Modern Data Integration Expert Session Webinar

Modern Data Integration
William McKnight
Jake Freivald
Information Builders
McKnight Consulting Group
Expert Sessions

Copyright © 2018 McKnight Consulting Group, LLC All Rights Reserved Slide 2
Unlock Potential
William McKnight
www.mcknightcg.com
214-514-1444
Modern Data Integration - Expert Sessions
@williammcknight

Data is the Most Important Asset
in the World
• We trade it for services instead of money
• Our information is exploding
• Business is moving to real-time, all the time
• Our information differentiates us from our
competitors
• Information is a key business asset

Corporate Initiatives
 80% of Initiatives That Matter are about DATA
• Budget
• Energy
 80% of Initiatives should be Business-Focused
• ROI
• Resource-Leveled

Copyright © 2018 McKnight Consulting Group, LLC All Rights Reserved Slide 5Copyright © 2018 McKnight Consulting Group, LLC All Rights Reserved Slide 5
Data Maturity is Highly Correlated to
Business Success
Data
Maturity
Business
Success

The Money Tree Doesn’t
Exist
Hitch your Architecture and Maturity Efforts to an Application Budget

AI is disruptive
Data is the Foundation

Choosing a Platform: 3
Major Decisions
 Decision #1: The Data Store Type
• The largest factor for distinguishing between databases and file-based scale-out system utilization is the data profile. The latter is
best for data that fits the loose label of 'unstructured' (or semi-structured) data, while more traditional data -- and smaller
volumes of all data -- still belong in a relational database.
 Decision #2: Data Store Placement
• You must also decide where to place your data store -- on-premises or in the cloud (and which cloud). In the past, the only clear
choice for most organizations was on-premises data. However, the costs of scale are gnawing away at the notion that this
remains the best approach for a data platform. For more on why databases are moving to the cloud, please read this article.
 Decision #3: The Workload Architecture
• Finally, you must keep in mind the distinction between operational or analytical workloads. Short transactional requests and
more complex (often longer) analytics requests demand different architectures. Analytics databases, though quite diverse, are
the preferred platforms for the analytics workload.
8

Data Everywhere
And in Numerous Technical Forms
And in Numerous Clouds

,
Low Maturity Data
Integration

Leverageable Vehicles
 Data Warehouse
 Master Data Management
 Data Lake

Points of Data Integration
• Into the Data Warehouse(s)
• Into the Data Marts/cubes that do not integrate with the data warehouse
• Into the Data Marts/cubes that do integrate with the data warehouse
• Into Big Data platforms from sensor, clickstream, other systems
• Into Big Data platforms from Data Stream Processing
• Into the Master Data Management Hub from publishing/master systems
• From the Master Data Management Hub to every subscribing system (ERPs, NoSQL, Hadoop, data
warehouse, analytical databases, etc.)
• Between analytical stores
• Between operational stores
• Summaries of Big Data for the data warehouse and other analytical stores
• Data migrations for setting up new environments
• Etc.!

Modern Realities of Data
Integration
 Desire for consolidated methods for data integration
 New types of data sources
• Logs, sensors, etc.
 We have more than OLTP and OLAP
• Distributed data platforms
 Desire for real-time data
 High-velocity data increasingly needs integration
 Traditional approaches, without Stream Processing, turn
into ETL+custom scripts+middleware+MQ

Real-Time Data
 A.k.a. messaging, live feeds, real-time, event-driven
 Comes in continuously and often quickly, so we call
it streaming data.
 Needs special attention and can be of immense
value, but only if we are alerted in time.
 Foundation for Artificial Intelligence
• Stream data forms the core of data for artificial
intelligence

Message-Oriented Middleware / Message
Queueing Technology
 An architectural component that deals with messages
 Manage and distribute streaming data
• Any kind of data wrapped in a neat package with a very simple
header
• Sent by “producers”—systems, sensors, or devices that generate
the messages—toward a “broker”.
• Routes them into queues according to the information enclosed in the
message header or its own routing process
• “Consumers” retrieve the messages from the queues to which they
subscribe
• Open the messages and perform some kind of action on them.

Streaming Architecture
Apps
Streaming
Platform
Change logs
Streaming data pipelines
Messaging or
Stream processing
Request - Response
DW Hadoop

Every Project is Burdened
(with Grander Opportunity)

Data Success Measurement
User Satisfaction
Business ROI and
growth instigated
Data Maturity
(Long-term User Sat
and Bus ROI)
Misc.

“Beyond the Mountain is
another mountain.”

Champion Initiatives That
Matter
 Every single item on a company mission statement
relates to data at some level
 It is from the position of data expertise that the
mission will be executed and company leadership
will emerge
 The data professional is absolutely sitting on the
performance of the company in this information
economy and has an obligation to demonstrate the
possibilities and originate the architecture, data
and projects that will deliver.
 It’s not enough to be responsive to urgent requests
and be the data leader that companies need.

Unlock Potential
William McKnight
www.mcknightcg.com
214-514-1444
Modern Data Integration - Expert Sessions
@williammcknight

Problems with Normal Data Integration Processes
Data modeling. Too much time spent coping with slight changes
in our business data
Business/IT alignment. Data architects, DBAs, and others can’t
communicate with businesspeople
Processes. Too much detail lost by handing off responsibility for
business data to different people

Problem: Data Modeling
Too much time spent coping with slight changes in our business data
Johann Sebastian Bach
Given Middle Family

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Too much time spent coping with slight changes in our business data
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Repeated changes in operational systems’ row-and-column structures

Ripple effects of changes in one system lead to changes in others
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Given Middle FamilyHon
Mohamed el
Muhammad Qasabgial
Patronymic Art
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Operational, designed for transactions
Data warehouse, designed for abstractions
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Data mart, designed for analysis
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Given Middle FamilyHn
Mohamed el
Muhammad Qasabgial
Patronymic Art
Ludwig vn Beethoven
ChenYi
Mougi
Given Middle Family
Dmitri Shostakovich
Mohamed
Muhammad Qasabgi
Ludwig Beethoven
ChenYi
Sebastian
Mougi
Given Middle Family
Dmitri Shostakovich
Mohamed
Muhammad Qasabgi
Ludwig Beethoven
ChenYi
Sebastian
Sebastian
Sebastian
Sebastian
Sebastian

Problem: Business/IT Alignment
Data people often can’t communicate with businesspeople
Data architect thinks
 Model the data
 Govern the data
 Watch out for “quick fixes”
IT:
Gets it
That modeling stuff
we just talked about
Business:
Hates it
Business thinks
 Modeling, metadata are hindrances
 Analytical tools best without governance
 IT slows them down

Problem: Processes
Too much information lost by distributing responsibility for business data
Cleansing occurs in transformation step: Different rules being fired
Different tools and metadata being used by platform
Loss of timestamps, context, before-and-after: No cross-platform auditability
No comprehensive rollback, alternate history, what-if
Operational
application
Data
warehouse
Cloud
application
F
a
m
i
l
y
Transformation
Cleansing
Standardization
Transformation
Cleansing
Standardization

F
a
m
i
l
y
F
a
m
i
l
y
How much time do we
spend mapping one set
of rows and columns
to another?
A modern solution:
post-relational for data capture, transformation,
subject-oriented storage (perhaps), and exchange,
rich documents instead of relational models
Operational
application
Data
warehouse
Analytics

How much time do we
spend mapping one set
of rows and columns
to another?
A modern solution:
post-relational for data capture, transformation,
subject-oriented storage (perhaps), and exchange,
rich documents instead of relational models
Operational
application
Data
warehouse
Analytics

Operational
application
Data
warehouse
Analytics
A modern solution:
ELT capture/integrate to capture data as it is,
time-stamped apply trustworthy processes to it,
subject-oriented and share it in trusted ways
How much info
do we lose
by distributing
ETL processes?

Operational
application
Analytics
Data Capture/Transformation Hub
Transformation
Cleansing
Standardization
Application
to business
use cases
How much info
do we lose
by distributing
ETL processes?
A modern solution:
ELT capture/integrate to capture data as it is,
time-stamped apply trustworthy processes to it,
subject-oriented and share it in trusted ways

Modern Data Integration: The Omni-Gen Approach
We built software to make ourselves successful
 Immediate capture in automatically generated data hub
 Master data: business-user-oriented, subject-oriented
 Rapid, integrated data quality rules
 Mastered and transactional subjects
 Rapid cycle times to keep the business engaged
 Support and automatically apply best practices

Modern Data Integration: The Omni-Gen Approach
Extending Value
We built “persona models” for customer and supplier
Everything you get in Omni-Gen, plus
 Pre-built models
 Pre-built data quality rules
 Pre-built match/merge rules
 Pre-built data governance
 Immediate 360° core view, unlimited extensions
 Supports different consumers with different, but trusted, data

Omni-Gen: More Value in Far Less Time
12-181-3 4-6
Project timeline, in months
Traditional
Data management tools
Build-it-yourself development environment
Omni-Gen
Software solution with built-in best practices
MDM, DQ, integration software with rules,
automatically generated data vault, remediation portal,
360° viewer, history, data interfaces, APIs, and feeds
Omnifor
Persona
Software solution with built-in best practices and complete master data models
Data vault model, data onramps; MDM, data quality, and integration software; MDM
and data quality rules, remediation portal, 360° viewer; Data interfaces, APIs,
history, & feeds; Analytical foundation for dashboarding, advanced analytics, more.

Modern Data Integration Expert Session Webinar

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