Opportunities in the digital economy

Opportunities in the digital economy Plamen Nedeltchev, Ph.D., Cisco IT Distinguished Engineer Skolkovo, Russia October 27, 2016 Cloud Infrastructur...
Author: Lee Fox
3 downloads 0 Views 5MB Size
Opportunities in the digital economy Plamen Nedeltchev, Ph.D., Cisco IT Distinguished Engineer Skolkovo, Russia October 27, 2016

Cloud Infrastructure Challenges Agenda 1

Evolution of the revolutions

2

Internet of things

3

“Every successful business will be digital”

4

Cisco leading the way

Evolution of the revolutions Revolution

Year

Outcome

1st

1784

Steam, water, mechanical production equipment

2nd

1870

Division of labor, electricity, mass production

3rd

1969

Electronics, IT, automated production

4th

?

Digital, Cyber-physical, Cyber-bio systems

The pace is exponential – why and why now? The Laws

The Enablers “Fixed” Computing

Mobility/BYOD

go to the device) BW(The device goes with you) IPv6,(You Storage,

Internet of Things

Internet of Things

(Age of Devices)

(People, Process, Data, Things)

50B things 200M

Doubled every 1.4 years

10B

Doubles every ? years

Renewable, Doubled everydistributed energy 1.3 years

1995 2000 Driverless cars, drones

2011

2020

Global Food Waste

Region

Waste (tons)

North Africa

36M

North America/Oceania

113M

Latin America

134M

Sub-Sahara Africa

137M

Europe

207M

East Asia

381M

West/South/Southeast/Central Asia

449M

Global Water Waste Brazil

USA

8,233 Gm /year 3

FAO More Code

Country

Avg precipitation 1961-1990 (km3 /year)

Internal Resources: surface (km3 /year)

than 2.8 billion people in 48 countries will face water stress or scarcity Brazil conditions by15236 2025. By the5418 21 middle of the century, this will have reachedRussian almost 7 billion. 185 7855 4037 Source: WaterFootprint.org and WWF Federation

Russia

4,507 Gm /year 3

Internal resources: groundwater (km3 /year)

Internal resources: overlap (km3 /year)

Internal resources: total (km3 /year)

1874

1874

788

China

3,069 Gm /year 3

Indonesia

2,896 Gm /year

2,838 Gm 3/year

3

External resources: natural (km3 /year)

External resources: actual (km3 /year)

Total resources: natural (km3 /year)

Total resources: actual (km3 /year)

IRWR/ inhab. (m3 /year)

5418

2815

2815

8233

8233

31795

512

4313

195

195

4507

4507

29642

33

Canada

5352

2840

370

360

2850

52

52

2902

2902

96662

101

Indonesia

5147

2793

455

410

2838

0

0

2838

2838

13381

41

China, mainland

5995

2712

829

728

2812

17

17

2830

2830

2245

44

Colombia

2975

2112

510

510

2112

20

20

2132

2132

50160

231

Continental USA

5800

1862

1300

1162

2000

71

71

2071

2071

7153

170

Peru

1919

1616

303

303

1616

297

297

1913

1913

62973

100

India

3559

1222

419

380

1261

647

636

1908

1897

1249

Inching toward Utopia…

A harvest alert could let vintners know precisely when grapes are perfectly ripened?

Factories could produce better goods more efficiently?

We could bring healthcare to where there is none and better scale existing resources?

We could eliminate food waste and feed millions more people?

We could track any device – virtual or physical – and eliminate theft and loss?

We could track and better conserve our essential natural resources?

10 emerging technologies will lead the way

INTERNET of

THINGS

IoT Value at stake

$4.6T

$19T

$14.4T

Cisco estimates for VAS of IoT = $19T PROCESS

People to People (P2P)

PEOPLE People to Things (P2T)

Architectures Cloud / Mobile / Secure Big Data / Analytics THINGS

Network value = #Connections2 200M à 10B à 50B à 500B2

Data to Process (D2P)

DATA

Machine to Machine (M2M)

What is the value? IoT – Fast Innovation Manufacturing

Retail

Oil + Gas

$3.9T

$1.5T

$504B

Finance

Healthcare

$1.3T

$1.1T

Manufacturing

Retail

Oil + Gas

Finance

Healthcare

• • • • •

• • • • • •

• • • •

• NG Business Models • Service Profitability • Cloud and ACI à Deploy with Speed • Cross Selling

• • • • •

Productivity + Efficiency Improved Security Improved Visibility and Control Optimized Operations Decreased Downtime

Mobile/BYOD Video/Social/Voice Change Customer Experience Connected Marketing Increase Sales, Productivity Lower Operating Costs

Reduced TCO of IT Reduced Downtime Improved IT, Asset Utilization Enhanced Security

Improved Productivity Operational Efficiency Resource Utilization Compliance Improved Outcome

IoT Country by Country Value at Stake (VAS) Country

VAS-Public ($B)

VAS-Private ($B)

Total ($B)

India

$116.2

$23.5

$139.7

Africa

$128

$362

$490

Middle East

$210

$442

$652

Russia

$57.1

$216

$273.1

UAE

$6.9

$46.0

$52.9

Germany

$177.8

$736

$913.8

Netherlands

$49.7

$188

$237.7

USA

$585

$585

Japan

$109.2

$109.2

China

$291.5

$1757.2

$2,048.7

South Korea

$45.7

$204.8

$250.5

France

$182.6

$537.4

$720

UK

$173.4

$537.4

$710.8

Australia

$25.9

$241.7

$267.6

Mexico

$34.3

$163

$197.3

Canada

$92.8

$92.8

Russia: $57.1B VAS Public Sector $4.6T

$7.1B

Citizens • Chronic Disease • Telework • Smart Payments • Counterfeit Drugs

$57.1B

$50B

Cities • Cyber Security • Mobile Collaboration • Transmission Grid • BYOD

Implementing an IoT for the Public Sector in Russia could generate an estimated $57.1B of value

Russia: $57.1B VAS Public Sector Detailed public sector use case values ($M) Video Surveillance

$540

Disaster Response

$148

Smart Parking

$380

Smart Buildings

$1,082

Smart Street Lighting

$0

Correction Visits

$145

Waste Management

$89

Bridge Maintenance

$21

Road Pricing

$162

Wildfire Suppression

$25

Public Transport

$235

Fleet Management

$141

Offender Transport

$25

Local Metro

$160

Telework

$2,029

Travel

$3,584

BYOD

$4,605

Payments

$1,922

Connected Museum

$2

Smart Tollbooths

$10

Connected Learning

$771

Chronic Disease

$4,191

Gas Monitoring

$1,320

Inpatient Monitoring

$61

Water Management

$431

Counterfeit Drugs

$1,620

Smart Xmission Grid

$12,575

Cybersecurity

$9,871

Mobile Collaboration

$10,438

Drug Compliance

$239

Smart Lotteries

$85

Virtual Desktop

$206

Russia: $216B VAS Private Sector $14.4T

$157B

Vertical • Connected Marketing/Advertisement • Smart Factories • Connected Gaming/Entertainment • Innovative Payments

$216B

$59B

Cross-Vertical • Future of Work • Time-to-Market • Supply Chain Efficiency • Travel Avoidance

Implementing an IoT for the Private Sector in Russia could generate an estimated $216B of value

Russia: $216B VAS Private Sector Detailed private sector use case values ($B) Connected Commercial Vehicles

$7.8

Smart Buildings

$5.7

Smart Farming

$4.3

Wealth Management

$7.3

Physical/Logical Security

$17.8

Next-Gen Retail Bank Branches

$0.3

Smart Factories

$26.7

Next-Gen Vending Machines/Digital Malls

$1.5

Business Process Outsourcing

$12.1

Connected Gaming/Entertainment

$19.2

Innovative Payments

$18.6

Connected Marketing/Advertising

$31.9

Future of Work

$18.5

Digital Signage

$1.2

Travel Avoidance

$8.4

Virtual Attendants

$2.7

Supply Chain Efficiency

$15.6

Time-to-Market

$16.9

Every successful business will be digital

Why Companies Are Focused on Digitization Top of Mind OPERATIONAL EFFICIENCY

46% 34%

CUSTOMER SERVICE INTRAORGANIZATIONAL COLLABORATION

31%

STRATEGIC DECISION MAKING

29%

PROFITABILITY

25%

REVENUE GROWTH

24%

INNOVATIVENESS

23%

EMPLOYEE SATISFACTION

22% 15%

DATA AND PHYSICAL SECURITY INTERORGANIZATIONAL COLLABORATION

13%

TIME TO MARKET

12%

DO NOT KNOW

1% 0

Source: Business Insider/Cisco (global survey of 7000 executives)

0,05

0,1

0,15

0,2

0,25

0,3

0,35

0,4

0,45

0,5

“Digitizing” the Operating Model

Simplification

Monitor & Adapt

Automation

Continuous Innovation

Security Strategic Outcomes Reengineered

Flexible Assets E2E Policy-Based Architecture

Federated Analytics Measurable Insights (Machine Learning)

Collaboration (People & Machine)

IT and OT integration

Remote Access

Industrial Router

Local Access

Industrial Switch

WAN

Industrial Access Point

Data Center

Industrial Video

Cloud

Industrial Sensor 22

The Data Grows Exponentially Big Data is the New Normal

30

Data Center

Cloud Data: 64% will be in the Cloud (UP from 40% in 2012); Only 36% will be in traditional DCs (DOWN from 60% in 2012)

25

Global Mobile Traffic reached 190 EB/year by 2008 and will reach 25 EB/year by 2020

20

16,1

15

10,7

10 5

24,3

57% CAGR 2014-2019

2,5

4,2

6,8

0 2014 2015 2016 2017 2018 2019

Exabytes per Month

Mobile

Cloud Major consumers / generators of traffic will be M2M, wearables, smartphones, tablets and laptops

Mobile Video will be 70% of all the traffic. Cloud will host 90% of all the mobile traffic

Smart Home

Big Data is the new normal – the triple “V” The “N” dimension – the N data is the Big Data

What do I do with all this IoT data? Corp. Traffic 60%

90% Unstructured

On the edge

40% 80% of traffic will be inside the Data Center; 7% DC to DC; 13% DC to Users

Widely-distributed, short shelf life, too big to move

Streaming data at massive scale Store and analyze Analyze before you store Replicate, Parse

New Skill Set in IT Cisco Data Preparation

Clean and change

Business Analysts Data Engineers

Data Virtualization Files Databases SaaS Apps

Combine

Explore

Data Scientists

Share & Govern DBaaS

AnswerSet

Add data

XML Docs Hadoop NoSQL

Publish Desktops

Shape

Enrich

The IT professionals Hownew do you prepare? Techniques § § § §

A/B Testing Crowdsourcing Data fusion and integration – Data integration. Genetic Algorithms – In the field of artificial intelligence, a genetic algorithm (GA) is a search heuristic that mimics the process of natural selection

New Skill set • • • • • •

Machine Learning Natural Language Processing Signal Processing Simulation Time series analysis Visualization

Languages and Solutions: • • • • • •

PIG, Go, R, Python MapReduce Column-oriented databases Schema-less database (NoSQL databases) Hadoop Hive 26

Cisco’s IoT Architecture and Control Point Distributed Platform … Computing, Storage, Networking Data Center Cloud Core Networking and Services

Multi-Service Edge

Embedded Systems, Machine-to-Machine Sensors

Centralized Intelligence

Distributed Intelligence

Distributed Intelligence: Fog Computing

IP/MPLS Core

Field Area Network

Smart Things Smart Things NetworkNetwork

Big Data challenge in Security Today, in DNS and TCP/IP queries alone, • What’s on the Cisco Network? about 0.5 TB of raw data is collected daily; with new architecture, that number will increase to 4TB • • • • • •

1.1M public IPv4* addresses plus 1.7M private addresses 125,000 Windows, 72,000 Linux, 50,000 Cisco devices, 43,000 “other” 120,000 IP phones 30,000 Data Center hosts 1820 labs, 100,000+ devices 2400+ IT applications supporting 835 service offerings

• 16 major Internet connections, ~32 TB bandwidth used daily • 66k employees + 33K contractors in 165+ countries (475+ offices) • 294 partners using 547 IT extranet connections into Cisco • 400+ cloud/ASP providers used (officially) *Most users do not have IPv6 dual-stack support; the transition to IPv6 is in progress

28

Types of Analytics

How can we make it happen?

Value

What will happen?

What happened?

Why did it happen?

Predictive Analytics

Diagnostic Analytics

Descriptive Analytics

Difficulty

Prescriptive Analytics

Cisco leading the way

Digital IT and Technologies of Digital Economy The top 10 IoT Technologies for 2017-2018 IoT Security

IoT Analytics

IoT Device Management

Low-Power, Short-Range IoT Networks

Low-Power, Wide-Area Networks

IoT Processors

IoT Operating Systems

Event Stream Processing

IoT Platforms

Standards and Ecosystems

The top 12 IoT Common Services for 2017-2018 Location

Virtual Reality

Haptics, ZUI, Voice, Vision

Presence

Contextual Services

Gesture Recognition

Behavior

Artificial Intelligence

Voice Recognition

Image Recognition

Proximity

Reality Augmentation

Cisco IT Digital Architecture Business Intelligence

Advanced Threat Defense and Risk Mitigation

Business Process Management

Application-Centric IT/IoT SDN Controller

Policy Management

Distributed Fast Data Processing

Infrastructure Data Foundation

Foundational Network

Domain Management and Orchestration

Predictive Analytics Data Science

Emerging Technologies of the Digital Economy Artificial Intelligence

Robotics

Internet of Things

Autonomous Vehicles

3D Printing

Nanotechnology

Biotechnology

Material Science

Energy Storage

Quantum Computing

Who is who in IoT The opportunities of the technological companies in the new world – June 2016 Business and Technology Consulting

IoT Apps/Sls

OT Services/ Edge OEMs

IoT Platforms

CSPs

Infrastructure/ Semiconductors

Digital Operations Management

Boston Consulting Group

Accenture

Black & Veatch

Ayla Networks

AT&T

Amazon Web Services

Genpact

Deloitte

Atos

Bosch

IBM

Deutsche Telekom

Cisco

HCL Tech

EY

Capgemini

GE

Microsoft

Sigfox

Fujitsu

HPE

KPMG

Cognizant

Hitachi

Oracle

Telefónica

Google

IBM

PwC

IBM GBS

L&T Infotech

SAP

Vodafone

Intel

Taleris

McKinsey & Co.

Infosys

Rockwell

Telit

Verizon

Microsoft

UPS

Mindtree

Schneider

ThingWorx

Salesforce

Zerox

TCS

Tech Mahindra

Xively

SAP

Wipro

Source: Gartner (June 2016)

Cisco opportunities by Gartner Security

Data Management and Analytics e.g. Big Data, Data Mining, Analytics, Machine Learning

Enterprise Systems

Business Systems e.g. ERP, Custom Applications, Industrial Control Systems, Websites

IoT Middleware and Platforms IoT Cloud Services Telco M2M Platform

Gateway/Aggregation

“Things” On-Device Functions

Source: Gartner (June 2016)

On-Device Agents

External Devices and Apps (e.g. Mobile, PC)

IoT Technology Stack Cloud and Fog

Applications

Analytics

Software Platform IoT Software IoT Platform Applications

Security and Identity Management

Things Open and Infrastructure Infrastructure Programmability (APIs) Ease of use and Management

Things

From Raw Data to Better Business Outcomes Cisco Data Preparation Clean and change

Business Analysts Data Engineers

Data Virtualization Files Databases SaaS Apps

Combine

Explore

Data Scientists

Share & Govern DBaaS

AnswerSet

Add data

XML Docs Hadoop NoSQL

Publish Desktops

Shape

Enrich

35

Databases ALL other Sources

Conventional Data Platform Architecture Sources

Storage and Processing

Consumption

Cisco Data Virtualization (Composite)

Integration Toolkit

Logical Data Abstraction Layer across transactional, SaaS, Big Data & DW B2B

Relational Databases

Agile Analytics

Golden Gate

SAP HANA on UCS

NoSQL Databases

Hadoop & Spark on UCS

SaaS Applications • • •

Unstructured Data, IoT

• •

Big Data Platform



In-Memory Columnar DB Fast Agile Development

Network of Truth Mission Critical Reporting

Enterprise Data Lake Data Archive Warehouse Expansion Multiple frameworks for Processing

Legacy EDW • • •

Financial SSOTs Stable core Controlled Change

Partner / Customer Integration

APIs & ESB

CIS Web Services

Cisco Data Virtualization (Composite)

Ingestion

(Mobile / Browser / Data Service)

Experience Toolkit

Rapid Prototyping / Logical Warehouse Self Service (Exploration & Dashboards) High Value / Fast response Real time Predictive

Large scale data processing + Self Service Machine Learning, Statistical Analysis Structured + Unstructured Data Analysis Advanced Analytics HANA

Mission Critical Operational Reports

Mahout, Spark SAS IT App & System Logs & Config.

Operational Intelligence

Index & Search

H2O

Financial Reporting & Extract Ops Intelligence & Data Acceleration

IoT Data Collection and Distributed Processing Edge / DC

real-time

(network/ computer/storage, domain managers, 3rd party monitoring apps)

OSS/NMS apps

IoT

Policy and Autoscale Manager

Publish/subscribe

Analytics

Telemetry (netflow)

Logs (app, web)

IoT Stream Data (mobile devices, sensors, processes, people)

Data Source

Cloud/DC

Data Collection Platform (DCP)

Events

Metrics (local data aggregation)

Collectors w/ Protocol Adaptors (Netflow, Syslog, HTTP, MQTT, DDS, CoAP, etc.)

Fog (IoT)

Data Transformer (data clean and repurpose)

Large Data Downloader

Data Aggregation

Distribution

37

Operations Dashboards

Network Analytics (SDN, SLN)

Business Reports

Log Analysis (Clap, Splunk)

Service Assurance Analytics Short term, transactional data

Local Analytics

Sensor Telemetry

Data Lake

(location, sensors, etc.)

(Perf, Fault, Change, Capacity, Dependency)

Orchestration

(SLA, Fulfillment)

Remedy Ticketing



Long term, historical data

Security and Threat Analytics

IoT BI Apps

Data Store

Analytics

Applications

Stealthwatch Stealthwatch Learning and Positioning Summary • • • •

We can sell both , and be clear on SLNL unique Cisco embed features Multiple AD Engines used in different places is not a bad thing, and it’s actually a good thing Machine Learning in SLNL is a new technology showing Cisco innovation in the NBAD Space Roadmap does call for phasing SCA into SMC, and preserving unique feature like PCAP Stealthwatch Learning Agent Manager (SCA)

Stealthwatch Management Console (SMC)

ISE Manager

Best of Breed Portfolio

Private / Public Network

• PCAP Packet Analysis • Mitigation ACL

Branch Network

Any Routers with Netflow

ISR4K with Stealthwatch DLA Agent

Unique Architecture Choices

Contextual Services Deliverables Distribution platform for continuous delivery

Self-learning Contextual Services Engine and Personalization Services Engine Cross-platform real-time contextual services clients

Business Benefits / ROI Seamless Workflow

Insights for more focused and relevant customer interactions Real-time information to help close deals

Engage With Us blogs.cisco.com/ciscoit

facebook.com/ciscoit

twitter.com/ciscoit

cisco.com/go/ciscoit

youtube.com/cisco

Suggest Documents