The Importance of Secure Analytics & AI

Transcription

1 The Importance of 2014 Secure Analytics & AI

2 Rick Hutley Program Director, Professor of Practice, Data Science, University of the Pacific Vice President of IoT, Cisco Systems Vice President of Innovation, Cisco Systems CIO, British Telecom

3 What s All the Fuss About? Master of Science in Analytics

4 1800s 1950s 2000s We are Entering the New Era of Data Technology Mechanical

5 Leverage Data or Die The Data revolution is happening we cannot ignore it Allow it to overwhelm us Analyze & Leverage it

6 What is Big Data Master of Science in Analytics Is Data Different Today?

7 Three Main Sources of Data Traditional Systems Traditional Data Relational Databases: Finance HR Sales.

8 Three Main Sources of Data Social Media YouTube Facebook Twitter. Traditional Systems A LOT of Data Relational Databases: Finance HR Sales.

9 Three Main Sources of Data Social Media YouTube Facebook Twitter. Traditional Systems Relational Databases: Finance HR Sales. Internet of Things Sensors on EVERYTHING. BIG Data

10 Mind Blowing Social Media Factoids Global Population: 7.6 Billion 2.1 Billion active users 28% of the entire planet - EVERY MONTH 500,000,000 Tweets sent every day Over 90% of all of the data in world, was created in the last 2 years 80% of the time spent on social platforms happens on mobile By 2020, over 75% of the world's mobile data traffic will be video.

11 The Initial Internet Era - ebusiness Data Leveraging data into more useful information for decision making Process Delivering the right information to the right person (or machine) at the right time

12 Social Media & the Internet of Things Data Leveraging data into more useful information for decision making Process Delivering the right information to the right person (or machine) at the right time People Connecting people in more relevant, valuable ways >35 Times Impact of the Internet Things Physical devices and objects connected to the Internet and each other for intelligent decision making

13 Data IS Different Today! Unimaginable quantities of data VOLUME New types of data: Challenge Structured (traditional rows & columns) Unstructured (e.g. voice & video) VARIETY BIG DATA VELOCITY VACINITY Data generated & processed in real time Processed locally, not just in a data center

14 The Internet is a BIG Place. But exactly how big is it??

15 The Internet is a BIG Place. IPv4: 2 32 => 4,294,967,296 device addresses (last one given out in 2015)

16 The Internet is a BIG Place. IPv4: 2 32 => 4,294,967,296 device addresses (last one given out in 2015) IPv6: => 340,282,366,920,938,463,463,374,607,431,768,211,45 device addresses But What Does That Really Mean

17 The Internet is a BIG Place. 340,282,366,920,938,463,463,374,607,431,768,211,456 addresses 2.4 E12 addresses for every piece of paper ever created 4.25 E28 addresses per person on the planet 6.7 E19 addresses square centimeter of the earths surface EVERYTHING could have an address

18 Unimaginable Change Ahead! 1.5T Things we could usefully connect >99% of Things remain unconnected 0.6% B Things will be connected by 2020 Source: Cisco Systems 2012

19 You can now see your world in ways you couldn t even Imagine before

20 Data Doesn t Help Master of Science in Analytics You Need Insights

21 Past Present Future The Analytics Hierarchy Prescriptive Predictive What should we do to achieve a desired outcome What will happen, when Monitoring Diagnostic What is happening Why did it happen Business Intelligence Data Science Descriptive / Reporting What happened

22 The Analytics Spectrum Reports Sentiment Analysis Risk Analysis Segmentation Alerts Intruder Detection Fraud Detection Forecasting Predictive Maintenance Historic Near Real Time Future Text Mining Time Series Credit Scoring Consumer Behavior Real Time Monitoring.and so much more! Classification Machine Learning Simulation

23 Artificial Intelligence, Master of Science in Analytics Machine Learning & Other Scary Stuff

24 Machine Learning: An Example of AI Artificial Intelligence Vision Natural Language Processing Neural Networks (Deep Learning) Machine Learning Robotics Regression Clustering Decision Trees Dimensional Reduction We can do some amazing, and scary things with AI today

25 Two Basic Types of Machine Learning

26 Two Basic Types of Machine Learning (Classification) (Clustering)

27 Teaching Cars to be Intelligent Self driving cars Collision / Pedestrian avoidance Lane warning / assist Self parking Auto-braking (faster than human reactions) LIDAR Sensor Predictive analysis of emerging dangers Intelligent highways Congestion avoidance StrataThought 2014

28 Using Data Ethically is Critical

29 The Red Face Test Ethical considerations: driven by a company s values belief system A responsible Organization is one that handles data in accordance with it s values AND is perceived by others to have done so

30 Using Data Securely is Critical But locking data away is NOT the answer: Cannot leverage it s power May as well not have it! The next panel will discuss in depth

31 Building Your Analytics / Master of Science in Analytics Data Science Capabilities

32 It Takes Two to be Successful Technology Skilled Workforce

33 Build Data Skills at Every Level Data Literate leadership: Understand the power of data Understand the dangers of inappropriate handling Understand their corporate responsibilities around data Highly skilled Data Scientists Understand data Can design and build complex analytic models Can turn data into actionable business insights

34 Leveraging Data = Future Success Data is the Economic Engine of the future We cannot ignore it Data insights are immensely valuable Embrace it, control it, leverage it

35 Questions..

36 Your Data University