Data-Ed Webinar: Best Practices with the DMM

Copyright 2013 by Data Blueprint
Welcome: Data Management Maturity - Achieving Best Practices using DMM
The Data Management Maturity (DMM) model is a framework for
the evaluation and assessment of an organization's data
management capabilities. The model allows an organization to
evaluate its current state data management capabilities, discover
gaps to remediate, and strengths to leverage. The assessment
method reveals priorities, business needs, and a clear, rapid path
for process improvements. This webinar will describe the DMM,
its evolution, and illustrate its use as a roadmap guiding
organizational data management improvements.
Key Takeaways:
• Our profession is advancing its knowledge and has a wide
spread basis for partnerships
• New industry assessment standard is based on successful
CMM/CMMI foundation
• Clear need for data strategy
• A clear and unambiguous call for participation 
Date: July 14, 2015 
Time: 2:00 PM ET 
Presented by: Melanie Mecca & Peter Aiken
1

Presented by Melanie Mecca & Peter Aiken, Ph.D.
Data Management Maturity
Achieving Best Practices using DMM

Your Presenters
Melanie Mecca
• CMMI Institute/ 
Director of Data 
Management  
Products and Services
• 30+ years designing and
implementing strategies and
solutions for private and public
sectors
• Architecture/Design experience in:
– Data Management Programs
– Enterprise Data Architecture
– Enterprise Architecture
• DMM primary author from Day 1
Peter Aiken
• 30+ years data mgt.
• Multiple Int. awards/recognition
• Founding Director,  
Data Blueprint (datablueprint.com)
• Associate Professor of IS (vcu.edu)
• Past, President, DAMA
International (dama.org)
• 9 books and dozens of articles
• 500+ empirical practice
descriptions
• Multi-year immersions w/
organizations as diverse as
US DoD, Nokia, Deutsche Bank,
Wells Fargo, Walmart, and the
Commonwealth of Virginia
7

• Motivation
- Are we satisfied with current performance of DM?
• How did we get here?
- Building on previous research
• What is the Data Management Maturity Model?
- Ever heard of CMM/CMMI?
• How should it be used?
- Use Cases and Value Proposition
• Where to next?
• Q & A?
Outline: Design/Manage Data Structures
8

DMMSM Structure
9
Data  
Governance
Data  
Management 
Strategy
Data  
Quality
Data  
Operations
Platform 
Architecture
Supporting 
Processes

‹#›
DMM Primer
• Reference model of foundational data management
practices
• Measurement instrument to evaluate capabilities and maturity
• Answers the question: “How are we doing?”
• Guidelines for: “What should we do next?”
• Baseline for: Integrated strategy & high-value specific
initiatives / improvements
• By CMMI Institute with our Sponsors – Booz Allen
Hamilton, Lockheed Martin, Microsoft, and Kingland
Systems - and many contributing experts
!10

‹#›
DMM Themes
• Architecture and technology neutral – applicable to legacy, DW, SOA,
unstructured data environments, mainframe-to-Hadoop, etc.
• Industry independent – usable by every organization with data assets,
applicable to every industry
• Emphasis on current state – organization is assessed on the
implemented data layer and existing DM processes
• Launch collaborative and sustained capability improvement – for the life
of the DM program [aka, forever].
If you manage data, the DMM will benefit you
11

Maslow's Hierarchy of Needs
12

Data Management Practices Hierarchy
You can accomplish Advanced
Data Practices without
becoming proficient in the
Foundational Data
Management Practices
however this will:
• Take longer
• Cost more
• Deliver less
• Present  
greater 
risk 
(with thanks to Tom DeMarco)
Advanced  
Data  
Practices
• MDM
• Mining
• Big Data
• Analytics
• Warehousing
• SOA
Foundational Data Management Practices
13Copyright 2015 by Data Blueprint Slide #
Data Platform/Architecture
Data Governance Data Quality
Data Operations
Data Management Strategy
Technologies
Capabilities

‹#›
Foundation for Business Results
• Trusted Data – demonstrated and independently measured
capability to assure customer confidence in the data assets
• Improved Risk and Analytics Decisions – a comprehensive and
measured DM strategy ensures decisions are made based on
accurate data
• Cost Reduction/Operational Efficiency – clarity about current
and target states supports elimination of redundant data and
streamlining of DM processes and data stores
• Regulatory Compliance – independently evaluated and
measured DM capabilities to meet regulator requirements and
provide a yardstick within industries.
14

• Motivation
• Where to next?
• Q & A?
Outline: Data Management Maturity
15

Motivation
• "We want to move our data management
program to the next level"
– Question: What level are you at now?
• You are currently managing your data,
– But, if you can't measure it,
– How can you manage it effectively?
• How do you know where to put time, money,
and energy so that data management best
supports the mission?
"One day Alice came to a fork in the road and
saw a Cheshire cat in a tree. Which road do I
take? she asked. Where do you want to go?
was his response. I don't know, Alice
answered. Then, said the cat, it doesn't
matter."
Lewis Carroll from Alice in Wonderland
16

DoD Origins
• US DoD Reverse Engineering
Program Manager
• We sponsored research at the
CMM/SEI asking
– “How can we measure the
performance of DoD and our
partners?”
– “Go check out what the Navy is up to!”
• SEI responded with an integrated
process/data improvement
approach
– DoD required SEI to remove the data
portion of the approach
– It grew into CMMI/DM BoK, etc.
17

Acknowledgements
version (changing data into other forms, states, or
products), or scrubbing (inspecting and manipulat-
ing, recoding, or rekeying data to prepare it for sub-
Increasing data management practice maturity levels can positively impact the
coordination of data ﬂow among organizations,individuals,and systems. Results
from a self-assessment provide a roadmap for improving organizational data
management practices.
Peter Aiken, Virginia Commonwealth University/Institute for Data Research
M. David Allen, Data Blueprint
Burt Parker, Independent consultant
Angela Mattia, J. Sergeant Reynolds Community College
s increasing amounts of data ﬂow within and
between organizations, the problems that can
result from poor data management practices
Measuring Data Management
Practice Maturity:
A Community’s
Self-Assessment MITRE Corporation: Data Management Maturity Model
• Internal research project: Oct ‘94-Sept ‘95
• Based on Software Engineering Institute Capability
Maturity Model (SEI CMMSM) for Software Development
Projects
• Key Process Areas (KPAs) parallel SEI CMMSM KPAs, but
with data management focus and key practices
• Normative model for data management required; need to:
– Understand scope of data management
– Organize data management key practices
• Reported as not-done-well by those who do it
18

‹#›
CMMI Institute Overview
• Owned by Carnegie Mellon University
• Formed & evolved from Carnegie Mellon’s Software
Engineering Institute (SEI) - a federally funded research and
development center (FFRDC)
• Continues to support and provide all CMMI offerings and
services delivered over its 20+ year history at the SEI
• Now for-profit, streamlined and focused on responding to
business & market requirements
• $10 MM business, 24 full-time employees with dedicated
training, partner and certification teams to support the
ecosystem
19

CMMI – Worldwide Process Improvement
• Quick Stats:
– Over 10,000  
organizations
– 94 countries
– 12 national  
governments
– 10 languages
– 500 Partners
– 1373  
appraisals  
in 2013
20

Source: Applications Executive Council, Applications Budget, Spend, and Performance Benchmarks: 2005 Member Survey Results, Washington D.C.: Corporate Executive Board 2006, p. 23.
Percentage of Projects on Budget
By Process Framework Adoption
…while the same pattern generally holds true for on-time performance
Percentage of Projects on Time
By Process Framework Adoption
Key Finding: Process Frameworks are not Created Equal
With the exception of CMM and ITIL, use of process-efficiency  
frameworks does not predict higher on-budget project delivery…
21

CMMI Model Portfolio
22
Establish, Manage, and
Deliver Services
Product Development /
Software Engineering
Acquire and integrate
products / supply chain
Workforce development
and management
Rearchitecting to present a more unified/modular offering

‹#›
DMM Drivers and Bio
• Data management is broad and
complex = challenging
• An effective DM program requires a
planned strategic effort – not a
Project, or a separate Program – a
lifestyle.
• Organizations needed a
comprehensive reference model to
precisely evaluate data
management capabilities
• DMM unifies understanding and
priorities of business, IT, and data
management.
• Foundation for collaborative and
sustained capability building.
Late 2009 – Gleam in the eye
Jan 2011 – Launch development
Sep 2012 – CMMI Transformation
Apr 2014 – Industry Peer Review
Aug 2014 – DMM 1.0 Released
DMM Timeline
Now–2016 – DMM Ecosystem
23

Who Wrote It and Why
• Authors with deep knowledge and experience in designing and
implementing data management
– Industry skills - MDM, DQ, EDW, BI, SOA, big data, governance,
enterprise architecture, data architecture, business and data
strategy, platform implementation, business process engineering,
business rules, software engineering, etc.
• Consortium approach – proven approaches
– Broad practical wisdom - What works
– DM experts combined with reference model architects and business
knowledge experts from multiple industries
– Extensive discussions resulting in consensus
• We wrote it for all of us
– To quickly and accurately measure where we are
– To accelerate the journey forward with a clear path  
and milestones
24

‹#›
DMM and DMBOK
CMMI Institute and DAMA International are forming a
collaborative partnership to:
• Eliminate any confusion between the two tools and highlight
their complementarity
• Extend and enhance data management training for
organizations and professionals
• Provide benefits to DAMA members (members receive a
discount for our public training classes)
• Harmonize DMM and DMBOK offerings as they develop
25

• Motivation
• Where to next?
• Q & A?
26

‹#›
You Are What You DO
• Model emphasizes behavior
• Creating effective, repeatable processes
• Leveraging and extending across the
organization
• Activities result in work products
• Processes, standards, guidelines,
templates, policies, etc.
• Reuse and extension = maximize value,
lower costs, happier staff
• Process Areas were
designed to stand alone for
evaluation
• Reflects real-world organizations
• Flexible for multiple purposes
• Whole model
• Selected Category(ies)
• Specific Process Areas
• Relationships are indicated
because operationally,
“everything is connected”
28

One concept for process
improvement, others include:
• Norton Stage Theory
• TQM
• TQdM
• TDQM
• ISO 9000 
and focus on understanding
current processes and
determining where to make
improvements.
DMM Capability Maturity Model Levels
Our DM practices are informal and ad hoc,
dependent upon "heroes" and heroic efforts
Performed
(1)
Managed
(2)
Our DM practices are defined and
documented processes performed at
the business unit level
Our DM efforts remain aligned with
business strategy using
standardized and consistently
implemented practices
Defined
(3)
Measured
(4)
We manage our data as a asset using
advantageous data governance practices/structures
 
Optimized
(5) 
DM is strategic organizational capability,
most importantly we have a process for
improving our DM capabilities
29

‹#›
DMM Capability Levels
Performed
Managed
Defined
Measured
Optimized
Level
1
Level
2
Level
3
Level
4
Level
5
Risk
Quality
Ad hoc
Reuse
Stress
Clarity
30

‹#›
Capability and Maturity Disambiguation
Capability – “We can do this”
• Specific Practices – “We’re doing it well”
• Work Products – “We’ve documented the processes we are
following” (processes, work products, guidelines, standards, etc.)
Maturity – “….and we can prove it”
• Process Stability – “Take it to the bank”
• Ensures Repeatability
• Policy
• Training
• Quality Assurance, etc.
31

‹#›
DMM Structure
Core Category
Process Area
Purpose
Introductory Notes
Goal(s) of the Process Area
Core Questions for the Process Area
Functional Practices (Levels 1-5)
rRelated Process Areas
Example Work Products
Infrastructure Support Practices
eExplanatory Model Components Required for Model Compliance
32

Maintain fit-for-purpose data,
efficiently and effectively
DMM℠ Structure of  
5 Integrated  
DM Practice Areas
33
Manage data coherently
Manage data assets professionally
Data architecture
implementation
Data lifecycle
implementation
Organizational support

DMM Process Areas 
34
Name Description
Data Management Strategy Goals, objectives, principles, business value, prioritization,
metrics, and sequence plan for the data management program
Communications

Communications strategy for data management initiatives and
mechanisms to ensure business, IT, and data management
stakeholders are aligned with bi-directional feedback
Data Management Function Structure of data management organization, responsibilities and
accountability, interaction model, staffing for data management
resources, executive oversight
Business Case Decision rationale for determining what data management
initiatives should be funded based on benefits to the
organization and financial considerations
Data Management Funding Funding justification for the data management program and
initiatives, operational and financial metrics
Create, communicate, justify and fund a unifying vision for data management

Data Governance
35
DataGovernance
GovernanceManagement Structure of data governance, governance processes and
leadership, metrics development and monitoring
BusinessGlossary Creation, change management, and compliance for terms,
definitions, and properties
Metadata Management Strategy, classification, capture, integration, and accessibility of
business, technical, process, and operational metadata
Active organization-wide participation in key initiatives and critical decisions
essential for the data assets

Data Quality
36
Data Quality

Data Quality Strategy Plan and initiatives for the data quality program, aligned with
business objectives and impacts
Data Profiling Analysis of semantic data content in physical data stores for
meaning and defect detection
Data Quality Assessment Assessment and improvement of data quality, business rules
and known issues analysis, measuring impact and costs
Data Cleansing Mechanisms to clean data, reporting and tracking of data
issues for correction with impact and cost analysis
A business-driven strategy and approach to assess quality, detect defects, and cleanse data

Platform & Architecture
Architectural Approach Architectural strategy, frameworks, and standards for implementation
planning
Architectural Standards Data standards for representation, access, and distribution
Data Management Platform Technology and capability platforms selection for data distribution and
integration into consuming applications
Data Integration Integration and reconciliation of data from multiple sources into target
destinations, standards and best practices, data quality processes at point
of entry
Historical Data, Archiving and
Retention
Management of historical data, archiving, and retention requirements
37
A collaborative approach to architecting the target state  
with appropriate standards, controls, and toolsets

Data Operations
38
Data Operations
Data Requirements Definition Process and standards for developing, prioritizing, evaluating, and
validating data requirements
Data Lifecycle Mapping of data to business processes as data flows from one process to
another
Provider Management Standardization of data sourcing process, SLAs, and management of data
provisioning from internal and external sources
Systematic approach to address business drivers and processes,  
building knowledge for maximizing data assets

Supporting Processes
39
Supporting Processes Adapted from CMMI
Measurement and Analysis Establishing and reporting metrics and statistics for each
process area within the data management program, supports
managing to performance milestones
Process Management Management and enforcement of policies, processes, and
standards, from creation to dissemination to sun-setting
Process Quality Assurance Evaluation and audit to ensure quality execution in all data
management process areas
Risk Management Identifying, categorizing, managing and mitigating business and
technical risks for the data management program
Configuration Management Establishing and maintaining the integrity of data management
artifacts and products, and management of releases
Systematic approach to address business drivers and processes,  
building knowledge for maximizing data assets

• Motivation
• Where to next?
• Q & A?
40

‹#›
Natural events for employing the DMM
• Use Cases - assess current capabilities before:
• Developing or enhancing DM program / strategy
• Embarking on a major architecture transformation
• Establishing data governance
• Expansion / enhancement of analytics
• Implementing a data quality program
• Implementing a metadata repository
• Designing and implementing multi-LOB solutions:
• Master Data Management
• Shared Data Services
• Enterprise Data Warehouse
• Implementing an ERP
• Other multi-business line efforts.
Like an Energy audit or an
executive physical
41

Assessment Components
Data Management Practice Areas
Data Management
Strategy
DM is practiced as a
coherent and
coordinated set of
activities
Data Quality
Delivery of data is
support of
organizational
objectives – the
currency of DM
Data  
Governance
Designating specific
individuals caretakers
for certain data
Data Platform/
Architecture
Efficient delivery of
data via appropriate
channels
Data Operations
Ensuring reliable
access to data
Capability
Maturity Model
Levels
Examples of practice
maturity
1 – Performed
Our DM practices are ad hoc and
dependent upon "heroes" and
heroic efforts
2 – Managed
We have DM experience and have
the ability to implement disciplined
processes
3 – Defined
We have standardized DM
practices so that all in the
organization can perform it with
uniform quality
4 – Measured
We manage our DM processes so
that the whole organization can
follow our standard DM guidance
5 – Optimized
We have a process for improving
our DM capabilities
42

Industry Focused Results
• CMU's Software  
Engineering Institute (SEI) Collaboration
• Results from hundreds organizations in
various industries including:
✓ Public Companies
✓ State Government Agencies
✓ Federal Government
✓ International Organizations
• Defined industry standard
• Steps toward defining data management
"state of the practice"
43
Data Governance
Data Quality
Data Operations
Focus:
Implementation
and Access
Focus:
Guidance and
Facilitation
Optimized(V) 
Measured(IV) 
Defined(III) 
Managed(II) 
Initial(I)

Development guidance
Data Adminstration
Support systems
Asset recovery capability
Development training
0 1 2 3 4 5
Client Industry Competition All Respondents
Data Management Practices Assessment
Challenge
Challenge
Challenge
Data Program
Coordination
Organizational Data
Integration
Data Stewardship
Data Development
Data Support
Operations
44

High Marks for IFC's Audit
45
Leadership & Guidance
Asset Creation
Metadata Management
Quality Assurance
Change Management
Data Quality
0 1 2 3 4 5
TRE ISG IFC Industry Benchmarks Overall Benchmarks

1
2
3
4
5
DataProgramCoordination
OrganizationalDataIntegration
DataStewardship
DataDevelopment
DataSupportOperations
2007 Maturity Levels 2012 Maturity Levels
Comparison of DM Maturity 2007-2012
46

Measurement = Confidence
• Activity-focused and
evidence-based evaluation
of the data management
program
• Allows organizations to gauge
their data management
achievements against peers
• Fuels enthusiasm and funding
for improvement initiatives
• Enhances an organization’s
reputation – quality and
progress
47

Starting the Journey - DMM Assessment Method
• DMM can be used as a standalone guide
• To maximize its value as a catalyst - forging shared perspective and accelerating
the program, our method:
– Provides interactive launch collaboration event with broad range of stakeholder
– Evaluates capabilities collectively by consensus affirmations
– Facilitates unification of factions - everyone has a voice / role
– Solicits key business input through supplemental interviews
– Verifies capability evaluation with work product reviews (evidence)
– Report and executive briefing presents Scoring, Findings, Observations, Strengths,
and targeted specific Recommendations.
• In the near future, audit-level rigor will be introduced to serve as a benchmark of
maturity, leveraging the CMMI Appraisal method.
To date, over 200 individuals from business, IT, and data management in early adopter organizations have
employed the DMM - practice by practice, work product by work product - to evaluate their capabilities.
48

‹#›
DMM Assessment Summary 
Sample Organization
49

‹#›
Next Step Sample – DM Roadmap
Comprehensive and Realistic Roadmap for the Journey
50

‹#›
Cumulative Benchmark – Multiple organizations
51

‹#›
Summary - Why Do a DMM Assessment?
• Engage the lines of business through education
• Tour de force – learn precisely “How are we doing?”
• Clarifies priorities – “What should we do next?”
• Industry-wide standard begets confidence
• Heals factions and silos – (i.e., improves climate for an
organization-wide program)
• Creates:
• Common concepts, perspective, and terminology
• A shared vision and purpose
• Baseline for monitoring progress over time
52

53
Establishing a Common Data Management Language
Data Management Maturity Model
Microsoft

Strategic Enterprise
Architecture
Data
Manag
ement
Operat
ions Platfor
m &
Archite
cture
Data
Quality
Data
Gover
nance
Data
Manag
ement
Strateg
y
54
CMMI Assessment Recommendations
• Unified effort to maximize data
sharing and quality
• Monitor and measure adherence to
data standards
• Top-down approach to prioritization
• Up-stream error prevention
• Common Data Definitions
• Leverage best practices for data
archival and retention
• Maximize shared services utilization
• Map key business processes to
data
• Leverage Meta Data repository
• Integrate data governance structures
• Prioritize policies, processes,
standards, to support corporate
initiatives
Microsoft

Strategic Enterprise
Architecture
▪ In the world of Devices and Services, Data Management is a pillar of
effectiveness
▪ DMM is a key tool to facilitate the Real-Time Enterprise journey
▪ Active participation of cross-functional teams from Business and IT is
key for success
▪ Employee education on the importance of data and the impact of data
management is a good investment
▪ Build on Strengths!
55
Key Lessons
Microsoft IT Annual Report may be found at:
http://aka.ms/itannualreport
Microsoft

How the DMMSM Helps the Organization
56
Gradated
path -step-
by-step
improveme
nts
Unambiguo
us practice
statements
for clear
understandi
ng
Functional
work
products to
aid
implementa
tion
Common
language
Shared
understandi
ng of
progress
Acceleration

‹#›
How the DMMSM helps the DM Professional
“Help me to help you” – education for roles, complexity,
connectedness
Integrated 360 degree program level view – launches
collaboration, increased involvement of lines of business
Actionable and implementable initiatives
Strong support for business cases
Certification path – defined skillset and industry recognition
57

‹#›
DMM Ecosystem - Product Suite Overview
• Data Management Maturity Model
o Comprehensive document with
descriptions, practice statements and
work products
o Enterprise license option
• Assessments
o Structured, facilitated working sessions
resulting in detailed current/future state
executive report
• Training & Certification
o Introductory, Advanced and Expert
courses with associated certifications
• Formal Measurement/Appraisal (2016)
o Benchmark measurement and scoring of
capability/maturity level
59

‹#›
DMM Ecosystem – Training
Results / Assets
Partner Program / Outreach
Certifications
Product Suite
DMM
Training Classes
• Building EDM Capabilities (3 days)
• eLearning Building EDM Capabilities (self-
paced, web-based) (10 hours)
• Mastering EDM Capabilities (5 days)
• Enterprise Data Management Expert (5
days)
• Future – EDM Lead Appraiser (5 days)
On-site courses available at your location
60

‹#›
DMM Ecosystem - Certifications
Certifications:
Credentials and Credibility
• Enterprise Data Management Expert
(EDME) – Assessing and Launching
the DM Journey
• DMM Lead Appraiser (DMM LA) –
Benchmarking and Monitoring
Improvements
61

‹#›
DMM Ecosystem – Partner Program
62

‹#›
DMM Ecosystem – Results and Assets
Results
• Case studies
• Best Practice Examples
• Benchmarking
• Web publication of approved
appraisals
DMM Assets
• Translations (#1 Portuguese)
• Seminars (RDA, Governance,
Quality)
• DMM Compass
• Profiles – Regulatory
• Academic Courses
• White Papers / Articles
63

• Motivation
• Where to next?
• Q & A?
64

Top
Operations
Job
Top Data Job
65
 
Top Job
 
Top 
IT 
Job
 
Top
Marketing
Job
 
Data Governance Organization
 
Top 
Data  
Job
• Dedicated solely to data asset leveraging
• Unconstrained by an IT project mindset
• Reporting to the business
• There is enough work to justify the function
and not much talent
• The CDO provides significant input to the
Top Information Technology Job
• 25 Percent of Large Global Organizations
Will Have Appointed Chief Data Officers By
2015 Gartner press release. Gartner website (accessed May 7, 2014). January 30, 2014. http://www.gartner.com/
newsroom/ id/2659215?
• By 2020, 60% of CIOs in global
organizations will be supplanted by the
Chief Digital Officer (CDO) for the delivery
of IT-enabled products and digital services
(IDC)
The Case for the
Chief Data Officer
Recasting the C-Suite to Leverage
Your MostValuable Asset
Peter Aiken and
Michael Gorman
 
Top  
Finance  
Job

The "waterfall" development model
- creates more, new data siloes
66
SoftwareData

Data is not a Project
• Durable asset
– An asset that has a usable  
life more than one year
• Reasonable project  
deliverables
– 90 day increments
– Data evolution is measured in years
• Data
– Evolves - it is not created
– Significantly more stable
• Readymade data architectural components
– Prerequisite to agile development
• Only alternative is to create additional data siloes!
67

Evolving Data is Different than Creating New Systems
68
Common Organizational Data  
(and corresponding data needs requirements)
New Organizational
Capabilities
Systems
Development
Activities
Create
Evolve
Future State
(Version +1)
Data evolution is separate from,
external to, and precedes system
development life cycle activities!

Executive Perspective
• The TDJ’s best friend
– Lines of business forge a shared
perspective
– Lines of business understand
current strengths and
weaknesses
– Lines of business understand  
their roles
– Reveals critical needs for the  
data management program
– Winning hearts and minds -
motivates all parties to
collaborate for improvements
69

For more information
• Feel free to email me:
• mmecca@cmmiinstitute.com
• And visit our web site:
• http://cmmiinstitute.com/DMM
71

Trends in Data Modeling
August 11, 2015 @ 2:00 PM ET/11:00 AM PT
Data Quality Engineering
Sepember 8, 2015 @ 2:00 PM ET/11:00 AM PT
Sign up here:
www.datablueprint.com/webinar-schedule
or www.dataversity.net
72
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