Big Data Analytics is the big differentiator in IoT.The value in IoT and the biggest revenue opportunities lie not in IoT sensors, modules, components and devices but in the associated Analytics applications.This Report examines the crucial role that IoT-enabled predictive and prescriptive Analytics will play in helping organizations sift through the data deluge to make informed decisions and derive real business value from their applications. Analytics can make businesses more efficient, enabling them to cut costs and lower ongoing operational expenditures and ultimately become more competitive and drive top line revenue.

This Report examines the business and technology drivers propelling organizations to adopt the new predictive and prescriptive capabilities in Analytics. It also presents the best use cases for deploying Big Data Analytics in crucial verticals like banking/finance, healthcare, retail, industrial, manufacturing and transportation to turn the results of analytic models into tactical, practical and strategic actions that benefit business’ top and bottom lines.  IoT enabled Big Data predictive and prescriptive Analytics can help organizations:

• Identify and Analyze Data to make informed decisions in real-time
• Make Tactical, Proactive Responses to Cut Costs, Drive Operational Efficiencies
• Improve Business Performance and Customer Satisfaction
• Make long-range forecasts
• Take Strategic Action to Drive Top-line revenue

1.       Executive Summary  5

2.       Introduction  6

2.1     Analytics is the big differentiator between M2M and IoT networks. 6

2.2     IoT-enabled and embedded analytics is a game changer for vendors and corporate customers. 7

3.       Analytics: Choosing the Right Tool for Your Business Requirements  9

3.1.1       Batch Analytics  9

3.1.2       Operations Analytics  10

3.1.3       Stream Analytics  10

3.1.4       Real-Time Analytics/Machine Learning Analytics for IoT  11

4.       Big Data Analytics: The Big Picture Forecast 13

4.1     Big Data Worldwide Revenue Forecast 2015 – 2022 by Vertical Segment 14

4.2     Big Data Analytics Worldwide Revenue Forecast 2015 – 2022 by Region  16

5.       Big Data Analytics Use Cases  21

5.1     Industrial and Manufacturing  22

5.2     Retail 24

5.3     Transportation  25

5.4     Healthcare  26

5.5     Fraud Detection and Security  26

6.       Big Data Analytics Vendors: The Big Three  28

6.1     IBM   28

6.2     SAP  30

6.3     SAS  31

7.       Big Data Analytics Challengers Cisco, GE, Microsoft, Oracle making headway  33

7.1     Cisco  33

7.2     GE  35

7.3     Microsoft 36

7.4     Oracle  36

8.       Analytics Vendor Contenders  38

8.1     Alteryx  38

8.2     Angoss Software  38

8.3     Big Cloud Analytics  38

8.4     KNIME  39

8.5     Predixion  39

8.6     Mnubo  40

8.7     Rapid Miner 40

9.       Big Data and Database Vendors  41

9.1     Cloudera  41

9.2     Databricks  41

9.3     Datameer 41

9.4     Dell 42

9.5     Hortonworks  42

9.6     MapR  42

9.7     MemSQL  43

9.8     MongoDB  43

9.9     Platfora  43

9.10   Palantir 43

9.11   Premise Data  44

9.12   Splunk  44

9.13   Teradata  44

9.14   Trifacta  44

10.     IoT and Analytics Mergers and Acquisitions Ramp Up  46

11.     Big Data Analytics Security & Data Privacy  50

12.     Big Data Deployment Challenges  52

13.     Conclusions  53

14.     Contact the Author of This Report 55