Watch full webinar here: https://bit.ly/38F77WN A successfully implemented self-service initiative means that business users have access to holistic and consistent views of data regardless of its location, source or type. However, companies must also ensure that while unlocking the full potential of data for business users, they maintain security requirements. Data virtualization as a governed data service layer can not only help organizations achieve a unified data access layer that provides integrated views of data to business users in real time, but also allows the organization to establish governance protocols and specify authoritative sources. What will we talk about? - Challenges faced by business users - How data virtualization enables self-service analytics - A demonstration - A customer case study

1. How to Achieve Self-Service
Analytics with a Governed Data
Service Layer (UK)
Paul Moxon
SVP Data Architectures & Chief Evangelist, Denodo
5th February 2020

2. Paul Moxon
SVP Data Architectures & Chief
Evangelist, Denodo
Speakers

3. 1. Data Challenges
2. Self-Service Initiatives
3. Governed Self-Service
4. Demo
5. Q&A
6. Next Steps
Agenda

4. Data Challenges

5. 5
The Economist, May 2017
The world’s most valuable resource
is no longer oil, but data.

6. 6
Data – Like Oil – Is Not Easy To Extract and Use

7. 7
The Data is Somewhere in Here…

8. 8
Business Dependency on IT to Deliver Data
IT DepartmentBusiness
“You’re too slow, too
expensive, and never
deliver what I want.”
“You can’t make up your
mind, keep adding
features, and never see
the big picture.”
Casual User: “Just
forget it.”
Power User: “Just give
me a data dump.”
BU Leader: “We’ll do it
ourselves.”
“I’d rather be doing
something else than
taking your order.”
“You’ll come crawling
back to us soon.”

9. Self-Service Initiatives

10. 10
• Let business users access the data
that they need and stop IT being a
bottleneck
• That’s the vision as sold by many BI
tool and Data Prep vendors
• i.e. give me the tools and
access to the data and stand
back ☺
The Promise of Self-Service

11. 11
Self-Service to the Rescue!
Not so fast!
• No backlogs and waiting
• Better tools
• Faster time to insight
IT Department
• Happy users
• Offload reports
• Focus on data
Business Users

12. 12
Challenges of Self-Service Initiatives
• Too many reports
• Duplicate reports
• Conflicting data
• Users don’t trust
reports
• Data extract hell NO STANDARDS OR GOVERNANCE
A TOWER OF BABEL

13. Governed Self-Service

14. 14
Needs of Different User Communities
60% of employees
Data Consumers Data Explorers
30% of employees
8% of employees
Data Analysts
CASUAL USERS
POWER USERS
Data Scientists
2% of employees
‘WHITE
GLOVE’
SERVICE
SELF
SERVICE
TopDownBottomUp

15. 15
Casual Users
• Want answers…not data
• Pre-integrated, pre-calculated, curated results
• Usually not technical users
• *Might* understand SQL, etc. – but not always…
• Want to use the mechanisms and tools that they are familiar with
• e.g. Reports or dashboards
• e.g. Excel (load data into Pivot Table)
• Do not want to ‘wrangle’ the data to get the results
• Do not make life difficult for them!
Data Consumers Data Explorers

16. 16
Power Users
• They want data!
• Want to analyze data, look for patterns, similarities, etc.
• Typically technical – most understand SQL and how to query data
• Want to use existing tools to access data
• BI tools (Tableau, Qlik, etc.)
• Analytics tools and languages (Statistica, SAS, Python, etc.)
• Don’t mind wrangling data, but don’t want this to be time consuming
• Often have their own data sets they want to integrate
Data Analysts Data Scientists

17. 17
The Challenge of Self-Service
Governance
Standards
Architecture
Operations
Centralization
Self-Service
Speed
Agility
Innovation
Decentralization
Create a data-
driven enterprise
that balances
dueling
imperatives.
Create a data-driven
organization that
balances dueling
imperatives.

18. 18
Power User Sandboxes
Data Virtualization – Governed Self-Service
Systems of Record Data Lake Data Warehouse
Common (or Core) Views
Curated Views (Virtual Data Marts)Customer VIews Promotion Views

19. 19
Common (or Core) Data Views
• Foundation of architecture to support
casual and power users
• Base views and derived views built on
top of data sources
• Hosted on ‘central’ Denodo server(s)
• Owned and maintained by IT (CoE Team)
• Available to all users as needed

20. 20
Curated Data View (Virtual Data Marts)
• Views created for specific users or
applications
• ‘Fit for purpose’ curated data sets
• Virtual Data Marts
• Hosted on central Denodo server(s)
• Owned and maintained by IT (CoE)
• Available to all users as needed

21. 21
Power User Sandbox
• Sandbox environment for a ‘power user’
• Local copy of Denodo running on desktop
• Dedicated Virtual Database (VDB) running in
shared Denodo sandbox server
• Dedicated Virtual Database running on central
Denodo server
• Integrate local data with common or curated data
from central Denodo server
• Keeps ‘shadow’ data off central server
• Provides guard rails for accessing common or
curated views
• IT can control what power users can and cannot
do via Resource Manager policies or constraints
in ‘self-service views’

22. 22
The Need for a Consumer Data Catalog
• Data Virtualization Platform delivers (a lot of)
data to users
• How do users know what data is available?
• What is the schema of the data?
• Where did the data come from? (Lineage)
• Is it an ‘approved’ data set?
• Can they get a sample of the data?
• Can they select only some of the data? (rows
and columns)

23. 23
Data Catalog with Data Access

24. Demo

25. 25
What We Will Do in the Demo
1. Customer Birthday Cards Project
• As a ‘casual user’ (Joe Smith), we’ll browse the data catalog to find the customer information
that we need
• Use Excel to get the data for the customer (name, address, DoB) to send them a birthday card
on their birthday
2. Customer Sentiment by Household Demographic Project
• As a ‘power user’ (Mary Weaver), browse the data catalog to find the customer data that we
can use for our sentiment analytics
• Access the customer data from the ‘corporate’ Denodo Server using our Denodo ‘sandbox’
environment
• Combine this with our local Twitter data (CSV file) to perform sentiment analysis

26. Summary

27. 27
Key Takeaways
FIRST
Takeaway
Self-service analytics can unleash the power and creativity
of your users
SECOND
Takeaway
Ungoverned self-service is a recipe for chaos and untrusted
analytics
THIRD
Takeaway
Data Virtualization provides a governance and management
infrastructure necessary for successful self-service initiatives
FOURTH
Takeaway
Coupled with a ‘consumer’ data catalog, Data Virtualization
enables ‘self-service with guard rails’

28. Q&A

29. 29
Next Steps
Access Denodo Platform in the Cloud!
Take a Test Drive today!
www.denodo.com/TestDrive
GET STARTED TODAY

30. Thanks!
www.denodo.com info@denodo.com
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