DAMA International Webinar: 10 Keys to World-Class Metadata Management20150616 meta data dataversity 1 hr webinar

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© 2015 Enterprise Warehousing Solutions, Inc. (EWSolutions) – 1
EWSolutions
Strategic Partner & Systems Integrator
Intelligent Business Intelligencesm
10 Keys to World-Class
Meta Data Management
By David Marco
President
EWSolutions

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Contact us at info@EWSolutions.com
EWSolutions’ Background
EWSolutions is a Chicago-headquartered strategic partner and full life-cycle
systems integrator providing both award winning strategic consulting and full-
service implementation services. This combination affords our clients a full
range of services for any size enterprise information management, meta data
management, data governance and data warehouse/business intelligence
initiative. Our notable client projects have been featured in the Chicago Tribune,
Federal Computer Weekly, Journal of the American Medical Informatics
Association (JAMIA), Crain’s Chicago Business, and won the 2004 Intelligent
Enterprise’s RealWare award, 2007 Excellence in Information Integrity Award
nomination and DM Review’s 2005 World Class Solutions award.
For more information on our Strategic Consulting Services, Implementation Services,
or World-Class Training, email us at info@EWSolutions.com or call at 630.920.0005
Best Business Intelligence Application
Information Integration
Client: Department of Defense
World Class
Solutions Award
Data Management
2007 Excellence in Information
Integrity Award Nomination

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EWSolutions’ Partial Client List
For more information on our Strategic Consulting Services, Implementation Services,
or World-Class Training email us at Info@EWSolutions.com
AFLAC
Arizona Supreme Court
Bank of Montreal
BankUnited
Basic American Foods
Becton, Dickinson and Company
Blue Cross Blue Shield companies
Booz Allen Hamilton
Branch Banking & Trust (BB&T)
British Petroleum (BP)
California DMV
California State Fund
Canadian National Railway
Capella University
Cigna
College Board
Comcast
Corning Cable Systems
Countrywide Financial
Defense Logistics Agency (DLA)
Delta Dental
Department of Defense (DoD)
Driehaus Capital Management
Eli Lilly and Company
Environment Protection Agency
Farmers Insurance Group
Federal Aviation Administration
Federal Bureau of Investigation (FBI)
Fidelity Information Services
Ford Motor Company
GlaxoSmithKline
Harbor Funds
Harris Bank
The Hartford
Harvard Pilgrim HealthCare
Health Care Services Corporation
Hewitt Associates
HP (Hewlett-Packard)
Information Resources Inc.
International Paper
Janus Mutual Funds
Johnson Controls
Key Bank
LiquidNet
Loyola Medical Center
Manulife Financial
Mayo Clinic
McDonalds
Microsoft
MoneyGram
NASA
National City Bank
Nationwide
Neighborhood Health Plan
NORC
Physicians Mutual Insurance
Pillsbury
Quintiles
Sallie Mae
Schneider National
Secretary of Defense/Logistics
Singapore Defense Science & Technology
Agency
Social Security Administration
South Orange County Community College
Standard Bank of South Africa
SunTrust Bank
Target Corporation
The Regence Group
Thomson Multimedia (RCA)
Thrivent Financial
United Health Group
United Nations (ICAO)
United States Air Force
United States Army
United States Department of State
United States Navy
United States Transportation Command
University of Michigan
University of Wisconsin Health
USAA
US Cellular
Waste Management
Wells Fargo
Wisconsin Department of Transportation
Zurich Cantonal Bank

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David Marco – Professional Profile
Best known as the world’s foremost authority on meta data management and the father of the
Managed Meta Data Environment, he is an internationally recognized expert in the fields of data
governance, big data, data warehousing, master data management and enterprise information
management (EIM). In 2004 David Marco was named the “Melvil Dewey of Metadata” by Crain’s
Chicago Business as he was selected to their very prestigious “Top 40 Under 40” list. David
Marco has authored several books including the widely acclaimed “Universal Meta Data Models”
(Wiley, 2004) and the classic “Building and Managing the Meta Data Repository: A Full Life-
Cycle Guide” (Wiley, 2000).
 2014 EWSolutions was inducted into the Hinsdale business Hall-of-Fame after 6
consecutive years of receiving “Best of” awards in Enterprise Information
Management
 Selected to the prestigious 2004 Crain’s Chicago Business “Top 40 Under 40”
 2008 DAMA Data Management Hall of Fame (Professional Achievement Award)
 2007 DePaul University named him one of their “Top 14 Alumni Under 40”
 Presented hundreds of keynotes/seminars across four continents
 Published hundreds of articles on information technology
 Author of several best selling information technology books
 Taught at the University of Chicago and DePaul University
 Holds both a CDMP and a CBIP certification
Email: DMarco@EWSolutions.com

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Agenda
 Meta Data Management Fundamentals
 Business vs. Technical Meta Data
 Meta Data Management Use Cases
 Meta Data Return on Investment (ROI)
 Managed Meta Data Environment (MME)
Fundamentals
 Top 10 List
 Real-World Case Studies

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Meta Data
Management
Fundamentals

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Meta Data vs. Data
 Meta Data: Meta data contains the knowledge that
a 1) field is called “Customer_Name”, is 40
characters in length, and exists in systems A, B, and
C; 2) that our company has 3 systems which
contain customer master data. These systems
are…
 Data: Data would be a specific instance of
“Customer_Name” equaling “John Doe”
 Information: Data that is meaningful to a business
user. They understand it and they know what to do
with it

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Meta Data Management Fundamentals
(content) (context)

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Managed Meta Data
Environment ROI

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Managed Meta Data Environment ROI
“The key to your company’s prosperity is how well
you gather, retain and disseminate knowledge”
“Managed meta data environments are the key to
gathering, retaining and disseminating knowledge”

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 Meta Data for the Business (business meta
data)
 Meta Data for the IT Department (technical
meta data)

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Business Meta Data

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Meta Data for the Business
 Reduces training costs
 Makes strategic information (e.g. data warehousing,
CRM, SCM, etc.) much more valuable as it aids
analysts in making more profitable decisions
 Create actionable information
 Limits incorrect decisions
 Assists business analysts in finding the information
they need, in a timely manner
 Bridges the gap between business users and IT
professionals
 Increases confidence in the IT system data

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Meta Data Providing the
Semantic Layer

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2015 Monthly Global Sales Report February 7, 2015
Month Sales $ (in thousands)
U.S
Sales $ (in thousands
International
Product Category Sales $ (in thousands
Total
December TV 22,101
DVD
12,190
Digital 4,002
1,209Miscellaneous
Cellular Phone
11,190
10,200
7,193
1,301
870
4,300
32,301
19,383
5,303
2,079
15,490
November TV 42,000
DVD
28,193
Digital 8,901
2,730Miscellaneous
Cellular Phone
21,190
22,200
12,193
2,901
1,530
9,878
64,200
40,386
11,802
4,260
31,068
October TV 70,100
DVD
41,700
Digital 20,000
4,850Miscellaneous
Cellular Phone
31,900
32,950
17,550
4,100
2,850
14,878
103,050
59,250
24,100
7,700
46,778
“Sales $ U.S.” is comprised of
aggregated sales revenues from the
United States, Canada, and Mexico,
but does not subtract sales dollars
from returned orders

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2015 Monthly Global Sales Report February 7, 2015
Month Sales $ (in thousands)
U.S
Sales $ (in thousands
International
Product Category Sales $ (in thousands
Total
December TV 22,101
DVD
12,190
Digital 4,002
1,209Miscellaneous
Cellular Phone
11,190
10,200
7,193
1,301
870
4,300
32,301
19,383
5,303
2,079
15,490
November TV 42,000
DVD
28,193
Digital 8,901
2,730Miscellaneous
Cellular Phone
21,190
22,200
12,193
2,901
1,530
9,878
64,200
40,386
11,802
4,260
31,068
October TV 70,100
DVD
41,700
Digital 20,000
4,850Miscellaneous
Cellular Phone
31,900
32,950
17,550
4,100
2,850
14,878
103,050
59,250
24,100
7,700
46,778
Information Quality Tracking Statistics
8.4% of the dollar values were not loaded
1.7% of the records were not loaded

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Technical Meta Data

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Meta Data for the IT (Information Technology) Department is
Technical Meta Data
 Help IT departments better manage, maintain and grow their
IT systems and assets

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What Do Our Current IT
Systems Look Like?

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Islands of Data
Operational
Applications
Islands of
Data
End User
Reporting
Systems
Claims
Pharmacy
Financial Analysis
Health & Medical
Services (CQM, HSA)
Actuarial & Underwriting
Analysis
Sales & Mktg /
Network Development
Utilization
Membership/Enrollment &
Billing
Standard
Adhoc
Standard
Adhoc
Standard
Adhoc
Standard
Adhoc
Standard
Adhoc
Standard
Adhoc
Standard
Adhoc
Standard
Adhoc
North E/B
TOPPS
ES-9000
IPS- Inter Practice System
VAX(Burlington)
PSIMED
HPE-980
MGD Claims
AS/400
Query
Pharmacare (outside vendor)
AS/400
Pharmaview (outside vendor)
NED E/B
ACPS- Automatic Claims
Processing SystemPASS - Patient Appointment
Scheduling SystemENCOUNTER
FFS - Fee For Service
REF - Referrals
HP-992 NEDClinical Computer
Pharmacy DB
Provider Master
AMISYS
MARS
•Hospital Summary
•Quality Assurance Request
HP (9 series)
•Network & Medical Request
•Actuarial End User Request
•Claims
Prov DB
Drug DB
AMRS-Advanced Medical
Record SystemRIS- Radiology Information
SystemLAB/LIS- Laboratory
Information System
VAX Cluster
RX42
HPE-980
MHUM - Mental Health
Utilization Management
AST 486 server
Healthchex
Pentium PC
Bulletin Board System (BBS)
NED Datawarehouse
HP937
HSA dept. Lan Server
NED Multiview GL and AP
PHC Pharmacy Server
PHC Actuarial Analysis Server
Analytic Database
Quantum
Dec Alpha
MAMSYS/PAPSYS
PHC JV Finance Server
PHC Network Development Server
PHC Medical Services Server
PHC Multi-view GL.
(14) Foxpro Applications
ASAP - Actuarial System
Analysis Program
MS SQL Server
DB2 and SAS dataset
Decision Analyzer
GL
SAS , SAS screens,
Viewpoint
GMIS-Claim Check
Actuarial SAS dataset
ES-9000
Medical
Groups
HCD MGD NED PHC
Legend Multiple colors indicate the system is
used by multiple divisions.

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Meta Data for the IT Department
 Dramatically reduces the probability of project failure
 Speeds system’s time-to-market
 Reduce system development life-cycle time
 Limit redundant data
 Limit redundant processes
 Managing IT portfolios
 Leverage work done by other teams
 Reduced rework
 Reduce research time
 Reduce unproductive work
 Lowers the impact of staff turnover

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Technical
Impact Analysis

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Impact Analysis Report January 7, 2015
Impact Field Tables/Files
Impacted
Fields ImpactedProgram Impacted
Customer_Name CUSTOMER_PR02 DW_CUSTOMER T
I02_CUSTOMER
Cust_Name_First
Cust_Name_Middle
Cust_Name_Last
Source
System
Order Entry
Table
Type
I Cust_Name_First
Cust_Name_Middle
Cust_Name_Last
CUSTOMER_PR01
Source
Table
CUST
Customer_Addr DW_CUSTOMER T
I02_CUSTOMER
Cust_Name_Address
Cust_Name_City
Cust_Name_State
I Cust_Name_Address
Cust_Name_City
Cust_Name_State
CUSTOMER_PR02
CUSTOMER_PR01
Cust_Name_Zip
Cust_Name_Zip
Question: Show all decision support tables/files, programs, and fields impacted by a change to the
“CUST” table in the “Order Entry” system
*Legend
“T” = Target
“I” = Intermediate
“S” = Source

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Impact Analysis Report January 7, 2015
DomainTables/Files Fields
Alphanumeric 20
DW_CUSTOMER
Order Entry
ORDER_HEADER
CUST_NAME
CUST_NAME
CUST_NAME
Field
Customer Name
System
CUST_NAME
CUST_NAME
Cust_Name
EXPENSES
I01_CUSTOMER
Cust_Name
Cust_Name
Cust_Name
Cust_Name
DM_Cust_Name
DM_Cust_Name
Cust_Name
DM_Cust_Name
Question: Show all systems, tables/files, fields, and their domains impacted by a change to the length of
all occurrences of the Customer_Name field
General Ledger
Data Warehouse
Data Mart - Marketing
Alphanumeric 20
Alphanumeric 20
Alphanumeric 20
Alphanumeric 20
Alphanumeric 20
Alphanumeric 20
Alphanumeric 20
I02_CUSTOMER
I03_CUSTOMER
DM_CUSTOMER
I01_DM_CUSTOMER
I02_DM_CUSTOMER
CUST_ACCOUNTS
CUSTOMER
ORDER_DETAIL
CUSTOMER_SHIP_TO
CUSTOMER_SELL_TO
CUSTOMER_BILL_TO
Cust_Name
Alphanumeric 20
Alphanumeric 20
Alphanumeric 20
Alphanumeric 20
Alphanumeric 35
Alphanumeric 35
Alphanumeric 35

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MME For Systems
Consolidation

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August 15, 2015Systems Consolidation Report
Small Town BankBigCity Bank
Attribute
Name
Attribute
Definition
Entity
Name
System
Name
Attribute
Name
Attribute
Definition
Entity
Name
System
Name
Cust_Nbr Cust_Nbr is the attribute of
record for BigCity Bank
customer numbers
Cust_Tbl Central
Customer
System
CUSTNUM Customer numbers from
the deposit system.
CUSTTABLE CUSTAPPL
Purchase_No Customer numbers from
the purchase in the legacy
deposit system
Purch_Tbl CUSTSYS
Borwr_No Customer numbers from
the loan system.
Borrower_File LoanSys
Cust_Type Cust_Type is the attribute
of record for BigCity Bank
customer types (affluent,
upward, standard, high
risk).
Cust_Tbl Central
Customer
System
CUSTCDE Customer types from the
general ledger system.
GL_CUST GLAPPL
Cust_Card_Ind Cust_Card_Ind is the
attribute of record for
BigCity Bank customer ‘s
that have a BCB credit
card.
Cust_Tbl Central
Customer
System
None applicable
Cust_Crdt_Ratg Cust_Crdt_Ratg is the
BigCity Bank customer
credit ratings (Superior
Risk, Low Risk, Standard
Risk, High Risk, Extreme
Risk).
Cust_Tbl Central
Customer
System
Credit_Rate Customer rate is from the
general ledger system and
refers to the credit
rating/worthiness of a
customer.
GL_CUST GLAPPL
BigCity Bank

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August 15, 2015Systems Consolidation Report
Small Town BankBigCity Bank
Entity
Name
Attribute
Definition
Attribute
Name
Domain
Value
Attribute
Name
Transformation
Rules
Entity
Name
Domain
Value
Cust_TypeCust_Type is the attribute
of record for BigCity Bank
customer types:
1 = affluent
2 = upward
3 = standard
4 = high risk
Cust_Tbl
1 CUSTCDECust_Type = 1 WHEN
CUSTCDE = 3 AND
CUSTBAL > 500,000
GL_CUST3
CUSTBAL GL_CUSTHigh cardinality field
CUSTCDE = 4 AND
CUSTBAL <= 500,000 AND
CUSTBAL > 200,000
GL_CUST3
CUSTCDE = 1 or 2 AND
CUSTBAL <= 200,000 AND
CUSTBAL > 75,000
GL_CUST3
CUSTCDE = 0 AND
CUSTBAL < 75,000 AND
Credit_Rate < 22
GL_CUST3
Credit_Rate GL_CUSTHigh cardinality field
Cust_Card_Ind Cust_Card_Ind is the
BigCity Bank customer ‘s
that have a BCB credit
card.
Cust_Tbl
BigCity Bank

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The Cost of Redundancy
 Large healthcare insurance company
 Has a $1.6 billion IT budget
 They estimate it costs them $2 per month to store
each gigabyte of data
 $8 per month if you add in services and
maintenance
 They estimate that they have 1.6 petabytes of
redundant data
 What does this cost them yearly? Simple math
 $8 x 12 months x 1,000,000 (1.6 petabytes) =
$153,600,000

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“We Build Systems To Manage Every Aspect
Of Our Business, Except One To Manage The
Systems Themselves.”
“A Managed Meta Data Environment Is A
System That Manages Our Systems.”

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MME ROI
 Intel finds huge ROI in managing meta data
 Estimates $6 in savings for every $1 spent
• Emphasis on reducing developer’s average research time of 30%
• Uses “Centralized” architecture
• Key to success is based on regular, frequent “scan updates”
Source: Computer World magazine – July 2005
 A Canadian government agency achieved:
 More than 90 percent reuse of existing data definitions
 85 percent improvement in application integration
 25 percent reduction in DW analysis and design
 Impact analysis study in 2 hours vs. 36 person-days of consulting
“Knowledge workers spend up to 2.5 hours each day looking for information… but find
what they are looking for only 40% of the time.”
Kit Sims Taylor – Economist and Researcher in “Economics of information and networks”
Bellevue Community College, WA

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50% of developer time today is spent “searching”
i.e: Determining the effects and depth of changes/ enhancements/ new
development. Understanding system problems, semantics, etc.
 20% of the 50% (10% overall) can easily be reduced by
implementing and utilizing a Metadata management solution.
Source: Gartner group
MME ROI

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Managed Meta Data
Environment

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Managed Meta Data Environment
Managed Meta Data Environment (MME): The
managed meta data environment represents the
architectural components, people and processes
that are required to properly and systematically
gather, retain and disseminate meta data
throughout the enterprise.

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Managed Meta Data Environment
Meta Data Sourcing Layer
Meta Data
Extract
Meta Data
Repository
Messaging/Transactions
(EAI, web services, XML, etc.)
Software
Tools
Meta Data Delivery Layer
Meta Data
Management LayerDocuments/
Spreadsheets
End Users
(business and technical)
Third Parties (business partners, vendors,
customers, government agencies)
Meta Data
Extract
Meta Data
Extract
Meta Data
Extract
Meta Data
Extract
M
e
t
a
D
a
t
a
I
n
t
e
g
r
a
t
i
o
n
L
a
y
e
r
Data Warehouse/
Data Mart(s)
Websites/E-Commerce
Third Parties (vendors, customers,
government agencies)
Meta Data
Marts
End Users
Business Users
Applications (CRM, ERP, etc.)
End Users
Websites/
E-Commerce
Applications
(CRM, ERP, data warehouses, etc.)
Meta Data
Extract
Messaging/Transactions
(EAI, web services, XML, etc.)
Software Tools

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Six Components Of The MME
 Meta Data Sourcing Layer
 Meta Data Integration Layer
 Meta Data Management Layer
 Meta Data Repository
 Meta Data Marts
 Meta Data Delivery Layer

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RBC Financial

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RBC Financial Group
 Canada’s largest financial institution as measured by market
capitalization and assets, and is one of North America’s
leading diversified financial services companies
 Provides personal and commercial banking, wealth
management services, insurance, corporate and investment
banking, and transaction processing services on a global
basis
 Employs 60,000 people, who serve more than 12 million
personal, business, and public sector clients through offices in
North America and some 30 countries around the world
 Has millions of active Canadian online customers

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RBC Financial Group
Challenges/Opportunities
 RBC Financial Group’s Data Warehouse Environment (DWE) consists of
five databases and contains more than 5 terabytes of data
 DWE end users required a tool to gain knowledge of the data content as
well as an understanding of the data sources to effectively utilize the DWE
environment
 RBC Financial Group has hundreds of applications, all interfacing and
sharing data
 Need to reduce the risk and effort necessary to modify their IT systems
during application changes
 Must improve their product’s time to market
 Many applications within the organization are well known by only a handful
of key individuals. Therefore, they needed to capture this information
(meta data) from these individuals before the corporate knowledge is lost

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RBC Financial Group
MME Overview
 Enterprise-level MME that provides an inventory of the
company’s information assets, a documentation facility for IT
applications, the capability for enterprise-wide impact
analysis, and a map for data movement
 This meta data management solution was directly supported
by RBC Financial Group’s executive management
 Deployed in 1993
 MME provides value to both business and technical users via
a customized Web front end
 The MME’s use has grown significantly in recent years and
has become ingrained into the work processes at the bank

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RBC Financial Group
Custom
Web Interface
Managed Metadata
Environment
(Vendor Products and
In-house Developed)
Data
Warehouse
Environment
(Enterprise Data
Warehouse, Data
Marts)Catalog Info,
ETL Mapping,
Transformation
Application
Database
Catalogs
Catalog
Information
Data Models
- Logical models
- Physical models
- Standards
Tool
Interface
Mainframe
Metadata
Legacy
COBOL
NightlyScansChange
Management Tool
Batch
Interfaces
Extract
Update
Metadata
Rationalization
Extract/
Reload
Non-Mainframe Metadata
- XML tags
- Distributed Systems
- External Sources
Import
Development
Community
Business
Community
Definitions,Technical
information
BusinessMetadata
SecurityClassifications
Research
/
understandingResearch
/
understanding
Load
Business Users
Data Stewards

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RBC Financial Group
MME ROI
 RBC Financial Group believes that the largest part of application support and maintenance is
spent on manual impact analysis
 Therefore, they focused their MME on reducing the cost of their IT environment. This MME
accomplishes this by providing the following benefits:
 Documentation, source-to-target mapping, and change notification for the DWE
 Identification, publication, and maintenance of over 4,900 batch interfaces among more than 200
applications
 Assisting applications in mapping and gap analysis. One particular project generated more than
10,000 hits to the web site, preventing thousands of phone calls
 Rationalization of the MME data elements, which allows impact analysis across 200 applications
and can be completed in hours or days as opposed to weeks or months
 Identification and publication of data element standards
 In the late 1990’s, a major credit card company asked RBC Financial Group to change their
credit card number from 13 bytes to 16 bytes
 This company assigned three people for 4 to 6 months to identify the 122 changes they needed to
make to their systems
 Using the MME RBC Financial Group identified 144 changes that would be needed to its systems
and accomplished this analysis in 15 minutes
 Typically, large banks spend 15% to 22% of non-interest expenses (NIE) on their IT systems,
while RBC Financial Group spends approximately 13% of NIE on their IT systems—See next
slide

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RBC Financial Group
12
CITIGROUP
J P MORGAN CHASE
BANK OF AMERICA
BANK ONE
WELLS FARGO
WACHOVIA
FLEET BOSTON
RBCFG
PNC GROUP
5.3 US$ Billion
4.6
3.9
2.0
1.9
1.6
1.1
0.9
U.S. Industry Total (2002): US$32.1 Billion
For largest US banks (assets over
US$70 Billion) IT spending ranges from
15 - 22% of NIE
RBCFG estimate is 13%
Projected 2002 IT Spending by U.S. Banks
0.6
(1)
(1) IT spending defined as excluding Voice Telecom. RBC estimate is $1,484MM CAD less $130MM Voice Telecom =
$1,354MM / 1.55 = $0.9Billion USD

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What To Avoid When
Building A Managed
Meta Data
Environment

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What To Avoid
Top 10 List
#10 MME team creates standards none of the
supporting teams can follow
#9 Not creating a MME team and/or trivializing
the MME effort
#8 Failing to have an experienced meta data
project manager/architect leading the project
#7 Neglecting to change the “status quo”

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What To Avoid
#6 Creating meta data silos – “Islands of Meta Data”
#5 Too many manual meta data integration processes
#4 Not providing EASY access to the meta data
#3 Selecting a meta data tool without conducting an
evaluation or defining requirements
#2 Trying to do a “Big Bang” (waterfall)
implementation…Boiling the Ocean

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What To Avoid
Not defining tangible business/technical objectives of the managed
meta data environment!!
Number #1 Reason
Good Business Drivers

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Department of Defense
World Class Solutions Award
Data Management

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How Large is the DoD Supply Chain?
 $480 Billion Dollars Budget
 $80 Billion Supply Chain
 3 Million People
 8 Million Parts
 Global – 150 Countries
 Dynamic Supply Chain
World Class Solutions Award
Data Management

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What’s the Challenge
 We Can’t Account for Several Billion Dollars a
Year
 We Have Data Quality Issues
 We Fix or Refine a Process and Break Several
Other Processes
 Never Planned the Enterprise
 The Enterprise “Just Grew”

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DoD Enterprise Architecture is Based on:
Support rapid, agile deployment, employment,
sustainment and reset/reconstitution Total Force,
within acceptable risk, across the full
spectrum of operations in a cost-
effective manner (ROI)

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Enterprise Integrated Data Environment (EIDE)
Provide an enhanced environment
that enables the DoD Logistics
Enterprise to execute practices,
processes, applications and
decision support tools to achieve
logistics interoperability and allow
for information exchange within and
between internal and external DoD
business partners.
- Non-system dependent transactions
- Consolidation and reuse of
Interfaces
- Data integration/sharing
- Leverage Modernization Efforts
- Data Standards not Standard Data

www.EWSolutions.com
DoD Enterprise Logistics
DMMSS(G005M)
ATLASSII+
CEMS1(D042A)
REMIS
CAEMS
MUMMS-SS06
LOGMOD
OWRMR(D072)
RSSL(D040)CMOS
JFAST
GDSSTOPS
WPS
IBS
AMS(MTMC)
CCSSGFM(CFM-HOST)
TC-AIMSII
SDS
MRDB MRDB
SAMMS EMACS
WARRS
IMWRP(D035A)CMOS
CALM
LOGMOD
WSMIS-ETADS
(O132A)
CEMS1
(D042A)
MUMMS-SS10
ATLASSII+
TMS
MUMMS
-SS06
CAEMS
MRP(MC)
DISMSDISMS
DSS DAISY
FAS
HMIS
TOPS
GTN JFAST
WPS
IBS
MISILCISIL
TC-ACCIS
SDSSARSS-1 MTMS
MRDBSPBS-R
AFMIS
COMPASS
ERSSARSS-2AC/2BAMS(MTMC)
TC-AIMSII
SAAS-MOD
AALPS
ETADS
DSS FAS
DAISY
HMIS
TOPS
WPS IBS
JFAST
GTN
MISILCISIL
SARSS-GATEWAY
COMPASS
TC-AIMSIIAALPS TC-ACCIS
AMS(MTMC)SARSS-1
MIDAS
AMS(MTMC)
CCSS
SAAS-MODCOMPASS SPBS-R
SARSS-GATEWAYTC-ACCIS
AFMIS
MIDAS
SARSS-2AC/2BSARSS-2AD SARSS-1
SDS
SUPMIS
ROLMS TANDEM
RAM
UICP-RESYS
LOGMOD
REMISMIICS(D043)
WARRSIMWRP(D035A)
JAMSSRSSL(D040)
CMOS
SBSS/ILS-SAFPS(D220)
SCS(D035)
WSMIS-ETADS
(O132A)
CALMETADS
OWRMR(D072)
CEMS1(D042A)
ITEMAPPS
MRP(MC)MUMMS-SS07
MUMMS-SS10
SASSYMAARSII
ATLASSII+
CAEMS
DAISY
DSS
BOSS
EMACS
FAS
DISMS
SAMMS
TOPS
WPS IBS
JFAST
GTN
AFMIS
TC-ACCIS
AMS(MTMC)CCSS TC-AIMSII
COMPASSMIDAS
SPBS-R
AALPS
SARSS-GATEWAYSARSS-1 SARSS-2AD
SARSS-2AC/2B
SAAS-MOD
SDS
TANDEM
RAM UICP-RESYS
SUPMIS
ROLMS
CALM
WSMIS-ETADS(O132A)LOGMOD
CMOSREMIS
AFPS(D220)MIICS(D043)
ETADS
SBSS/ILS-SSCS(D035)
IMWRP(D035A)CEMS1(D042A)
JAMSSWARRS
OWRMR(D072)
RSSL(D040)
MUMMS-SS07
ATLASSII+MAARSII
ITEMAPPS
MRP(MC)MUMMS-SS10
SASSY
CAEMS
BOSS
DISMS
DSS
FAS
SAMMS
EMACS
DAISY
JFAST
TOPS WPS
GTN
IBS
ACCLAIMS COMPASS
ATAVGCSS-A LIDB
TC-AIMSIISAAS-MODELIST
IC3
CMOS
BROKERCEMS1(D042A)
LOGMODMANPER-B
MAGTFII
CAEMS
LMIS
ATLASSI
MCDSS
JFRGIIMDSSII
MUMMS-SS06
WRS
GCCSICIS
TOPS
AMPAPODModel
IBS
WPSGDSS
JFASTBDSS
OWRMR(D072)
RSSL(D040)
SAMMS
SARSS-2AC/2B
SARSS-2ADSARSS-1
SPBS-RSDS
SARSS-GATEWAYSAAS-MOD
MIDASAMS(MTMC)
CCSSATS
UITATAV
DESCBS-X
AFMIS
ROLMS
SUPMIS
FACTSUICP-RESYS
HMMS
CAIMSMMS
RAM
TANDEM
MIICS(D043)
CEMS1(D042A)
AFPS(D220)REMIS
JTAVSAMMS
BOSSDFAMS
DAISY
DESEXDSS
DISMS
FAS EMACS
SDSCCSS
REMISDMMSS(G005M)
ATLASSI
ATLASSII+
MDSSII
EMACS
SDSCCSS
REMIS
OWRMR(D072) RSSL(D040)
DMMSS(G005M)
ATLASSII+
MDSSII
ATLASSI
EMACSSAMMS
DAMMS-RBLKIII
GFM(CFM-HOST)
AMS(MTMC) ELIST
TC-ACCIS
GOPAX
FACTSNAOMIS
CMOS
GATES LOGMOD
GTN21
TC-AIMS
CAEMS MDSSII
MAGTFII
JFRGII
WPS
JFAST GTN
GDSS
IBS
SDSIFS
CCSS
DMMSS(G005M)
EMACS
DBMS
DAMMS-RBLKIIIELIST
TC-AIMSIICOMPASS
GFM(CFM-HOST)GOPAX
TC-ACCISAMS(MTMC)
AALPS
FACTSNAOMIS
GATES
ETADS
CALM
WSMIS-ETADS
(O132A)
LOGMOD
CMOS
GTN21
JFRGII
MAGTFII CAEMS
TC-AIMS
MDSSII
JFAST
WPS
GTN
GDSS
TOPS
IBS
TRAC2ES
SARSS-2AD
CCSSCBS-X
DESUIT
SDSATS
SARSS-GATEWAYSARSS-2AC/2B
SARSS-1ATAV
SPBS-RAFMIS
MIDASAMS(MTMC)
SAAS-MOD
UICP-RESYS
RAM
ROLMSTANDEM
HMMS
CAIMSSUPMIS
MMS
FACTS
REMIS
MIICS(D043)
AFPS(D220)OWRMR(D072)
JAMSSSIS(D035T)
RSSL(D040)
SBSS/ILS-SCEMS1(D042A)
SCS(D035)SSRAC(D169)
WARRS
IMWRP
(D035A)
MUMMS-SS07
MRP(MC)MUMMS-SS10
SASSY
MUMMS-SS06
ATLASSII+
MDSSIIATLASSI
ITEMAPPS
MAARSII
DISMS
DESEXDSS
DFAMS
DAISY
BOSS
JTAVFAS
SAMMS
EMACS
SARSS
-2AC/2B
SARSS-
GATEWAY
GFM
(CFM-HOST)
SARSS-1 ACLDBFEDLOG
ERSMIDAS SDS
SB-
700-
20
SPBS-R
SAAS
-MOD
AFMISCCSS EIC
LIF MRDBMILSTEP
SARSS-2AD
MMS
ROLMS
SUPMIS
UICP-RESYS
RAM
TANDEM
MIICS(D043)
AFERS(D143C)REMIS
CSCPS(D155)CE-CM(D042E)
CEMS1(D042A)WARRS
FSSSYSTEM
SSRAC(D169)
DMMSS(G005M)
SBSS/ILS-SSCS(D035)
IMWRP
(D035A)
MIIDB(D043A)
JAMSS
RSSL(D040)
ATLASSII+
ITEMAPPS
MRP(MC)MUMMS-SS05
TDMS
SASSY
SAMMS
EMACS
MEDALSFAS
DAISYBOSS
BSM
DSSDISMS
DESEXDFAMS
TOPS
RASFIARS
CISILMISIL
MOCAS
MTMS
SDSERS SPBS-R
MRDBSAAS-MOD CCSS
AFMISSARSS-1SARSS-2AC/2B
JCALS
ATIS(32BIT)
ATIS(16BIT)
MSPECS
MCA
SETS
JEDMICS
CE-CM(D042E)
CEMS1(D042A)
REMIS
DMMSS(G005M)
IMWRP(D035A)
WARRS
OLVIMS
MUMMS-SS10
ATLASSII+SASSY
MUMMS-SS06
TMSTDMS
MRP
(MC)
CAEMS
TMPODS
DSSEMACS
MEDALS
HMISFAS
DISMS
DAISY
MISILCISIL
MIIDB(D043A)
JAMSSRSSL(D040)
FSSSYSTEMOWRMR(D072)
MIICS(D043)SBSS/ILS-S
CEMS1(D042A)
WARRS
AFPS(D220)
CE-CM(D042E)IMWRP(D035A)
SCS(D035)REMIS
CSCPS(D155)DMMSS(G005M)
SSRAC(D169)
AFERS(D143C)
ATLASSII+
MAARSII
SASSYMRP(MC)
ATLASSI
ITEMAPPS
TDMS
MUMMS-SS05
MUMMS
-SS10
MUMMS-SS07
MUMMS-SS06
TMPODS
FLIS
CALM
ETADSIMWRP(D035A)
CEMS1(D042A)
WARRSCMOS
LOGMOD
WSMIS-ETADS
(O132A)
MRP(MC)
ATLASSII+
MUMMS
-SS06
TMS
MUMMS-SS10
CAEMS
SDS
SPBS-RAFMIS CCSS
SAAS-MOD
SARSS-2AC/2B
SARSS-2AD
ROLMS
RAM TANDEM
UICP-RESYS
SUPMIS
AFPS(D220)
CALMOWRMR(D072)
RSSL(D040)JAMSS
ETADSREMIS
SBSS/ILS-S
WSMIS-ETADS
(O132A)
CEMS1(D042A)
MIICS
(D043)
WARRS
IMWRP(D035A)SCS(D035)
LOGMOD
CMOS
MAARSII
CAEMSSASSY
MUMMS-SS10
MUMMS-SS07MRP(MC)
ITEMAPPS
ATLASSII+
FAS
DAISY
DSS
SAMMS
EMACS
BOSS
DISMS
IBS
GTN WPS
TOPS
JFAST
AALPS
TC-AIMSII
OWRMR(D072)JAMSS
SIS(D035T)
RSSL(D040)SSRAC(D169)
SCS(D035)WARRS
IMWRP(D035A)
SBSS/ILS-S
MUMMS-SS10
MDSSII
SASSYATLASSII+
MRP(MC)
MUMMS-SS07MAARSII
ATLASSI
ITEMAPPS
MAARSII
MUMMS-SS10ATLASSI
MUMMS-SS07
MUMMS-SS06
DFAMS
EMACS
FLISDSS
MEDALSDISMS
SAMMS
DESEXDAISY
BSMBOSS
TMPODS
FAS
TOPS
CISIL
RASFIARS MOCAS
MISIL
CCSS
AFMISGFM(CFM-HOST) EIC
SDSSPBS-R MILSTEP
LIFMRDB SARSS-2AC/2B
ERSFEDLOG
SARSS
-GATEWAY
SB-700-20SARSS-1 SAAS-MOD
SARSS-2ADMIDAS
ACLDB
SUPMIS
MMS
ROLMS
TANDEM
RAM
UICP-RESYS
WARRS
SCS(D035)REMIS
JAMSSRSSL(D040)
IMWRP
(D035A)
SBSS/ILS-S
CEMS1
(D042A)
AFERS(D143C)
CSCPS(D155)
SSRAC(D169)
DMMSS
(G005M)
MIIDB(D043A)MIICS(D043)
FSSSYSTEM
CE-CM(D042E)
ATLASSII+
SASSY
TDMSMUMMS-SS10
MUMMS-SS07
MUMMS-SS06
MUMMS-SS05
MRP(MC)ITEMAPPS
ATLASSI
MAARSII
DISMS
FLIS
ELISTATAV
SAAS-MOD
ACCLAIMSGCSS-A
COMPASS
IC3
MANPER-BBROKER
MCDSSWRS
JFRGII
MAGTFIIMDSSII
ATLASSILMIS
GCCSICIS
AMPAPODModel
SDSAFMIS SAAS-MOD
SARSS-1
SARSS-2AC/2B
ERS SPBS-R
MTMSJCALSCCSS
MSPECS
JEDMICS
ATIS(16BIT)
ATIS(32BIT)
MCA
SETS
WARRS
IMWRP
(D035A)
CE-CM(D042E)
OLVIMS
FASDSS
SAMMSEMACS
MEDALS
BSMDAISY
DFAMSBOSS
TMPODS
DESEX
TOPS
MOCAS
MISILCISIL
RASFIARS
SAAS-MOD
AFMIS LIF
GFM
(CFM-HOST)
SARSS-
2AC/2B
MIDAS
MILSTEPMRDB SPBS-R
FEDLOG
ERSSDS ACLDB
SB-700-20CCSS SARSS-2AD
SARSS-GATEWAY
SARSS-1
EIC
RAM
SUPMIS
TANDEM
UICP-RESYS
MMS
ROLMS
DMMSS(G005M)
REMIS
CEMS1(D042A)
ATLASSII+MRP(MC)
MUMMS-SS10
SASSYTMS
MUMMS-SS06
TDMS
CAEMS
HMIS
EMACSMEDALS
TMPODS
DAISYFAS
DISMS
DSS
MISILCISIL
SPBS-RSDS AFMIS
SARSS-2AC/2B
CCSS
MTMS JCALS
SARSS-1SAAS-MODERS
SETS
JEDMICS
ATIS(16BIT)
ATIS(32BIT)
MCA
MSPECS
WARRS
IMWRP(D035A)
DMMSS(G005M)
OLVIMS
REMIS
CE-CM(D042E)
CEMS1(D042A)
MUMMS-SS06
ATLASSII+TMS
MUMMS-SS10
MRP(MC)TDMS
SASSY
CAEMS
DSS
DAISYEMACS
TMPODS
DISMSMEDALS
FAS
HMIS
CISILMISIL
COMPASS
MTMSSPBS-R SDS
TC-ACCISAMS(MTMC)
SARSS-2AC/2B
SARSS-1
SAAS-MODAALPS TC-AIMSII
MRDB
ERS
AFMIS
CMOS
LOGMODCALM
IMWRP(D035A)
WSMIS-ETADS
(O132A)
WARRS
ETADS
CEMS1(D042A)
TMS
ATLASSII+
CAEMS
MRP(MC)
MUMMS-SS06
MUMMS-
SS10
DAISY
HMIS FAS
DISMS
DSS
GTN
WPS IBS
TOPS
JFAST
CISILMISIL
SAAS-MOD
AFMISERS AMS(MTMC)
TC-ACCISMTMS
AALPS
SARSS-1
COMPASSSARSS-2AC/2BTC-AIMSII
SDS
MRDB
SPBS-R
ARMY
NAVY
AIR FORCE
(USAF)
MARINE CORPS
(USMC)
DLA
TRANSCOM
OTHER
PLAN SOURCE MAKE DELIVER RETURN ENABLE
Plan
Supply
Chain
Plan
Source
Plan
Make
Plan
Deliver
Plan
Return
Source
Stocked
ProductSource
Make-to-Order
Product
Source
Engineer-to-Order
Product
Make-to-Stock
Make-to-Order
Engineer-to-Order
DeliverStocked
Product
Deliver
Make-to-Order
Product
Deliver
Engineer-to-Order
Product
Return
Defective
Product
Return
MRO
Product
Return
Excess
Product
EnablePlan
EnableSource
EnableMake
EnableDeliver
Enable
Return
DoD ENTERPRISE LOGISTICS
Operational Activity to System Function Traceability Matrix (SV-5)
NOTE: The data displayed in this
diagram is collected from the LSM's
Future Logistics Enterprise OV-5.
= System with Operational
to System Process Mapping
= System with Operational
to System ActivityMapping

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Distributed Meta Data Architecture (DMDA)
End User -
Reporting Tool
(e.g. Data
Shopper, Cognos,
Crystal Reports)
COI Data
Champion
NavyDLA/DSS
Global Meta Data
Public Meta Data
Private Meta Data
Other
Repositories
Air force
Meta Data Sources
Global Data
Champion
Logistics Domain
Global Repository
DOD
Meta Data
Registry
Msg/Trans
Broker
Rules
Engine
Army Marines
Weapons Platforms
Suppliers
Other Domains
(Acquisition)

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Summary
 Plan Your Enterprise Around the “To Be Business
Processes” not your “As Is” (80-20 Rule)
 Use Commercial Processes and “COTS” Products
 Data Strategy is the Key to Data Interoperability
Across the Enterprise
 No Meta Data Management, No Data Strategy
 Compliance is an “On-Going Process”
 Your Business Processes Should Not Be Unique
Information IntegrationWorld Class Solutions Award
Data Management

www.EWSolutions.com
Questions

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DAMA International Webinar: 10 Keys to World-Class Metadata Management20150616 meta data dataversity 1 hr webinar