All Together Now: Connected Analytics for the Internet of Everything

Grab some coffee and enjoy the pre-show banter

before the top of the

hour!

The Briefing Room

All Together Now: Connected Analytics for the Internet of Everything

Twitter Tag: #briefr The Briefing Room

Welcome

Host: Eric Kavanagh

[email protected] @eric_kavanagh


  Reveal the essential characteristics of enterprise software, good and bad

  Provide a forum for detailed analysis of today’s innovative technologies

 Give vendors a chance to explain their product to savvy analysts

  Allow audience members to pose serious questions... and get answers!

Mission


Topics

August: REAL-TIME DATA

September: HADOOP 2.0

October: DATA MANAGEMENT


Outside In

Ø  Centrifugal Reality

Ø  Responsiveness Required

Ø  Customization Paramount

Ø  Context is King


Analyst: Mark Madsen

Mark Madsen is president of Third Nature, a technology research and consulting firm focused on business intelligence, data integration and data management. Mark is an award-winning author, architect and CTO whose work has been featured in numerous industry publications. Over the past ten years Mark received awards for his work from the American Productivity & Quality Center, TDWI, and the Smithsonian Institute. He is an international speaker, a contributor to Forbes Online and on the O’Reilly Strata program committee. For more information or to contact Mark, follow @markmadsen on Twitter or visit http://ThirdNature.net


Cisco

  Cisco Systems is a known leader in the design, manufacturing and sales of networking equipment

  Cisco offers Connected Analytics, a comprehensive platform designed to deliver a complete infrastructure for streaming analytics

  The platform supports open APIs for custom solutions and application development


Guest: Kim Macpherson

Kim Macpherson is the Director of Analytics Engineering in the Data and Analytics Business Unit at Cisco Systems. She is developing analytics technologies and architectures for devices and fog nodes for platforms, applications and solutions that deliver business value for infrastructure, operations and the line of business. She is contributing to Cisco’s software platform strategy by embedding analytics software into Cisco devices, creating analytics services and delivering analytics applications to service providers, enterprises and vertical industries through digitization and hyper-distributed processing. Kim joined Cisco via the 2012 acquisition of Truviso, Inc., a venture-funded startup company providing streaming real-time analytics, where Kim was Senior Vice President of Engineering and Business Operations. Before joining Cisco, Kim served as Vice President of Engineering for Consumer Security products at EMC and Vice President of Global Professional Services at RSA Security Systems.

Kim MacPherson

August 18, 2015

Data and Analytics Briefing Room

2 © 2014 Cisco and/or its affiliates. All rights reserved. Cisco Confidential

Data & Analytics Unlock the Value in the Internet of Everything

Data Monetization

Customer Engagement

Optimized Operations

Organizational Effectiveness

Threat Minimization

Source: Cisco Consulting Services primary research, 2013

of the $19T IoE Opportunity over the Next 10 Years will be Realized Through Data & Analytics $7.3T


Getting insight from the Data is the Client’s Biggest Challenge

Data Effectively capturing, storing, and analyzing data generated by connected “things” (e.g., machines, devices, equipment)

Things Connecting the right “things”

(e.g., machines, devices, equipment) to capture useful data

Process Updating our business and operational processes to benefit from IoT solutions

People Enabling workers to effectively

use IoT solutions through means such as training and providing

user-friendly systems

1230 Respondents

Source: Cisco Consulting Services Global IoT Study, 2014

20%

27%

13% 40%

In Which of the Following Areas Does Your Organization Need to Improve the Most to Make Effective Use of IoT Solutions?


Widely Distributed, Streaming, Short Shelf Life, Too Big to Move

“Most data will be processed at the edge” (mobile devices, appliances, routers)

Digital Enterprises See an Explosion of Data at the Edge

Three years from now, where will most data generated by IoT solutions be processed?

1230 respondents Source: Cisco Consulting Services Global IoT Study, 2014

37%


Connecting Diverse Types of Data is Required to Uncover the Most Valuable insight

Structured Data Unstructured Data Data Streaming at the Edge

Traditional Data Warehouse

Big Data Store


However, a New Approach is Needed to Reach and Analyze That Data

Structured Data Unstructured Data Data Streaming at the Edge

Traditional Data Warehouse

Big Data Store

Analytics 1.0 Analytics 2.0 Hours/Minutes/Seconds Days/Hours

Analytics 3.0 Seconds/Milliseconds


Operational Consistency Data Mobility Optimized Form Factors

UCS Mini

UCS Mini

UCS for Enterprise

UCS for Hadoop

Nexus Family

ISR

APIC EM

AP MS

CGR

IE

Video

Cloud Services and Applications

Partner Clouds

Intercloud Fog & Edge Core Data Center

Only Cisco Delivers the Connected Infrastructure You Need to Reach from the Data Center to the Edge


Integrated Infrastructure From Data Center to Edge

On-Demand Performance & Scalability

Integrated Security

Unified Management

Providing End-to-end Consistency, Performance, and Security Across Analytic Applications

Data Center to Edge Analytics Infrastructure, Lowest Analytics TCO


We are Investing in Three Ways to Turn Data from Connected Infrastructure into Extraordinary Outcomes

Real-time Connected Solutions 3

Analytics Packages for Optimized Infrastructure

2

Agile & Pervasive Data Access 1

Network

Collaboration

Contact Center

Video

Sensor-based Real-time

Location / Mobility

Energy Management

Threat

Analytics from Core Cisco Competencies


Cisco Solution Support Services

Cisco Advanced Services

Data Science Services

Partner Designed Solutions

Cisco Consulting Services

Streaming Analytics Intellectual Capital Data Virtualization Work-load Automation

Agile & Pervasive Data Access

Integrated Infrastructure and Networking

Big Data & Analytics Ready Infrastructure

Real-time Connected Solutions Connected

Analytics for Events Connected Analytics

for IT Connected

Analytics for Retail Connected

Analytics for SP

Analytics Packages for Optimized Infrastructure

Cisco Connected Analytics for the Internet of Everything

1

3

Contact Center

Video Analytics

Threat Analytics

Location Analytics

Collaboration Analytics

Network Analytics

Energy Analytics 2

NEW

NEW

Devices Wi-Fi Video POS Sensors Social 3G/4G

Access Wired | Small Cell | Wi-Fi

Data Center | Intercloud Core/Edge | IoT | IoX

Mobile Packet Core/SON I Unified Communication

Collaboration | Contact Center


Integrated Streaming query processing supports active, continuous monitoring of live data. Get

instantaneous real-time analysis and action, and efficient use of compute

resources.

Scalable Offers the framework and interfaces for use case development across a

wide variety of business and network management functions and

industries.

React intelligently Provides real-time insights with Big Data views to support actionable

events and dynamic dashboards to help you get more value out of your

data.

What is Cisco Connected Streaming Analytics? An analytics platform delivering predictive, actionable insights in high velocity streams of live data

from multiple sources, enabling real-time governance and immediate actions. It adapts to customer environments and use cases for a wide range of industries and business applications.


Ultra-fast performance, real-time analytics

for multiple data sources

Flexible data integration framework including

infrastructure-ready adapters

Customizable live dashboards and reporting framework that let

you look at data your way

Reliability with high availability, failover, and online backup and

restore

A variety of deployment architectures for cloud, data

center and fog

Cisco Connected Streaming Analytics Offers

Seamless persistent data management for historical

reporting, big data integration


•  Dynamic dashboards •  Real-time trending •  Predictive analytics integration •  Continuous queries •  Event generation

•  Combine information from network and applications

•  Seamlessly query live and historic data

•  Historic reporting framework

•  Complex queries from fact streams and dimensional data

•  North/south and east/west interfaces for customization

•  Multivendor extensibility

Cisco Connected Streaming Analytics Drives

Big Data Flexibility Real-Time


Value of Real-Time Connected Streaming Analytics

… Enabling closed-loop use cases:

Make it actionable in real time

Make it scale without sacrificing latency

Integrate advanced predictive analytics and machine learning

Transparently combine both live and historic data

Collect Analyze Alert and store Adjust

Make your data work for you

Usage data


Real-time Streaming Analytics

•  Mine (fetch) •  Analyze •  Report Usage Data

!

Generate an Actionable

Event

that is sent to the Policy System, Management System, etc. to allow immediate control

Next Generation Analytics Applies predicates, aggregations, and joins with metadata tables and contextual data to

identify and match trends.

Querybase Waiting for Data Store raw data or filtered data for further mining.

Database Waiting for Queries Store raw data for further mining.

Traditional Analytics Model Store first, and query later.

Usage Data

•  Mine (fetch) •  Analyze •  Report


Alerts

Predictive

Pattern Detection

Aggregation engine for pre-computation and reporting

Massive data reduction

Connected Streaming Analytics Capabilities Live Data Expertise

Decouple “Data Processing” from “Result Consumption”

Immediate Consumption True real-time streaming analytics

Delayed Consumption Heavy lifting in real time Pre-aggregated results on demand

High-cardinality analytics: Internet-scale dimensions •  Compressed bitmap aggregates for uniques •  Per-actor analysis Location

Deviations based on history, thresholds and cohort analysis

Dynamic model scoring, classification and categorizing

Finding patterns and detecting complex events

High-performance spatial indexing


Prebuilt Adapters Available & Framework to easily create

other adapters

Business Data

Operations Data

Infrastructure Data

Sensor Data

Analytics for Streaming & Historical Data

Multiple Data Sources

Continuous & Interactive Query Processing over Streaming &

Historical Data

Intelligent Actions / Events

XML, alarms …

Customizable visualizations


Sample Cisco Streaming Analytics Dashboard

Customizable Dashboards Look at the Data Your Way Live and Historical Dashboards

•  Web-based drag and drop dashboard design including rich navigation, drilling and a library of filter controls.

•  Custom build dashboards uniquely tailored for your business.

•  Portal and mash-up integration seamlessly connects business analytics with other applications.


Historical Reporting Capabilities •  Self-service report creation allows you to easily

select data; sort, filter, and group data; and generate reports from a web browser (prepackaged sample reports are provided)

• Reporting administration capabilities include:

  Export to PDF, CSV, XLS, and other formats

  Report scheduling

  Publishing by email or on a local server

• Customization: Drag-and-drop report creation and impromptu reporting


Customization and Application Building • Cisco Connected Streaming Analytics

supports open APIs and commercial database methodologies to develop solutions and custom applications

• Developers can use commercial tools and standard APIs such as SQL, XML, and Java to develop use cases, applications, and system integrations

• Cisco knows the infrastructure data, and enterprises and service providers know their business data

Each organization is unique and has it’s own specific analytics needs:

•  Use Cases •  Data sources •  Customers •  Infrastructure


Use Case - Mobility KPI Dashboard Connected Analytics for Mobility (CAM) • Unique and new clients trend analysis by date, week or month

• Real-time active session information in real-time to identify network usage pattern or manage promotional programs

• Authenticated vs. unauthenticated associations to identify potential customers

• At-a-glance information on total data usage to identify network anomaly or usage spikes

• Drill down for further analysis

Product currently under development. Actual Production UI may vary.


Use Case - Shopper Insights: Dwell Time Analysis Connected Analytics for Retail (CAR)

Shopper insights create competitive advantage for retailers- shows where, how and why buyers make decisions on path to purchase •  Measure shopper

engagement with product and promo displays

•  Insights into store areas where shoppers spend most time


What can you do?

• Correlate information from multiple sources to derive

best representation of what is occurring in your stadium

• Customer Behavior Analytics, Attendance Trends & Wi-

Fi Body Language of your Fans

Benefits:

•  Unprecedented insight & trends into your Fans

•  Better understanding of who your Fans are

•  Measure sponsor & advertisement impact and response

Use Case - Fan Behavior and Device Usage Connected Analytics for Events (CAE)

Product currently under development. Actual Production UI may vary.


Horizontally & hierarchically scalable yet embeddable

Integrate advanced predictive analytics and machine learning

Immediate Insight: True real-time with low-latency

Data acquisition & mediation from the Internet of Everything

Blends live & historical sources from Big Data ecosystem

Open APIs and highly customizable: SQL, R, JDBC etc.

Connected Streaming Analytics Advantage


Want to learn more?

Register and attend: Cisco Data and Analytics Conference October 20-22, Chicago Hilton http://cisco.com/go/daconf Kim MacPherson, Cisco Dir. Engineering, Data and Analytics [email protected]


Perceptions & Questions

Analyst: Mark Madsen

11

All Together Now: Connected Analy4cs for the Internet of Everything Analyst comments August, 2015 Mark Madsen Third Nature @markmadsen

Copyright Third Nature, Inc.

The data warehouse. More of a CostCo, really. Came about in a pre-‐event streaming market


Core idea: centralize: that solves all problems!

But there are tradeoffs:

Creates bottlenecks

Causes scale problems

Enforces a single model

Injects latency


New terminology: Data is the new oil


New terminology: Data Exhaust


New terminology: the data lake


There is a different assump4on with these terms

“Data isn’t something we make, it’s just there.” But the prevailing model for management and use is sJll “centralize”


There is a different assump4on with these terms

“Data isn’t something we make, it’s just there.” But the prevailing model for management and use is sJll “centralize” This won’t work


In a mostly-‐connected world, events occur in different 4me frames, follow different cycles of use

19 Source: Noumenal

Disconnected

Milliseconds Minutes Hours+



20 Source: Noumenal

Disconnected Every event is

persisted for some period of Jme before

it is forgoRen or forwarded




21 Source: Noumenal

Disconnected

Local context and control,

local decisions, local latency




22

Disconnected

Source: Noumenal Milliseconds Minutes Hours+

Bigger context, likely correlated, more complex rules, external monitoring



23

Disconnected


Broad context, human intervenJon, diagnosis and analyJcal tasks that have to be coordinated.



24

Disconnected


Data lives in mulJple places, at mulJple levels of detail, for differing duraJons. Unlikely to all be in one place. Nor should it be.


Flowing Persisted

Sliding window of “now”

Persisted but not yet loaded into a platform

Queryable history

Managed history

Streaming isn’t either-‐or, it’s part of IT architecture

A DB can get you to within minutes (at large scale) but it won’t be easy or cheap, mainly lives in the realm of history

Event streams, in-mem stores, CEP streaming SQL can be used for these

Real time monitoring doesn’t use only real time data: windows, restarts, detecting deviation, so the above boundaries are crossed.

ESB Cache/Queue Database / platform


Stream

If you want to do real4me and s4ll manage your data effec4vely then you need to rethink data architecture

Collect Refine Manage Deliver

Flowing Managed history Persisted

Microservices Metadata Metadata & reuse?

Flow, persisted, managed define different access, processing, storage and retrieval requirements


Rethinking soLware and data architectures is required. Enterprise IT needs to be renovated.



Upcoming Topics

www.insideanalysis.com

August: REAL-TIME DATA

September: HADOOP 2.0

October: DATA MANAGEMENT


THANK YOU for your

ATTENTION!

Some images provided courtesy of Wikimedia Commons and http://lylawarren.com/2012/06/denver-comic-con/

Technology

All Together Now: Connected Analytics for the Internet of Everything