Data Architecture Best Practices for Today’s Rapidly Changing Data Landscape

Data Architecture Best Practices for
Today’s Changing Data Landscape
Donna Burbank, Managing Director
Global Data Strategy, Ltd.
May 24th, 2018
Follow on Twitter @donnaburbank
Twitter Event hashtag: #DAStrategies

DATA INTEGRATION SOFTWARE ❏
❏
❏

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 - on demand Modern Metadata Strategies
• April - on demand The Rise of the Graph Database
• 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
• With the rise of the data-driven organization, the pace of innovation in data-centric technologies
has been tremendous. New tools and techniques are emerging at an exponential rate, and it is
difficult to keep track of the array of technological choices available to today’s data management
professional.
• At the same time, core fundamentals such as data quality and metadata management remain
critical in order for organizations to obtain true business value from their data.
• This webinar will help demystify the options available:
• from Data Lake to Data Warehouse
• to Graph database
• to NoSQL, and more
• and how to integrate these new technologies with core architectural fundamentals
• that will help your organization benefit from the quick wins that are possible from these exciting
technologies, while at the same time build a longer-term sustainable architecture that will
support the inevitable change that will continue in the industry.
4

What is Data Architecture?
• The DAMA Data Management Body of Knowledge (DMBOK), defines data architecture as the
following:
• “Data Architecture is fundamental to data management. Because most organizations have more
data than individual people can comprehend, it is necessary to represent organizational data at
different levels of abstraction so that it can be understood and management can make decisions
about it.
• … Data Architecture artifacts include specifications used to describe existing state, define data
requirements, guide data integration, and control data assets as put forth in data strategy.
• An organization’s Data Architecture is described by an integrated collection of master design
documents at different levels of abstraction, including standards that govern how data is
collected, stored, arranged, used, and removed. It is also classified by descriptions of all the
containers and paths that data takes through an organization’s systems.”
5
DMBOK Definition

What is Data Architecture?
• Survey respondents from a 2017 DATAVERSITY survey on “Emerging Trends in Data Architecture”.
also provided a range of relevant definitions including:
“…the practice of examining the enterprise strategy, and identifying the key Data Integration points
that need to be enabled to execute that strategy, and laying out a roadmap for creating the core
capabilities to deliver those integrations, so that companies can leverage data as a strategic
asset.”
“…the science of assessing where your company data is, and the art of designing the best future
way of storing data across the enterprise.”
“…the discipline to help businesses manage their data in the most effective, secure, compliant, and
profitable way.”
6
Survey Respondents Provided a Range of Views
From Trends in Data Architecture 2017, by Donna Burbank & Charles Roe

Business Drivers for Data Architecture
• As more organizations see
data as a strategic asset, and
with the drive towards Digital
Business Transformation on
the rise, the need to analyze,
understand & govern core
data assets continue to be a
key goal.
7
What’s Driving the Need?

Global Data Strategy, Ltd. 2018 8
A Successful Data Strategy links 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
Copyright 2018 Global Data Strategy, Ltd
Aligning Business Strategy and Data Strategy

Developers
Managers
Data Architecture: Finding the Right Fit
9
A Successful Data Architecture Enforces Fit for Purpose Solutions
Operational Data Reporting & Analytics Master & Reference Data Metadata
CRM
Customer X orders
Product Y at 2pm on
Oct 24, 2017 Sales
CRM
ERP
Customer
Care
IoT
Customer X calls
Support at 1pm on
Nov 1, 2017
Inventory consists of x
number of Product X
components on Oct
24, 2017
Supply
Chain
Customer turns on
foot warmer at 11pm
on Oct 30, 2017
Product
Team
Customer
CRM
& other
systems
DW
What were total
sales for Product X
in 2016 by region?
Lake
Operational Reporting
Enterprise Historical Reporting
Analytics & Discovery
What variables
most influence
customer repeat
purchases?
Limited Personal Use
Limited ad hoc analysis
for small data sets.
Not recommended
for enterprise data
management.
Customer
Master
Product
Master
“Golden Record” for Customer,
Product, etc.
Mary Smith lives on 101 Main ST,
Minneapolis, MN and has been a
customer since 2011
SuperProd has a product code of
SS720 & a suggested retail price
of $11,000 USD.
Business & Technical Context &
Descriptions
ELT
How many support
calls are currently
open?
Analytics
Team
Managers
Reference Data
Hierarchies
The Sales management reporting
hierarchy is structured as follows.
Valid Return Codes are “X, Y, & Z”
State Codes include MA, MD MI …
Applications
DW Etc
DW Etc
DW Etc
Business
Glossary How is Total Sales
calculated?
What is a Qualified
Lead?
Business
Users
Data
Models
How do we uniquely
identify a customer?
Can a customer have
more than 1 email?
Data
Dictionary
What is this DW table
used for?
The standard length for
customer ID is CHAR(12)
Developers
Data
Lineage
How was this field
calculated?
What will break
downstream if I make a
change?
Developers
Developers
Business
Users
Access

Industry Trends: Data Platforms are Currently in Use?
• A wide range of technologies are
currently in use:
• Relational databases most common
o Both Cloud & On-Premises
• Spreadsheets ubiquitous
10
“Which of the following data sources or platforms are you currently using?
[Select all that apply]
Relational Databases
are still clearly the
leader.
Spreadsheets are
ubiquitous
More Legacy
platforms (44.6%)
than Big Data (42.2%)
From Emerging Trends in Data Architecture, DATAVERSITY, by
Donna Burbank & Charles Roe, October 2017

Industry Trends: Emerging Technologies
11
“Which of the following do you plan to use in the future that you are not using
currently? [Select all that Apply]”
Many looking to Big
Data Platforms
Movement to the
Cloud is popular
Uncertainty is
common.
• For those looking at new
technologies, there is a wide range
of responses.
• Big Data Platforms a leader
• Move to Cloud RDMBS
• Graph Database
• Real-time Streaming
• Internet of Things (IoT)
• Many are still uncertain, indicating
the vast rate of change and wide
array of choices available.
From Emerging Trends in Data Architecture,
DATAVERSITY, by Donna Burbank & Charles Roe,
October 2017

Big Data a Growing Trend
• Over 70% of organizations are
either using Big Data solutions, or
planning to in the future.
• Analysis & Discovery are leading
trends including:
• Data Science & Discovery
• Reporting & Analytics
• “Sandbox” Exploration
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Analysis & Discovery are Key Drivers

Big Data Concerns
13
• The Complexity of current Big
Data solutions & the Skills
Required to manage them
were also common issues.
• Security is a leading concern, and Data
Governance was a top write-in response.

Data Lakes
• There was a mixed response
to Data Lake Implementation
14

Data Warehouse vs. Data Lake
15
Data Warehouse Data Lake
A Data Lake is a storage repository that holds a vast
amount of raw data in its native format, including
structured, semi-structured, and unstructured data.
The data structure & requirements are not defined until
the data is needed.
A Data Warehouse is a storage repository that holds current
and historical data used for creating analytical reports. Data
structures & requirements are pre-defined, and data is
organized & stored according to these definitions.

Integrating the Data Lake & Traditional Data Sources
• The Data Lake has a different architecture & purpose than traditional data sources such as data
warehouses.
• But the two environments can co-exist to share relevant information.
16
Data Analysis & Discovery – Data Lake Enterprise Systems of Record
Data Governance & Collaboration
Master &
Reference Data
Data Warehouse
Data MartsOperational Data
Security & Privacy
Sandbox
Lightly Modeled
Data
Data
Exploration
Reporting & Analytics
Advanced
Analytics
Self-Service BI
Standard BI
Reports

The Data Ecosystem
• Know what to manage closely and what to leave alone
• The more the data is shared across & beyond the organization, the more formal governance needs to be
17
Core Enterprise
Data
Functional & Operational
Data
Exploratory Data
Reference &
Master Data
Core Enterprise Data
• Common data elements used by multiple
stakeholders colleges, departments, etc. (e.g. DW)
• 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. Student name, Address, Courses, etc.)
Master & Reference Data
• Common data elements used by multiple stakeholders
across functional areas, applications, etc.
• Highly governed
• Highly published & shared
Examples
• Reference Data: Department Codes, Country Codes, etc.
• Master Data: Student, Faculty, etc.
Exploratory analysis
uses core data sets
when applicable
Derived variables of
value can be fed into
Core Enterprise, or
even Master Data.
PublishPromote

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,
Exploratory and/or 3rd party data sets

Crowdsourcing Governance & Metadata
• Many data governance projects (& vendors) are embracing the concept of “crowdsourcing”. i.e. The
Wikipedia vs. Encyclopedia approach
• Open editing
• Popularity & Usage Rankings
• Dynamically changing
19
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

Who is Responsible for Creating a Data Architecture?
• With a greater business focus on
data and a wider range of
technologies associated with Data
Management…
• … it is not surprising that there is a
concomitant rise in the diversity of
roles responsible for developing a
Data Architecture.
• … the role of the data architect, not
surprisingly, continues to play a
large role.
20
Wide Range of Responses shows Need for Collaboration
Collaboration is Key
From Trends in Data Architecture 2017, by Donna
Burbank & Charles Roe
Wide range
of roles

Moving to the Cloud
• As indicated in the previous response re: Future Technologies, adoption of Cloud Technologies is
on the rise, with over 75% of respondents currently implementing a Cloud strategy, or planning
to in the future.
21
A Growing Trend
Scalability & Cost Savings are leading reasons for moving to the Cloud

Cloud Migration not without its Concerns
• Security & Privacy are
leading concerns for those
moving to the Cloud.
22
What are your Concerns regarding moving data to the Cloud? [Select All that Apply]

Master Data Management
• As more and more organizations struggle with obtaining a single, consistent view of core data
such as Product, Customer, Vendor, and Location, Master Data Management (MDM) is seeing a
rise in implementations with over 65% of respondents pursuing a MDM strategy.
23
• Master Data is the consistent and uniform set of identifiers and
extended attributes that describes the core entities of the
enterprise including customers, prospects, citizens, suppliers, sites,
hierarchies and chart of accounts (sic).
• Master data management (MDM) is a technology-enabled
discipline in which business and IT work together to ensure the
uniformity, accuracy, stewardship, semantic consistency and
accountability of the enterprise's official shared master data assets.
- Source GartnerFrom Trends in Data Architecture 2017, by Donna

Master Data – the Opportunity
24
A 360 Degree View through Data
Stefan Krauss
Age = 31
Occupation = Ski Instructor Purchased €500 in
outdoor gear in 2016
100% of purchases online
Top Finisher in Engadin Ski
Marathon 2010-2015
Member of Loyalty
Program since 2010
Prefers Text Message
Address = Pontresina, Switzerland

Optimizing Restaurant Revenue through Menu Data
• An international restaurant chain realized through its digital strategy that:
• While menus are the core product that drives their business…
• They had little control or visibility over their menu data
• Menu data was scattered across multiple systems in the organization from supply chain to kitchen prep to marketing,
restaurant operations, etc.
• Menu data was consolidated & managed in a central hub:
• Master Data Management created a “single view of menu” for business efficiency & quality control
• Data Governance created the workflow & policies around managing menu data
25
Managing the Data that Runs the Business
Product Creation &
Testing
Menu Display &
Marketing
Supply Chain Point of Sale &
Restaurant Operations

Transaction Data vs. Master Data
Customer Date Product Code Price Quantity Location
Stefan Kraus 1/2/2017 Scarpa Telemark Ski Boot SC1279 €250 1 St. Moritz, CH
Donna Burbank 1/5/2017 Scarpa Telemark Ski Boot SCU1289 $150 1 Boulder, CO
Stefan Kraus 1/2/2017 North Face Down Jacket NF8392 €450 1 Zurich, CH
Stefan Kraus 1/2/2017 Garmin Sports Watch GM29384 €200 2 Zurich, CH
Wendy Hu 3/4/2017 Prana Yoga Pant PN82734 $51 5 New York, NY
Joe Smith 4/1/2017 Garmin Sports Watch GM29384 $150 1 Albany, NY
26
Consider the following retail transaction data
Transaction Data
• Describes an action (verb): E.g. “buy”
• May include measurements about the action: (Who, When,
What, How Many, Where, How Much, etc.)
• E.g. Stefan Kraus, 1/2/2017/, Scarpa Telemark Ski Boot, St.
Moritz, CH, €250
Master Data
• Describes the key entities (nouns), e.g. Customer, Product,
Location
• Provides attributes & context for these nouns
• e.g. Wendy Hu, age 25, female, resident of New York, NY,
Customer since 2005, preferred customer card, etc.

Customer Date Product Code Price Quantity Location
Stefan Kraus 1/2/2017 Scarpa Telemark Ski Boot SC1279 €250 1 St. Moritz, CH
Donna Burbank 1/5/2017 Scarpa Telemark Ski Boot SCU1289 $150 1 Boulder, CO
Stefan Kraus 1/2/2017 North Face Down Jacket NF8392 €450 1 Zurich, CH
Stefan Kraus 1/2/2017 Garmin Sports Watch GM29384 €200 2 Zurich, CH
Wendy Hu 3/4/2017 Prana Yoga Pant PN82734 $51 5 New York, NY
Joe Smith 4/1/2017 Garmin Sports Watch GM29384 $150 1 Albany, NY
Transaction Data vs. Master Data
27
Master Data:
Customer
Master Data: Product
Master Data: Location
Reference Data:
Country Codes
Reference Data:
State Codes
Transaction
Data

Relational Databases & Transaction Data
• Relational Databases are well-suited for transaction data:
• Avoid Redundancy & Duplication: Through referential integrity, normalization
• ACID Transactions (Atomicity, Consistency, Isolation, Durability) - guarantee validity even in the event
of errors, power failures, etc.
• Ease of Query via SQL: Table-based structure allows for ease of query.
28
Relational Databases are the workhorses of many organizations
Relational Database

ETL
Master Data Solutions Can Help Create a
Customer “Golden Record”
29
CRM In-Store
Sales
MarketingFinance Online
Sales
Supply
Chain
MDM
“Golden Record”
Data
Warehouse BI & Reporting
Data Model
Lookup
End User Applications
Reference
Data Sets
Data Quality
& Matching
Publish & Subscribe

Graph Databases
• With graph databases, the relationships between data
points often matter more than the individual points
themselves. In order to leverage those data relationships,
your organization needs a database technology that stores
• A graph database uses a set of nodes, edges, and
properties to represent and store data.
• These relationships can help you discover new insights
from your data.
• Common use cases include:
• Social Networks
• “Enterprise Knowledge Graph”
• Fraud Detection
• Recommendation Engines
30
You bought this…
You may also like
this…

No SQL
• No SQL databases such as key value stores can store customer information such as Web session
logs, user preference and profile stores, etc.
31
Fast response and high volume for web sessions, profiles, etc.
Web pages from scarpa.com

Data Models
• Data Models can provide a holistic view of the organization
• Provide a helpful overlay against
• Business Process
• Technical Architecture
• Organizational Capability
• Etc.
32
Address
Customer
Contact
Order
Request
Product
Schedule
Company
Invoice
Payment
Sales Rep

Data Modeling is Hotter than ever
33
In a recent DATAVERSITY survey,
over 96% of were engaged in Data
Modeling in their organizations.
Sexiest job of the 21st Century?

Mapping Data to Business Process
• Process models are a helpful tool for describing core business processes.
• “Swimlanes” outline organizational considerations
• Data can be mapped to key business processes to understand creation & usage of information.
34
Identifying key data dependencies in core business processes

Business Capability Models
• A business capability model outlines the core functional areas of the organization.
• Note: this is not the same as an organizational chart
• Capabilities can be overlayed with key data domains to create a “heat map” of cross-functional data usage.
35
Core Business
Shared Services
Artful Art’s Business Capabilities
Etc. – sample subset only
Product Development
R&D
Product
Management
Product
Manufacturing
Packaging
Marketing
Product
Messaging
Branding
Product
Launch
Campaign
Development
Lead
Generation
Pricing
Sales
Pipeline
Management
Customer
Relationship
Quotes &
Tenders
Research & Development Branding & Go-to-Market
Partner
Management
Sales & Distribution
Human Resources
Recruitment
Employee
Training
Performance
Management
Legal
Compliance
Contract
Management
Data Domains
Customer
Product
Account
Etc.

Data-Driven Merger for Financial Services
The combined information assets of both companies is one of our biggest strategic advantages.
- CEO
• A key driver for a recent merger of two large financial institutions was the integration of
data assets
• Streamlining the merger of the two organizations by integrating the data assets
• Identify ways in which data can be used to strategic advantage
• Organizational Structure & Business Capability Alignment were critical
• Understanding how data was used across the organization
• Identifying efficiencies & opportunities for collaboration Business Capabilities
Company A
Business Capabilities
Company B
Common Data Foundation

CRUD Matrix
Product
Development
Supply Chain
Accounting
Marketing Finance
Product Assembly Instructions C R
Product Components C R
Product Price C U R
Product Name C U,D
Etc.
37
Create, Read, Update, Delete
• CRUD Matrices shows where data is Created, Read, Updated or Deleted across the
various areas of the organization
• This can be a helpful tool in data governance & data quality.

Process Models & CRUD Fit Well Together
• Business Process Models describe key activities within the organization.
• Linking these processes to the data that is Created, Updated, or Deleted (CRUD) is important
to understanding data usage.
Customer Order Account Invoice Product
Receive Customer Order R C C, R
Process Customer Order C,R,U R,U R
Fill Order R,U R,U R,U
Send Invoice R,U R,U C
CRUD Matrix
Business Process Model

Architecture helps create “Quick Wins”
• Data Architecture aligned with business architecture helps identify key pain points &
opportunities
Customer Order Account Invoice Product
Receive Customer Order R C C, R
Process Customer Order C,R,U R,U R
Fill Order R,U R,U R,U
Send Invoice R,U R,U C
CRUD Matrix
Business Process Model
• Customer Contact Information is not Consistent throughout the Customer Journey
• In other words, we can’t follow up with our customers to:
• Process their orders
• Send an invoice
• Join a Loyalty Program
• Etc.

Metadata Drives FTLB1 – Faster Time to Light Bulb
Comprehensive, documented metadata helps organizations make better, faster, more cohesive decisions.
40
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.

Optimizing Customer Experience through Better Data
• A major Retail Vendor wanted to become a data-driven company
• Enhancing the customer experience by mapping the Customer Journey to the Data Lifecycle
• Using IoT product data to improve product design & customer service
• Optimizing product supply chain & delivery
• Developing a Tactical Data Strategy determined that
• Master Data Management was needed to manage customer contact data throughout the Customer Journey
• Data Governance was required to manage data across organizational siloes: product development, marketing, sales, etc.
• Data Architecture was needed to understand the data ecosystem: data flow diagrams, data models, process models, etc.
41
Improving Customer Contact Data to Build Customer Loyalty & Increase Sales
Customer SupportCustomer Discovery &
Purchase
Product Delivery &
Tracking
Product Usage Tracking Product Development

Summary
• A Successful Data Architecture creates fit for purpose solutions using the right
technology for the right job
• “Fit for Purpose” depends largely on business drivers and use cases
• Collaboration is key as more and more roles are involved in data architecture
• Data Architecture fundamentals such as:
• Data models
• Process models
• Organizational capability models
• and more…
are critical in aligning business goals with data implementations
• A Robust Data Architecture fundamentals helps identify & solve “quick wins”, and
communicate the issues effectively across the enterprise.

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.
43
Data-Driven Business Transformation
Business Strategy
Aligned With
Data Strategy
www.globaldatastrategy.com

White Paper: Trends in Data Architecture
44
Free Download
• Download from
www.globaldatastrategy.com
• Under ‘Resources/Whitepapers’

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
45
This Year’s Line Up for 2018 – Join Us Next Month

Questions?
46
Thoughts? Ideas?

Data Architecture Best Practices for Today’s Rapidly Changing Data Landscape

Data Architecture Best Practices for Today’s Rapidly Changing Data Landscape

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