamps
– Auditing information
– Contextual information
– Geographic information
– Demographic information
– Psychographic information
DQ Tools
6. Reporting
• Good reporting supports:
– Inspection and monitoring of conformance to data quality
expectations
– Monitoring performance of data stewards conforming to data quality
SLAs
– Workflow processing for data quality incidents
– Manual oversight of data cleansing and correction
• Associate report results w/:
– Data quality measurement
– Metrics
– Activity

terms of:
• CASE Tools
• Repositories
• Data Life Cycle
Data acquisition activities Data usage activitiesData storage
Traditional Data Life Cycle

DataLifeCycleModel
Metadata
Creation
Data
Assessment
MetadataRefinement
DataRefinement
Data
Manipulation
DataCreation
Data
Utilization
Metadata
Structuring
Data Storage
Metadata Data
Dimension Focus/Phase: Refinement Creation Structuring Creation Manipulation Refinement Utilization Assessment
Data
Architecture
Quality
Data architecture quality
is the focus of metadata
creation & refinement
efforts.
↵ ↵ ↵
Data Model
Quality
Data model quality is the
focus of metadata
refinement & structuring
efforts
↵ ↵ ↵
Data Value
Quality
Data value quality is the
focus of the data
creation, manipulation,
and refinements phases.
↵ ↵ ↵ ↵
Data
Representation
Quality
Data representation
quality is the focus of
data utilization phase.
↵ ↵
Dimensions Related to Phases
• Data architecture quality is the focus of metadata creation and refinement efforts.
• Data model quality is the focus of metadata structuring efforts
• Data value quality is the focus of the data creation, manipulation, and refinements phases.
• Data architecture and model quality are the focus of metadata refinement efforts.
• Data representation quality is the focus of data utilization and assessment phase.

terms of:
• CASE Tools
• Repositories
• Data Life Cycle
Other Technologies
Data Integration Definition:
• Pulling together and reconciling dispersed data for
analytic purposes that organizations have maintained in
multiple, heterogeneous systems. Data needs to be
accessed and extracted, moved and loaded, validated
and cleaned, standardized and transformed.
• Other tools include:
– Servers
– EII technologies
– Portals
– Conversion tools
Source: http://www.information-management.com

Portal Options
[Adapted from Terry Lanham Designing Innovative Enterprise Portals and Implementing Them Into Your Content Strategies Lockheed
Martin’s Compelling Case Study Web Content II: Leveraging Best-of-Breed Content Strategies - San Francisco, CA 23 January 2001]
Legacy Systems Transformed Into Web-services Accessed Through a Portal
Organizational Portal
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Web 
Service 5.2
Web 
Service 5.3

Top Tier Demo
Portals as a Data Quality Tool

Defining Spaces
• ETL Extract Transform, Load
– delivers aggregated data to a  
new database
• EAI Enterprise Application Integration
– connects applications to other applications in a
predictable manner using  
pre-established connections
• EII Enterprise Information Integration
– between ETL and EAI - delivers tailored views of
information to users at the time that it is required
Meta-Matrix Integration Example

understanding of data management technologies and their
use as part of a people process & technology 3-legged stool
in terms of:
• CASE Tools
• Repositories
• Data Life Cycle
Gartner Key Findings
• Data assets continue to drive strategic cloud service
providers’ offerings
• Machine learning is increasingly popular–key uses:
– Data integration tools,
– Database management systems,
– Data quality tools and
– Metadata management solutions
• Increasing use of cloud for production applications
requiring that database in the cloud
• Organizations applying a combination of data warehouses,
data lakes and data hubs can achieve greater flexibility to
support a range of use cases compared to those applying
only one.

+ =
Questions?
It’s your turn!  
Use the chat feature or
Twitter (#dataed) to submit
your questions now!
IT Business
Data
Perceived State of Data

Data
Desired To Be State of Data
IT Business
The Real State of Data
Data
IT Business

It isn't possible to go digital
Digital
aBy just spelling 'data'
Dat

It requires more work
Data
a
Lady Ada Augusta King Rule
https://people.well.com/user/adatoole/bio.htm

Recent Technology Realization
GarbageIn➜
GarbageOut!Recent
GI➜GO!
Perfect  
Model
Garbage  
Data
Garbage  
Results
Data
Warehouse
Machine
Learning
Business
Intelligence
Block ChainAIMDM
Data
Governance
AnalyticsTechnology

GI➜GO!
Perfect  
Model
Garbage  
Data
Garbage  
Results
Data
Warehouse
Machine
Learning
Business
Intelligence
Block Chain
AI
MDM
Analytics
Technology
Data
Governance
Quality In ➜ Quality Out!
Perfect  
Model
Quality  
Data
Good  
Results
Data
Warehouse
Machine
Learning
Business
Intelligence
Block Chain
AI
MDM
Analytics
Technology
Data
Governance

More Data Management Tools
More Data Management Tools

Gartner Recommendations
Leaders seeking to advance their strategies and deliver effective solutions
must:
• Validate both product capabilities and data availability
• Leverage automation to free up scarce specialist resources
• Update policies and governance  
standards to address cloud and  
database platform as a service  
(dbPaaS) before purchasing
• Favor providers with a clear  
roadmap for ML enabling better  
business outcomes or service levels
• Don’t expect a single piece of  
infrastructure to meet all of your  
needs. Plan the core by  
assessing use case type,  
processing flexibility and  
semantic requirements
Upcoming Events
May Webinar 
Data Management Maturity:  
Achieving Best Practices using DMM 
May 14, 2019 @ 2:00 PM ET
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Achieving Best Practices using DMM 
June 11, 2019 @ 2:00 PM ET
 
Sign up for webinars at:  
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DataEd Slides: Approaching Data Management Technologies

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