Copyright © 2013, SAS Institute Inc. All rights reserved. ANALYTICS AND OPEN DATA THROUGH A CASE STUDY SAS MIDDLE EAST CAREL BADENHORST HEAD OF INFORMATION.

Slides:



Advertisements
Similar presentations
Value-at-Risk: A Risk Estimating Tool for Management
Advertisements

Copyright © 2012, SAS Institute Inc. All rights reserved. INTRODUCTION TO DATA AND TEXT MINING ANDREW PEASE, 8 MARCH 2013.
Verdict – 27 years of retail research Think Retail, Think Verdict.
UNECE Gender Statistics Session, October Dissemination and Use of Time Use Data The New Zealand Experience UNECE Gender Statistics Session Geneva,
Improving Disability Claims Management with Predictive Modeling May 15, 2008 Claim Analytics Inc. Barry Senensky FSA FCIA MAAA Jonathan Polon FSA
Data Analytics : A powerful insight into your donors’ giving potential Insight SIG 19th February, 2013.
Evaluating Inforce Blocks Of Disability Business With Predictive Modeling SOA Spring Health Meeting May 28, 2008 Jonathan Polon FSA
Norman Washington Garrick CE 2710 Spring 2014 Lecture 07
Copyright © 2012 IHS Inc. All Rights Reserved. Company Confidential Leveraging Social Media Data as Real-Time Indicators of X September 7,
SAS solutions SAS ottawa platform user society nov 20th 2014.
McGraw-Hill/Irwin Copyright © 2013 by The McGraw-Hill Companies, Inc. All rights reserved. Chapter 7: Demand Estimation and Forecasting.
Introduction to Management Information Systems Chapter 9 Business Intelligence and Knowledge Management HTM 304 Fall 07.
2006 Impacts of Michigan’s Small Business and Technology Development Centers April 13, 2007.
Economic Indicators. Concepts  Variables that provide information about the state of the economy.  Every economic indicator has a story to tell.  Need.
CRM Chapter 9 Analytics. Analytics  Collection, extraction, modification, measurement, identification, and reporting of information designed to be useful.
Database Processing for Business Intelligence Systems
Business Cycles, Unemployment, and Inflation
Beyond Opportunity; Enterprise Miner Ronalda Koster, Data Analyst.
Chapter 2 Data Patterns and Choice of Forecasting Techniques
ECONOMIC INDICATORS. Understanding Economic Indicators  Background Economic Theme: Recognize the stage of the business cycle.
Managerial Accounting by James Jiambalvo
Testimony for Hearings on FY 2010 Revenues Yolanda K. Kodrzycki Senior Economist and Policy Advisor Federal Reserve Bank of Boston Presented to: Massachusetts.
BGS Customer Relationship Management Chapter 7 Database and Customer Data Development Chapter 7 Database and Customer Data Development Thomson Publishing.
Title: Spatial Data Mining in Geo-Business. Overview  Twisting the Perspective of Map Surfaces — describes the character of spatial distributions through.
 BA_EM Electronic Marketing – Pavel
Data Mining Techniques As Tools for Analysis of Customer Behavior
Understanding Data Analytics and Data Mining Introduction.
Chapter 18 Business Cycles and the entrepreneur
Chapter 4 Global Economies 1 Section 4.2 Understanding the Economy Marketing Essentials.
3 Objects (Views Synonyms Sequences) 4 PL/SQL blocks 5 Procedures Triggers 6 Enhanced SQL programming 7 SQL &.NET applications 8 OEM DB structure 9 DB.
Version 1.2 Copyright © 2000 by Harcourt, Inc. All rights reserved. Requests for permission to make copies of any part of the work should be mailed to:
© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke Slide 1 Chapter 9 Competitive Advantage with Information Systems for Decision Making.
Population Census Topics included in the 2011 Population and Housing Census for Jamaica Presented by: Valerie Nam Director, 2011 Population and Housing.
1 BA 275 Quantitative Business Methods Housekeeping Introduction to Statistics Elements of Statistical Analysis Concept of Statistical Analysis Statgraphics.
Dominic Adorno and Michael Salvatore Optimizing Requester Deployment.
economic indicator  Statistics about the economy that allows analysis of economic performance and predictions of future performance.  Usually calculated.
economic indicator  A statistic about the economy.  Allows analysis of economic performance and predictions of future performance.  Include various.
Highline Class, BI 348 Basic Business Analytics using Excel, Chapter 01 Intro to Business Analytics BI 348, Chapter 01.
INTRODUCTION TO DATA MINING MIS2502 Data Analytics.
Mrs.Shefa El Sagga F&BMP110/2/ Problems with the VaR Approach   Bankers The first problem with VaR is that it does not give the precise.
Norman W. Garrick Transportation Forecasting What is it? Transportation Forecasting is used to estimate the number of travelers or vehicles that will use.
Banking on Analytics Dr A S Ramasastri Director, IDRBT.
Confidential. This presentation is provided for the recipient only and cannot be reproduced or shared without Fair Isaac Corporation's express consent.
Copyright, © 2004, Theresa M. Welbourne, Ph.D. 1 HR Confidence June Leadership Pulse Dr. Theresa M. Welbourne Preliminary Report June 16, 2004.
Copyright © 2008, SAS Institute Inc. All rights reserved. Interactive Analysis and Data Visualization Using JMP −Dara Hammond, Federal Systems Engineer.
Copyright © 2012, SAS Institute Inc. All rights reserved. ANALYTICS IN BIG DATA ERA ANALYTICS TECHNOLOGY AND ARCHITECTURE TO MANAGE VELOCITY AND VARIETY,
OESAI COMPREHENSIVE LIFE INSURANCE TECHNICAL TRAINING.
Arkansas Consumer Confidence Third Quarter Report The Arkansas Consumer Confidence Report is conducted by Talk Business and Hendrix College and is sponsored.
MIS2502: Data Analytics Advanced Analytics - Introduction.
FISCAL CLIFF & ECONOMIC INDICATOR By: Claire Murray.
Conclusions. Why Data Mining? -- Potential Applications Database analysis and decision support – Market analysis and management target marketing, customer.
Decision Tree Algorithms Rule Based Suitable for automatic generation.
Copyright © 2015, SAS Institute Inc. All rights reserved. Business & Analytics unite VS.
Monday, February 22,  The term analytics is often used interchangeably with:  Data science  Data mining  Knowledge discovery  Extracting useful.
Mean Mean  = Sum of all the values The number of values Eg. Find the mean of the following values: 2, 5, 4, 7, 5, 7, 8, 4, 5, 6, 12, 2, 4, 5, 7, 5 Mean.
Advanced Analytics Turin April, Index 2 ■ Advanced Analytics Approach –Architecture Overview –Methodology –Professional Skills ■ Impacted Areas.
Data Mining Techniques Applied in Advanced Manufacturing PRESENT BY WEI SUN.
Show Me Potential Customers Data Mining Approach Leila Etaati.
EC 827 Module 2 Forecasting a Single Variable from its own History.
Data Resource Management – MGMT An overview of where we are right now SQL Developer OLAP CUBE 1 Sales Cube Data Warehouse Denormalized Historical.
9/24/2017 7:27 AM © Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN.
Contextual Intelligence as a Driver of Services Innovation
© 2016 Global Market Insights, Inc. USA. All Rights Reserved Fuel Cell Market size worth $25.5bn by 2024 Text Analytics Market share.
BUS 308 Competitive Success-- snaptutorial.com
BUS 308 Education for Service-- snaptutorial.com
BUS 308 Teaching Effectively-- snaptutorial.com
MIS5101: Data Analytics Advanced Analytics - Introduction
Advanced Analytics. Advanced Analytics What is Machine Learning?
Leverage Real-Time Payments Intelligence to Identify and Keep Great Customers March 2019 Parag Patil.
Presentation transcript:

Copyright © 2013, SAS Institute Inc. All rights reserved. ANALYTICS AND OPEN DATA THROUGH A CASE STUDY SAS MIDDLE EAST CAREL BADENHORST HEAD OF INFORMATION TECHNOLOGY PRACTICE MIDDLE EAST

Copyright © 2013, SAS Institute Inc. All rights reserved. SAS AGENDA Analytics and Open Data Analytics example - UN Global Pulse Case Study

Copyright © 2013, SAS Institute Inc. All rights reserved. Discover relevant themes and relationships in social media, call notes and for deeper insights and improved business management Understand and find relationships in data to make accurate predictions about the future Leveraging historical time series data to drive better insight into decision-making for the future Make appropriate business decisions by understanding dynamics and utilize resources the best way FORECASTING DATA MINING TEXT ANALYTICS OPTIMIZATION STATISTICS INFORMATION MANAGEMENT Copyright © 2011, SAS Institute Inc. All rights reserved. ANALYTICS LIFE CYCLE….NOT BI

Copyright © 2013, SAS Institute Inc. All rights reserved. SAS/ UN GLOBAL PULSE BACKGROUND OF THE CASE STUDY The UN Global Pulse- SAS research had a few questions Does the sum total of what we say online add up to anything meaningful? Do online conversations correlate in any way with official government statistics? Specifically can unemployment patterns be predicted based on certain chatter topics and correlated with govt open data to derive meaningful statistics?

Copyright © 2013, SAS Institute Inc. All rights reserved. SAS/ UN GLOBAL PULSE METHODOLOGY Online social media conversations over a period in US and Ireland Government Open Data to validate experiment ie. Employment history statistics Mood Scoring based on conversations Text Analytics - words used in each conversation were mined in order to assign one or more topical categories Sentiment Analysis undertaken to classify conversations as happy, sad, anxious etc Dynamic Correlation between mood scores with unemployment scores

Copyright © 2013, SAS Institute Inc. All rights reserved. SAS SAMPLE INSIGHT GENERATED FROM THE RESEARCH RESULTS An uptake in social media conversation on topics such as cutting back on groceries and other essentials or downgrading one’s mode of transportation can predict an impending unemployment spike. After a spike, an increase in chatter about foreclosures, reduced spending for health care and canceled vacations can offer insights on the effects of a down economy. Better understanding of demographical areas, gender, age and income characteristics based on social techniques such as mood scoring

Copyright © 2013, SAS Institute Inc. All rights reserved. SAS SAMPLE INSIGHT GENERATED FROM THE RESEARCH In the US: Huge increase in depressed mood conversations four months before a spike in unemployment ( calculated and validated within 95 percent ). Talk about loss of housing increases two months after an unemployment spike ( calculated and validated within 95 percent ). Talk about auto repossession increases three months after an unemployment spike ( calculated and validated within 95 percent ). In Ireland: Anxious moods increase five months before a spike in unemployment ( calculated and validated within 90 percent ) Talk about travel cancellations increases three months after an unemployment spike ( calculated and validated within 95 percent ). Talk about changing housing situations for the worse increases eight months after unemployment increases ( calculated and validated within 90 percent ).

Copyright © 2013, SAS Institute Inc. All rights reserved. SAS TRANSLATING INTO In the US: 95 Confidence EARLY WARNING SIGN KPI (four months) for unemployment increase 95 Confidence EARLY WARNING SIGN KPI (six months – four plus two months) for mortgage repayment default increase (down to the demographics) 95 Confidence EARLY WARNING SIGN KPI (seven months – four plus three months) for car manufacturers and retail re new sales and potential default increase (down to the demographics) Increased potential in social welfare needs down to a specific demographic level……and the most important value Using further analytics statistical, data mining, prediction and optimization algorithms to start predicting pre-emptive actions and their outcome in case these patterns are detected Analytics is amazing if you allow it to tell you stories....

Copyright © 2013, SAS Institute Inc. All rights reserved. sas.com THE STORIES ANALYTICS WILL HELP YOU TELL USING OPEN DATA IS ENDLESS… QUESTIONS?