Date: 13th November 2018 Location: Governance and MDM Theatre Time: 11:50 - 12:20 Speaker: Mark Pritchard Organisation: Denodo About: Self-service analytics promises to liberate business users to perform analytics without the assistance of IT, and this in turn promises to free IT to focus on enhancing the infrastructure. Join us to learn how data virtualization will allow you to gain real-time access to enterprise-wide data and deliver self-service analytics. We will explore how you can seamlessly unify fragmented data, replace your high-maintenance and high cost data integrations with a single, low-maintenance data virtualization layer; and how you can preserve your data integrity and ensure data lineage is fully traceable.

1. Mark Pritchard, Sales Engineering
November, 2018
Realising the Promise of
Self-Service Analytics
with Data Virtualization

2. Self-Service Analytics
The Challenge
1

3. 2
Self-Service Heaven
The Promised Land of Self-Service Initiatives
§ Let business users access the data that they need
§ Stop IT being a bottleneck
That’s the vision promoted by many BI tool vendors
§ Give me the tools and access to the data and stand back J
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4. 3
Liberate business users to perform analytics without the assistance of IT
The Promised Land of Self-Service Analytics
MarketingSales ExecutiveSupport
Access to complete information:
business entities and pre-integrated
views
Access to related information: discovery
and self service
Access data in real-time from different
tools, applications and devices
Customers
Invoices Products
Service
Usage

5. 4
The Reality for Many Business Users and Consumers
Tools are designed for data analysts & power users
§ Who are happy finding, wrangling, cleansing data
§ Who can create calculations, aggregations & transformations
What about the other business users?
§ People who don’t want to spend hours fighting the spreadsheet…
Spreadsheets and desktop tools are isolated
§ Sitting on one desktop or shared via email
Ultimately, can you trust the numbers?
§ Where did the data come from? How has is been manipulated?

6. 5
Challenges of Delivering Self-Service Analytics
Fragmented data spread across different
sources, systems
Multiple, high-maintenance data-integration
initiatives
Data delays from days to multiple months
Poor Data Integrity
Untraceable data lineage
MarketingSales ExecutiveSupport
Database
Apps
Warehouse Cloud
Big Data
Documents AppsNo SQL

7. 6
Rob van der Meulen, Gartner
December 2015
https://www.gartner.com/smarterwithgartner/managing-the-data-chaos-of-self-service-analytics/
Gartner predicts that by 2018 most business users will have access
to self-service tools, but that only one in 10 initiatives will be
sufficiently well-governed to avoid data inconsistencies that
negatively impact the business.

8. Building a Platform for Self-Service
Analytics
The Solution
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9. 8
Self-Service with Guardrails
Don’t build just for the ‘data cowboys’
Create a common and consistent semantic layer
§ Everyone is using the same definitions and metrics
Create pre-integrated, pre-calculated data services
§ Save the user having to do this themselves
§ Ensures consistency of calculations, etc.
But allow the cowboys to ‘roam and wrangle’
§ Even the cowboys can only access ‘approved’ data sources

10. 9
Self-Service Platform Design
A Few Simple Rules…
1. Remember users come in all shapes and sizes
§ Who are they? What data do they need? What flexibility do they want?
2. Connect to all of the data (but start with the most important)
§ What data is needed by the users? Open access or pre-aggregated and pre-
calculated?
3. Use the language that the business understands
§ e.g. to Finance it’s an ‘account’, but to Customer Care it’s a ‘customer’. Don’t force
people to change terminology…support multiple semantic mappings (to the language
of the consumer)

11. Self Service Reference Architecture
10

12. 11
Faclitating the Self-Service Architecture
Five Essential Capabilities of Data Virtualization
4. Self-service data services
5. Centralized metadata, security
& governance
1. Data abstraction
2. Zero replication, zero relocation
3. Real-time information

13. 12
1. Data Abstraction
Abstracts access to disparate data sources.
Acts as a single virtual repository.
Abstracts data complexities like location,
format, protocols
…hides data complexity for ease of data access by business
Enterprise architects must revise their data architecture to meet
the demand for fast data.”
– Create a Road Map For A Real-time, Agile, Self-Service Data
Platform, Forrester Research

14. 13
2. Zero Replication, Zero Relocation
…reduces development time and overall TCO
The Denodo Platform enables us to build and deliver data
services, to our internal and external consumers, within a
day instead of the 1 – 2 weeks it would take with ETL.”
– Manager, DrillingInfo
Leaves the data at its source; extracts only what is
needed, on demand.
Diminishes the need for effort-intensive ETL
processes.
Eliminates unnecessary data redundancy.

15. 14
3. Real-Time Information
Provisions data in real-time to consumers
Creates real-time logical views of data across many
data sources.
Supports transformations and quality functions
without the latency, redundancy, and rigidity of legacy
approaches
…enables timely decision-making
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

16. 15
4. Self-Service Data Services
Facilitates access to all data, both internal and external
Enables creation of universal semantic models reflecting
business taxonomy
Connects data silos to provide best available information to
drive business decisions
…enables information discovery and self-service
Impressively quick turn around time to "unlock“ data from
additional siloes and from legacy systems - Few vendors (if any) can
compete with Denodo's support of the Restful/Odata standard -
both to provide data (northbound) and to access data from the
sources (southbound).”
– Business Analyst, Swiss Re

17. 16
5. Centralized Metadata, Security & Governance
Abstracts data source security models and enables single-point
security and governance.
Extends single-point control across cloud and on-premises
architectures
Provides multiple forms of metadata (technical, business,
operational) to facilitate understanding of data.
…simplifies data security, privacy, audit
Our Denodo rollout was one of the easiest and most successful rollouts of critical
enterprise software I have seen. It was successful in handling our initial, security,
use case immediately, and has since shown a strong ability to cover additional
use cases, in particular acting as a Data Abstraction Layer via it's web service
functionality.”
– Enterprise Architect, Asurion

18. 17
Denodo Data Virtualization Architecture

19. Self Service Architecture
18

20. 19
Data Virtualization as the Unified Semantic Layer
• Move data integration and semantic layer to
independent Data Virtualization platform
• Purpose built for supporting data access across
multiple heterogeneous data sources
• Separate layer provides semantic models for
underlying data
§ Physical to logical mapping
• Enforces common and consistent security and
governance policies
19
Data Virtualization as Data
Integration/Semantic Layer
Data Virtualization
EDW ODS

21. Customer Case Study
20

22. 21
Large Mutual Funds Company
Unifed Semantic Layer with Data Virtualization
• Lacked common and consistent view of key business metrics
• Different answers depending upon which tool or report was used
• Too much time discussing veracity of data and not addressing business issues
• Management tasked IT with providing a consistent view of data used to drive the
business – irrespective of channel used to access the data
• Implemented a unified semantic layer using Data Virtualization

23. 22
Customer Architecture
Data
Connectivity
Data Modeling
Layer
Unified
Semantic Model

24. Realising the Promise of Self-
Service Analytics
Summary
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25. 24
Summary
Key Takeaways
1. Universal semantic model provides a common and consistent view of data across
organization
§ No more arguments about data sources and veracity J
2. Data Virtualization allows you to build a flexible semantic model quickly and easily
§ Provides a platform for self-service with guardrails
§ Supports both ‘data cowboys’ (with limits) and regular business users
3. Accelerates self-service initiatives – no more analysis silos – while retaining control
and governance
24

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