Business Intelligence_ week 7

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Chapter 14:

Business Analytics: Emerging Trends and Future Impacts

Business Intelligence and Analytics: Systems for Decision Support

(10th Edition)

Business Intelligence and Analytics: Systems for Decision Support

(10th Edition)

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Learning Objectives

Explore some of the emerging technologies that may impact analytics, BI, and decision support

Describe how geospatial and location-based analytics are assisting organizations

Describe how analytics are powering consumer applications and creating a new opportunity for entrepreneurship for analytics

Describe the potential of cloud computing in business intelligence

(Continued…)

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Learning Objectives

Understand Web 2.0 and its characteristics as related to analytics

Describe the organizational impacts of analytics applications

List and describe the major ethical and legal issues of analytics implementation

Understand the analytics ecosystem to get a sense of the various types of players in the analytics industry and how one can work in a variety of roles

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Opening Vignette…

Oklahoma Gas and Electric Employs Analytics to Promote Smart Energy Use

Company background

Problem description

Proposed solution

Results

Answer & discuss the case questions...

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Questions for the Opening Vignette

Why perform consumer analytics?

What is meant by dynamic segmentation?

How does geospatial mapping help OG&E?

What types of incentives might the consumers respond to in changing their energy use?

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Geospatial Analytics

Geocoding

Visual maps

Postal codes

Latitude & Longitude

Enables aggregate view of a large geographic area

Integrate “where” into customer view

Location-Based Analytics

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Location-Based Analytics

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Location-based databases

Geographic Information System (GIS)

Used to capture, store, analyze, and manage the data linked to a location

Combined with integrated sensor technologies and global positioning systems (GPS)

Location Intelligence (LI)?

Interactive maps that further drill down to details about any location

Location-Based Analytics

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Retailers – location + demographic details combined with other transactional data can help …

determine how sales vary by population level

assess locational proximity to other competitors and their offerings

assess the demand variations and efficiency of supply chain operations

analyze customer needs and complaints

better target different customer segments

Use of Location-Based Analytics

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Global Intelligence

U.S. Transportation Command (USTRANSCOM)

track the information about the type of aircraft

maintenance history

complete list of crew

equipment and supplies on the aircraft

location of the aircraft

 well-informed decisions for global operations

Overlaying weather and environmental data

Teradata, NAVTEQ, Tele Atlas …

Use of Location-Based Analytics

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Application Case 14.1

Great Clips Employs Spatial Analytics to Shave Time in Location Decisions

Questions for Discussion

How is geospatial analytics employed at Great Clips?

What criteria should a company consider in evaluating sites for future locations?

Can you think of other applications where such geospatial data might be useful?

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Geospatial Analytics Examples

Sabre Airline Solutions’ application

Traveler Security

Geospatial-enabled dashboard

Assess risks across global hotspots

Interactive maps

Find current travelers

Respond quickly in the event of any travel disruption

Telecommunication companies

Analysis of failed connections

See the Multimedia Exercise, next

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A Multimedia Exercise in Analytics Employing Geospatial Analytics

Go To Teradata University Network (TUN)

Find the BSI Case video on “The Case of the Dropped Mobile Calls”

Watch the video via TUN or at YouTube youtube.com/watch?v=4WJR_Z3exw4

Also, look at the slides at

slideshare.net/teradata/bsi-teradata-the-case-of-the-dropped-mobile-calls

Discuss the case

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Real-Time Location Intelligence

Many devices are constantly sending out their location information

Cars, airplanes, ships, mobile phones, cameras, navigation systems, …

GPS, Wi-Fi, RFID, cell tower triangulation

Reality mining?

Real-time location information = real-time insight

Path Intelligence (pathintelligence.com)

Footpath – movement patterns within a city or store

How to use such movement information

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Application Case 14.2

Quiznos Targets Customers for Its Sandwiches

Questions for Discussion

How can location-based analytics help retailers in targeting customers?

Research similar applications of location-based analytics in the retail domain.

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Real-Time Location Intelligence

Targeting right customer based on their behavior over geographic locations

Example Radii app

Collects information about the user’s favorite locations, habits, interests, spending patterns, …

Radii uses the Gimbal Context Awareness SDK

Combines time + place + duration + action + …

Assigns Location Personality  Recommendation

New members receive 10 “Radii” to spend

Radii can be earned and spent on those locations

For more info, search for radii app on the Internet

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Real-Time Location Intelligence

Augmented reality

Cachetown - augmented reality-based game

Encourage users to claim offers from select geographic locations

User can start anywhere in a city and follow markers on the Cachetown app to reach a coupon, discount, or offer from a business

User can point a phone’s camera toward the virtual item through the Cachetown app to claim it

Claims  free good/discount/offer from a nearby business

For more info, go to cachetown.com/press

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Explosive growth of the apps industry

iOS, Android, Windows, Blackberry, Amazon, …

Directly used by consumers (not businesses)

Enabling consumers to become more efficient

Interesting Examples

CabSense – finding a taxi in New York City

Rating of street corners; interactive maps, …

ParkPGH – finding a parking spot

Downtown Pittsburgh, Pennsylvania

For a related example, see Application Case 14.3, next

Analytics Applications for Consumers

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Application Case 14.3

A Life Coach in Your Pocket

Questions for Discussion

Search online for other applications of consumer-oriented analytical applications.

How can location-based analytics help individual consumers?

How can smartphone data be used to predict medical conditions?

How is ParkPGH different from a “parking space–reporting” app?

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Other Analytics-Based Applications

In addition to fun and health...

Productivity

Cloze – email in-box management

Intelligently prioritizes and categorizes emails

The demand and the supply for consumer-oriented analytic apps are increasing

The Wall Street Journal (wsj.com/apps) estimates that the app industry has already become a $25 billion industry

Privacy concerns?

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Recommendation Engines

People rely on recommendations by others

Success for retailer line Amazon.com

Recommender systems

Web-based information filtering system that takes the inputs from users and then aggregates the inputs to provide recommendations for other users in their product or service selection choices

Data

Structured  ratings/rankings

Unstructured  textual comments

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Recommendation Engines

Two main approaches for recommendation systems

Collaborative filtering

Based on previous users’ purchase/view/rating data

Collectively deriving user  item profiling

Use this knowledge for item recommendations

Techniques include user-item rating matrix, kNN, correlation, …

Disadvantage – requires huge amount of historic data

Content filtering

Based on specifications/characteristics of items (not just ratings)

First, characteristics of an item are profiled, and then the content-based individual user profiles are built

Recommendations are made if there are similarities found in the item characteristics

Techniques include decision trees, ANN, Bayesian classifiers

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The Web 2.0 Revolution and Online Social Networking

Web 2.0?

Advanced Web - blogs, wikis, RSS, mashups, user-generated content, and social networks

Objective – enhance creativity, information sharing, and collaboration

Changing the Web from passive to active

Consumer is the one that creates the content

Redefining what is on the Web as well as how it works

Companies are adopting and benefiting from it

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Representative Characteristics of Web 2.0

Allows tapping into the collective intelligence of users

Data is made available in new or never-intended ways

Relies on user-generated/user-controlled content/data

Lightweight programming tools for wider access

The virtual elimination of software-upgrade cycles

Users can access applications entirely through a browser

An architecture of participation and digital democracy

A major emphasis is on social networks and computing

Strong support for information sharing and collaboration

Fosters rapid and continuous creation of new business models

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Social Networking

Social networking gives people the power to share, making the world open/connected

Facebook, LinkedIn, Google+, Orkut, …

Wikipedia, YouTube, …

A social network is a place where people create their own space, or homepage, on which they write blogs (Web logs); post pictures, videos, or music; share ideas; and link to other Web locations they find interesting

Mobile social networking

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Enhancing marketing and sales in public social networks

Using Twitter to Get a Pulse of the Market

Listening to the public for opinions/sentiments

Product/service brand management

Text mining, sentiment analysis

How – built in-house or outsource

reputation.com

Share content in a messaging ecosystem

WhatsApp, Draw Something, SnapChat, …

Social Networks - Implications of Business and Enterprise

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Cloud Computing and BI

A style of computing in which dynamically scalable and often virtualized resources are provided over the Internet.

Users need not have knowledge of, experience in, or control over the technology infrastructures in the cloud that supports them.

Cloud computing = utility computing, application service provider grid computing, on-demand computing, software-as-a-service (SaaS), …

Cloud = Internet

Related “-as-a-services”: infrastructure-as-a-service (IaaS), platforms-as-a-service (PaaS)

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Cloud Computing Example

Web-based email  cloud computing application

Stores the data (e-mail messages)

Stores the software (e-mail programs)

Centralized hardware/software/infrastructure

Centralized updates/upgrades

Access from anywhere via a Web browser

e.g., Gmail

Web-based general application = cloud application

Google Docs, Google Spreadsheets, Google Drive,…

Amazon.com’s Web Services

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Cloud Computing Example

Cloud computing is used in

e-commerce, BI, CRM, SCM, …

Business model

Pay-per-use

Subscribe/pay-as-you-go

Companies that offer cloud-computing services

Google, Yahoo!, Salesforce.com

IBM, Microsoft (Azure)

Sun Microsystems/Oracle

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Cloud Computing and BI

Cloud-based data warehouse

1010data, LogiXML, Lucid Era

Cloud-based ERP+DW+BI

SAP, Oracle

Elastra and Rightscale

Amazon.com and Go Grid

SaaS

DaaS

SaaS

DaaS

+ IaaS

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Cloud Computing and Service-Oriented Thinking

Service-oriented thinking is one of the fastest-growing paradigms today

Toward building agile data, information, and analytics capabilities as services

Service orientation + DSS/BI

Component-based service orientation fosters

Reusability, Substitutability, Extensibility, Scalability, Customizability, Reliability, Low Cost of Ownership, Economy of Scale,…

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Service-Oriented DSS/BI

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Major Components of Service-Oriented DSS/BI

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Major Components of Service-Oriented DSS/BI

Data-as-a-Service (DaaS)

Accessing data “where it lives”

Enriching data quality with centralization

Better MDM, CDI

Access the data via open standards such as SQL, XQuery, and XML

NoSQL type data storage and processing

Amazon’s SimpleDB

Google’s BigTable

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Major Components of Service-Oriented DSS/BI

Information-as-a-Service (IaaS)

“Information on Demand”

Goal is to make information available quickly to people, processes, and applications across the business (agility)

Provides a “single version of the truth,” make it available 24/7, and by doing so, reduce proliferating redundant data and the time it takes to build and deploy new information services

SOA, flexible data integration, MDM, …

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Major Components of Service-Oriented DSS/BI

Analytics-as-a-Service (AaaS)

“Agile Analytics”

AaaS in the cloud has economies of scale, better scalability, and higher cost savings

Data/Text Mining + Big Data  Cloud Computing

Storage and access to Big Data

Massively Parallel Processing

In-memory processing

In-database processing

Resource polling, scaling, cost and time saving, …

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Impacts of Analytics in Organizations: An Overview

New Organizational Units

Analytics departments

Chief Analytics Officer, Chief Knowledge Officer

Restructuring Business Processes and Virtual Teams

Reengineering and BPR

Job Satisfaction

Job Stress and Anxiety

Impact on Managers’ Activities/Performance

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Issues of Legality, Privacy, and Ethics

Legal issues to consider

What is the value of an expert opinion in court when the expertise is encoded in a computer?

Who is liable for wrong advice (or information) provided by an intelligent application?

What happens if a manager enters an incorrect judgment value into an analytic application?

Who owns the knowledge in a knowledge base?

Can management force experts to contribute their expertise?

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Issues of Legality, Privacy, and Ethics

Privacy

“the right to be left alone and the right to be free from unreasonable personal intrusions”

Collecting Information About Individuals

How much is too much?

Mobile User Privacy

Location-based analysis/profiling

Homeland Security and Individual Privacy

Recent Issues in Privacy and Analytics

“What They Know” about you (wsj.com/wtk)

Rapleaf (rapleaf.com), X + 1 (xplusone.com), Bluecava (bluecava.com), reputation.com, sociometric.com...

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Issues of Legality, Privacy, and Ethics

Ethics in Decision Making and Support

Electronic surveillance

Software piracy

Invasion of individuals’ privacy

Use of proprietary databases

Use of knowledge and expertise

Accessibility for workers with disabilities

Accuracy of data, information, and knowledge

Protection of the rights of users

Accessibility to information

Personal use of corporate computing resources

… more in the book

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Analytics Industry Clusters

Data Infrastructure Data Warehouse Providers

Middleware/BI Platform Industry

Data Aggregators/Distributors

Analytics-Focused Software Developers

Application Developers or System Integrators

Analytics User Organizations

Analytics Industry Analysts and Influencers

Academic Providers and Certification Agencies

An Overview of The Analytics Ecosystem

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Analytics Ecosystem

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Analytics Ecosystem - Titles of Analytics Program Graduates

Masters Degrees

UG Degrees

Certificate Programs

…

Data Scientist

…

Decision Science

Marketing Analytics

Management Science

…

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End-of-Chapter Application Case

Southern States Cooperative Optimizes its Catalog Campaign

Questions for Discussion

What is the main business problem faced by Southern States Cooperative?

How was predictive analytics applied in the application case?

What problems were solved by the optimization techniques employed by Southern States Cooperative?

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End of the Chapter

Questions, comments

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All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher. Printed in the United States of America.

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