Leap to Next Generation Data Management with Denodo 7.0

27/03/2018
Copyright © Intelligent Business Strategies 1992-2018, All Rights Reserved 1
Simplifying Data Access While Reducing Time
To Value In A Modern Digital Enterprise
Mike Ferguson
Intelligent Business Strategies
Denodo Fast Data Summit
London
March 2018
2
Copyright © Intelligent Business Strategies 1992-2018

Topics
§ Business trends
§ Challenges and requirements
§ How data virtualisation helps solve problems, embrace
trends and deliver value
§ Conclusions

27/03/2018
3

Business Trends
§ Digitalisation
§ Customer centricity - the customer intelligent front office
• Customer 360o, customer communities, self-service,
personalisation, integrated customer service in digital channels,
chatbots, dynamic pricing, cross-sell/up-sell
§ New business models via real-time access to data & analytics
§ Reducing operational cost
• Process automation and real-time smart business operations
• B2B process integration
• Reduce IT cost and improve agility
§ Risk mitigation
• Get more data to understand business operations
• Real-time analytics, automated actions and process automation
4

Today’s Digital Enterprise is Storing Data And Running
Applications In A Hybrid Computing Environment
On-premises
Digitalization is happening in a hybrid computing environment
One or more clouds
Digitalization is the process of moving to a digital business by making
use of digital technologies to change ways of operating and to create new
insights that provide new revenue and value-producing opportunities

27/03/2018
5

Product /
service line n
Product /
service line 4
Product /
service line 3
Product /
service line 2
Product/
service line 1
Marketing
Service
Credit
Verification
HR
Finance
Planning
Procurement
SupplyChain
Front Office BackOffice
OperationsShipping
Sales
Operational Digital Transformation - Cloud Operational
Application Adoption Has Grown Rapidly
Suppliers
Customers,
Partners
Employees
Things
Things(Smartproducts)
data
data
data
data
data
6

Challenge – Processes Now Span Cloud & On-Premises Making
Transaction Data Hard To Access To Manage Operations
order
credit
chec
k
fulfil ship invoice paymentpackageschedule
Order entry
system
Credit
control
system
Production
planning &
scheduling
CAM
system
Inventory
system
Distribution
system
Billing Gen
Ledger
Orders data Customer data Product data
Order-to-Cash Process
What order changes in the last 10 mins?
What shipments are impacted by the changes
e.g. lack of inventory or shipping capacity?
Which customers are affected?
Operational reporting
is not timely
Inability to respond quickly
to problems
Problems not seen until long after they
happen e.g. incorrect shipments
Operational oversights cause processing
errors & unplanned operational cost
Inability to see across multiple instances of a
system can cause errors & duplication of effort
Business
impact
customer
app

27/03/2018
7

Product/service line 1
Product/ service line 3
Enterprise
Many Companies Have Business Units, Processes &
Systems Organised Around Products And Services
order credit
check
fulfill ship invoice paymentpackage
order credit
check
order credit
check
Order
(product line 1)
Order
(product line 2)
Order
(product line 3)
How
can
you
becom
e
custom
er centric
when
data
is fractured
and
m
ay also
be
duplicated
and
inconsistent?
XYZ
Corp.
Customers/
Prospects
Channels/
Outlets
Requirement is now to become customer centric
8

Sales Force
automation apps
Customer facing hotel
reservation apps
Front-Office Operations
Customer
service apps
Customers
Need to improve
customer engagement
Digital channels are
generating big data
E-commerce
application
M-commerce
Mobile apps
Social commerce
applications
E-commerce
application
M-commerce
Mobile apps
Social commerce
applications
Application
/ System data
customer app
Customer interaction – the new way
- Need customer intelligent
applications
- Need access to low latency data
Application/
System data
Customer interaction – the old way
customer employee UI
Digital Channels Are Now The Focal Point For Customer
Interactions And The New Battleground For Revenue

27/03/2018
9

The Requirement Now Is To Capture, Integrate And Analyse
More Data For Deeper Customer Insights To Drive Value
OMNI channel analysis – analyse all
customer interactions across all channels
identity
data
(master
data)
behavioural
data
(on-line,
location,
product usage)
social data
(opinion,
relationships)
Customer “DNA”
transactional
activity
(channel
purchases)
Needs to be integrated in near real-time to
maximise competitive advantage
10

Popular Types of Data That Businesses Now Want to
Analyse to Improve Customer Engagement & Reduce Cost
Type Of Data Examples
Machine data • Clickstream data, e-commerce logs
• IVR logs, App Server logs, DBMS logs
Connected things
(Consumer & Industrial
IoT), Sensor data
• Product usage behaviour data, product
performance data
• Location, temperature, movement, vibration, liquid
flow, pressure
Web data • Social network data e.g., Twitter, LinkedIn,
Facebook, Instagram…
• Competitor web site data
Unstructured data ( e.g.
Social network Text)
Semi-structured data e.g., JSON, BSON, XML
Open government data
Weather data
Master data Customer, product, employee, supplier, site,…..
Transaction data Orders, shipments, returns, payments, adjustments..

27/03/2018
11

Data Deluge – The Data Warehouse Is Not Ideal For All
Types Of Data And All Analytical Workloads
Data.Gov
Enterprise
Data Warehouse
RDBMS
EDW
Unstructured?
Streaming?
Schema variant?
Huge volume?
RESTRICTED
AREA
DO NOT
ENTER
PRODUCTION SYSTEM
- AUTHORISED
PERSONNEL ONLY
12

DW Challenges – Slow To Change, High Total Cost Of
Ownership, Too Many Data Copies & Physical Data Marts
Sales DW
(E/R model)
UK DE FR NL
ESP CH IT BEUS
ETL ETL ETL ETL
ETL ETL ETL ETL ETL
Business Impact
Very high total cost of ownership
No Agility – Takes time and is costly to change
Risk of inconsistency across DWs and marts
No drill down across marts
Pace of change can’t keep pace with the demand for new data
E.g. Country
Specific Data
Marts
BI Tools
Inventory
DW
(E/R model)
UK DE FR NL
ESP CH IT BEUS
ETL ETL ETL ETL
ETL ETL ETL ETL ETL
Data Warehouses need modernising to reduce the impact of
change, reduce data copying, fuel agility and simplify access
BI Tools

27/03/2018
13

Challenges –Data Is Being Ingested Into Multiple Types Of
Data Store Both On-Premises And In The Cloud
Enterprise
cloud
storage
Data.Gov
C
R
U
prod cust
asset
D
MDM
NoSQL
DBMS DW
I
D N
A G
T E
A S
T
14

An Analytical Ecosystem Has Emerged With Different
Platforms Optimised For Different Analytical Workloads
Multiple platforms now being used for analytical processing
– This has increased complexity of managing and governing data
Streaming
data
Hadoop
data store
Data Warehouse
RDBMS
NoSQL
DBMS
EDW
DW & marts
NoSQL DB
e.g. graph DB
Advanced Analytic
(multi-structured data)
mart
DW
Appliance
Advanced Analytics
(structured data)
Analytical
RDBMS
Traditional
query,
reporting &
analysis
Data mining,
model
development
Streaming
analytics
Real-time
stream
processing &
decision m’gmt
Investigative
analysis,
Data refinery
Graph
analysis
C
R
U
D
Prod
Asset
Cust
MDM
Master data
management
Data mining,
model
development

27/03/2018
15

Analytical Digital Transformation – This Analytical
Ecosystem Is Now Also Available In The Cloud
Data Warehouse
RDBMS
EDW
DW & marts
mart
DW
Appliance
Advanced Analytics
(structured data)
Analytical
RDBMS
NoSQL
DBMS
Hadoop
data store
NoSQL DB
e.g. graph DB
Advanced Analytic
(multi-structured data)
C
R
U
D
Prod
Asset
Cust
MDM
Several vendors now offer the entire analytical ecosystem on the cloud
Alternatively it can be a hybrid setup
Streaming
data
Streaming
analytics
16

Challenge – Improving Profitability And Agility Is Proving Difficult
When Captured Data Is Becoming More Fractured
§ Data in different locations and no way to
find anything
§ Data in different data storage
technologies
§ Different APIs and query languages
needed to access data
§ Data in different data structures
§ Different data definitions for the same
data in different data stores
§ Excessive use of ETL to copy data
• Expensive and not agile
§ Synchronization nightmare
<XML>Text</XML>
Digital
media
RDBMSs
Web
content
E-mail
Flat files
Packaged
applications
Office
documents
Legacy
applications
DW/BI
systems
Big Data applications
Cloud based
applications
ECMS
“Where is all the
Customer
Data?”
Accessing, governing and managing
data is becoming increasingly complex
as it becomes more distributed
IoT

27/03/2018
17

Data Deluge - With 000’s of Data Sources IT Is Becoming
a Bottleneck Driving Demand For Self-Service Data Prep
IT
OLTP
systems
Web
logs
web
DQ/DI
job
DQ/DI
job
DQ/DI
job
Open data
IoT
machine data
social & web
C
R
U
prod cust
asset
D
MDM
DW
Data
warehousing
cloud
Data virtualization
Can business analysts &
Data Scientists help?
DQ/DI
job
DQ/DI
job
DQ/DI
job
???
Bottleneck?
Should IT be expected
to do everything?
Big Data
18

Challenges - The Danger Of Self- Service Data Preparation
Is Chaos The Enterprise And Lack of Trust In Data
social
Web
logs
web cloud
sandbox
Data Scientists
sandbox
Data Scientists
sandbox
Data Scientists
HDFS
ETL
/ DQ
Self-service
BI tools with ETL
ETL
new
insights
SQL on
Hadoop
DW
ETL
/ DQ
DW
marts
ETL
SCM
CRM
ERP
ETL/D
Q
marts Self-service
BI tools with ETL
ETL/D
Q
Built by IT
ETL/
DQETL/
DQETL/
DQ
Everyone is blindly integrating data with little
or no attempt to share what they create !!

27/03/2018
19

Topics – Where Are We?
§ Business trends
§ Challenges and requirements
ØHow data virtualisation helps solve problems, embrace
trends and deliver value
§ Conclusions
20

Data Virtualization Makes It Easy To Find, Access And Report
on Data Across Processes To Manage Business Operations
Order-to-Cash Process
Data virtualization and Virtual Data Services
Benefits
Simplified access to real-time data across the process
Agile and responsive
Avoid unplanned operational costs
See across on-premises & cloud apps
Simplify building new business models on top of APIs
Catalog enables self-service ad hoc operational queries
cost
Agility
order credit
check
fulfil ship invoice paymentpackageschedule
customer
app
Information

27/03/2018
21

XYZ
Corp.
Data Virtualisation Provides Customer Orientation - Views Of
Orders, Shipments And Payments Across All Lines Of Business
Customers/
Prospects
order credit
check
fulfil ship invoice paymentpackage
Product/ service line 3
Channels
/Outlets
order credit
check
order credit
check
Order
(product line 1)
Order
(product line 2)
Order
(product line 3)
Enterprise
Datavirtualization
Datavirtualization
Datavirtualization
22

Data Virtualisation Eases DW Modernisation – E.g. Migration
To Cloud, Data Vault, Virtual Marts All Without Users Noticing
Sales DW
(E/R model)
UK DE FR NL
ESP CH IT BEUS
ETL ETL ETL ETL
ETL ETL ETL ETL ETL
Business Impact
Very high total cost of ownership
No Agility – Takes time and is costly to change
Risk of inconsistency across DWs and marts
No drill down across marts
E.g. Country Specific Data Marts
BI Tools
Sales DW
UK DE FR NL
ESP CH IT BEUS
Data Vault model
Business Impact
Low total cost of ownership
Agility – Easy to change
No inconsistency across DWs and marts
Virtual aggregation and drill down across
marts
Migrate &
change
the design
BI Tools
Data Virtualisation

27/03/2018
23

Governing Self-Service Data Prep – Data Virtualisation
Reduces Reinvention, Enforces Security & Increases Agility
C
R
U
prod client
asset
D
MDM Systems
C
R
Urisk
rates
pricing
Country
codes
D
RDM Systems
Business User
Self-Service BI/Data Prep
Important to make
sure business users
re-use rather than
re-invent AND have
simplified access to
data
RDBMS Cloud
XML,
JSON
web
services
NoSQL Files
Data Virtualisation
IT Data Architect
24

Data and Analytics Have Moved to the Heart of the
Enterprise for Use Everywhere
Data and
Analytics
HR
Sales
Marketing
Service
Finance
Procure
-ment
Operations Distribution
Partners
Customers
Suppliers
Employees
Things
The Intelligent
Business
Access to commonly
understood, trusted data,
insights and analytics across the
business is critical to success

27/03/2018
25

Simplifying Data Access Via A Logical Data Warehouse
- Using DV To Catalog And Access Data In Multiple Stores
Analytical tools &
applications
Integrated Self-Service and IT Data Management Tools (Business & IT)
Logical Data Warehouse (Data Virtualisation with a common vocabulary)
feedsIoT
XML,
JSON
RDBMS Files office docssocial Cloud
clickstream
web logs web
services
NoSQL
Information
Commonly understood, trusted data and insights
EDW
DW & marts
NoSQL DB
e.g. graph DB
mart
DW
Appliance
Advanced Analytics
(structured data)
Advanced
Analytics
Streaming
data
RT Analytics
C
R
U
prod cust
asset
D
master data
26

The Logical Data Warehouse - Integrated Customer Insight
LDW Business Impact? - On-Demand Integrated Customer
Insight As A Service In All Front Office Channels
EDW
DW & marts
NoSQL DB
e.g. graph DB
mart
DW
Appliance
Advanced Analytics
(structured data)
Advanced
Analytics
Streaming
data
RT Analytics
C
R
U
prod cust
asset
master data
Customer sentiment,
interactions,
online behaviour,
& new data
Customer
relationships*,
social network
influencers
Customer real-
time location,
product usage &
on-line behaviour
Customer
master data
Customer
purchase activity
& transaction
history
Customer predictive
analytical model
development
Sales Force
automation apps
Customer facing
bricks & mortar apps
Front-Office Operations
Customer
service apps
Customers
Improve customer
engagement
E-commerce
application
M-commerce
Mobile apps
Social commerce
applications
Digital channels are
generating big data

27/03/2018
27

Integrating IoT And Customer Data Using DV
- Property and Casualty Insurance Example
• By leveraging connected devices, P&C insurers increase risk assessment precision
and provide value-added services to customers:
– E.g., Security First Insurance’s Security First Mobile app helps customers plan
their routine in case of adverse weather conditions. The interactive hurricane
tracking feature helps customers plot their property address on the map and
check an active storm’s projected path. It also shares information on Red Cross
shelters and evacuation routes5
• Another example of proactive risk mitigation and increased customer
engagement is that of Esurance, which launched its DriveSafe teen driver
program to help parents monitor their teens’ driving behavior through a
telematics device. This can be done by setting preferences on their personalized
Esurance DriveSafe site where they can opt for customized alerts for unsafe
driving behavior and review trip details6
Implications
• With real-time data from connected technologies on customer risk exposure,
P&C insurers can respond with timely interventions at any sign of hazard or
peril for risk reduction
• With the help of smart home ecosystems and telematics, P&C insurers are
providing value-added services in multiple areas such as home monitoring and
assistance, driving assistance, and so on
• P&C insurers carry significant benefits of providing value-added services to
customers such as competitive differentiation, increased customer retention,
additional revenue streams, and lower claim costs:
– Value-added services benefit P&C insurers as a means for competitive
differentiation. Providing innovative and useful services can help insurers
stand out among their competitors and realize increase in customer mindshare
– As value-added services enhance customer experience through relevant and
meaningful engagement, customer retention could be improved for the insurers
– As many of the value-added services are directed toward proactive risk
mitigation, they can also help lower the overall cost of claims for P&C insurers
by helping customers avoid risks in their daily lives
5 Security First website, accessed October 2017 at http://www.securityfirstflorida.com/resources-and-advice/security-first-mobile
6 Esurance website, accessed October 2017 at http://blog.esurance.com/introducing-esurance-drivesafe
Exhibit 1: Connected Devices in P&C Insurance
Source: Capgemini Financial Services Analysis, 2017
Connected Devices Customers
Smart Home Sensors
(Ex: Leak Detectors,
Smoke Detectors)
Proactive
Risk Mitigation
Value-added
Services
Real-Time
Customer Data
Data
Analytics
Customized
Oﬀerings
Sensors at Oﬃces,
Factories, and Worksites
Telematics Devices
(Automobiles)
6 Top 10 Trends in Property & Casualty Insurance 2018
Streaming
IoT data
C
R
U
prod cust
asset
D
master data
Advanced
Analytics
EDW
DW & marts
mart
Data Virtualisation
28

Data
sources
Performance - Parallel Processing In Data Virtualisation Speeds
Up Integration Of Insights Across Analytical Systems
parallel processing in the source
DV = data virtualisation
EDW
DW & marts
NoSQL DB
e.g. graph DB
mart
DW
Appliance
Advanced Analytics
(structured data)
Advanced
Analytics
Streaming
data
RT Analytics
C
R
U
prod cust
asset
master data
DV Slave DV Slave DV Slave DV Slave
SQL
Cost based
optimizer
DV
master
DV Slave
BI
Tool
Application
In memory caching PLUS
in-memory parallel
processing of aggregations
pushdown pushdown pushdown pushdown
Data
virtualisation
server
in memory
DV needs parallel pushdown and MPP
in-memory processing of cached and
aggregate dataSQL or REST
pushdown

27/03/2018
29

Conclusions
§ There is a need to remain agile while accommodating change to
reduce cost, enable customer engagement and reduce risk
§ We now have
• Hybrid business processes across multiple clouds and data centres
• An analytical ecosystem consisting of multiple data stores optimised for
different analytical workloads which may also be in a hybrid environment
§ Data management is much more complex
§ Data access is hard and users are forced to integrate data
§ Data virtualisation
• Simplifies access to data in hybrid operational business processes
• Hides the complexity of multiple analytical systems while enable high
performance query processing
• Provides a catalog for users to easily find and query data unaided
• Enables low cost, low impact modernisation or systems
• Consistent information services to use in building new business models
30

Thank You!
www.intelligentbusiness.biz
mferguson@intelligentbusiness.biz
@mikeferguson1
(+44)1625 520700
Thank You!
Mike Ferguson is Managing Director of Intelligent
Business Strategies Limited. As an independent analyst
and consultant he specializes in business intelligence,
analytics, data management and big data. With over 35
years of IT experience, Mike has consulted for dozens
of companies, spoken at events all over the world and
written numerous articles. Formerly he was a principal
and co-founder of Codd and Date Europe Limited – the
inventors of the Relational Model, a Chief Architect at
Teradata on the Teradata DBMS and European
Managing Director of DataBase Associates.

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