This document provides a summary of a presentation about managing the explosion of data. It discusses challenges around isolating valuable data from large and diverse datasets, including "dark data" that has not been analyzed. It also notes challenges around a lack of unified IoT standards. The presentation proposes that Qlik's platform can help organizations gain insights from big data and IoT by providing accessible self-service analytics, rapidly delivering value from existing data, and offering flexible solutions as data volumes and technologies continue to evolve. It demonstrates how on-demand app generation can allow users to dynamically create analytic apps from subsets of large datasets.

1. Managing the explosion of data
Adam Mayer
Technical Product Marketing

2. Legal Disclaimer
This presentation includes certain statements, estimates, targets and projections that reflect Qlik’s management’s assumptions
concerning anticipated future performance of Qlik. Such statements, estimates, targets and projections are based on significant
assumptions and subjective judgments concerning anticipated results, which are inherently subject to risks, variability and
contingencies, many of which are beyond the control of Qlik, Thoma Bravo and any of Qlik’s current or future direct or indirect
owners.
These assumptions and judgments may or may not prove to be correct and there can be no assurance that any projected results
are attainable or will be realized. Qlik, Thoma Bravo, and each of their respective representatives and affiliates expressly
disclaim any and all liability with respect to information contained in or omitted from this presentation. None of Qlik or Thoma
Bravo, nor their respective representatives or affiliates, make any representations and/or warranties, whether express or implied,
with respect to this presentation, the information contained herein or omitted herefrom.
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new information, future events or otherwise, and such statements should not be relied upon as representing Qlik's views as of
any date subsequent to the date of this presentation. Further, this presentation is intended to outline Qlik’s general product
direction and should not be relied on in making a purchase decision, as the development, release, and timing of any features or
functionality described for our products remains at our sole discretion.
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3. One simple
guiding belief…
Business Intelligence is
when you harness
the collective
across an organization.

4. QLIK
Founded in Lund,
Sweden in 1993
Headquartered in
Radnor, PA, USA
40,000 customers and
1,700 partners in more
than 100 countries
* FY 2016 $700M USD RevenueMore than 2,400
employees
40,000
1,700
100
*Results are unaudited and do not reflect certain U.S. GAAP purchase accounting adjustments related to the company’s August 2016
acquisition by Thoma Bravo which will be reflected in the company’s audited financial statements for 2016

5. 55
Agenda
• Big Data and IoT challenges
• Qlik Customer examples
• Qlik solution for IoT and Big Data
• On Demand App Generation

6. The explosion of data
Big Data
Volume
Velocity
Variety

7. The explosion of data - Volume
1 zettabyte =
1 trillion gigabytes
Zettabyte
• 1 trillion gigabytes
• 35 - 45 Zettabytes of data
will be created by 2020
Big Data
Volume
Velocity
Variety 1 petabyte =
1 million gigabytes

8. The explosion of data - Velocity
24 milliseconds for $850 Million
• Fiber optic network being built
through Northwest passage
• London - Tokyo: 154 milliseconds 
130 milliseconds
Big Data
Volume
Velocity
Variety
Zettabyte

9. The explosion of data - Variety
7,721 Tweets, 797 Instagram
photos, 231 PayPal transactions,
30 blogs in WordPress
• New Internet content created in 1 sec.
Big Data
Volume
Velocity
Variety
Zettabyte
24 milliseconds for $850 Million

10. Industry Challenges
“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

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Industry Challenges
• Isolating the megabyte of data a month that’s really valuable
• Dark data – “Unstructured, untagged and untapped data that is found in data repositories and
has not been analyzed or processed.”
Technopedia

12. Industry Challenges
• Isolating the megabyte of data a month that’s really valuable
• Dark data
Lack of unified IoT standards and protocols
– Infrastructure (ex: 6LowPAN, IPv4/IPv6, UDP, QUIC, Aeron, uIP, DTLS, ROLL / RPL, NanoIP, CCN, TSMP)
– Identification (ex: EPC, uCode, IPv6, URIs)
– Communication / Transport (ex: Wifi, Bluetooth, LPWAN, WirelessHart, DigiMesh, ISA100.11a, NFC, ANT, IEEE
802.15.4, Eddystone, ZigBee, EnOcean, WiMax, Weightless, NB-IoT, LTE-MTC, EC-GSM-IoT, LoRaWAN, RPMA)
– Discovery (ex: Physical Web, mDNS, DNS-SD, HyperCat, UPnP)
– Data Protocols (ex: MQTT, CoAP, AMQP, Websocket, MQTT-SN , Mosquitto, SMCP, STOMP, XMPP, XMPP-IoT,
Mihini/M3DA, DDS, JMS, LLAP, LWM2M, SSI, ONS 2.0, RESTful HTTP, HTTP/2, SOAP)
– Device Management (ex: TR-069, OMA-DM)
– Semantic (ex: JSON-LD, SensorML, Web Thing Model, IOTDB, Wolfram, RAML, SENML, LsDL)
– Multi-layer Frameworks (ex: Alljoyn, IoTivity, Weave, Homekit, IEEE P2413, Thread, IPSO, OMA, Telehash)

13. 1313
• Expanding access
─ Most potential users are not Engineers or Data Scientists
─ Business users want simple, guided access
• Rapidly deliver more value
─ Maximizing the value of your investment
─ Allow users to quickly find relevant and contextual information
• A solution for today and tomorrow
─ Big Data & IoT landscapes continues to rapidly evolve
─ “One method does not fit all”
• Different methods for different data volumes and complexities
Customer Challenges

14. Expanding access

15. Discover new potential
Japan
Discover Data
Driven
possibilities
• 430 restaurants
• 40,000 workers
• 120 million customers,1 billion plates of sushi every year
• 4 billion records of sushi sales data per year
Akindo Sushiro - Top Japanese household name in conveyor-
belt sushi restaurants. All sushi plates are fitted with RFID
sensors creating 4 billion records of sushi sales data per year
Deliver insight
to your
organisation
Qlik empowers the
IoT data innovator
to clearly see the
whole story in your
data
“Development of new products accelerated by decision
makers conducting analysis themselves”
“Our thinking was nothing more than a preconception. The truth only
came to light once we had analyzed sales of related products on Qlik.”
– Akihiro Onoe, Corporate Planning Division

16. Gained new
insights from
existing data
Payback in
1.5 years
Increased
loyalty program
value
European Oil & Gas Company
• 7 European Markets. 6500 Sites. 25+ Million
Customers.
• Cloud + Big Data (15TB / 75 Billion rows in cloud)
• Challenge - Find new ways to increase margins as
profitability was under pressure
• Users given a 360 degree view of customer activity
• 14% increase in year 1 active customer base

17. Rapidly deliver more value

18. Deliver customer value
• Centrica have installed 3.5m smart meters with a single smart meter
generating 3 million energy usage data points per annum
• Centrica used Qlik® to analyse consumers energy usage from smart
metering data via IoT Connectivity
• Centrica used Qlik to prove customers could save money in a highly
regulated industry
• Connecting IoT data to everything else Centrica knew about their
customers paving the way for future personalised tariffs
Centrica is UK’s
leading energy
services
business
UK Government
directive - a smart
meter installed in
every household by
2020
Customers can
now receive free
electricity with
Home Energy
Free Time

19. Gain deeper insightGain deeper
insights from IoT
data
Make solid data
driven decisions
• Bticino is a leading Italian manufacturer of electrical wiring
systems and home automation
• Electricity consumption data is collected for 9 production
locations in Italy via smart meters, and is analyzed together with
production BOM and production schedules
• The Qlik® energy dashboard analysis have enabled savings of
3% annually
Qlik easily joins all
your IoT data
together to deliver
deeper insights

20. A solution for today
and tomorrow

21. Gather data
• Integrated Connectivity
• Broad partnerships
• Certification
• Single Sign On
─ SAP
─ Cloudera

22. Qlik Big Data Methodologies
Different data volumes and complexities are best met using different methods
Different methods ensure an optimized
experience for the user for every situation
Methods can be combined to meet
different use cases
Methods vary in deployment complexity
Data Volume
• Size (rows)
• Dimensions
(columns)
• Cardinality
(uniqueness)
App Complexity
• Computational
complexity such
as set analysis
• Object density
Segmentation
Chaining
In-Memory
On-Demand
App Generation
On-Demand App
Generation (API’s)

23. Qlik® in a Big Data Architecture
Analyze
Refinement
Initial Processing
Gather
HADOOP
DATA SOURCES
ACCELERATORS
Qlik’s Associative Engine
Unstructured
data
Structured
data
Standards-based or application-specific connector
NON-HADOOP

24. IoT platform
Data in motion Data at rest
Data in
use
Technology partners
AnalyticsConnectivity Operations
OEM partners
Wi-Fi, Bluetooth,
3G/4G, LTE, etc
Qlik® platform
In the Enterprise
Data store(s) e.g Cloudera,
Hadoop,
SAP HANA,
etc
IoT Devices
Online portals
Qlik® within the IoT ecosystem

25. On Demand App
Generation

26. On-Demand App Generation
• A template app summarizes the
entire big data environment
• Users can select subsets of data
and dynamically generate new
apps for analysis
• Analysis apps offer fully
unrestricted search and
exploration
Make selections to segment
Big Data and generate
analysis apps on the fly

27. On Demand App Generation
Big Data
Repository
Summary
Data1. View summary data in a ‘Selection
App’ and select a slice of data
2. Request the Analysis App to be built
3. Source data is extracted and Analysis
App is created
Repeat steps 1-3 as many times as
needed
Selection
App
1
Request
Request
2
Analysis
App
3

28. On Demand App
Generation Demo

29. Managing the explosion of data
Accessible
Empowering more individuals
across the organization
A platform for the
future
A solution for today and tomorrow
The Qlik® Platform
Discover more value in your Big Data and IoT data
using the power of Qlik’s platform based approach
Rapid Value
Maximizing the value of your
investment

30. Introducing Associative
Big Data Indexing
Can be distributed across a cluster
for speed and scale
Incremental index optimizing
for associative queries and
speed, leaving the data in place
Full associative experience
on top of Big Data sources
N E X T S T E P I N Q L I K ’ S
B I G D A T A E V O L U T I O N
TECHNOLOGY
FUTURE
This document and Qlik‘s strategy and possible future developments are subject to change and may be changed by Qlik at any time for any reason without notice.
This document is provided without a warranty of any kind. The document may not be copied, distributed, or otherwise shared with any third party.

32. Adam Mayer
Senior Manager Technical Product
Marketing
www.qlik.com
@amayerwrk

33. Stand 515
Thank you!