Big Data Strategies Creating Customer Value In Utilities
|
|
- Roy Williams
- 8 years ago
- Views:
Transcription
1 Big Data Strategies Creating Customer Value In Utilities National Conference ICT For Energy And Utilities Sofia, October 2013 Valery Peykov Country CIO Bulgaria Veolia Environnement г.
2 One Core Business: Environmental Solutions 4 Divisions No. 1 worldwide No. 1 in Europe No. 2 worldwide No. 1 in Europe N 1 provider of water utility services to municipalities in the world 158 years experience (activities started in 1853) 67 countries; employees worldwide 2
3 Summary Shaping-up utility services of tomorrow from current challenges to future opportunities Big data: the next frontier for innovation, productivity and competition Applying Big Data strategies to improve customer management and reduce operational costs Where science meets business 3
4 1 Shaping-up Utility Services of Tomorrow From Current Challenges To Future Opportunities
5 Environmental Efficiency vs. Financial Performance Utility companies deal with natural resources threatened by scarcity and long term environmental strategies aim to rationalize the use of these resources (water, gas and energy sources). Decreasing trend of end-users consumption, due to: the wish to reduce expenses related to utilities; more efficient management of internal networks; use of devices with reduced consumption (cost reduction and environmental awareness). Utility companies need to manage more efficiently the raw sources they use, before delivering the service to the final customer and reduce the losses - network management performance. 5
6 From Captive Customers to Increased Awareness and Power of Choice Customers of utility companies have become more aware of their rights and their level of expectations is constantly increasing. The monopolies are no longer an indestructible myth in utilities EU legislation on deregulation of the market increasing competition and allowing customers to choose the supplier Alternative supply solutions between utilities can make companies lose their customers: e.g. electrical or gas heating instead of centralized heating Performance of customer management is a differentiation factor between utility companies Utility companies must therefore discover who the customers are, what their specific needs are and how to address them. 6
7 Customer Trust in Utility Companies Frequently, when customers hear from a utility company is when is sending a bill, a disconnection warning, or notice of a rate increase. It is no surprise then that in an age of increasing customer importance, trust in utility companies is the lowest level it has been in years. Source: Google trends 7
8 In Bulgaria Source: Google trends 8
9 2 2 Big Data: The Next Frontier For Innovation, Productivity And Competition
10 Big Data The amount of data in our world has been exploding. Utility companies already capture trillions of bytes of information about their customers and operations, and millions of networked sensors and devices are being embedded in the physical world in devices sensing, creating, and communicating data. Big data is often described as data sets so large and complex that it becomes difficult to manage and analyze them with the traditional data processing tools. The problem how this tidal wave of information can be captured, communicated, aggregated, stored, and finally analyzed to create value is now part of every sector and function of the global economy. We create more data in a day then we did from the dawn of man through 2003 and approximately 90% of all the world's data has been created in the past 2 years. 10
11 Google search results for the term Big Data Data from Google Trends 11
12 Becoming a Data-Driven Organization To cope with the changing nature of information, organizations must transition from an application-driven focus to a data-driven approach. Innovative, data-centric companies view information as an asset as valuable as buildings, employees, production equipment and intellectual capital. It should be considered the value of the information as a corporate asset. Evolving technologies in the utilities industry, including smart meters, smart grids, loggers can provide companies with unprecedented data volume, speed and complexity. To manage and use this information to gain insight, utility companies should turn to Big data strategies involving technologies capable of high-volume data management, advanced analytics and scientific methods applied to the business. One of the components of the Big Data movement is the emerging role of the data intelligence and since. 12
13 3 2 Applying Big Data Strategies To Improve Customer Management And Reduce Operational Costs Processes, Technologies and Science Techniques to Manage Customer Performance
14 Technology Architecture Turing Data Into Information - In order to support Big Data initiatives hardware and software solutions are needed to capture, store, process, analyze and report on large volume of business data to support the decision making at operational, tactical and strategic level. The so called Decision Support Systems - Business Intelligence and Data Warehouse Systems. Business Intelligence according to Wikipedia is the ability for an organization to take all its capabilities and convert them into knowledge, ultimately getting the right information to the right people, at the right time, via the right channel. 14
15 Technology Architecture Data Warehouse is a subject-oriented, integrated, time-variant and non-volatile collection of data in support of management's decision making process. According to: Ralph Kimball: Data Warehouse is a copy of transaction data specifically structured for query and analysis. Bill Inmon: Data Warehouse is a subject-oriented, integrated, timevariant and non-volatile collection of data in support of management's decision making process. 15
16 Information Needs at Operational, Tactical and Strategic Level 16
17 4 3 Customer Performance Management Action Insight 360 Customer View
18 Customer Debt Management Strategy - part of the Customer Performance Management Cycle Customer Performance is a measurable monetary or non-monetary result of all customer relationships in a defined period. Customer Performance Management: is a CRM function comprising all customer performance indicators, instruments, processes, software tools and systems to analyze and control Customer Performance. To optimize customer performance we need deep customer insight based on technologies capable of high-volume data management, advanced customer analytics and applied scientific methods. The developed by Veolia Bulgaria team Customer Performance Management Cycle includes also Customers Behavior Analysis, Profiling, Scoring & Segmentation, Automated Risk Management, Customer Debt Management, Consumption Predictive Models etc. Customer Strategies and Campaigns implemented by in house developed information systems. 18
19 360 Customer View To optimize the customer performance first we need 360 View on our customers. Customer Data Warehouse has been build in Sofia Water Company which daily process hundreds of millions of customer records. Business Intelligence solution supports decision makers to get customer and enterprise performance insight at tactical and strategic level with a single version of the truth. A project to integrate all corporate customer, financial and operational data is ongoing. The project is supported entirely by internal IT resources and centers of competence. 19
20 5 2 Sofia Water Business Intelligence - Data Warehouse Project Turning Data Into Information
21 Sofia Water Business Intelligence / DWH Initiative OPERATIONAL & MANAGEMENT REPORTS DYNAMIC DASHBOARDS EXECUTIVE MANAGEMENT & REGULATOR REPORTS BUSINESS ANALYSES CUSTOMER ANALYTICS STATISTICS & DATA MINING ANALYSIS STRUCTURED DATA STRUCTURED DATA Finance data STRUCTURED DATA Business Intelligence & Sofia Water DWH Customer Interactions STRUCTURED DATA Call Center Interactions Web Site Statistics Customer Service Centers Payment Chanels Transactions STRUCTURED DATA Billing data Water& Sewage connections GIS Devices Objects SCADA SYSTEMS Assets& Operational management Clients Addresses Devices Objects DATA LOGERS ERP SYSTEM CC&Billing Budgeting, Financials, Management accounting, Procurement, Supply Chain, Warehouse Management, Projects, Operation Management, Assets Management REVENUE VAT 21
22 Network Management Analysis in Business Intelligence Solution 22
23 Payment Channels Analysis Customers Debt Aging & Pareto Analysis 23
24 Customer Debt Management Strategy - current issues and proposed approach Usually companies react to decreased collection rates and search post factum improvement measures Prevent accumulation of the debt, by taking actions before loyal customers turn into debtors refine customer segmentation, through big data analysis Collection methods are limited: phone calls, letters, site visits, court actions, disconnection using the right measure to the right type of situation obtain maximum results from limited methods Companies usually deal with the effect of increased debt, without considering the real causes, at the very individual level (e.g.: discontent about the service) identifying causes which make customers stop paying the bills 24
25 Customers Risk Analysis & Segmentation Implementing Real Time Customer Behavior & Performance Analytics. Customer Risk Management; Customer Debt Internal Controls Monitoring 25
26 Debt Collection Campaigns Initialization based on customer behavior analytics Customer Reactions Sensitive Analysis based on Customer Analytics 26
27 Campaign Management System - In House Solution Solution Built around best of the Veolia Bulgaria team experience, the in house Campaign Management System provides Customer Service teams with the tools to turn customer insight into customer strategies, actionable plans and performance controls. Integration The solution integrates with the Business Intelligence/ Customer Data Warehouse and based on the customer behavior and performance analytics, fine tuned with Data Mining techniques allows the executions of a number of customer campaigns through different channels, touch points, and interactions. 27
28 Optimization Customer INTERACTIONS Business Intelligence/ DWH Artificial Intellect Machine Learning Statistics OPERATIONS FINANCIAL STATUS Data Mining Campaign Management System ECONOMICAL & ENVIRONMENTAL FACTORS CHARACTERISTICS TRANSACTIONS Analysis
29 6 4 When Science Meets Business Turning Since Into Customer Value
30 Turning Information Into Knowledge a step beyond.. Adopting Data Mining, Statistics analytic techniques and Mathematical Models designed to explore large amounts of business data known as Big Data. Data Mining (also called knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information. Given today s explosion of Big Data, companies need more advanced methods for leveraging their data methods that don t rely solely on tribal knowledge, personal experience or best guesses. What s needed are new technologies and purpose-built solutions that reveal questions to answers no one even knew to ask. 30
31 What is Data Mining Data Mining is a multidisciplinary field Data Mining involves the use of sophisticated techniques for analyzing data to discover previously unknown, existing patterns and relationships in large databases. Data Mining techniques include statistical models, mathematical algorithms, methods for machine learning (algorithms that improve their performance automatically based on experience) and artificial intellect data analysis. Data Mining includes also prediction models. 31
32 Addressing Data Mining to Customer s Debt Being part of the Customer Performance Management, customers debt is analyzed in Sofia Water Company with data mining techniques. Correlations have been analyzed between customer management data, operational data, assets, internal teams and subcontractors' meter readings performance. Have been also analyzed the correlations, variables weight and influence of customer behavior and performance KPIs and interactions on the customer debt. Case Study: Sofia Water Company In the process of Data Mining research projects sometimes are done unexpected foundlings. By analyzing data across SW company It has been found strong correlation between a Revenue Meter Diameter and Customer Debt. Further investigation and internal interviews explored years ago not well finished by a meter reading subcontractor task, multiplied during many years and not noticed later, passed through the business processes and information systems and finally allowed a group of customers to generate a huge volume of customers debt. 32
33 Primary Data Analysis (descriptive statistics) on Debtors Data Frequency table: Average Billed Consumption (m3) (15 000_Random_Domestic-Sof_Debtors_Red) K-S d=,46345, p<,01 Count Cumulative - Count Percent - of Valid Cumul % - of Valid % of all - Cases Cumulative % - of All -5000,00<x<=0, ,000000<x<=5000, ,000<x<=10000, ,00<x<=15000, ,00<x<=20000, ,00<x<=25000, Missing
34 Statistical Correlation Analysis on Customer, Operational and Performance Data 34
35 Who Our Real Customers Are? Most companies segment customers based on their performance - financial value, debt, geographic, products and services classification. For the needs of the internal reporting, it is OK. But do the customers really behave and group themselves in alignment with our internal reporting? If we want to change the numbers, we need the answer.. To influence on customers and generate successful customer strategies, campaigns, actions and loyalty programs, we must understand and anticipate customer behavior in relationship with internal and external factors and get deep customer insight. We need to identify the factors influencing on customers behavior and performance - Consumption, Satisfaction and Customer Debt. 35
36 Hidden clusters of customers Appling advanced statistical analysis revealed hidden clusters of debtors based on unknown behavioral models. 36
37 Hidden Clusters of Debtors - follow up During the scientific research, have been identified the parameters describing every cluster. Their values will be programed into the BI in order to uncover who those customers are. What are their similarities and the factors influencing on their behavior in order to generate successful and customized campaigns to them. 37
38 Customer Risk Management Based on scientific researches and tests on real customer data have been identified the most proper advanced Data Mining techniques to automate the Customer Risk Management. The developed Data Mining risk classification models, optimize the percentage of correct classified risk customers with more than 60% compared with the conventional methods based only on human experience and logic.(based on the our tests and observations). The analysis applied during the customers risk management allows to be identified per customer the critical threshold after which the same turns into a permanent debtor. Those thresholds could be implemented into the BI solution as internal controls alerting Customer Service departments proactively to act and prevent the accumulation of new debt. 38
39 Analyzing Water Consumption Study of the correlation of the consumption data with the factors "average temperature" and "average monthly rainfall." Two-dimensional diagram of correlation fields for the analyzed variables 39
40 Thank you for your attention 40
BIG Data Analytics Move to Competitive Advantage
BIG Data Analytics Move to Competitive Advantage where is technology heading today Standardization Open Source Automation Scalability Cloud Computing Mobility Smartphones/ tablets Internet of Things Wireless
More informationI. TODAY S UTILITY INFRASTRUCTURE vs. FUTURE USE CASES...1 II. MARKET & PLATFORM REQUIREMENTS...2
www.vitria.com TABLE OF CONTENTS I. TODAY S UTILITY INFRASTRUCTURE vs. FUTURE USE CASES...1 II. MARKET & PLATFORM REQUIREMENTS...2 III. COMPLEMENTING UTILITY IT ARCHITECTURES WITH THE VITRIA PLATFORM FOR
More informationStatistics for BIG data
Statistics for BIG data Statistics for Big Data: Are Statisticians Ready? Dennis Lin Department of Statistics The Pennsylvania State University John Jordan and Dennis K.J. Lin (ICSA-Bulletine 2014) Before
More informationIntroduction to Business Intelligence
IBM Software Group Introduction to Business Intelligence Vince Leat ASEAN SW Group 2007 IBM Corporation Discussion IBM Software Group What is Business Intelligence BI Vision Evolution Business Intelligence
More informationElegantJ BI. White Paper. Operational Business Intelligence (BI)
ElegantJ BI Simple. Smart. Strategic. ElegantJ BI White Paper Operational Business Intelligence (BI) Integrated Business Intelligence and Reporting for Performance Management, Operational Business Intelligence
More informationBig Data, Physics, and the Industrial Internet! How Modeling & Analytics are Making the World Work Better."
Big Data, Physics, and the Industrial Internet! How Modeling & Analytics are Making the World Work Better." Matt Denesuk! Chief Data Science Officer! GE Software! October 2014! Imagination at work. Contact:
More informationChapter 6 - Enhancing Business Intelligence Using Information Systems
Chapter 6 - Enhancing Business Intelligence Using Information Systems Managers need high-quality and timely information to support decision making Copyright 2014 Pearson Education, Inc. 1 Chapter 6 Learning
More informationBusiness Intelligence and Big Data Analytics: Speeding the Cycle from Insights to Action Four Steps to More Profitable Customer Engagement
white paper Business Intelligence and Big Data Analytics: Speeding the Cycle from Insights to Action Four Steps to More Profitable Customer Engagement»» Summary For business intelligence analysts the era
More informationA STUDY OF DATA MINING ACTIVITIES FOR MARKET RESEARCH
205 A STUDY OF DATA MINING ACTIVITIES FOR MARKET RESEARCH ABSTRACT MR. HEMANT KUMAR*; DR. SARMISTHA SARMA** *Assistant Professor, Department of Information Technology (IT), Institute of Innovation in Technology
More informationCustomer Analytics. Turn Big Data into Big Value
Turn Big Data into Big Value All Your Data Integrated in Just One Place BIRT Analytics lets you capture the value of Big Data that speeds right by most enterprises. It analyzes massive volumes of data
More informationPredicting From the Edge in an
Predicting From the Edge in an IoT World IoT will produce 4,400 exabytes of data or 4,400 billion terabytes between 2013 and 2020. (IDC) Today, in the Internet of Things (IoT) era, the Internet touches
More informationIntelligent Government From Data to Decision. Robert Lindsley robert.lindsley@oracle.com Oracle, Public Sector Technology Group
Intelligent Government From Data to Decision Robert Lindsley robert.lindsley@oracle.com Oracle, Public Sector Technology Group Safe Harbor Statement The following is intended to outline our general product
More informationQlikView for utilities
QlikView for utilities Delivering unprecedented customer intelligence qlik.com QlikView for utilities: delivering unprecedented customer intelligence Collaboration, visibility and efficiency: necessities
More informationOverview, Goals, & Introductions
Improving the Retail Experience with Predictive Analytics www.spss.com/perspectives Overview, Goals, & Introductions Goal: To present the Retail Business Maturity Model Equip you with a plan of attack
More information3 Ways Retailers Can Capitalize On Streaming Analytics
3 Ways Retailers Can Capitalize On Streaming Analytics > 2 Table of Contents 1. The Challenges 2. Introducing Vitria OI for Streaming Analytics 3. The Benefits 4. How Vitria OI Complements Hadoop 5. Summary
More informationCUSTOMER RELATIONSHIP MANAGEMENT (CRM) CII Institute of Logistics
CUSTOMER RELATIONSHIP MANAGEMENT (CRM) CII Institute of Logistics Session map Session1 Session 2 Introduction The new focus on customer loyalty CRM and Business Intelligence CRM Marketing initiatives Session
More informationORACLE SUPPLY CHAIN AND ORDER MANAGEMENT ANALYTICS
ORACLE SUPPLY CHAIN AND ORDER MANAGEMENT ANALYTICS KEY FEATURES & BENEFITS FOR BUSINESS USERS Provide actionable information to conduct intelligent analysis of orders related to regions, products, periods
More informationChapter 4 Getting Started with Business Intelligence
Chapter 4 Getting Started with Business Intelligence Learning Objectives and Learning Outcomes Learning Objectives Getting started on Business Intelligence 1. Understanding Business Intelligence 2. The
More informationDecisyon/Engage. Connecting you to the voice of the market. Contacts. www.decisyon.com
Connecting you to the voice of the market Contacts www.decisyon.com Corporate Headquarters 795 Folsom Street, 1st Floor San Francisco, CA 94107 1 844-329-3972 European Office Viale P. L. Nervi Directional
More informationBusiness Intelligence services
Business Intelligence services 2013 Benefit from ScienceSoft BI expertise By offering analytic tool development & support, on-demand reporting and comprehensive data analysis, ScienceSoft helps its Customers
More informationSUSTAINING COMPETITIVE DIFFERENTIATION
SUSTAINING COMPETITIVE DIFFERENTIATION Maintaining a competitive edge in customer experience requires proactive vigilance and the ability to take quick, effective, and unified action E M C P e r s pec
More informationTaking A Proactive Approach To Loyalty & Retention
THE STATE OF Customer Analytics Taking A Proactive Approach To Loyalty & Retention By Kerry Doyle An Exclusive Research Report UBM TechWeb research conducted an online study of 339 marketing professionals
More informationBUY BIG DATA IN RETAIL
BUY BIG DATA IN RETAIL Table of contents What is Big Data?... How Data Science creates value in Retail... Best practices for Retail. Case studies... 3 7 11 1. Social listening... 2. Cross-selling... 3.
More informationOutline. BI and Enterprise-wide decisions BI in different Business Areas BI Strategy, Architecture, and Perspectives
1. Introduction Outline BI and Enterprise-wide decisions BI in different Business Areas BI Strategy, Architecture, and Perspectives 2 Case study: Netflix and House of Cards Source: Andrew Stephen 3 Case
More informationMarketing Advanced Analytics. Predicting customer churn. Whitepaper
Marketing Advanced Analytics Predicting customer churn Whitepaper Churn prediction The challenge of predicting customers churn It is between five and fifteen times more expensive for a company to gain
More informationIntroduction to Data Mining and Business Intelligence Lecture 1/DMBI/IKI83403T/MTI/UI
Introduction to Data Mining and Business Intelligence Lecture 1/DMBI/IKI83403T/MTI/UI Yudho Giri Sucahyo, Ph.D, CISA (yudho@cs.ui.ac.id) Faculty of Computer Science, University of Indonesia Objectives
More informationLoyalty. Social. Listening
Loyalty Social Listening Listen Understand Engage We integrate Social Listening data with existing research and other data to help our clients drive brand preference and customer loyalty Loyalty Social
More informationDATA MANAGEMENT FOR THE INTERNET OF THINGS
DATA MANAGEMENT FOR THE INTERNET OF THINGS February, 2015 Peter Krensky, Research Analyst, Analytics & Business Intelligence Report Highlights p2 p4 p6 p7 Data challenges Managing data at the edge Time
More informationEndeavour Dynamics Offering
Endeavour Dynamics Offering Microsoft Dynamics AX 2012 is recognised as a global leading ERP system that supports a single instance strategy for medium to large enterprise companies. Endeavour is proud
More informationORACLE LOYALTY ANALYTICS
ORACLE LOYALTY ANALYTICS KEY FEATURES & BENEFITS FOR BUSINESS USERS Increase customer retention and purchase frequency Determine key factors that drive loyalty and use that insight to increase overall
More informationMaximize Sales and Margins with Comprehensive Customer Analytics
Q Customer Maximize Sales and Margins with Comprehensive Customer Analytics Struggling to connect the dots between Marketing, Merchandising and Store Ops? With the explosion of customer interaction systems,
More informationFederico Rajola. Customer Relationship. Management in the. Financial Industry. Organizational Processes and. Technology Innovation.
Federico Rajola Customer Relationship Management in the Financial Industry Organizational Processes and Technology Innovation Second edition ^ Springer Contents 1 Introduction 1 1.1 Identification and
More informationInsurance customer retention and growth
IBM Software Group White Paper Insurance Insurance customer retention and growth Leveraging business analytics to retain existing customers and cross-sell and up-sell insurance policies 2 Insurance customer
More informationQlikView for utilities. Delivering Unprecedented Customer Intelligence
QlikView for utilities Delivering Unprecedented Customer Intelligence QlikView for utilities: Delivering unprecedented Customer Intelligence Collaboration, visibility and efficiency: necessities for efficient
More informationWhat s Trending in Analytics for the Consumer Packaged Goods Industry?
What s Trending in Analytics for the Consumer Packaged Goods Industry? The 2014 Accenture CPG Analytics European Survey Shows How Executives Are Using Analytics, and Where They Expect to Get the Most Value
More informationSAP Solution Brief SAP HANA. Transform Your Future with Better Business Insight Using Predictive Analytics
SAP Brief SAP HANA Objectives Transform Your Future with Better Business Insight Using Predictive Analytics Dealing with the new reality Dealing with the new reality Organizations like yours can identify
More informationWhite Paper HOW TO INCREASE YOUR COMPANY S VALUE WITH PREDICTIVE ANALYTICS. What is Predictive Analytics?
Investment Banking Valuation & Forensics White Paper A Mariner Holdings Company Business Advisory HOW TO INCREASE YOUR COMPANY S VALUE WITH PREDICTIVE ANALYTICS Predictive analytics has long been used
More informationNICE MULTI-CHANNEL INTERACTION ANALYTICS
NICE MULTI-CHANNEL INTERACTION ANALYTICS Revealing Customer Intent in Contact Center Communications CUSTOMER INTERACTIONS: The LIVE Voice of the Customer Every day, customer service departments handle
More informationNext Generation Business Performance Management Solution
Next Generation Business Performance Management Solution Why Existing Business Intelligence (BI) Products are Inadequate Changing Business Environment In the face of increased competition, complex customer
More informationCONCEPTUALIZING BUSINESS INTELLIGENCE ARCHITECTURE MOHAMMAD SHARIAT, Florida A&M University ROSCOE HIGHTOWER, JR., Florida A&M University
CONCEPTUALIZING BUSINESS INTELLIGENCE ARCHITECTURE MOHAMMAD SHARIAT, Florida A&M University ROSCOE HIGHTOWER, JR., Florida A&M University Given today s business environment, at times a corporate executive
More informationCleaned Data. Recommendations
Call Center Data Analysis Megaputer Case Study in Text Mining Merete Hvalshagen www.megaputer.com Megaputer Intelligence, Inc. 120 West Seventh Street, Suite 10 Bloomington, IN 47404, USA +1 812-0-0110
More informationFive Predictive Imperatives for Maximizing Customer Value
Five Predictive Imperatives for Maximizing Customer Value Applying predictive analytics to enhance customer relationship management Contents: 1 Customers rule the economy 1 Many CRM initiatives are failing
More informationDATA MINING TECHNIQUES SUPPORT TO KNOWLEGDE OF BUSINESS INTELLIGENT SYSTEM
INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS ISSN 2320-7345 DATA MINING TECHNIQUES SUPPORT TO KNOWLEGDE OF BUSINESS INTELLIGENT SYSTEM M. Mayilvaganan 1, S. Aparna 2 1 Associate
More informationSocial Media Implementations
SEM Experience Analytics Social Media Implementations SEM Experience Analytics delivers real sentiment, meaning and trends within social media for many of the world s leading consumer brand companies.
More informationORACLE TAX ANALYTICS. The Solution. Oracle Tax Data Model KEY FEATURES
ORACLE TAX ANALYTICS KEY FEATURES A set of comprehensive and compatible BI Applications. Advanced insight into tax performance Built on World Class Oracle s Database and BI Technology Design after the
More informationDAMA NY DAMA Day October 17, 2013 IBM 590 Madison Avenue 12th floor New York, NY
Big Data Analytics DAMA NY DAMA Day October 17, 2013 IBM 590 Madison Avenue 12th floor New York, NY Tom Haughey InfoModel, LLC 868 Woodfield Road Franklin Lakes, NJ 07417 201 755 3350 tom.haughey@infomodelusa.com
More informationwww.pwc.com/oracle Next presentation starting soon Business Analytics using Big Data to gain competitive advantage
www.pwc.com/oracle Next presentation starting soon Business Analytics using Big Data to gain competitive advantage If every image made and every word written from the earliest stirring of civilization
More informationHow to use Big Data in Industry 4.0 implementations. LAURI ILISON, PhD Head of Big Data and Machine Learning
How to use Big Data in Industry 4.0 implementations LAURI ILISON, PhD Head of Big Data and Machine Learning Big Data definition? Big Data is about structured vs unstructured data Big Data is about Volume
More informationData Mining Solutions for the Business Environment
Database Systems Journal vol. IV, no. 4/2013 21 Data Mining Solutions for the Business Environment Ruxandra PETRE University of Economic Studies, Bucharest, Romania ruxandra_stefania.petre@yahoo.com Over
More informationPredictive Analytics for Donor Management
IBM Software Business Analytics IBM SPSS Predictive Analytics Predictive Analytics for Donor Management Predictive Analytics for Donor Management Contents 2 Overview 3 The challenges of donor management
More informationBeyond listening Driving better decisions with business intelligence from social sources
Beyond listening Driving better decisions with business intelligence from social sources From insight to action with IBM Social Media Analytics State of the Union Opinions prevail on the Internet Social
More informationOracle Business Intelligence Applications Overview. An Oracle White Paper March 2007
Oracle Business Intelligence Applications Overview An Oracle White Paper March 2007 Note: The following is intended to outline our general product direction. It is intended for information purposes only,
More information<Insert Picture Here> Oracle Retail Data Model Overview
Oracle Retail Data Model Overview The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into
More informationIT and CRM A basic CRM model Data source & gathering system Database system Data warehouse Information delivery system Information users
1 IT and CRM A basic CRM model Data source & gathering Database Data warehouse Information delivery Information users 2 IT and CRM Markets have always recognized the importance of gathering detailed data
More informationBanking On A Customer-Centric Approach To Data
Banking On A Customer-Centric Approach To Data Putting Content into Context to Enhance Customer Lifetime Value No matter which company they interact with, consumers today have far greater expectations
More informationSAP Predictive Analysis: Strategy, Value Proposition
September 10-13, 2012 Orlando, Florida SAP Predictive Analysis: Strategy, Value Proposition Thomas B Kuruvilla, Solution Management, SAP Business Intelligence Scott Leaver, Solution Management, SAP Business
More information2012 AnnuAl MArket OutlOOk & FOrecAst SUMMARY REPORT
2012 Annual Market Outlook & Forecast SUMMARY REPORT SECTION TITLE 04 Executive Summary 06 Introduction 07 A closer look at online study respondents Table of Contents 09 Going a step further in-depth interviews
More informationBig Data: What You Should Know. Mark Child Research Manager - Software IDC CEMA
Big Data: What You Should Know Mark Child Research Manager - Software IDC CEMA Agenda Market Dynamics Defining Big Data Technology Trends Information and Intelligence Market Realities Future Applications
More informationDirect-to-Company Feedback Implementations
SEM Experience Analytics Direct-to-Company Feedback Implementations SEM Experience Analytics Listening System for Direct-to-Company Feedback Implementations SEM Experience Analytics delivers real sentiment,
More informationLoss Prevention Data Mining Using big data, predictive and prescriptive analytics to enpower loss prevention
White paper Loss Prevention Data Mining Using big data, predictive and prescriptive analytics to enpower loss prevention Abstract In the current economy where growth is stumpy and margins reduced, retailers
More informationORACLE FINANCIAL ANALYTICS
ORACLE FINANCIAL ANALYTICS KEY FEATURES & BENEFITS FOR BUSINESS USERS Receive intra-period information on income statement, cash flow, and balance sheet condition without having to perform consolidations
More informationSupply Chains: From Inside-Out to Outside-In
Supply Chains: From Inside-Out to Outside-In Table of Contents Big Data and the Supply Chains of the Process Industries The Inter-Enterprise System of Record Inside-Out vs. Outside-In Supply Chain How
More informationHexaware E-book on Predictive Analytics
Hexaware E-book on Predictive Analytics Business Intelligence & Analytics Actionable Intelligence Enabled Published on : Feb 7, 2012 Hexaware E-book on Predictive Analytics What is Data mining? Data mining,
More informationDecisioning for Telecom Customer Intimacy. Experian Telecom Analytics
Decisioning for Telecom Customer Intimacy Experian Telecom Analytics Turning disruption into opportunity The traditional telecom business model is being disrupted by a variety of pressures from heightened
More informationBPM for Structural Integrity Management in Oil and Gas Industry
Whitepaper BPM for Structural Integrity Management in Oil and Gas Industry - Saurangshu Chakrabarty Abstract Structural Integrity Management (SIM) is an ongoing lifecycle process for ensuring the continued
More informationORACLE BUSINESS INTELLIGENCE, ORACLE DATABASE, AND EXADATA INTEGRATION
ORACLE BUSINESS INTELLIGENCE, ORACLE DATABASE, AND EXADATA INTEGRATION EXECUTIVE SUMMARY Oracle business intelligence solutions are complete, open, and integrated. Key components of Oracle business intelligence
More informationWhite Paper. Data Mining for Business
White Paper Data Mining for Business January 2010 Contents 1. INTRODUCTION... 3 2. WHY IS DATA MINING IMPORTANT?... 3 FUNDAMENTALS... 3 Example 1...3 Example 2...3 3. OPERATIONAL CONSIDERATIONS... 4 ORGANISATIONAL
More informationSolve Your Toughest Challenges with Data Mining
IBM Software Business Analytics IBM SPSS Modeler Solve Your Toughest Challenges with Data Mining Use predictive intelligence to make good decisions faster Solve Your Toughest Challenges with Data Mining
More informationDATA MINING AND WAREHOUSING CONCEPTS
CHAPTER 1 DATA MINING AND WAREHOUSING CONCEPTS 1.1 INTRODUCTION The past couple of decades have seen a dramatic increase in the amount of information or data being stored in electronic format. This accumulation
More informationISSN: 2321-7782 (Online) Volume 3, Issue 7, July 2015 International Journal of Advance Research in Computer Science and Management Studies
ISSN: 2321-7782 (Online) Volume 3, Issue 7, July 2015 International Journal of Advance Research in Computer Science and Management Studies Research Article / Survey Paper / Case Study Available online
More informationThe Top 9 Ways to Increase Your Customer Loyalty
Follow these and enjoy an immediate lift in the loyalty of your customers By Kyle LaMalfa Loyalty Expert and Allegiance Best Practices Manager What is the Key to Business Success? Every company executive
More informationPentaho Data Mining Last Modified on January 22, 2007
Pentaho Data Mining Copyright 2007 Pentaho Corporation. Redistribution permitted. All trademarks are the property of their respective owners. For the latest information, please visit our web site at www.pentaho.org
More informationAlcatel-Lucent 8920 Service Quality Manager for IPTV Business Intelligence
Alcatel-Lucent 8920 Service Quality Manager for IPTV Business Intelligence IPTV service intelligence solution for marketing, programming, internal ad sales and external partners The solution is based on
More informationTHE STATE OF Social Media Analytics. How Leading Marketers Are Using Social Media Analytics
THE STATE OF Social Media Analytics May 2016 Getting to Know You: How Leading Marketers Are Using Social Media Analytics» Marketers are expanding their use of advanced social media analytics and combining
More informationSolve your toughest challenges with data mining
IBM Software IBM SPSS Modeler Solve your toughest challenges with data mining Use predictive intelligence to make good decisions faster Solve your toughest challenges with data mining Imagine if you could
More informationData Mining Applications in Higher Education
Executive report Data Mining Applications in Higher Education Jing Luan, PhD Chief Planning and Research Officer, Cabrillo College Founder, Knowledge Discovery Laboratories Table of contents Introduction..............................................................2
More informationBig Data Analytics. Copyright 2011 EMC Corporation. All rights reserved.
Big Data Analytics 1 Priority Discussion Topics What are the most compelling business drivers behind big data analytics? Do you have or expect to have data scientists on your staff, and what will be their
More informationData Refinery with Big Data Aspects
International Journal of Information and Computation Technology. ISSN 0974-2239 Volume 3, Number 7 (2013), pp. 655-662 International Research Publications House http://www. irphouse.com /ijict.htm Data
More informationEmpowering intelligent utility networks with visibility and control
IBM Software Energy and Utilities Thought Leadership White Paper Empowering intelligent utility networks with visibility and control IBM Intelligent Metering Network Management software solution 2 Empowering
More informationBEYOND BI: Big Data Analytic Use Cases
BEYOND BI: Big Data Analytic Use Cases Big Data Analytics Use Cases This white paper discusses the types and characteristics of big data analytics use cases, how they differ from traditional business intelligence
More informationDecisioning for Telecom Customer Intimacy. Experian Telecom Analytics
Decisioning for Telecom Customer Intimacy Experian Telecom Analytics Turning disruption into opportunity The traditional telecom business model is being disrupted by a variety of pressures. From heightened
More informationHow To Transform Customer Service With Business Analytics
IBM Software Business Analytics Customer Service Transforming customer service with business analytics 2 Transforming customer service with business analytics Contents 2 Overview 2 Customer service is
More informationPredictive Analytics. Noam Zeigerson, CTO
Predictive Analytics Noam Zeigerson, CTO Agenda The Predictive Analytics Need Innovative Technologies Business Solutions The problem: Inconsistent stream of revenue Available Data Sources ERP data Web
More informationCONNECTING DATA WITH BUSINESS
CONNECTING DATA WITH BUSINESS Big Data and Data Science consulting Business Value through Data Knowledge Synergic Partners is a specialized Big Data, Data Science and Data Engineering consultancy firm
More informationGerard Mc Nulty Systems Optimisation Ltd gmcnulty@iol.ie/0876697867 BA.,B.A.I.,C.Eng.,F.I.E.I
Gerard Mc Nulty Systems Optimisation Ltd gmcnulty@iol.ie/0876697867 BA.,B.A.I.,C.Eng.,F.I.E.I Data is Important because it: Helps in Corporate Aims Basis of Business Decisions Engineering Decisions Energy
More informationBusiness Intelligence Solutions for Gaming and Hospitality
Business Intelligence Solutions for Gaming and Hospitality Prepared by: Mario Perkins Qualex Consulting Services, Inc. Suzanne Fiero SAS Objective Summary 2 Objective Summary The rise in popularity and
More informationBusiness Intelligence
Business Intelligence What is it? Why do you need it? This white paper at a glance This whitepaper discusses Professional Advantage s approach to Business Intelligence. It also looks at the business value
More informationCis330. Mostafa Z. Ali
Fall 2009 Lecture 1 Cis330 Decision Support Systems and Business Intelligence Mostafa Z. Ali mzali@just.edu.jo Lecture 2: Slide 1 Changing Business Environments and Computerized Decision Support The business
More informationLocation Analytics for Financial Services. An Esri White Paper October 2013
Location Analytics for Financial Services An Esri White Paper October 2013 Copyright 2013 Esri All rights reserved. Printed in the United States of America. The information contained in this document is
More informationEnterprise Optimization
Enterprise Optimization Assets Energy Operations 1 Market needs what we are hearing from our customers Performance visibility across systems, buildings, and real estate portfolios Managed uptime and availability
More informationContent is essential to commerce
Content is essential to commerce IBM ECM helps organizations improve the efficiency of buy, market, sell and service processes Highlights: Analyze customer and operational data and build business processes
More informationData Mining Techniques and Opportunities for Taxation Agencies
Data Mining Techniques and Opportunities for Taxation Agencies Florida Consultant In This Session... You will learn the data mining techniques below and their application for Tax Agencies ABC Analysis
More informationZero-in on business decisions through innovation solutions for smart big data management. How to turn volume, variety and velocity into value
Zero-in on business decisions through innovation solutions for smart big data management How to turn volume, variety and velocity into value ON THE LOOKOUT FOR NEW SOURCES OF VALUE CREATION WHAT WILL DRIVE
More informationPatient Relationship Management
Solution in Detail Healthcare Executive Summary Contact Us Patient Relationship Management 2013 2014 SAP AG or an SAP affiliate company. Attract and Delight the Empowered Patient Engaged Consumers Information
More informationOPERA BI OPERA BUSINESS. With Enterprise and Standard Editions INTELLIGENCE SUITE
OPERA BI OPERA BUSINESS With Enterprise and Standard Editions INTELLIGENCE SUITE OPERA Business Intelligence Deployment Benefits Reduced Hardware Complexity OBI is built entirely on the same platform as
More informationOptimize your Workforce for Customer Contact in Social Marketplace
Optimize your Workforce for Customer Contact in Social Marketplace Spence Mallder GM Workforce Optimization & CTO The Contact Center Is The Cornerstone Of Customer Experience Our contact center strategy
More informationbirt Analytics data sheet Reduce the time from analysis to action
Reduce the time from analysis to action BIRT Analytics is the newest addition to ActuateOne. This new analytics product is fast and agile, and adds to the already rich Actuate BIRT product lineup the simpleto-use
More informationIBM Social Media Analytics
IBM Analyze social media data to improve business outcomes Highlights Grow your business by understanding consumer sentiment and optimizing marketing campaigns. Make better decisions and strategies across
More informationTRENDS IN THE DEVELOPMENT OF BUSINESS INTELLIGENCE SYSTEMS
9 8 TRENDS IN THE DEVELOPMENT OF BUSINESS INTELLIGENCE SYSTEMS Assist. Prof. Latinka Todoranova Econ Lit C 810 Information technology is a highly dynamic field of research. As part of it, business intelligence
More informationElevate Customer Experience and Engagement in the New Digital World
Elevate Customer Experience and Engagement in the New Digital World John Chan CRM Solutions Lead, Microsoft Business Solutions Microsoft Asia Customer buying behavior has fundamentally changed therefore,
More information