Modern Metadata Strategies

Modern Metadata Strategies
Donna Burbank, Managing Director
Global Data Strategy, Ltd.
March 22nd, 2018
Follow on Twitter @donnaburbank
Twitter Event hashtag: #DAStrategies

Global Data Strategy, Ltd. 2018
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 specializes 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, Past President
and Advisor to the DAMA Rocky Mountain
chapter, and was recently awarded the
Excellence in Data Management Award from
DAMA International in 2016.
Donna is also an analyst at the Boulder BI
Train Trust (BBBT) where she provides advice
and gains insight on the latest BI and
Analytics software in the market. She was on
several review committees for the Object
Management Group’s for key information
management and process modeling
notations.
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. She can
be reached at
donna.burbank@globaldatastrategy.com
Donna is based in Boulder, Colorado, USA.
2
Follow on Twitter @donnaburbank
Twitter Event hashtag: #DAStrategies

DATAVERSITY Data Architecture Strategies
• January - on demand Panel: Emerging Trends in Data Architecture – What’s the Next Big Thing?
• February - on demand Building an Enterprise Data Strategy – Where to Start?
• March Modern Metadata Strategies
• April The Rise of the Graph Database: Practical Use Cases & Approaches to Benefit your Business
• May Data Architecture Best Practices for Today’s Rapidly Changing Data Landscape
• June Artificial Intelligence: Real-World Applications for Your Organization
• July Panel: Data as a Profit Driver – Emerging Techniques to Monetize Data as a Strategic Asset
• August Data Lake Architecture – Modern Strategies & Approaches
• Sept Master Data Management: Practical Strategies for Integrating into Your Data Architecture
• October Business-Centric Data Modeling: Strategies for Maximizing Business Benefit
• December Panel: Self-Service Reporting and Data Prep – Benefits & Risks
3
This Year’s Line Up for 2018

What We’ll Cover Today
• Metadata is hotter than ever, according a number of recent DATAVERSITY surveys.
• More and more organizations are realizing that in order to drive business value from data, robust
metadata is needed to gain the necessary context and lineage around key data assets.
• At the same time, industry regulations are driving the need for better transparency and understanding
of information.
• While metadata has been managed for decades, new strategies & approaches have been
developed to:
• Support the ever-evolving data landscape
• Provide more innovative ways to drive business value from metadata.
• This webinar will provide an overview of metadata strategies & technologies available to today’s
organization, and provide insights into building successful business strategies for metadata
adoption & use.
4

5
Metadata: Definition &
Use Cases

What is Metadata?
Metadata is Data In Context
6

Metadata is the “Who, What, Where, Why, When & How” of Data
7
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?

Metadata is Part of a Larger Enterprise Landscape
8
A Successful Data Strategy Requires Many Inter-related Disciplines
“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

Metadata is Hotter than ever
9
A Growing Trend
In a recent DATAVERSITY survey, over 80%
of respondents stated that:
Metadata is as important, if not more
important, than in the past.

Metadata Management Use Cases
10
• Leading Use Cases were similar in 2016
& 2017, according to two recent
DATAVERSITY surveys1:
• Data Governance
• Data Quality
• Data Warehousing (DW) & Business
Intelligence (BI)
• Master Data Management (MDM)
• 2017 saw growth in:
• Regulation & Audit (e.g. GDPR)
• Master Data Management
1 Emerging Trends in Metadata Management, 2016, DATAVERSITY, by Donna Burbank and Charles Roe
Trends in Data Architecture, 2017, DATAVERSITY, by Donna Burbank and Charles Roe

Metadata Example: Financial Reporting
11
• An international retail chain was comparing its 4th Quarter Sales across regions.
• Typically the 4th quarter sees a spike in revenue, due to numerous holidays in the November &
December timeframes
• But Latin American sales from a newly-acquired subsidiary were particularly low that quarter,
prompting questions:
• Do we need to increase marketing in that region?
• Is this the wrong market for our products? Should we close retail stores?
• Further research determined that:
• the Latin American branch was using a Fiscal year of June – June
• … rather than the Calendar year used by the rest of the world.
• A metadata issue (mismatched definitions) caused business confusion and potentially misspent funds & effort
4th Quarter Sales
North America $5.1B
AsiaPac $3.9B
EMEA $2.1B
LatAm $0.1B
Quarter = Calendar Quarter (Dec – Dec)
Quarter = Fiscal Quarter (June – June)
Metadata Issue

Data Lineage: Reporting & Data Warehouse Example
• In the data warehouse example below, metadata exists in a number tools & data stores that are
used to generate the final figure on a given report – metadata can help show that path.
12
Audit and Traceability
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
Physical Data Model
Logical Data Model
Dimensional
Data Model
BI Tool
Total Sales for
Customer X this
Quarter are $1.5M

Impact Analysis & Where Used
• Impact Analysis shows the relationship between a piece of metadata and other sources that rely on that metadata to
assess the impact of a potential change.
• For example, if I change the length & name of a field, what other systems that are referencing that field will be affected?
-> reducing risk
• With this roadmap in place, it is easier to assess the impact of a proposed change, significantly reducing development
and maintenance time -> driving agility & responsiveness
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Showing the Impact of Change
What happens if I change the name &
length of the “Brand” field?
Brand CHAR(10)
MyBrand VARCHAR(30)
Customer Database
Oracle
Sales Application
Sales Database
DB2
Staging Area
ETL

Metadata & Big Data Analytics
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• What was the source for the weather data?
• Were readings taking daily, monthly, weekly? Averages or actuals?
• What was the original purpose & format for the readings?
• Were temperatures in Celsius or Fahrenheit?
• Etc.
• Were readings taken by meter readings or billing amounts?
• Were readings taking daily, monthly, weekly? Averages or actuals?
• Were temperatures in Celsius or Fahrenheit?
• Meter readings for were in completely different formats.
It took us weeks to standardize them.
• Etc.
• Is Usage by Address, by Individual,
or by Household?
• Are households determined by
residence or relationships?
• Etc.
“Our analysis shows that energy usage with Smart Meters increases by 5% for each percentage point decrease in
temperature compared to a 20% increase for traditional thermostat customers.”

Metadata Is Critical for Big Data Analytics & BI
15
The absence of commonly understood and shared metadata and data definitions
and the lack of data governance are cited as the main impediments to the success
of Data Lakes.
Source: Radiant Advisors

Business vs. Technical Metadata
• The following are examples of types of business & technical metadata.
16
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.

Metadata is Needed by Business Stakeholders
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Making business decisions on accurate and well-understood data
80% of users of metadata are from the business, according to
a recent DATAVERSITY survey1.
Business users often
“get” metadata more
than IT does!
How was this “Total
Sales” figure
calculated?
“Metadata helps both IT and business users understand the data
they are working with. Without Metadata, the organization is at
risk for making decision based on the wrong data.”1

Data is Only as Good as the Metadata
19
Open Data & Data Visualization Example
Road Safety - Vehicles by Make and Model

The Rise of Self-Service BI, Analytics, & Data Prep
• More and more organizations are moving to a Self-Service model for Reporting and Data Preparation
• Gartner predicts that by 2020, self-service data preparation tools will be used in over half of new data integration
efforts1
• Curated Metadata is needed in order for the Self-Service model to be successful
• According to Gartner estimates, by 2019, data and analytics organizations that provide agile, curated internal and
external data sets for a range of content authors will realize twice the business benefits of those that do not1
Metadata is key to the curation that makes self-service data analysis useful and efficient.
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1 Gartner Market Guide for Self-Service Data Preparation, August 2016, by Rita L. Sallam, Paddy Forry, Ehtisham Zaidi, and Shubhangi Vashisth ID: G00304870

Human Metadata
• Much business metadata and the history of the business exists in employee’s heads.
• It is important to capture this metadata in an electronic format for sharing with others.
• Avoid the dreaded “I just know”
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Avoid the dreaded “I just know”
Part Number is what used to
be called Component
Number before the
acquisition.
Business Glossary
Metadata Repository
Data Models
Etc.
Collaboration Tools

Metadata Matters
Even with today’s advanced hardware & storage options, self-service BI tools, and data
science skills & tools, attention needs to be paid to the quality, context, & structure of data
Raw data used in Self-Service Analytics and BI environments is
often so poor that many data scientists and BI professionals
spend an estimated 50 – 90% of their time cleaning and
reformatting data to make it fit for purpose.(4
Source: DataCenterJournal.com
Correcting poor data quality is a Data Scientist’s least favorite
task, consuming on average 80% of their working day
Source: Forbes 2016
(aka Data Models & Metadata)
If I have to reformat this spreadsheet one
more time to account for mismatched
Region Codes, I’m going to shoot myself.

The Self-Service User
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“If there are standardized
data sets, I’d love to use
them!”
e.g. Master Data, Data Warehouse
“Published documentation,
metadata, & standard
definitions are super-helpful!”
e.g. Glossaries, data models, etc.
“I want to integrate these data
sets with my own exploratory
data for analysis & modeling!”
e.g. Self-Service Data Prep & Analysis Tools
“How can I leverage what other
people have done, and see
what is most relevant?
e.g. Data Cataloguing & Crowdsourcing
Metadata
Also Metadata

Crowdsourcing Governance & Metadata Definitions
• Many metadata projects & vendors are embracing the concept of “crowdsourcing”.
i.e. The Wikipedia vs. Encyclopedia approach
• Open editing
• Popularity & Usage Rankings
• Dynamically changing
24
Encyclopedia Wikipedia
• Created by a few, then published as read-only
• Single source of “vetted” truth
• Static
• Created by a by many, edited by many
• Eventual consistency with multiple inputs
• Dynamic
For Standardized, Enterprise Data Sets For Self-Service Data Prep & Analytics

Finding the Right Balance
25
• When implementing metadata management in today’s rapidly-changing, self-service data
landscape, it is important to find a balance between:
Standards-based
Metadata &
Governance
The two methods work well together, using the right
approach depending on the data usage.
Collaboration-based
Metadata &
Governance
• Well-suited for enterprise-wide
data standards • Well-suited for self-service data
preparation & analytics

Metadata Drives FTLB1 – Faster Time to Light Bulb
Comprehensive, documented metadata helps organizations make better, faster, more cohesive decisions.
26
1 FTLB: Faster Time to Light Bulb. Not to be confused with:
Ft lb: foot-pound (ft-lb) energy measurement unit
FTLB: First Trust Hedged BuyWrite Income ETF (FTLB) investment fund.
Metadata matters!
Could Engineers have access
to Support logs to understand
product design issues & make
improvements?
Could we understand which Customers
are in the Loyalty Program and what
Products they’ve purchased?
Etc, etc. -- Once you have a
roadmap for your data assets,
you can truly innovate and
optimize the business.

Types of Metadata Managed in Today’s Organization1
28
Now Future
• Strong focus on:
• Data Warehousing
• Relational Databases
• Data Models
• Business Glossaries
• Business Intelligence
• ETL Tools
• Focus on DW, Relational, Glossaries, etc. continues.
• With more diverse sources added:
• Big Data Platforms
• Machine Learning/AI
• Semantic Technologies
• NoSQL Platforms
• Legacy Platforms (?!) – retirees?
• Social Media
• Media Files
• Etc.

Metadata Sources are Diverse
• In the following slides, let’s take a quick look at metadata for some of these new sources:
29
• Big Data Platforms
• Machine Learning/AI
• Semantic Technologies
• NoSQL Platforms
• Legacy Platforms (?!)
• Social Media
• Media Files
• Etc.

COBOL Copybook Metadata
30
• What is a COBOL Copybook? – In COBOL, a copybook file is used to define data elements that
can be referenced by many programs
• What is COBOL Copybook Metadata? – structure, definition
Metadata
Describes structure & format of data
The demand for COBOL & legacy
metadata is growing, according to
the recent DATAVERSITY survey.
Yes, and the title of this webinar IS
“Modern Metadata Strategies” ☺

Big Data Platform Metadata
31
• Big Data platforms (e.g. Hadoop-based) are typically based on system of files (HDFS)
• As a result, the detailed structure that is found in a relational database platform does not exist
• Metadata still exists for these platforms, however.
 Business Metadata
 Description of file
 Tags
 Technical Metadata
 Tree structure of HDFS directories
 Directory and file attributes (ownership, permissions, quotas, replication
factor, etc.)
 Metadata about logical data sets (e.g. format, statistics, etc.)
 Data ingest & transformation lineage
 There are components that allow you to add structure within the
Hadoop ecosystem (e.g. Hive)

NoSQL – Key Value Databases
• NoSQL Databases are often optimal solutions for flexibility & performance in certain scenarios.
• One common NoSQL database is a key-value pair database (e.g. Redis, Oracle NoSQL, etc.)
• They can support extremely high volumes of records & state changes per second through distributed
processing and distributed storage.
• Use cases include: Managing user sessions in web applications, online gaming, online shopping carts,
etc.
• The structure is often created by the application code, not within a database or metadata
structure.
• Metadata for NoSQL databases is typically minimal or non-existent.
• The structure & metadata is generally determined by the application code
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Key Value
1839047 John Doe, Prepaid, 40.00
9287320 01/01/2008, 50.00, Green

*
NoSQL Metadata – Document Databases
• Document databases are popular ways to store unstructured information in a flexible way (e.g.
multimedia, social media posts, etc. )
• Each Collection can contain numerous Documents which could all contain different fields.
33
• Some data modeling can be done, and some data modeling tools support this (e.g. MongoDB).
* Example from docs.mongodb.com
{type: “Artifact”,
medium: “Ceramic”
country: “China”,
}
{type: “Book”,
title: “Ancient China”
country: “China”,
}

Image Metadata
34
Camera: Apple iPhone 6 Plus
Lens:
iPhone 6 Plus back camera 4.15mm f/2.2
Shot at 4.2 mm
Digital Zoom: 5.006134969×
Exposure: Auto exposure, Program AE, 1/7,937 sec, f/2.2, ISO 32
Flash: Auto, Did not fire
Date:
April 13, 2016 5:35:53PM (timezone not specified)
(1 month, 11 days, 14 hours, 14 minutes, 46 seconds ago,
assuming image timezone of US Pacific)
File:
3,264 × 2,448 JPEG (8.0 megapixels)
800,782 bytes (782 kilobytes)
Technical Metadata
(Embedded in Photo)
• Metadata is critical for locating images online, as well as identifying copyright information, etc.
• Some information is system-generated, while other is user-defined.
Descriptive Metadata
(User Defined)
Administrative Metadata
(User Defined)
Title DATAVERSITY EDW 2016 San Diego
Keywords EDW 2016, San Diego, Bay Photos
Location San Diego
Author Donna Burbank
Copyright None
Licensing None

Social Media Metadata
35
• Metadata from Social Media, such as Twitter, can help identify trend and sentiment analysis, for
example.
Date/Time
Location
Language
Device Used
ID of Tweet
Etc.
Embedded Metadata
Text/Content of Tweet
Hash Tag
Author
100
# of Retweets

Metadata for Machine Learning/AI
• With Machine Learning (& Data Science), not only the data
needs to be governed with documented metadata, but the
models and algorithms themselves must be documented as
well.
• What data are we using and why?
• What algorithms are being used and what is the logic?
36
Source: David Robinson, Data Scientist at Stack Overflow

The Semantic Web & RDF
37
• The RDF (Resource Description Framework) model from the World Wide Web Consortium (W3C) provides a way to link resources on
the web (people, places, things). It provides a common framework for applications to share information without losing meaning.
• Search Engines
• Exchanging data between datasets
• Sharing information with applications / APIs
• Building social networks
• Etc.
• The goal is to move from a web of documents to a web of data.
• The Framework is a simple way to express relationships between resources.
• IRIs (International Resource Identifiers) (e.g. URI) identify resources
• Simple triples relate objects together in the format: <subject> <predicate> <object>
• These relationships create a connected Graph
• There are several serialization formats, with RDF XML being a common one. For example:
• Turtle is a human-friendly format
• RDF/XML
• JSON-LD
• Schemas define the vocabularies used to describe the objects
• Dublin Core and Schema.org are described further in the Standards section
Subject Object
Predicate
ACME
Publishing
RDF is
Easy
Is Publisher Of

Creating a Web of Data
38
@type: Place
Sheraton San Diego Hotel & Marina
1380 Harbor Island Drive
San Diego, California 92101 USA
"@context": "http://schema.org",
“location": {
"@type": "Place",
"name": "Sheraton San Diego Hotel & Marina",
"address": {
"@type": "PostalAddress",
"streetAddress": "1380 Harbor Island Drive",
"addressLocality": "San Diego",
"addressRegion": "CA",
"postalCode": "92101"
},
"telephone" : "+1-877-734-2726",
"image":
"http://edw2016.dataversity.net/uploads/ConfSiteAssets/72/im
age/sheraton.jpg",
"url":"http://edw2016.dataversity.net/travel.cfm"
},
"@context": "http://schema.org",
"location": {
"@type": "Place",
"name": "Sheraton San Diego Hotel & Marina",
"address": {
"@type": "PostalAddress",
"streetAddress": "1380 Harbor Island Drive",
"addressLocality": "San Diego",
"addressRegion": "CA",
"postalCode": "92101"
},
"telephone" : "+1-877-734-2726",
"image": “http://mysite.com/edw16photo.jpg",
"url":“http://mysite.com/myphotos"
},
* Script provided by: Eric Franzon, eric@smartdataconsultants.com
*

OK, We’re Done with the Race through Technologies
39
Note: Typical attendee at one of Donna’s presentations.

Technical Innovation in Metadata Management
Technical Innovation not only has created new sources of metadata.
…but it has created new ways to manage metadata as well.
40

Machine Learning & Metadata Discovery
• Machine Learning offers ways to automate
tedious tasks that may have been done
manually before:
• e.g. Data Mapping
• SSN -> Field1_SSN
• SSN -> Soc_Num
• Etc.
• Machine Learning Pattern Matching
• NNN-NN-NNNN -> Field_X follows this
pattern, it must be a SSN
41
Source kdnuggets.com
• There is a place for both methods:
• Sometimes you want to define specific mapping rules
• Sometimes you want a pattern-matching, discovery-
style approach.

Graph Relationships
• Graph databases are ideal for analyzing metadata relationships between objects and finding
patterns in those relationships.
42
Patterns & Interrelationships
• Common use cases for graph relationship metadata
analysis include:
• Fraud detection - e.g. financial transactions
• Threat detection - e.g. email and phone patterns
• Marketing – e.g. social media connections, product
recommendation engines
• Network optimization - e.g. IoT, Telecommunications

Tagging – Amazon S3 Example
• Some new platforms, such as Amazon S3, allow you to create user-defined metadata tags that can
be stored in a series of key-value pairs.
• For example, security classifications could as shown below. In this case, metadata tags were created for
both Retention Policy and Security Classification. Tags can be assigned at the bucket and file level.
43
Tracking Metadata with the Data
• Metadata can be assigned when uploading an object, manually, via PUT/POST requests, or via the
REST or SOAP APIs. Metadata tags can also be retrieved via the API.
• System-generated metadata is created automatically by the S3 platform and can including
information such as :
• Creation date
• Storage class configured for the object (e.g. Standard, Reduced Redundancy, Glacier, etc.)
• Whether the object has server-side encryption

44
Designing a Metadata Strategy
for Your Organization

Metadata Interfaces & Publication
45
• Technical users
• Interfaces & Export to in-use tools & technologies (e.g.
Data Modeling Tools, BI Tools, ETL Tools, etc.)
• Intuitive visualization of Impact Analysis, Lineage, etc.
• Publication of common standards
• Create a Technology Inventory & Usage Mapping
• Business Users
• Intuitive interfaces for business terms, glossary
information
• Easy search
• Integration with in-use tools & technologies (e.g. Self-
Service BI)
• Create a Stakeholder Matrix & Technical Inventory
• Align the two
When devising a strategy for metadata integration & publication, first consider the audience for the
metadata solution, and align that with your technical inventory & publication mechanisms:

Implement “Just Enough” Management
• Know what to manage closely and what to leave alone
• As a general rule, the more the data is shared across & beyond the organization, the more formal
metadata management & governance needs to be
46
Core Enterprise
Data
Functional & Operational
Data
Exploratory Data
Reference &
Master Data
Core Enterprise Data
• Common data elements used by multiple
stakeholders across Bus, LOBs, functional areas,
applications, etc.
• Highly governed
• Highly published & shared
Functional & Operational Data
• Lightly modeled & prepared data for
limited sharing & reuse
• Collaboration-based governance
• May be future candidates for core data
Exploratory Data
• Raw or lightly prepped data for
exploratory analysis
• Mainly ad hoc, one-off analysis
• Light touch governance
Examples
• Operational Reporting
• Non-productionized analytical model data
• Ad hoc reporting & discovery
Examples
• Raw data sets for exploratory analytics
• External & Open data sources
Examples
• Common Financial Metrics: for Financial & Regulatory Reporting
• Common Attributes: Core attributes reused across multiple areas
(e.g. Customer name, Account ID, Address)
Master & Reference Data
• Common data elements used by multiple stakeholders
across functional areas, applications, etc.
• Highly governed
• Highly published & shared
Examples
• Reference Data: Procedure codes, Country Codes, etc.
• Master Data: Location, Customer, Product

Defining a Roadmap Focusing on Business Value
• Develop a detailed roadmap that is both actionable and realistic
• Show quick-wins, while building to a longer-term goal
• Balance Business Priorities with Data Management Maturity
• Focus on projects that benefit multiple stakeholders
• Integrate traditional and innovative technologies
47
Maximize the Benefit to the Organization
Initiatives H1 '17 H2 '17 H2 '18 H2 '18
Strategy Development
Governance Lineage for
Privacy Rules
Business Glossary
Population & Publication
Data Warehouse Metadata
Social Media Metadata
Open Data Publication
IoT Integration
Ongoing Communication & Collaboration
Customer Product Location
Integrated
Customer View
Marketing
Sales
Customer Support
Executive Team

Summary
• Metadata is hotter than ever, supporting both business and IT
• Business users, particularly self-service users are key drivers of the growth in interest in
metadata
• Innovation supports both Business and IT – centric metadata management
• Business: Collaboration & Crowdsourcing
• Technical: New sources, new formats. Machine learning & automation.
• In today’s data-driven marketplace, metadata allows for FTLB – “Fast time to light bulb”
• Increasing Innovation
• Speeding Time to Market
• Reducing Risk

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 technological solution.
• Clear & Relevant: We provide clear explanations using real-world examples, not technical jargon.
• 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, and we attract high-
quality professionals with years of technical expertise in the industry.
49
Data-Driven Business Transformation
Business Strategy
Aligned With
Data Strategy
www.globaldatastrategy.com

White Paper: Trends in Data Architecture
50
Free Download
• Download from
• Under ‘Resources/Whitepapers’

White Paper: Emerging Trends in Metadata Management
• Download from
• Under ‘Resources/Whitepapers’
51
Free Download

DATAVERSITY Training Center
• Learn the basics of Metadata Management and practical tips on how to apply metadata
management in the real world. This online course hosted by DATAVERSITY provides a series of six
courses including:
• What is Metadata
• The Business Value of Metadata
• Sources of Metadata
• Metamodels and Metadata Standards
• Metadata Architecture, Integration, and Storage
• Metadata Strategy and Implementation
• Purchase all six courses for $399 or individually at $79 each.
Register here
• Other courses available on Data Governance & Data Quality
52
Online Training Courses
Metadata Management Course
Visit: http://training.dataversity.net/lms/

DATAVERSITY Data Architecture Strategies
• January Panel: Emerging Trends in Data Architecture – What’s the Next Big Thing?
• February Building an Enterprise Data Strategy – Where to Start?
• March Modern Metadata Strategies
• April The Rise of the Graph Database: Practical Use Cases & Approaches to Benefit your Business
• May Data Architecture Best Practices for Today’s Rapidly Changing Data Landscape
• June Artificial Intelligence: Real-World Applications for Your Organization
• July Panel: Data as a Profit Driver – Emerging Techniques to Monetize Data as a Strategic Asset
• August Data Lake Architecture – Modern Strategies & Approaches
• Sept Master Data Management: Practical Strategies for Integrating into Your Data Architecture
• October Business-Centric Data Modeling: Strategies for Maximizing Business Benefit
• December Panel: Self-Service Reporting and Data Prep – Benefits & Risks
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This Year’s Line Up for 2018 – Join Us Next Month

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