Watch: https://bit.ly/2FLc5I2 Being able to maintain a well managed and curated Data Warehouse, along with keeping up with all of the demands of a very sophisticated consumer group can be a challenge. The new user wants access to data, they want to experiment, fail fast and if they do find usable insights/algorithms they want them productionized. This puts pressure on an IT organization and pushes them closer to a Bimodal operation where the regular IT processes that are highly curated, well defined and managed contrast sharply with the demands of the more sophisticated user. In the recently published TDWI Best Practices Report ,“Data Management for Advanced Analytics”, Philip Russom, DM for Advanced Analytics some of these newer requirements for the more sophisticated user are discussed in some length. How can IT support traditional demands around traditional BI and Reporting, whilst enabling the business with more demand for data and Advanced Analytics in mind? Attend and learn: - How data virtualization enables this Bi-Modal approach to Data Management. - How data virtualization enables compelling use cases for data management and advanced analytics - How we can achieve this important balance with process and technology.

1. DATA VIRTUALIZATION PACKED LUNCH
WEBINAR SERIES
Sessions Covering Key Data Integration Challenges
Solved with Data Virtualization

2. Enabling a BiModal IT Framework with Data
Virtualization
Emma Stein
Sales Engineer, Denodo
Paul Fearon
Senior Solutions Consultant, Denodo

3. Agenda1. Bimodal IT – the Pro’s and the Con’s
• The challenge for Advanced Analytics
2. Virtualization and the Bimodal approach.
• Demonstration
3. Q/A

4. Bimodal IT – the Pro’s and the
Con’s

5. 5
Source : Gartner Kick-Start Bimodal by Launching Mode 2
“Bimodal recognizes that there are areas of the enterprise that have more
certainty, objectives and clear cause and effect is understood, we can
predict and plan – Mode 1. In other areas, requirements are unclear and
changing, the relationship between action and outcome is uncertain, and
things are less well understood at the start – Mode 2”
Why Bimodal?

6. 6
What is Bimodal?
Predictable vs Experimental
Agility
Revenue, Brand,
Customer
Experience
Agile, Kanban,
low-ceremony
IID
Empirical,
continuous,
process-based
Small, new
vendors, short-
term deals
Good at new and
uncertain
projects
Business-centric,
close to
customer
Short (days,
weeks)
Goal Value Approach Governance Sourcing Talent Culture Cycle Times
Reliability
Price for
Performance
Waterfall, V-
model, high-
ceremony IID
Plan driven,
approval based.
Enterprise
suppliers, long
term deals.
Good at
conventional
process, projects
IT centric,
removed from
customer.
Long (months)
Mode 2
Mode 1
“Mode 1 is predictable,
improving and renovating in
more well-understood areas.”
“Mode 2 is exploratory,
experimenting to solve new
problems. “

7. 7
Great idea but a challenge to implement
Not popular with leadership
“ In the digital era, CIOs not buying ‘this bimodal crap’ ” – CIO magazine*
Just make everything AGILE (i.e. lose waterfall and everything is delivered in sprints).
New roles, new processes, the setup of a Bimodal org can be prohibitive
Highly integrated systems can cause ownership issues.
Splitting teams can cause staff challenges (morale, resignations, etc.).
Budgetary challenges (who gets what?).
* Ref – Clint Boulton – CIO Magazine May 2, 2017 https://www.cio.com/article/3193793/in-the-digital-era-cios-not-buying-this-bimodal-crap.html

8. 8
Challenges still exist for Advanced Analytics
TDWI Best Practices Report – Data Management for Advanced Analytics

9. Bimodal approach with Data
Virtualization
Crossover with Advanced Analytics challenges

10. 10
What is Data Virtualization?
Consume
in business applications
Combine
related data into views
Connect
to disparate data sources
2
3
1
DATA CONSUMERS
DISPARATE DATA SOURCES
Enterprise Applications, Reporting, BI, Portals, ESB, Mobile, Web, Users
Databases & Warehouses, Cloud/Saas Applications, Big Data, NoSQL, Web, XML, Excel, PDF, Word...
Analytical Operational
Less StructuredMore Structured
CONNECT COMBINE PUBLISH
Multiple Protocols,
Formats
Query, Search,
Browse
Request/Reply,
Event Driven
Secure
Delivery
SQL,
MDX
Web
Services
Big Data
APIs
Web Automation
and Indexing
CONNECT COMBINE CONSUME
Share, Deliver,
Publish, Govern,
Collaborate
Discover, Transform,
Prepare, Improve
Quality, Integrate
Normalized views of
disparate data
“Data virtualization
integrates disparate
data sources in real
time or near-real
time to meet
demands for
analytics and
transactional data.”
– Create a Road Map For A
Real-time, Agile, Self-
Service Data Platform,
Forrester Research, Dec 16,
2015

11. 11
BI and Analytics Reference Architecture

12. 12
Data Virtualization scope of responsibility?
Business
consumer
IT
Provisioned

13. 13
Bimodal approach..
Business
consumer
IT
Provisioned

14. 14
Business
consumer
IT
Provisioned
IT Provisioned views
Model Prototyping Model
Operationalization
From prototype to production

15. 15
Sophisticated tools for Sophisticated users
AA Users/Developers
Data Virtualization
Developers
Data
Scientists/Analysts

16. 16
Empower the user
• Users are much more sophisticated
• Tools are much more intuitive and user friendly
• Easy Discovery/Collaboration via Data Catalogs
• Users can curate their own views of data
• Data Scientists & Analysts create new models & views for
general consumption
• AA is ubiquitous (All types of consumers use productionized
AI/ML algorithms)
• IT provides views to source data and manage
security/governance
• IT manages “production” process

17. Product Demonstration
17
Sales Engineer, Denodo
Emma Stein

18. 18
Data Scientist Flow
Identify useful
data
Modify
data into
a useful format
Analyze
data
Execute data
science
algorithms
(ML, AI, etc.)
Share with
business users
Prepare for
ML algorithm

19. 19
https://flic.kr/p/x8HgrF
Can we predict the usage of the NYC bike
system based on data from previous years?

20. 20
Data Sources – Citibike

21. 21
There are external factors to consider.
Which ones?
https://flic.kr/p/CYT7SS

22. 22
What We’re Going To Do…
1. Search through the Data Catalog to identify useful
data sets
2. Prepare the data in the Design Studio
3. Analyze our datasets using Apache Zeppelin
4. Using Python, read the 2017 data and run it through
our ML algorithm for training
5. Use 2018 data to test the algorithm
6. Save the results and productionize our findings for
other users to explore
join
join
Citi Bike Weather Date
Apache Zeppelin

23. Demonstration
23

24. 24
Work as advisor as well as provider
• Bimodal organization may be a stretch but a bimodal approach to
information sharing is possible.
• Start with “Island” Projects. Use them to polish processes and
methodologies, before expanding to more broader projects that
have dependencies etc.
• Connect business team with IT ambassadors (you already have
them) and define workable communication methodologies.
• Data is a complicated asset, use the tools and education to give
consumers important insight (a hammer is useless until you learn
how and when to use it).
• Define a change control process that makes it easy for AA users to
productionize insight

26. 26
Next Steps
Access Denodo Platform in the Cloud!
Take a Test Drive today!
www.denodo.com/TestDrive
G E T S TA R T E D TO DAY

27. Thanks!
www.denodo.com info@denodo.com
© Copyright Denodo Technologies. All rights reserved
Unless otherwise specified, no part of this PDF file may be reproduced or utilized in any for or by any means, electronic or mechanical, including photocopying and microfilm,
without prior the written authorization from Denodo Technologies.