3.kdb-비즈니스 인사이트 중심의 데이터분석 전략 진승의.ppt [호환 …

사물인터넷 환경에서 비즈니스 인사이트 중심의데이터 분석 전략

한국 IBM진승의 실장[email protected]

1.1. 사물인터넷으로사물인터넷으로 인한인한 데이터데이터 분석분석 및및 관리관리 환경의환경의 변화변화

2.2. Actionable InsightsActionable Insights의의 개념개념 및및 중요성중요성

3.3. Actionable Insights Actionable Insights 도출을도출을 위한위한 분석분석 고도화고도화 방안방안

4.4. 사물인터넷사물인터넷 환경에서의환경에서의 Actionable Insights Actionable Insights 기반기반 데이터데이터 분석분석 사례사례 연구연구

AgendaAgenda

© Copyright International Business Machines Corporation 2014, All rights reserved 1





The Internet of Things

Definition1

The Internet of Things refers to the use of sensors, actuators, and data communications technology built into physical objects - from roadways to pacemakers - that enable those objects to be tracked, coordinated, or controlled across a data network or the InternetThere are three steps in Internet of Things applications:§Capturing data from the object (for example,

simple location data or more complex information),

§Aggregating that information across a data network, and

§Acting on that information - taking immediate action or collecting data over time to design process improvements.


Source: 1. Disruptive Technologies, McKinsey Global Institute, May 2013

Definition1

The Internet of Things refers to the use of sensors, actuators, and data communications technology built into physical objects - from roadways to pacemakers - that enable those objects to be tracked, coordinated, or controlled across a data network or the InternetThere are three steps in Internet of Things applications:§Capturing data from the object (for example,

simple location data or more complex information),

§Aggregating that information across a data network, and

§Acting on that information - taking immediate action or collecting data over time to design process improvements.

Internet of Things continues to explode


Source: ABI Research, Cisco ‘The Internet of Things: How the Next Evolution of the Internet is Changing Everything’, Cisco Visual Networking Index (VNI) Global Mobile Data Traffic Forecast for 2010-2015, IBM Analysis

Internet of Things Opportunity

50BDevices20201> Compute Economics

+ Ubiquitous Connectivity+ Big Data Analytics

Today 85% of deployed systems are unconnected, do not share data with each other or the cloud.

And new devices are being added every day.


= BusinessTransformation

15BDevices2015

2BDevices

2006

1IDC*, Intel, United Nations3McKinsey Global Institute*

2IDC Digital Universe Study, Dec 2012*Other names and brands may be claimed as the property of others.

> Compute Economics+ Ubiquitous Connectivity+ Big Data Analytics

1 Connect2 Collect Data3 Analyze4 Transform

GSMA “Connected Life” forecast $4.5T in 2020

Connected Life is everything that is connected and how they interact: cars, mobile devices, buildings, sensors and people

Top Ten in 20201. Connected Car $600 billion2. Clinical Remote Monitoring $350 billion3. Assisted Living $270 billion4. Home and Building Security $250 billion5. Pay-As-You-Drive Car Insurance $245 billion6. New Business Models for Car Usage $225 billion7. Smart Meters $105 billion8. Traffic Management $100 billion9. Electric Vehicle Charging $75 billion10.Building Automation $40 billion


Connected Life is everything that is connected and how they interact: cars, mobile devices, buildings, sensors and people

Top Ten in 20201. Connected Car $600 billion2. Clinical Remote Monitoring $350 billion3. Assisted Living $270 billion4. Home and Building Security $250 billion5. Pay-As-You-Drive Car Insurance $245 billion6. New Business Models for Car Usage $225 billion7. Smart Meters $105 billion8. Traffic Management $100 billion9. Electric Vehicle Charging $75 billion10.Building Automation $40 billion

Source:http://www.globaltelecomsbusiness.com/article/2985699/Connected-devices-will-be-worth-45t.html

Internet of Things is turning Big Data into Massive Data


IoT use cases have many common requirements

Core Requirements:§ Easily on-board connected “things”§ Create a real-time communication channel with the “thing”§ Begin capturing data from the “thing”§ Visualize data from the “thing”§ Collect data in a historian DB§ Provide access to the collected data§ Manage the “things” and the connectivity to them§ Secure the data from the “thing” and control access to that that data§ Pay for the service based on usage

Extended Requirements:§ Perform analytics both in real-time and on historical trend data§ Trigger events based on specific data conditions§ Interact with the “thing” from business apps and/or from mobile devices§ Send commands to the “thing”


Core Requirements:§ Easily on-board connected “things”§ Create a real-time communication channel with the “thing”§ Begin capturing data from the “thing”§ Visualize data from the “thing”§ Collect data in a historian DB§ Provide access to the collected data§ Manage the “things” and the connectivity to them§ Secure the data from the “thing” and control access to that that data§ Pay for the service based on usage

Extended Requirements:§ Perform analytics both in real-time and on historical trend data§ Trigger events based on specific data conditions§ Interact with the “thing” from business apps and/or from mobile devices§ Send commands to the “thing”

Brand New Trends of Information Management

Back office, business transaction systems- Relational database- Relation of the entities described as relational tables- Rigid data schema

Mobile, social, Internet-of-People- Graph database, JSON store- Relation of the entities described as graphs and events/objects- Flexible data schema


Internet-of-Things, physical world- Relation among data entities follows physical model- Ideally the information management system should capture both the data entities and their relationship- Current solution: separate data and relationship (model), using RDB or file to store the data, leave model & analytics to applications

Imagine the Possibilities of Analyzing All this Data in Real-time


Predictive Maintenance & Quality

§ Estimate and extend component life

§ Increase return on assets

§ Improve maintenance, inventory and resource schedules

§ Improve quality and reduce recalls

§ Reduce time to identify issues

§ Improve readiness and service

ReduceReduceOperational costsOperational costs

ImproveImproveAsset productivityAsset productivity


§ Estimate and extend component life

§ Increase return on assets

§ Improve maintenance, inventory and resource schedules

§ Improve quality and reduce recalls

§ Reduce time to identify issues

§ Improve readiness and service

ImproveImproveAsset productivityAsset productivity

IncreaseIncreaseProcess efficiencyProcess efficiency

Predictive Maintenance & Quality (cont’d)

§ Monitor, maintain and optimize assets for better availability, utilization and performance

§ Predict asset failure to optimize product quality and supply chain processes

§ Remove guesswork from the decision-making process


§ Monitor, maintain and optimize assets for better availability, utilization and performance

§ Predict asset failure to optimize product quality and supply chain processes

§ Remove guesswork from the decision-making process

Combined with out-of-box models, dashboards, reports and source connectors





AgendaAgenda






Information Supply Chain of Internet of Things


Extracting Actionable Insights from Internet of Things

OPPORTUNITY:

We can realize the Smarter Planet vision by improving the intelligentpart

§ We can radically improve our ability to manage, control and optimize large-scale physical infrastructures

§ The business opportunities would be broad, applying to smarter buildings & cities, asset-intensive industries such as E&U, Oil&Gas, Automotive, etc.

§ The total value would be several $B for IBM software and services.

§ Profusion of sensor data and data sets from cities, buildings and homes

§ Scientific exploration: large open data sets for astronomy, meteorology, genome, etc.

§ More data collected from industry assets, e.g. for condition-based maintenance

TREND:

The physical world is becoming increasingly instrumented and interconnected


OPPORTUNITY:

We can realize the Smarter Planet vision by improving the intelligentpart

§ We can radically improve our ability to manage, control and optimize large-scale physical infrastructures

§ The business opportunities would be broad, applying to smarter buildings & cities, asset-intensive industries such as E&U, Oil&Gas, Automotive, etc.

§ The total value would be several $B for IBM software and services.

§ Transport & ingest unprecedented volumes of data from physical devices

§ New data stores and query languages for spatio-temporal and linked data

§ Cope with open world of data models; match data and analytics semantics and ontologies

§ Semi-automate extraction of statistical physics models

§ Real-time visualization & computational steering of what-if models

CHALLENGE:

We must intelligently manage & analyze big data from the physical world

Real-time Big Data Analytics of Internet of Things

Millions of events per second

Microsecond Latency

Real time insights

PowerfulAnalyticsAlgorithmic

TradingTelco ChurnPrediction

SmartGrid

CyberSecurity Government /

Law enforcement

ICUMonitoring

EnvironmentMonitoring


Millions of events per second

Microsecond Latency

IBM MessageSight

IBM InfoSphere Streams

Identify and leverage data needed for actionable insights

With new analytics, clients can better able to enter the world of predicting the next best offer, action or need

Demographicdata

Transactiondata

Pay Centers

Outbound calls

Call Centers

Events

Direct Mail

Kiosks

TransactionsOrders

Paymenthistory

Usage historyCharacteristics

Demographics

AttributesUtilities are capturing and leveraging internal and external data


Clients are working to understand how predictive insights about customers must be in order to succeed

With new analytics, clients can better able to enter the world of predicting the next best offer, action or need

Descriptive analyticsPredictive analyticsPrescriptive analytics

Interactiondata

Behavioraldata

Outbound calls

Website

Search

Online Advertising

MobileEmails

SMS/MMS

Social Media

Customer Service

Transaction

stage

E-mail / Chat

Call center notes

Web click-streamsIn-person

dialogs

Opinions

Preferences

Desires

Needs

Sample Actionable Insights Use Cases

Customer Targeting and Personalization

Customer Program Targeting

Customer Survey Selection

C&I Customer Experience and Engagement

Customer Recruitment

Program Eligibility Tracking

Targeted Marketing Campaigns

Business Capability Use Case Name


Customer Behavior Analysis

Identify Right Products and Services for Right Customer via Right Channel

Monitor Channel Preferences and Measure Channel Effectiveness

Monitor Transactions by Channel

Improve Targeting by Leveraging Customer Interaction and Usage

Correlate Channel Usage to Stated Preferences

Pay Arrangement and Credit

Branch Office Performance

Design Targeted Customer Offerings

Personalized Customer Service Via Customer Lifecycle Knowledge

Customer Engagement Initiative





AgendaAgenda






Managing IoT data using Timeseries

IBM Informix

Timeseries VTI Tables

IoT - Devices

Data Loader

JSONData Files

Middlew

are P

rocessing

JSON


Timeseries Tables

Timeseries VTI Tables

Data Loader

Middlew

are P

rocessing

type id Usage Timeseries(IFXTSBSON)

“XA” 12 (2014-01-01 01:21:000, {x:1,y:2}), (2014-02-02 01:23:000, {x:3, y:5, z:42})

“XB” 48 (2014-01-01 01:21:000, {c:1,d:”ACND”}), (2014-04-02 01:23:000, {c:92,d:”MCBS”, e:42})

“XC” 23 (2015-01-01 01:21:000, {p:1,q:2}), (2015-03-02 01:23:000, {p:3, y:5, z:42}),

Data

IoT - Devices

NoSQL used for IoT data

SQL {NoSQL:JSON}

Define Schema first Write the program first

Relational Key-value, Document, column family, graph and text

Changing schema is hard Assumes dynamic schema


Scale-up Scale-out

ACID consistency BASE consistency

Transactions No Transactions

SQL Proprietary API; Sometimes has the “spirit” of SQL

Timeseries also required for better IoT data analysis

SQL Timeseries

Define Schema first Create Timeseries Row Type

Relational Timeseries Optimized with projection to relational;

Often used with Spatial data

Changing schema is hard Changing schema is hard; Flexible with Timeseries({JSON})

Scale-up Scale-up & Scale-out


Scale-up Scale-up & Scale-out

ACID consistency ACID consistency

SQL SQL extensions; Relational projection

Timeseries also required for better IoT data analysis (cont’d)

• Instrument-generates large time-based Big Data– Data are time-based and time-serialized

– Stock trading, smart meters, network devices,

heavy industrial sensors. Etc.

• Characteristics of time-series data– Data ordered by time such as 15 minutes per read

– Data value varies based on time dimension


• Instrument-generates large time-based Big Data– Data are time-based and time-serialized

– Stock trading, smart meters, network devices,

heavy industrial sensors. Etc.

• Characteristics of time-series data– Data ordered by time such as 15 minutes per read

– Data value varies based on time dimension

ID, Values , Kwh

ID102，219，0.6

ID, Values , Kwh

ID102，220，0.6

ID, Values , Kwh

ID102，222，0.8

ID, Values , Kwh

ID102，219，0.7

01:00:00 01:15:00 01:30:00 01:45:00 Timeline

Examples of time-series data

ID101，220，0.5 ID101，220，0.8 ID101，215，0.6 ID101，218，0.5

Predictive Inspection Analytics

Improve Inspection Efficiency and Effectiveness: ØSupport Inspection with Data Driven Selection

• Examine all data attributes and rank them on importance to predicting successful investigations

ØPredictive Models and Advance Analytics• Find additional data attributes and predictive rule sets

that aren’t currently considering when selecting subjects for inspection

ØDecision Support and Integration• Recommended next best action in a complex

environment with different criteria and multiple inspection levels


Improve Inspection Efficiency and Effectiveness: ØSupport Inspection with Data Driven Selection

• Examine all data attributes and rank them on importance to predicting successful investigations

ØPredictive Models and Advance Analytics• Find additional data attributes and predictive rule sets

that aren’t currently considering when selecting subjects for inspection

ØDecision Support and Integration• Recommended next best action in a complex

environment with different criteria and multiple inspection levels

Predictive Inspection Analytics (cont’d)

Decision Management is a business discipline that applies advanced analytics to prioritize and optimize inspection selections.

§ Business rules to automate what you know

§ Models to predict what you don’t§ Optimization to make best use of

scarce resources§ Business events to identify

situations where action is needed


Decision Management is a business discipline that applies advanced analytics to prioritize and optimize inspection selections.

§ Business rules to automate what you know

§ Models to predict what you don’t§ Optimization to make best use of

scarce resources§ Business events to identify

situations where action is needed

IoT Mobility Analytics






AgendaAgenda






Well field optimization for Oil/Gas field service operations

§ Improve Production (reduce downtime or non-productive time) at lower costs AND

§Optimize the Safety Case (reduce operational risk)

§Operators must have both social and legal license to operate

§ Increasing reliance upon communications network and operational technology

Business Challenges

§Well head optimization§Public / private stakeholder visibility§Operational efficiency – remote monitoring with

pre-planned response plans§Reduce time to production and operational delays

thru more effective collaboration between operators, contractors, and public stakeholders

Opportunities for Innovation


§ Improve Production (reduce downtime or non-productive time) at lower costs AND

§Optimize the Safety Case (reduce operational risk)

§Operators must have both social and legal license to operate

§ Increasing reliance upon communications network and operational technology

Smar

t Dev

ices

What happens to the ‘well field optimization’ process if:§ The metering equipment fails? The communication network(s) fails? The application

or database servers have a performance issue ? Network security is breached?

Increasing supply chain efficiency from ‘Pit to Port’

§Decrease time to operation for new sites and improve return on capital investments

§ Improve production reliability (across supply chain)

§Operational compliance with environmental, health, and safety regulations

§ Increasing operational efficiency with real time visibility and process automation

Business Challenges

§Predict and act on potential failures§ Increasing production thru resource optimization§Operational efficiency – remote monitoring with

pre-planned response plans (internal / 3rd party)§Reduce time to production and operational delays

thru more effective collaboration between operators, contractors, and stakeholders



§Decrease time to operation for new sites and improve return on capital investments

§ Improve production reliability (across supply chain)

§Operational compliance with environmental, health, and safety regulations

§ Increasing operational efficiency with real time visibility and process automation

Managing complexity of connected cars to reduce costs and improve safety

§Deliver new vehicle innovation to maintain / extend market differentiation- Particularly software / electronics

§Reduce quality and reliability issues- >50% of life cycle warranty costs

§Ensure security of electronic systems and comply with safety critical engineering process regulations

Business Challenges

§Reduce life cycle warranty costs and improve product differentiation- Reduce dealership visits via OTA software

updates- Improving analytics for early problem detection

§Vehicle-to-vehicle awareness & safety§ Increased driving automated & assistance§Risk-based insurance



Integration to othersmarter systems

Remote diagnosticsand software updates

Vehicle as application platformgenerating new innovation

§Deliver new vehicle innovation to maintain / extend market differentiation- Particularly software / electronics

§Reduce quality and reliability issues- >50% of life cycle warranty costs

§Ensure security of electronic systems and comply with safety critical engineering process regulations

§Reduce life cycle warranty costs and improve product differentiation- Reduce dealership visits via OTA software

updates- Improving analytics for early problem detection

§Vehicle-to-vehicle awareness & safety§ Increased driving automated & assistance§Risk-based insurance

Visualisation of different groups of consumer based on their seasonal usage


Identifying customers for targeting of energy saving products and advice


Identifying customers to target efforts to protect potentially vulnerable users


Wrap-up

Step 1Data Understanding

Step 2Hypothesis Testing

Step 3Reporting


• Load the data • Explore and sense

check versus expectations

Step 1Data Understanding

• Define hypotheses using expert knowledge and exploratory learning

• Build and iterate models to test hypotheses

• Synthesize analysis results

• Demonstrate possible benefits

• Explain insights• Plan next steps

Step 2Hypothesis Testing

Step 3Reporting


Questions?Questions?

3.kdb-비즈니스 인사이트 중심의 데이터분석 전략 진승의.ppt [호환 …

Documents