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BIS 220 INTRODUCTION TO COMPUTER APPLICATIONS AND SYSTEMS WORKSHOP V, RAJI GOGULAPATI

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Computer Applications and Systems - Workshop V

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Page 1: Computer Applications and Systems - Workshop V

BIS 220 INTRODUCTION TO COMPUTER APPLICATIONS

AND SYSTEMS WORKSHOP V, RAJI GOGULAPATI

Page 2: Computer Applications and Systems - Workshop V

Summary

Emerging phenomenon in businesses and technologies,

effects on work

Workshop IV review

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Story of how businesses use technologies

with new approaches to

solve old problems.

Groups of online collaborative communities made all the difference !

Generating, capturing and

sharing knowledge

Organizations can use

technology to bring brains

together effectively

Enterprise 2.0

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Enterprise 2.0

& Next generation enterprise landscape Role of

IS/ IT in the

enterprise

world Online customer

evolution

What did we learn so far?

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Path to Progress

Technology is the “driver” not just the “enabler” of business success and opportunity

When people adopt technology, they merely do old things in new ways.

When people internalize technologies, they find new things to do.

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Current Drivers

Connectivity of people and things is rapidly expanding to being everywhere and anytime.

An ocean of information is getting collected, clever algorithms are sifting through it and finding insights

These large datasets are enabling a new set of applicationsthat elevate the consumer experience at every touch point

New products, services and value is being created and delivered rapidly over platforms

What you sell is no longer perceived as just a product or service by the consumer. It is about something at the center of the “total customer experience”.

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IN THE NEWS

KPCB, cnnmoney

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IN THE NEWS

money.cnn.com

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Top 10 Trends (Gartner)

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Cloud computing

Open Source

Enterprise 2.0 BI, Big Data

Social networking benefits and challenges

Apps World

E-learning

Platforms – Amazon, Apple, Facebook, Google

What is next?

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What is cloud computing

about?

Relevance to business

operations

Players

Technology enablers and architectural

elements

Cloud Computing

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Shift from supply push to demand pull of data, information

and services

Massive server farms made up of commodity

PC’s

Usage pattern with existing IT resources

SOA is key to cloud computing

Source: Enterprise cloud computing by Andy Mulholland, Jon Pyke and Peter Fingar

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http://www.csrc.nist.gov/groups/SNS/cloud-computing/index.html

NIST Cloud Computing Framework

Access the presentation at

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Framework

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Applications in the Cloud

Salesforce.com growth over the years

Virtual, collaborative learning, teaching and research environments in the cloud

Mobile enterprise in the cloud

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Social Network sites Professional network sites

Social News, book Marking sites

Forum sites

Business directory sites Photo and video sharing

Get connected

Mobile, Portable computing, mobile web browsing,

touch screen Embed computing into products,

mobile commerce connected Reality redefined, Life streaming

Cyborg, BYOD

Always on, always connected, plug yourself into cloud

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Cloud computing vendors

http://online.wsj.com/article/NA_WSJ_PUB:SB123802623665542725.html

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Analytics Big Data Enterpri

se BI

What is next?

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Describe Big Data

Incomplete

Fragmented

Not precise

Dynamic

External

Unmanageable

Democratic

???

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Data Science

Ultimate Goal – Improve Decision making

principles

Frameworks

Data analytic thinking

• Extract patterns• Mining for useful knowledge

• Models • Process • Stages

• Assess how data can improve performance

• Understand data science • See data oriented

competitive threats • Question

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Big Data Advantage – Analytics and decision management

Decision

making

Data deluge

Techniques

Solutions

Rewards

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Techniques

Statistical Visualization

Semantics

Automation

Predictive Analytics

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Plunge Issues

Problems

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Source: Forbes.com, Cloud Predictive Analytics most used to gain customer insight, 10/24/2013

Data types in a Big Data context

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Big Data Use Cases – Financial Sector

Source – HP Sponsored white paper, Case for Big Data in the Financial Services sector, IDC Financial Insights opinion 2012

Investment banks - speed post-trade settlement,

confirmation

Hedge funds - optimize price discovery

Retail banking - create merchant intelligence and assist in

optimizing offers and pricing

Social media tracking for marketing campaigns

Investment research to understand traffic patterns, trends

Community banksAnalyzing unstructured data to anticipate workloads in call centers

Social analytics towards product and service initiatives

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Big Data Use Cases – Government

Source – IBM, “Accelerate Analytics and harness Big data within government”

Improve citizen and business services -

Smarter social services. Fight fraud, abuse and errors

Manage resources effectively – tax compliance

Fight fraud, abuse and errors

Strengthen public safety – crime prediction and prevention

Strengthen national and security defense - Threat prediction and prevention, cyber security,Video analytics

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Big Data Capabilities – health care

Source: McKinsey Report titled Big Data Revolution in health care, exhibit 9

Risk stratification – patient identification for integrated care

Risk adjusted benchmark/ simulation of hospital productivity

Personal health records

Evaluation – identification of patients with negative drug-drug

interactions, potential diseases.

Systematic Reporting of misuse of drugs, systematic Identification of obsolete drug usage

Monitoring - Identification of inappropriate medication

Data mining – Why did it happen?Evaluation – clinical pathways, drug efficacy based on real world data

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Big Data Analytics capabilities – travel and transportation

Source: IBM, Big Data and Analytics in Travel and Transportation white paper, figure 4

Maintenance and Engineering – asset management data, Spec sheets, product data

Capacity and Pricing optimization

Call center – call logs, voice/ audio, incidents, email, text Images, videos, graphics – Track, rail segment images and video

Geospatial and temporal – GIS, GPS, weather, Environmental

Sensors, Actuators and detectors – Equipment, wheels, engines, Tracks

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Analytics - Explained

Source: Analytics 3.0 by Thomas H Davenport, HBR, Dec 2013

Analytics 1.0Era of business intelligence, go beyond intuition, fact based comprehension for decision making. Era of enterprise data warehouse. Dominant for about 50 years.

Analytics 2.0From about 2005 onwards, Internet based social network firms – Google, eBay, LinkedIn..Not only internal, externally sourced, sensors, public data initiatives, multi media recordings. Innovative technologies NoSQL, Hadoop, machine learning. Computational and analytical skills

Analytics 3.0 Data enriched offerings for every industry. Driven by analytics, rooted in enormous amounts of data.Co-existence of traditional and new.

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Ability to handle new varieties of data – voice, text, log files, images, video on a large scale

Sensors and operational data gathering devices in motion to optimize

Cost savings of storage – data base to database appliance to a Hadoop cluster

Big companies always wrestled with the data volume issues. Bigness is not new! Variety is new!

What is different from the past?

Source: Big Data in Big companies, May 2013: Authored by: Thomas H. Davenport, Jill Dyché

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Networked

Enterprise

Of Evolution

What is next?

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Search Engines – Google, Yahoo, Bing

Source: Mitra, K. (2010). What Next In Search?. Business Today, 19(9), 134.

User intentReal time

Localized

• Information Retrieval

• Match intent with search

• Platforms such as Twitter

• Search engines update databases searching thru tweets

• Mobile searches • Walk into a mall

and search for sales

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Collaboration Tools

For learning and sharing basics on subject matter • Blogs • Youtube

videos • Wikis

Office Tools• Open

source • Google

docs• MS word,

powerpoint• Presentatio

n tools such as Prezi

• Slideshare

Web Tools for Project management• Project

meeting agenda

• Fixing meetings

• Follow up

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Source: http://www.zdnet.com/the-major-enterprise-collaboration-platforms-and-their-mobile-clients-7000018519/

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Corporate Portals Re-imagine

Re-imagine knowledge Services and functionality in a framework

e-document management systems, exchanges for content,database management systems,data warehouse, communities of practicessocial communities of interestindividual communities of interest

Re-imagine organizing around this knowledge exchange

Re-imagine innovations, competitiveness with know-how

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Source: http://socialmediatoday.com/soravjain/195917/40-most-popular-social-networking-sites-world

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Source: http://www.wired.com/2013/05/mary-meekers-annual-state-of-the-internet-hoo-ha/

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Source: http://www.wired.com/2013/05/mary-meekers-annual-state-of-the-internet-hoo-ha/

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World

Apps

What is next?

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Apps for everything?

First, some reflections

Procter & Gamble's Oral B smartphone connected toothbrush - expect in the market soon...

I thought it was just the human brain and "some things" that needed connections... I will be bold and say - ridiculous!

For brushing teeth and eating breakfast, keep it simple...

Mar 8, 2014.

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Apps are Everywhere!

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1975 1985 1995 2008 2015

Main Frames

Operating Systems

Database

ERP

Apps

Data

Business Value Shifting to Apps

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Wearable Tech – Google Glass

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Platforms

What is next?

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PLATFORMS APPLE

Are they modern monopolies?

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AMAZON WHAT IT MEANS TO YOU

Network Effects

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PLATFORMS GOOGLE

What is Google’s business model?

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And more

Facebook

LinkedIn

Netflix

Salesforce

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PLATFORMS ARE ABOUT SOLUTIONS WITH CONVERGENCE OF TECHNOLOGIES

• CLOUD SERVICES ARE THE BACK-END DELIVERY MODEL FOR MOST MOBILE APPLICATIONS

• BIG DATA IS THE COLLECTION AND ANALYSIS OF OCEANS OF INFORMATION THAT IS NOW BEING PRODUCED

• AND “SOCIAL BUSINESS” REFLECTS THE FACT THAT CUSTOMERS HAVE TAKEN CONTROL OF THE BUYING CYCLE

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Open Source

What is next?

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Open Source Eco System

Open Source communities

Open Source users

Open Source Products

Foundations/ Trusts

Traditional Vendors

Support, services,

consulting, Systems

integration

Experts, Press, Opinions

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Musings on Open Source licensing

Interactive License Differentiator from Oxford Universities OSS Watch

Open Source License Differentiators – It’s complicated!

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E Learning

MOOCS

What is next?

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LCEs Shift Focus To Learning & Learner

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e-sources of knowledge at the UOP