In today’s digital economy, data drives the core processes that deliver profitability and growth - from marketing, to finance, to sales, supply chain, and more. It is also likely that for many large organizations much of their key data is retained in application packages from SAP, Oracle, Microsoft, Salesforce and others. In order to ensure that their foundational data infrastructure runs smoothly, most organizations have adopted a data governance initiative. These typically focus on the people and processes around managing data and information. Without an actionable link to the physical systems that run key business processes, however, governance programs can often lack the ‘teeth’ to effectively implement business change. Metadata management is a process that can link business processes and drivers with the technical applications that support them. This makes data governance actionable and relevant in today’s fast-paced and results-driven business environment. One of the challenges facing data governance teams however, is the variety in format, accessibility and complexity of metadata across the organization’s systems.

1. The Business Value of Metadata
for Data Governance
Donna Burbank
Managing Director, Global Data Strategy, Ltd
February 15, 2017

2. Global Data Strategy, Ltd. 2017
Donna Burbank
Donna is a recognised industry expert in
information management with over 20
years of experience in data strategy,
information management, data modeling,
metadata management, and enterprise
architecture. Her background is multi-
faceted across consulting, product
development, product management,
brand strategy, marketing, and business
leadership.
She is currently the Managing Director at
Global Data Strategy, Ltd., an international
information management consulting
company that specialises in the alignment
of business drivers with data-centric
technology. In past roles, she has served in
key brand strategy and product
management roles at CA Technologies and
Embarcadero Technologies for several of
the leading data management products in
the market.
As an active contributor to the data
management community, she is a long
time DAMA International member and is
Past President of the DAMA Rocky
Mountain chapter. She was also on the
review committee for the Object
Management Group’s Information
Management Metamodel (IMM) and a
member of the OMG’s Finalization
Taskforce for the Business Process
Modeling Notation (BPMN).
She has worked with dozens of Fortune
500 companies worldwide in the
Americas, Europe, Asia, and Africa and
speaks regularly at industry
conferences. She has co-authored two
books: Data Modeling for the
Business and Data Modeling Made Simple
with ERwin Data Modeler and is a regular
contributor to industry publications such
as DATAVERSITY, EM360, & TDAN. She can
be reached at
donna.burbank@globaldatastrategy.com
Donna is based in Boulder, Colorado, USA.
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Follow on Twitter @donnaburbank

3. Global Data Strategy, Ltd. 2017
Agenda
• The Business Value of Data Governance
• Making Data Governance “Actionable” through Metadata
• Integrating Application Data Sources for Enterprise Business Context
• Silwood Technology - introduction
• Summary & Questions
3
What we’ll cover today

4. Global Data Strategy, Ltd. 2017 4
What my friends think I do
What I think I do
What my mom thinks I do
What my coworkers think I do
What society thinks do
DATA GOVERNANCE
What I actually do
Driving the
Success of
the Business

5. Global Data Strategy, Ltd. 2017
Successful Data Governance Aligns with Business Goals
5
Linking Business Goals with Technology Solutions
“Top-Down” alignment with
business priorities
“Bottom-Up” management &
inventory of data sources
Managing the people, process,
policies & culture around data
Coordinating & integrating
disparate data sources
Leveraging & managing data for
strategic advantage

6. Global Data Strategy, Ltd. 2017
Data Governance – A Business-Driven Framework
Organization &
People
Process &
Workflows
Data Management &
Measures
Culture &
Communication
Vision & Strategy
Tools & Technology
Business Goals &
Objectives
Data Issues &
Challenges

7. Global Data Strategy, Ltd. 2017
Aligning Data Governance Goals to Corporate Goals
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Corporate Mission Corporate Vision
Goals & Objectives
To provide a full service online retail experience
for art supplies and craft products.
To be the respected source of art products worldwide,
creating an online community of art enthusiasts.
Artful Art Supplies ArtfulArt
C
External Drivers
Digital Self-Service
Increasing
Regulation Pressures
Online Community &
Social Media
Customer Demand
for Instant Provision
Internal Drivers
Cost Reduction
Targeted Marketing
360 View of
Customer
Brand Reputation Community Building
Revenue Growth
C
Accountability
• Create a Data Governance
Framework
• Define clear roles &
responsibilities for both
business & IT staff
• Publish a corporate
information policy
• Document data standards
• Train all staff in data
accountability
C
Quality
• Define measures & KPIs for
key data items
• Report & monitor on data
quality improvements
• Develop repeatable
processes for data quality
improvement
• Implement data quality
checks as BAU business
activities
C
Culture
• Ensure that all roles
understand their
contribution to data quality
• Promote business benefits
of better data quality
• Engage in innovative ways
to leverage data for
strategic advantage
• Create data-centric
communities of interest
• Corporate-level Mission & Vision
• May already be created or may
need to create as part of project.
• Project-level, Data-Centric Drivers
• External Drivers are what you’re
facing in the industry
• Internal Drivers reflect internal
corporate initiatives.
• Project-level, Data-Centric Goals
& Objectives
• Clear direction for the project
• Use marketing-style headings
where possible

8. Global Data Strategy, Ltd. 2017
Mapping Business Drivers to Data Governance Goals
8
Business-Driven Prioritization
Business Drivers
Digital Self Service
Increasing Regulation
Pressures
Online Community &
Social Media
Customer Demand for
Instant Provision
External Drivers
Internal Drivers
Targeted Marketing
360 View of Customer
Revenue Growth
Brand Reputation
Community Building
Cost Reduction
Challenges
Lack of Business Alignment
• Data spend not aligned to Business Plans
• Business users not involved with data
360 View of Customer Needed
• Aligning data from many sources
• Geographic distribution across regions
Data Quality
• Bad customer info causing Brand damage
• Completeness & Accuracy Needed
Cost of Data Management
• Manual entry increases costs
• Data Quality rework
No Audit Trails
• No lineage of changes
• Fines had been levied in past for lack of
compliance
Disparate Data Sources
• ERP systems difficult to integrate with DW
• Exploiting Unstructured Data
• Access to External & Social Data
Establish Governance Organization
• Create a Data Governance Steering Committee
• Appoint Head of Data Governance role
• Build a “Data Culture” where all staff using data
are committed to its quality.
• Build data-management best-practices into core
IT and reporting processes.
Standardize Shared Data Elements
• Identify the shared data elements most
important to the business.
• Agree on common business definitions.
• Align with technical implementations.
Data Governance Goals
Create Lineage & Audit Trails
• Build a complete technical data inventory
• Link data sources together in a metadata-driven
lineage view
• Create standards to build a common view
Build Business-Centric View of Data
• Build a complete technical data inventory
• Link data sources together in a metadata-driven
lineage view
• Create standards to improve consistency

9. Global Data Strategy, Ltd. 2017
Identify High-Priority Data Elements
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Align with Business Drivers
Launch of New Product – Marketing Campaign
requires better customer information
Customer Product
Region
Vendor
Partner
Identify Key
Business Driver
Filter Data Elements
Aligned with Business
Driver
Focus Governance
Efforts on Key Data
Targeted Project to
Show Short-Term
Results

10. Global Data Strategy, Ltd. 2017
Metadata is the “Who, What, Where, Why, When & How” of Data
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Who What Where Why When How
Who created this
data?
What is the business
definition of this data
element?
Where is this data
stored?
Why are we storing
this data?
When was this data
created?
How is this data
formatted?
(character, numeric,
etc.)
Who is the Steward of
this data?
What are the business
rules for this data?
Where did this data
come from?
What is its usage &
purpose?
When was this data
last updated?
How many databases
or data sources store
this data?
Who is using this
data?
What is the security
level or privacy level
of this data?
Where is this data
used & shared?
What are the business
drivers for using this
data?
How long should it be
stored?
Who “owns” this
data?
What is the
abbreviation or
acronym for this data
element?
Where is the backup
for this data?
When does it need to
be purged/deleted?
Who is regulating or
auditing this data?
What are the technical
naming standards for
database
implementation?
Are there regional
privacy or security
policies that regulate
this data?

11. Global Data Strategy, Ltd. 2017
Data Governance is a Key Driver for Metadata Usage
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A Key Use Case for Metadata Management
In a recent DATAVERSITY survey, over
60% of respondents stated that:
Data Governance is a key driver for their
use of Metadata.

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Business vs. Technical Metadata
• The following are examples of types of business & technical metadata.
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Business Metadata Technical Metadata
• Definitions & Glossary
• Data Steward
• Organization
• Privacy Level
• Security Level
• Acronyms & Abbreviations
• Business Rules
• Etc.
• Column structure of a database table
• Data Type & Length (e.g. VARCHAR(20))
• Domains
• Standard abbreviations (e.g. CUSTOMER ->
CUST)
• Nullability
• Keys (primary, foreign, alternate, etc.)
• Validation Rules
• Data Movement Rules
• Permissions
• Etc.

13. Global Data Strategy, Ltd. 2017
What is a Data Model?
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Translates Business Rules & Definitions… …to the Technical Data Systems & Structures that Support Them

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What is a Data Model?
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Translates Regulations, Policies & Procedures… …to the Technical Data Systems & Structures that Support Them
Regulation -
e.g. GDPR
Policy
“All Personally Identifiable
Information (PII) must be
anonymized for the purpose
of information sharing
between departments. “
Which data fields constitute PII
in our databases?

15. Global Data Strategy, Ltd. 2017
Technical & Business Metadata
• Technical Metadata describes the structure, format, and rules for storing data
• Business Metadata describes the business definitions, rules, and context for data.
• Data represents actual instances (e.g. John Smith)
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CREATE TABLE EMPLOYEE (
employee_id INTEGER NOT NULL,
department_id INTEGER NOT NULL,
employee_fname VARCHAR(50) NULL,
employee_lname VARCHAR(50) NULL,
employee_ssn CHAR(9) NULL);
CREATE TABLE CUSTOMER (
customer_id INTEGER NOT NULL,
customer_name VARCHAR(50) NULL,
customer_address VARCHAR(150) NULL,
customer_city VARCHAR(50) NULL,
customer_state CHAR(2) NULL,
customer_zip CHAR(9) NULL);
Technical Metadata
John Smith
Business Metadata
Data
Term Definition
Employee
An employee is an individual who currently
works for the organization or who has been
recently employed within the past 6 months.
Customer
A customer is a person or organization who
has purchased from the organization within
the past 2 years and has an active loyalty card
or maintenance contract.

16. Global Data Strategy, Ltd. 2017
Building a Holistic View
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Integrating Application data from ERP and CRM systems
• Integrating the data from ERP and CRM systems provides a more complete view of critical data
such as Customer data.
• Metadata creates the linkages between these systems for integration & reporting
Customer
Responded to 6
marketing campaigns
POS Data StoreCRM
POS
Purchased our flagship
product 12 times in the
past month.
ERP
Ordered £350K of total
product in the past year.
DW
Has been a Gold
customer for 17 years.
Data Warehouse

17. Global Data Strategy, Ltd. 2017
Marketing Database
Netezza
Creating a Technical Data Inventory
• Data models & the associated metadata can create a real-world inventory of the data storage
associated with key business data domains within the control of a data governance program.
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Linking business definitions to technical implementations
Customer
Customer Database
Oracle
Sales Database
DB2
SAP
Data Lake on
Hadoop
Customer Database
SQL Server
CRM Database
POS Data Store
• Some systems, such as ERP and CRM
applications can be a particular challenge to
extract technical & business definitions.
• Due to their complex & proprietary
architectures, they can be very “black box”.

18. Global Data Strategy, Ltd. 2017
ERP/CRM and Packaged Application Metadata
• Packaged applications such as CRM and ERP systems (e.g. Salesforce, Oracle PeopleSoft, etc.) hold critical information about
Customers, Employees, Sales, and more.
• But extracting information from these systems can be complex
• Thousands of disparate tables
• Relationships not clearly defined
• Technical names don’t reflect business definitions or values
• No business definitions
• It is therefore difficult to integrate this critical information with other key systems such as a Data Warehouse, Reporting Data
Mart, and/or MDM hub.
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Technical Metadata Business Metadata

19. Global Data Strategy, Ltd. 2017
Align Data Elements with Business Drivers
• With thousands of tables in a typical packaged application, it is important to be able to categorize them
by business area and function.
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General Ledger
Accounts Payable
Filter Data Elements Aligned
with Business Drivers

20. Global Data Strategy, Ltd. 2017
Focus on the Business Meaning of Data
• Translating often cryptic table structures into meaningful business terminology is critical to understand
and integrate application data sources into a larger data governance initiative.
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Technical Structures Business Meaning

21. Global Data Strategy, Ltd. 2017
Data Lineage – Providing an Audit Trail for Critical Data
• Metadata helps create a data lineage from critical reports to the source systems that created them.
• The typical enterprise technical infrastructure can be complex
• ERP and CRM systems are a key part of the infrastructure
• Understanding the data flow and data lineage between these systems is critical for audit trail and
business context.
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Sales Report
CUSTOMER
Database Table
CUST
Database Table
CUSTOMER
Database Table
CUSTOMER
Database Table
TBL_C1
Database Table
Business Glossary
ETL Tool ETL Tool
Physical Data Model
Logical Data Model Dimensional
Data Model
ERP
System
T128
Database Table
How were
Regional Sales
calculated?

22. Global Data Strategy, Ltd. 2017
Technical Metadata Makes Data Governance Actionable
• Metadata & Data models can help take the business rules & definitions defined in policies and
make them actionable in physical systems, maintaining a lineage & audit trail.
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Policies & Procedures Business Rules & Definitions Technical Implementation Audit & Lineage

23. Global Data Strategy, Ltd. 2017
Summary
• Data Governance Manages the Data that Runs the Business
• “You can’t manage what you can’t measure”
• Metadata is a key requirement in measuring & managing information
• Metadata supports the policies & procedures defined by data governance
• Business definitions
• Technical data structures
• Data lineage & impact analysis
• Metadata supports actionable data governance through
• Linking business & technical definitions & business rules
• Providing standardization & consistency
• Supporting data lineage & audit trails
• Application metadata for ERP and CRM systems can be a particular challenge
• Solutions do exist to manage them
• Integrating ERP and CRM data helps manage some of the most business-critical metadata around
customers, sales, and more
• Business Value can be achieved once a managed, integrated set of information is curated &
understood

24. Global Data Strategy, Ltd. 2017
Silwood Technology - introduction
• Helping customers and partners to
answer the question “Where’s the data”?
• Making sense of packaged ERP and CRM
metadata
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25. Global Data Strategy, Ltd. 2017
Sample customers

26. Global Data Strategy, Ltd. 2017
Silwood Safyr® – Application Metadata Software
• Shorten time to project value
• Cut cost of data discovery
• Improve accuracy
• Reduce risk
• Gives control to data professionals

27. Global Data Strategy, Ltd. 2017
Silwood Safyr® – Application Metadata Software

28. Global Data Strategy, Ltd. 2017
What next?
• Engage with Global Data Strategy
www.globaldatastrategy.com
• Download the White Paper which
accompanies this webinar. See the
Handouts section on GoToWebinar
• Sign up for the Safyr product webinar
Wed 1st March 4-4.30pm
• Visit www.silwoodtechnology.com
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29. Global Data Strategy, Ltd. 2017
About Global Data Strategy, Ltd
• Global Data Strategy is an international information management consulting company that specializes
in the alignment of business drivers with data-centric technology.
• Our passion is data, and helping organizations enrich their business opportunities through data and
information.
• Our core values center around providing solutions that are:
• Business-Driven: We put the needs of your business first, before we look at any technology solution.
• Clear & Relevant: We provide clear explanations using real-world examples.
• Customized & Right-Sized: Our implementations are based on the unique needs of your organization’s
size, corporate culture, and geography.
• High Quality & Technically Precise: We pride ourselves in excellence of execution, with years of
technical expertise in the industry.
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Data-Driven Business Transformation
Business Strategy
Aligned With
Data Strategy
Visit www.globaldatastrategy.com for more information
Proud to be a Silwood
Strategic Partner

30. Global Data Strategy, Ltd. 2017
Questions?
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Thoughts? Ideas?