How to Prepare for 2025's Intelligence Technology

The Intelligence Collaborativehttp://IntelCollab.com #IntelCollab

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How To Prepare for 2025’s Intelligence

Technology

A Complimentary Webinar from Aurora WDC

12:00 Noon Eastern /// Wednesday 16 December 2015

~ featuring ~

Nova Spivack JP Ratajczak


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Nova Spivack is a technology futurist, serial entrepreneur, angel investor, and one of the leading voices on the next-generation of search, AI, big data, and the Web. He was recently ranked in the top 20 futurists based on an analysis of social and Web influence. He is a frequent speaker and blogger, and writes guest-articles for publications such as TechCrunch, Mashable, GigaOM, and SiliconAngle. Nova was named to Re/Code’s List of LA Power Players in Tech in 2014.

Nova is presently CEO of Bottlenose, which uses big data mining to discover emerging trends for large brands and enterprises. He also advises and/or invests in several startups including Klout (acquired by Lithium), The Daily Dot, Next IT, WhoKnows, Live Matrix (acquired by OVGuide), Chronos Trading, PublishThis, Sensentia, Cambrian Genomics, and Energy Magnification Corporation.

Twitter: @novaspivackBlog: novaspivack.com

The Intelligence Collaborative is the online learning and networking community powered by Aurora WDC, our clients, partners and other friends and dedicated to exploring how to apply intelligence methods to solve real-world business problems.

Apply for a free 30-day trial membership at http://IntelCollab.com or learn more about Aurora WDC at http://AuroraWDC.com.


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Taken from Why 2015 Was a Breakthrough Year in Artificial Intelligence


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Nova Spivack is a technology futurist, serial entrepreneur, angel investor, and one of the leading voices on the next-generation of search, AI, big data, and the Web. He was recently ranked in the top 20 futurists based on an analysis of social and Web influence. He is a frequent speaker and blogger, and writes guest-articles for publications such as TechCrunch, Mashable, GigaOM, and SiliconAngle. Nova was named to Re/Code’s List of LA Power Players in Tech in 2014.

Nova is presently CEO of Bottlenose, which uses big data mining to discover emerging trends for large brands and enterprises. He also advises and/or invests in several startups including Klout (acquired by Lithium), The Daily Dot, Next IT, WhoKnows, Live Matrix (acquired by OVGuide), Chronos Trading, PublishThis, Sensentia, Cambrian Genomics, and Energy Magnification Corporation.

Twitter: @novaspivackBlog: novaspivack.com




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Questions, Commentary & Content

http://tweetchat.com/room/IntelCollab

http://slideshare.net/IntelCollab

http://intelcollab.com/


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ENTER CODE: BOTTLENOSE to receive $200 Discount

Enterprise IntelligenceDecember 2015

Confidential, Copyright (c) 2015, Bottlenose, Inc. All Rights Reserved

Data is Exploding and Analysts Can’t Keep Up

Unstructured data projected to grow

to 40 Zettabytes by 2020 (Source: IDC)

Challenge:By 2018, the United States will face a shortage of up to 190,000 data scientists with advanced training in statistics and machine learning as well as 1.5 million managers and analysts with enough proficiency in statistics to use big data effectively (Source: IDC)

Zettabytes(Trillion Gigabytes)

Sources: “Worldwide Big Data and Technology Forecast 2015 - 2019”, October 2015,The Digital Universe of Opportunities,” April, 2014, International Data Corp (IDC). Unlocking the Value in your Big Data,” Accenture, 2012; “Big data: The next frontier for innovation, competition, and productivity,” McKinsey Global Institute, May 2011. and Wikipedia https://en.wikipedia.org/wiki/Zettabyte#cite_note-12

2010

2015

2020

40

0

10

20

30

World Wide Web = 4 ZB in 2013(Source: Wikipedia)

Opportunity:International Data Corporation sees the big data technology and services market growing at a compound annual growth rate (CAGR) of 23.1% over the 2015-2019 forecast period with annual spending reaching $48.6 billion in 2019, or almost six times the growth rate of the overall information technology market over this period.


http://www.idc.com/getdoc.jsp?containerId=259532

http://idcdocserv.com/1678

http://www.slideshare.net/oscarrenalias/unlocking-value-in-your-big-data

https://en.wikipedia.org/w/index.php?title=Zettabyte&oldid=689903332

https://en.wikipedia.org/wiki/Zettabyte#cite_note-12

NEW PRESSURE

Stream Analytics is a Growing Imperative for the Enterprise

A growing segment of the unstructured data challenge is comprised of fast moving data streams – what we call “stream data”.

Stream data is rapidly growing and is likely to become the majority of unstructured data in the future

Within stream data lies vital information critical to detecting emerging threats and opportunities

Enterprises need to be able to detect these emerging threats and opportunities in real-time, while it is still possible to act on them


THE PROBLEM

First generation big data technologies were not designed for stream data.

They were designed to compute in “batch mode” on static big data sets, rather than continuous analytics on temporal data

They require extensive IT involvement to install, integrate, configure and use

They often require time-consuming query construction to explore data

They don’t show moving dynamic analytics and visualizations over time


THE PROBLEM

Human analysts cannot keep up with constantly moving data

The velocity of streaming data overwhelms the capacity of human brains to monitor and analyzeWhile analysts work on a question, so much new data flows in that the answer is obsoleteAnalyst expertise & learning is difficult to capture, automate, and deploy

Analysts need automated intelligent assistance to cope with streaming data at scale


A Radical Solution is Needed

Enterprises need a new kind of big data technology that is natively designed for temporal analytics on streaming data.

Make it less costly and time-

consuming to get insights out of any kind of streaming

data

Reduce the need for manual

integration of raw technologies

and data sources

Continuously generate live

dynamic analytics and

visualizations from streaming

data

Automate the discovery,

learning, and distribution

of insights to help analysts

work more efficiently


Defining Data Intelligence

Reporting

Analytics

Data Mining

Semi-Supervised Learning

Autonomous Agents

Structured Data & Metrics

Interactive Search & Dimension Aggregation

Semi-structured Data Enrichment

Clustering & Trainable Classifiers

Use-case Specific Automated Actions

Bottlenose Enterprise Intelligence Solution

Our core IP automates insights by continuously mining to find the signal in the noise and our platform makes it radically easier to handle data variety and velocity.

Trend Detection:• Threats• Opportunities• Patterns• Relationships• Predictions

Access Points:• Dashboards• Email Reports• Email Alerts• Other apps


Ingest Augment Store

Analyze

Learn

APIAutonomous Agents

Discovery Engine

Bottlenose Intelligence Platform

Ingest Augment Store

Analyze

Learn

Detect, Anticipate, Contextualize, Cluster, Predict

Supervised & Unsupervised Machine Learning, Topic Modeling & Classification

API

Metrics, Dimensions, Aggregations & Query

Autonomous AgentsDiscovery Engine

StreamSense™Data Science Workbench

Enterprise AppEnterprise AppEnterprise Apps

Nerve Center™

Visualization SDK

Processing Engine

APIDiscovery Engine

Bottlenose Platform

Hydrant™Data Push

Raw Data

Insights

Insights

Insights

Autonomous Agents

Demo of Nerve Center

Enterprise Intelligence Trends for 2025Intelligence Becomes ContinuousEvery medium and large enterprise will need a streaming, real-time unstructured data intelligence process to remain competitive

Learning Will be More AutomatedThe enterprise intelligence process will increasingly depend on automated machine learning

The Democratization of Data Science and AnalyticsMore of what analysts and data scientists do will be automated and provided directly to domain experts and decision-makers

Intelligence Moves Closer to the EdgeIntelligence capabilities will be seamlessly integrated into most workflows & applications

Key Recommendations for 2025 Enterprise Intelligence

Deploy a data intelligence capability that can collect and analyze all forms of structured & unstructured streaming data Use machine learning to automate discovery and analysis in real-time at scale

Enable domain experts to access intelligence on-demand, without relying on human analysts, data-scientists and IT in the middle

Integrate data intelligence into other applications and processes, where the work actually gets done


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Twitter: @novaspivackBlog: novaspivack.comEmail: [email protected]



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How to Prepare for 2025's Intelligence Technology