Apache Hadoop is an open source software framework for distributed storage and processing of large datasets across clusters of computers. It allows businesses to combine multiple types of analytics on the same data at massive scale. Forrester predicts 100% of large enterprises will adopt Hadoop and related technologies like Spark for big data analytics in the next two years due to benefits like solving storage problems and being a mature technology. Combining big data and analytics through Hadoop allows companies to optimize operations, gain new business insights, and build data-driven products and services.

1. A Revolution in
Business Analytics
The Zika Virus Example
October 2016

2. Data is growing…and it effects everyone

3. How Big Is Big?

4. What is Big Data?
4

5. What is Hadoop?
Apache Hadoop is an open source, Java-based programming
framework that supports the processing and storage of extremely
large data sets in a distributed computing environment.
• Hadoop is an ecosystem of open source components that fundamentally changes the way
enterprises store, process, and analyze data.
• Unlike traditional systems, Hadoop enables multiple types of analytic workloads to run on
the same data, at the same time, at massive scale on industry-standard hardware.
IBM BigInsights, AWS, MapR, Microsoft HDInsight, Intel, Datastax,
Teradata, Pivotal HD
Hortonworks Data Platform (HDP) is a secure, enterprise-ready open
source Apache™ Hadoop® distribution based on a centralized architecture
(YARN). HDP addresses the complete needs of data-at-rest, powers real-
time customer applications and delivers robust analytics that accelerate
decision making and innovation.
Cloudera provides Apache Hadoop-based software, support and
services, and training to business customers for enterprise-class
deployments . It is the most popular distribution of Hadoop and
related projects in the world
Others

6. Market Growth … does it match your budget?
The global Hadoop market, which spans hardware, software, and
services, is expected to grow to $85B by 2021 (CAGR of 63% from 2016)
-Allied Market Research

7. Prediction of Adoption
“Forrester estimates that
100% of all large enterprises
will adopt it (Hadoop and
related technologies such as
Spark) for big data analytics
within the next two years.”
--The Forrester WaveTM, Big Data Hadoop
Distributions, Q1 2016
100% of all large enterprises will adopt Hadoop
and related technologies such as Spark

8. Why does Forester predict that there will be 100% adoption
• With Hadoop the storage volume problem is solved
• We are nearing the peak in the life cycle—it is no longer a
emerging technology
Source: Hortonworks June 2015

9. What can be done with all the data?
Operational
Efficiency
New Business
Value
OPERATIONS
DATAMANAGEMENT
UNIFIED SERVICES
PROCESS,ANALYZE, SERVE
STORE
INTEGRATE
Store and process
unlimited data fast and
cost-effectively.
Data
Integration
Explore, analyze, and
understand all your data.
Data
Discovery and
Analytics
Build data-driven products
to deliver real-time
insights.
Real-Time
Data
Applications
The real issue is not about acquiring large amounts of data,
It's about what is done with the data.

10. Big Data Implementation Examples
Combining Big Data with Analytics:
By combining Big Data and high-powered analytics,
it is possible to:
• Determine root causes of failures, issues and defects in near-real time,
potentially saving billions of dollars annually.
• Optimize routes for many thousands of package delivery vehicles while
they are on the road.
• Analyze millions of SKUs to determine prices that maximize profit and clear
inventory.
• Generate retail coupons at the point of sale based on the customer's
current and past purchases.
• Send tailored recommendations to mobile devices while customers are in
the right area to take advantage of offers.
• Recalculate entire risk portfolios in minutes.
• Quickly identify customers who matter the most.
• Use clickstream analysis and data mining to detect fraudulent behavior.
10

11. How do we do it?
• What are the technologies?
• How do we combine them to form a Modern Data
Architecture?
• How do we leverage the technologies in place to
combine Big Data with High Powered Analytics?
• Let’s start by examining the Analytics Value Chain

12. The Analytics Value Chain
It’s what we do.
Apply, Artificial Intelligence (AI), Machine
Learning (ML), predictive, prescriptive and
geospatial analytics to further leverage
your data assets.
Identify patterns, relationships and
outliers in vast amounts of data in
visually compelling ways.
Democratize your data further by
empowering business users to prep their
data for analysis. Enable DaaS with
governance.
Modernize your architecture to take
advantage of a schema-less data-lake
approach that rapidly adjusts to changing
business requirements.
The Bardess Analytics
Value Chain is a
systematic approach
to conceptually
visualize the strategic
journey to insightful
business analytics
Enterprise-Ready
Solutions

13. BUSINESSVALUE
Enterprise-Ready
Solutions
Database & Storage
Modernizing the data architecture to
take advantage of a schema-less
data-lake approach that rapidly
adjusts to changing business
requirements providing scalability,
security and governance.

14. BUSINESSVALUE
Data Preparation
Enterprise-Ready
Solutions
Enable DaaS with governance.
• Data Preparation
• Data Wrangling
• Data Quality Improvement
• Data Enrichment
Democratize data further by empowering business
users inn:

15. BUSINESSVALUE
Solution Provider
of the year 2016
Visual Analytics
Enterprise-Ready
Solutions
Identifying patterns,
relationships and outliers in
vast amounts of data in visually
compelling ways.
User-driven Analysis

16. BUSINESSVALUE
Advanced Analytics
Enterprise-Ready
Solutions
Further leveraging data assets by
applying advanced
• Machine Learning
• Predictive Analytics
• Prescriptive Analytics
• Artificial Intelligence

17. The Zika Virus Example
This Zika analysis is an example of
the Value Chain in action…
• A modern data architecture that
provides agility to adjust to changing
business requirements, and a
repository for really disparate data.
Enterprise-Ready
Solutions
• Security and governance.
• A compelling story about Zika and
what's been done about it, presented
in Qlik Sense.
• A foundation for future analytics including
Machine Learning and predictive analytics.

18. What is Zika?

19. The Spread of Zika

20. How is Zika Spreading?

21. Getting the Message Out

22. The Zika Virus Demo
Zika Virus Demo

23. See the whole story
that lives within your data

24. Visualization alone is insufficient
A modern analytics platform like Qlik provides the vehicle
to solve a multitude of business analytics challenges.

25. Founded in Lund,
Sweden in 1993
Headquartered in
Radnor, PA, USA
39,000 customers and
1,700 partners in more
than 100 countries
10 years’ growth
outpacing market
More than
2,500
employees
39,000
1,700
100
What Qlik
®
delivers

26. 26
Gartner Recognizes Qlik® as a Leader in Magic
Quadrant for BI & Analytics Platforms
This graphic was publishedby Gartner,Inc. as part of a larger researchdocumentand shouldbe evaluatedin the context of the entire document.The Gartner documentis availableupon request fromQlik. Gartner does not endorseany vendor,productor service depictedin its
research publications,and does not advise technologyusers to select only those vendorswith the highest ratings or other designation.Gartner researchpublicationsconsistof the opinionsof Gartner's research organizationand shouldnot be construed as statementsof fact.
Gartner disclaimsall warranties,expressed or implied,with respect to this research,includingany warrantiesof merchantabilityor fitnessfor a particularpurpose.
GARTNER is a registeredtrademarkand servicemarkof Gartner,Inc. and/or its affiliatesin the U.S. and internationally,and is used herein with permission.All rights reserved.
Source: Gartner,Magic Quadrant for BusinessIntelligenceand Analytics
Platforms,JoshParenteau et al, February 4, 2016. The Gartner documentis
availableupon request fromQlik.

27. 27
The Qlik Portfolio
Associative Engine Technology, APIs, Toolkit…
Analytics
Guided, embedded, self-
service, collaborative
Data
Access, smart load,
governed data, big data
Cloud
Create & share, managed cloud, value
added services, ecosystem
CapabilitiesProductsPlatform

28. 28
• Self-service visualization and discovery
• Associative model
• Smart visualizations
• User-driven, drag-and-drop creation
• Sharing of knowledge and insights
• Centralized sharing and collaboration
• Data storytelling and reporting
• Anywhere, anytime mobility
• A platform for the entire enterprise
• Embedded analytics, custom apps and
extensions
• Robust data integration
• Enterprise-class governance
With Qlik’s patented Associative Data Indexing technology at its core,
Qlik Sense delivers:
Qlik® Sense

29. 29
• Most Big Data Users are not Data Scientists
─ Business users want simple, guided access
• Helping the user find relevant and contextual information
─ Instead of having to search through everything
• Ensuring the solution can accommodate today and tomorrow
─ Big Data landscape continues to rapidly evolve
• Able to use different methods for different data volumes and
complexities
─ “One method does not fit all”
Challenge - Providing Big Data to everyone
“A car may produce an exabyte of data a year (a billion gigabytes), but most is
completely meaningless. Isolating the megabyte of data a month that’s really
valuable, and then figuring out what you can do with it, that’s the challenge of Big
Data.”
Scott McCormick, president of the Connected Vehicle Trade Association and industry adviser to the U.S. Secretary of Transportation, September 2013

30. 30
The Qlik platform – for all users
Most Big Data Users are not Data Scientists
Deep drilling
Mostly drilling, some exploration
Mostly exploration,
some drilling
Data Experts
Data Scientists
Breadth of Coverage
DepthofCoverage
Data Explorers
Descriptive, diagnostic and predictive analytics
(“What happened?”, “Why did it happen?” and “What is likely to happen?”

31. 31
Qlik & Cloudera
Benefits For Customers
.
Fast
For Business
Easy
To Manage
Secure
Without Compromise
Make Big Data
Accessible
Deliver Big Data
In Context
Keep Big Data
Relevant
Faster and Higher Big Data
ROI

32. 32
Qlik within a Big Data Architecture
Analyze
Refinement
Initial Processing
Gather
HADOOP
DATA SOURCES
ACCELERATORS
QIX Associative Engine
Unstructure
d data
Structured
data
NON-HADOOP
Standards-based or application-specific connector