[callout: no organization can afford to deny itself the power of business intelligence ]

Size: px
Start display at page:

Download "[callout: no organization can afford to deny itself the power of business intelligence ]"

Transcription

1 Publication: Telephony Author: Douglas Hackney Headline: Applied Business Intelligence [callout: no organization can afford to deny itself the power of business intelligence ] [begin copy] 1 Business Intelligence 1.1 Definition Business Intelligence (BI) is a market that will be worth $148 billion dollars by 2003, according to Survey.com. It is growing at exponential rates; is mission critical to every business, regardless of size; has every major technology company as a player; features e-everything delivery and interaction; offers exponentially more capability at orders of magnitude lower prices than just a year ago; is commercializing top-secret technology from formerly off-limits US government programs; has some of the hottest initial public offerings (IPO) and best performing companies of the last two years; and has nowhere to go but up. BI consists of all activities related to organizing and delivering information and analysis to the business. This includes data mining, knowledge management, analytical applications, reporting systems, data warehouses, etc. The BI space is an exciting place to be today, but only if you leverage it to provide high-impact solutions that solve specific business problems. 2 Architectures 2.1 Monolithic The primary components in a BI infrastructure are data warehouse (DW) systems. These systems combine and integrate data from a wide variety of operational systems. They cleanse the data to remove errors; they standardize the data so that key entities (such as product and customer) and metrics / measures (such as revenue and net profit) are consistent across the system; and they integrate the data so that information from different systems (such as accounting data and marketing data) can be combined to yield very high impact information and analysis (such as lifetime value of a customer or profit by product). In the early to mid '90s, many organizations attempted to build their BI infrastructure data warehouse elements in a "top-down" monolithic fashion (see figure one). This approach attempted to model the enterprise, then incrementally build a central mega-data warehouse resource. This has become known as the "dream of homogeneity" as it assumes and demands a consistency of systems, data and architectures that is inconsistent with the heterogeneous nature of a business environment. These large scale enterprise-class projects had trouble delivering value to the business, with studies showing failure rates from 30% (Meta Group) to 80% (DWN and OTR). Enterprise data warehouses aren't the only large scale projects having troubles. A

2 recent Boston Consulting Group (www.bcg.com) study showed 70% lack of success in largescale enterprise projects involving ERP, CRM, etc. systems. These high failure rates led to the development of an alternative approach to achieve the goal of the enterprise data warehouse called "bottom up." It involves the creation of a series of highly targeted, architected data marts that are integrated into the resulting data warehouse system. This approach has proven to be very popular and effective. The BCG study found that small, targeted solutions are five times more likely to be rated as a success by the business. It is surprising that in the face of these statistics that there is an ongoing fixation with some oldthink DW guru adherents that the enterprise, top-down, monolithic DW approach is the only viable way to achieve the goal of an integrated information resource. This group is blind to five key factors: 1) both methods, top-down and bottom-up, are viable given a suitable political and cultural environment; 2) top-down monolithic approaches are sure death in organizations that lack the senior level support, long-term sustainable political will, and political and communication skills required to be a success; 3) top-down monolithic approaches are incapable of accommodating today's heterogeneous mix of custom DW/data marts (DM), turn-key, packaged DW/DMs, data mining and analytical applications (see figure two); 4) technological considerations such as architectures, approaches, tools, technologies, etc. are meaningless to the business - it is fast, measurable high-impact on the business that counts; and 5) the business makes the rules, not the technologists.

3 2.2 Federated BI Architecture The current BI market is built on the foundation of a modern BI infrastructure, consisting of a federated BI architecture accommodating all the components of a contemporary BI system: packaged/turnkey data warehouses (DW) and data marts (DM), packaged/turnkey analytical applications (AA), custom built DWs and DMs, custom built AAs, data mining, online analytical processing (OLAP) tools, query and reporting (Q&R) tools, production reporting tools, data quality tools, extraction transformation and load (ETL) tools, system management tools, information delivery tools, enterprise information portals, reporting systems, knowledge management systems, database systems, etc. The federated BI architecture is the "big tent" that provides the foundation and environment to facilitate and enable business information flow, analysis and decision making. As the internet is a network of networks, a federated DW architecture is an architecture of architectures (see figure three). It provides a framework for the integration, to the greatest extent possible, of disparate DW, DM and analytical application systems. A federated DW architecture is the most pragmatic route to provide the maximum amount of architecture possible given the political and implementation realities of real-world sites. A federated DW architecture shares as much core information among the various systems as possible. This is accomplished by sharing critical "master files" or dimensions, common metrics and measures and other high impact data across all systems that can make use of the information. It is usually accomplished via an enterprise class ETL tool, which provides a common meta data repository, and the use of common data staging areas. 2.3 Sample Telecommunications Architecture A packet based telecommunications company has high demands for BI, and often has a business model based on core BI functionality, such as bandwidth/utilization based billing, real time configuration, etc. To accommodate these needs, a federated BI architecture is required to accommodate the heterogeneous BI requirements inherent in providing the near-real time

4 analysis required by the networking organization along with service / support team requirements and the billing, utilization and analysis needs (see figure four). Telephony and packet BI systems face special challenges in the areas of data volume and realtime data streams. While typical data warehouse systems are considered large if they contain a terabyte of data, a packet system can easily contain ten terabytes or more. To provide support for provisioning, support and dynamic billing the system must also manipulate very large volumes of data in near-real time. The data must be gathered from a worldwide network of devices, cleansed, integrated and aggregated within minutes. These requirements are well beyond the sundry run-of-the-mill architectures, ETL tools and server systems found in everyday data warehouse systems and require special expertise, experience, techniques and technologies to be successful. 3 Solutions No BI system, regardless of its technical elegance or purity of design vision, has a prayer of survival if it does not provide direct business value and solve a specific business problem. The most popular ways to achieve this goal are via analytical applications and data mining. 3.1 Analytical Applications The most popular form of BI utilization from the business perspective is via packaged, turn-key analytical applications. A true, high-business-impact, analytical application is defined by the following characteristics: 1. Architected, integrated data from multiple sources (internal & external) An analytical application includes (or, at a minimum, can include) information from multiple sources, both native OLTP applications, as in the case of an analytical application offered by an ERP vendor, and external information from heterogeneous OLTP systems or 3rd party vendors. Note that many ERP vendor supplied analytical application offerings have no capability to

5 capture, leverage or utilize external data of any kind. This shortcoming cannot be overly emphasized as you consider the implications of an environment made up of disparate, nonarchitected analytical applications, each with its own semantics, business rules, etc. 2. Flexible, multi-dimensional analysis, drill (up, down, across) and reporting Analytical applications allow business users a flexible environment to view business metrics and measures by any number of pertinent dimensions, with any required number of members. Analytical applications allow seamless drill through into pertinent detailed transactions and flexible and easy movement across dimensions and measures. They also provide the capability to view and report information in all forms required by the applicable business processes, i.e. detailed lists as well as summary cross tab. 3. Turnkey package / short time to market Analytical applications feature rapid deployment, with easy data extraction and/or integration into OLTP packages and data sets; indigenous OLAP or native support for industry standard OLAP engines; pre-formatted, pre-defined relevant business metrics, measures, Key Performance Indicators (KPI), etc.; and implementation ready agents, reports, and aggregations. 4. Integrated business processes Analytical applications provide domain specific solutions to specific business challenges, including internal representations of relevant business processes. Analytical applications provide an interactive environment to interact with the business process by presenting applicable metrics and measures of processes, as well as the ability to interact with, and alter, process values and measures. 5. Self measuring (internally monitored ROI, etc.) Analytical applications provide internal value measurement of the relevant business processes and of the analytical application itself. They monitor the ongoing utilization of the analytical application, and it's effects on the business process. In doing so, they provide ongoing ROI analysis of the business process, and the analytical application. In addition, they monitor the utilization of the analytical application, and provide an active monitor into the propagation of the tool throughout the organization, the relative sophistication of the usage of the system, optimization of the system and identification of best practices regarding usage of the system. 6. Closed loop system An analytical application provides a closed loop, feeding new inputs back into the host OLTP or data warehouse / data mart system. As the users interact with the business process, they introduce new information or alter existing information, as in a budgeting and forecasting system. These new values are then fed back into the source systems as new or modified information for use by all users of the source system and all downstream BI systems. Note that this new or altered information must flow back into the analytical application in real time or near real time. This places extraordinary challenges on the technical infrastructure of data warehouse and data mart systems more accustomed to relatively leisurely monthly, weekly or daily information refreshes. It also places heavy demands for massive re-calculation and reallocation of data, as in budget vs. actual calculations or performance against plan. An even greater challenge is that these write-back, flow-through prerequisites require a level of process rigor and structure that is

6 diametrically opposed to the free-form flexibility required of a successful BI system. This is a key technological and cultural hurdle that many teams cannot overcome. 3.2 Data Mining Data mining solutions are a key weapon in the BI arsenal. They are used to reveal trends and relationships, and predict future outcomes. They are built on variations of artificial intelligence such as neural networks, machine learning and genetic algorithms. Data mining tools are a powerful technological and competitive weapon and form the underpinnings of powerful product offerings, and infrastructure and support capabilities for packet companies. Most organizations use data mining tools for the discovery of previously unknown relationships, trends and anomalies, as well as to predict future outcomes. On the customer side of the house these capabilities are used for target marketing, churn management, fraud detection and promotion management. Packet content BI systems can also use data mining tools to track, trend and predict network volumes, spot significant outlier behavior, optimize system configuration and performance, and optimize the structure and design of customer offerings. 4 Conclusion A federated BI system is a prerequisite to survive and thrive in today's fast changing and evolving market. Without the capabilities provided by integrated data, powerful analytical tools and insightful data mining applications companies are at a tremendous disadvantage and find themselves unable to compete with their better informed and capable competitors. With the players, the customers and the fundamental possibilities of the market changing daily, no organization can afford to deny itself the power of business intelligence. [end copy] Enterprise Group, Ltd. Enterprise Group, Ltd. is a servicemark and should be treated as such. We build business intelligence is a servicemark of Enterprise Group, Ltd. and should be treated as such.

7 Other company and product names may be trademarked, servicemarked or registered, and should be treated as such. Copyright 2000, Enterprise Group, Ltd. All rights reserved.

CONCEPTUALIZING 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 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 information

Data Warehouse: Introduction

Data Warehouse: Introduction Base and Mining Group of Base and Mining Group of Base and Mining Group of Base and Mining Group of Base and Mining Group of Base and Mining Group of Base and Mining Group of base and data mining group,

More information

BUSINESS INTELLIGENCE. Keywords: business intelligence, architecture, concepts, dashboards, ETL, data mining

BUSINESS INTELLIGENCE. Keywords: business intelligence, architecture, concepts, dashboards, ETL, data mining BUSINESS INTELLIGENCE Bogdan Mohor Dumitrita 1 Abstract A Business Intelligence (BI)-driven approach can be very effective in implementing business transformation programs within an enterprise framework.

More information

MDM and Data Warehousing Complement Each Other

MDM and Data Warehousing Complement Each Other Master Management MDM and Warehousing Complement Each Other Greater business value from both 2011 IBM Corporation Executive Summary Master Management (MDM) and Warehousing (DW) complement each other There

More information

Integrating SAP and non-sap data for comprehensive Business Intelligence

Integrating SAP and non-sap data for comprehensive Business Intelligence WHITE PAPER Integrating SAP and non-sap data for comprehensive Business Intelligence www.barc.de/en Business Application Research Center 2 Integrating SAP and non-sap data Authors Timm Grosser Senior Analyst

More information

LITERATURE SURVEY ON DATA WAREHOUSE AND ITS TECHNIQUES

LITERATURE SURVEY ON DATA WAREHOUSE AND ITS TECHNIQUES LITERATURE SURVEY ON DATA WAREHOUSE AND ITS TECHNIQUES MUHAMMAD KHALEEL (0912125) SZABIST KARACHI CAMPUS Abstract. Data warehouse and online analytical processing (OLAP) both are core component for decision

More information

Big Data and Your Data Warehouse Philip Russom

Big Data and Your Data Warehouse Philip Russom Big Data and Your Data Warehouse Philip Russom TDWI Research Director for Data Management April 5, 2012 Sponsor Speakers Philip Russom Research Director, Data Management, TDWI Peter Jeffcock Director,

More information

Making Business Intelligence Easy. Whitepaper Measuring data quality for successful Master Data Management

Making Business Intelligence Easy. Whitepaper Measuring data quality for successful Master Data Management Making Business Intelligence Easy Whitepaper Measuring data quality for successful Master Data Management Contents Overview... 3 What is Master Data Management?... 3 Master Data Modeling Approaches...

More information

Business Intelligence Solutions for Gaming and Hospitality

Business 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 information

Moving Large Data at a Blinding Speed for Critical Business Intelligence. A competitive advantage

Moving Large Data at a Blinding Speed for Critical Business Intelligence. A competitive advantage Moving Large Data at a Blinding Speed for Critical Business Intelligence A competitive advantage Intelligent Data In Real Time How do you detect and stop a Money Laundering transaction just about to take

More information

Managing Big Data with Hadoop & Vertica. A look at integration between the Cloudera distribution for Hadoop and the Vertica Analytic Database

Managing Big Data with Hadoop & Vertica. A look at integration between the Cloudera distribution for Hadoop and the Vertica Analytic Database Managing Big Data with Hadoop & Vertica A look at integration between the Cloudera distribution for Hadoop and the Vertica Analytic Database Copyright Vertica Systems, Inc. October 2009 Cloudera and Vertica

More information

Turnkey Hardware, Software and Cash Flow / Operational Analytics Framework

Turnkey Hardware, Software and Cash Flow / Operational Analytics Framework Turnkey Hardware, Software and Cash Flow / Operational Analytics Framework With relevant, up to date cash flow and operations optimization reporting at your fingertips, you re positioned to take advantage

More information

QLIKVIEW DEPLOYMENT FOR BIG DATA ANALYTICS AT KING.COM

QLIKVIEW DEPLOYMENT FOR BIG DATA ANALYTICS AT KING.COM QLIKVIEW DEPLOYMENT FOR BIG DATA ANALYTICS AT KING.COM QlikView Technical Case Study Series Big Data June 2012 qlikview.com Introduction This QlikView technical case study focuses on the QlikView deployment

More information

www.ducenit.com Analance Data Integration Technical Whitepaper

www.ducenit.com Analance Data Integration Technical Whitepaper Analance Data Integration Technical Whitepaper Executive Summary Business Intelligence is a thriving discipline in the marvelous era of computing in which we live. It s the process of analyzing and exploring

More information

Enterprise Solutions. Data Warehouse & Business Intelligence Chapter-8

Enterprise Solutions. Data Warehouse & Business Intelligence Chapter-8 Enterprise Solutions Data Warehouse & Business Intelligence Chapter-8 Learning Objectives Concepts of Data Warehouse Business Intelligence, Analytics & Big Data Tools for DWH & BI Concepts of Data Warehouse

More information

Applied Business Intelligence. Iakovos Motakis, Ph.D. Director, DW & Decision Support Systems Intrasoft SA

Applied Business Intelligence. Iakovos Motakis, Ph.D. Director, DW & Decision Support Systems Intrasoft SA Applied Business Intelligence Iakovos Motakis, Ph.D. Director, DW & Decision Support Systems Intrasoft SA Agenda Business Drivers and Perspectives Technology & Analytical Applications Trends Challenges

More information

Data Warehousing and Data Mining in Business Applications

Data Warehousing and Data Mining in Business Applications 133 Data Warehousing and Data Mining in Business Applications Eesha Goel CSE Deptt. GZS-PTU Campus, Bathinda. Abstract Information technology is now required in all aspect of our lives that helps in business

More information

Data warehouse and Business Intelligence Collateral

Data warehouse and Business Intelligence Collateral Data warehouse and Business Intelligence Collateral Page 1 of 12 DATA WAREHOUSE AND BUSINESS INTELLIGENCE COLLATERAL Brains for the corporate brawn: In the current scenario of the business world, the competition

More information

Decision Support and Business Intelligence Systems. Chapter 1: Decision Support Systems and Business Intelligence

Decision Support and Business Intelligence Systems. Chapter 1: Decision Support Systems and Business Intelligence Decision Support and Business Intelligence Systems Chapter 1: Decision Support Systems and Business Intelligence Types of DSS Two major types: Model-oriented DSS Data-oriented DSS Evolution of DSS into

More information

Making Business Intelligence Relevant for Mid-sized Companies. Improving Business Results through Performance Management

Making Business Intelligence Relevant for Mid-sized Companies. Improving Business Results through Performance Management Making Business Intelligence Relevant for Mid-sized Companies Improving Business Results through Performance Management mydials Inc. 2009 www.mydials.com - 1 Contents Contents... 2 Executive Summary...

More information

www.sryas.com Analance Data Integration Technical Whitepaper

www.sryas.com Analance Data Integration Technical Whitepaper Analance Data Integration Technical Whitepaper Executive Summary Business Intelligence is a thriving discipline in the marvelous era of computing in which we live. It s the process of analyzing and exploring

More information

Database Marketing, Business Intelligence and Knowledge Discovery

Database Marketing, Business Intelligence and Knowledge Discovery Database Marketing, Business Intelligence and Knowledge Discovery Note: Using material from Tan / Steinbach / Kumar (2005) Introduction to Data Mining,, Addison Wesley; and Cios / Pedrycz / Swiniarski

More information

Automated Financial Reporting (AFR) Version 4.0 Highlights

Automated Financial Reporting (AFR) Version 4.0 Highlights Automated Financial Reporting (AFR) Version 4.0 Highlights Why Do 65% of North American CAT Dealers Use AFR? Without formal training, our CFO conducted quarterly statement reviews with all of our operating

More information

Torquex Customer Engagement Analytics. End to End View of Customer Interactions and Operational Insights

Torquex Customer Engagement Analytics. End to End View of Customer Interactions and Operational Insights Torquex Customer Engagement Analytics End to End View of Customer Interactions and Operational Insights Rob Witthoft Torquex {Pty) Ltd 10/1/2015 Torquex Customer Engagement Analytics Torquex Customer Engagement

More information

Business Intelligence. A Presentation of the Current Lead Solutions and a Comparative Analysis of the Main Providers

Business Intelligence. A Presentation of the Current Lead Solutions and a Comparative Analysis of the Main Providers 60 Business Intelligence. A Presentation of the Current Lead Solutions and a Comparative Analysis of the Main Providers Business Intelligence. A Presentation of the Current Lead Solutions and a Comparative

More information

BI for the Mid-Size Enterprise: Leveraging SQL Server & SharePoint

BI for the Mid-Size Enterprise: Leveraging SQL Server & SharePoint AUGUST 2012 BI for the Mid-Size Enterprise: Leveraging SQL Server & SharePoint Defining Business Intelligence and How it Can Transform Organizations of All Sizes About Perficient s Microsoft Practice Perficient

More information

Escape from Data Jail: Getting business value out of your data warehouse

Escape from Data Jail: Getting business value out of your data warehouse Escape from Data Jail: Getting business value out of your data warehouse Monica Woolmer, Catapult BI, (Formally Formation Data Pty Ltd) Does your organisation have data but struggle with providing effective

More information

Business Intelligence: Effective Decision Making

Business Intelligence: Effective Decision Making Business Intelligence: Effective Decision Making Bellevue College Linda Rumans IT Instructor, Business Division Bellevue College lrumans@bellevuecollege.edu Current Status What do I do??? How do I increase

More information

Data Virtualization A Potential Antidote for Big Data Growing Pains

Data Virtualization A Potential Antidote for Big Data Growing Pains perspective Data Virtualization A Potential Antidote for Big Data Growing Pains Atul Shrivastava Abstract Enterprises are already facing challenges around data consolidation, heterogeneity, quality, and

More information

Contents. visualintegrator The Data Creator for Analytical Applications. www.visualmetrics.co.uk. Executive Summary. Operational Scenario

Contents. visualintegrator The Data Creator for Analytical Applications. www.visualmetrics.co.uk. Executive Summary. Operational Scenario About visualmetrics visualmetrics is a Business Intelligence (BI) solutions provider that develops and delivers best of breed Analytical Applications, utilising BI tools, to its focus markets. Based in

More information

Speeding ETL Processing in Data Warehouses White Paper

Speeding ETL Processing in Data Warehouses White Paper Speeding ETL Processing in Data Warehouses White Paper 020607dmxwpADM High-Performance Aggregations and Joins for Faster Data Warehouse Processing Data Processing Challenges... 1 Joins and Aggregates are

More information

Paper DM10 SAS & Clinical Data Repository Karthikeyan Chidambaram

Paper DM10 SAS & Clinical Data Repository Karthikeyan Chidambaram Paper DM10 SAS & Clinical Data Repository Karthikeyan Chidambaram Cognizant Technology Solutions, Newbury Park, CA Clinical Data Repository (CDR) Drug development lifecycle consumes a lot of time, money

More information

BI Dashboards the Agile Way

BI Dashboards the Agile Way BI Dashboards the Agile Way Paul DeSarra Paul DeSarra is Inergex practice director for business intelligence and data warehousing. He has 15 years of BI strategy, development, and management experience

More information

Data Warehousing Systems: Foundations and Architectures

Data Warehousing Systems: Foundations and Architectures Data Warehousing Systems: Foundations and Architectures Il-Yeol Song Drexel University, http://www.ischool.drexel.edu/faculty/song/ SYNONYMS None DEFINITION A data warehouse (DW) is an integrated repository

More information

How the Past Changes the Future of Fraud

How the Past Changes the Future of Fraud How the Past Changes the Future of Fraud Addressing payment card fraud with models that evaluate multiple risk dimensions through intelligence Card fraud costs the U.S. card payments industry an estimated

More information

Application of Business Intelligence in Transportation for a Transportation Service Provider

Application of Business Intelligence in Transportation for a Transportation Service Provider Application of Business Intelligence in Transportation for a Transportation Service Provider Mohamed Sheriff Business Analyst Satyam Computer Services Ltd Email: mohameda_sheriff@satyam.com, mail2sheriff@sify.com

More information

Enterprise Data Quality

Enterprise Data Quality Enterprise Data Quality An Approach to Improve the Trust Factor of Operational Data Sivaprakasam S.R. Given the poor quality of data, Communication Service Providers (CSPs) face challenges of order fallout,

More information

A Service-oriented Architecture for Business Intelligence

A Service-oriented Architecture for Business Intelligence A Service-oriented Architecture for Business Intelligence Liya Wu 1, Gilad Barash 1, Claudio Bartolini 2 1 HP Software 2 HP Laboratories {name.surname@hp.com} Abstract Business intelligence is a business

More information

Need for Business Intelligence

Need for Business Intelligence Wisdom InfoTech Need for Business Intelligence INFORMATION AT YOUR FINGER TIPS May 2007 ABRAHAM PABBATHI Principal Consultant BI Practice Wisdom InfoTech 18650 W. Corporate Drive Suite 120 Brookfield WI

More information

Oracle Data Integrator 12c (ODI12c) - Powering Big Data and Real-Time Business Analytics. An Oracle White Paper October 2013

Oracle Data Integrator 12c (ODI12c) - Powering Big Data and Real-Time Business Analytics. An Oracle White Paper October 2013 An Oracle White Paper October 2013 Oracle Data Integrator 12c (ODI12c) - Powering Big Data and Real-Time Business Analytics Introduction: The value of analytics is so widely recognized today that all mid

More information

Business Intelligence

Business Intelligence Transforming Information into Business Intelligence Solutions Business Intelligence Client Challenges The ability to make fast, reliable decisions based on accurate and usable information is essential

More information

W H I T E P A P E R B u s i n e s s I n t e l l i g e n c e S o lutions from the Microsoft and Teradata Partnership

W H I T E P A P E R B u s i n e s s I n t e l l i g e n c e S o lutions from the Microsoft and Teradata Partnership W H I T E P A P E R B u s i n e s s I n t e l l i g e n c e S o lutions from the Microsoft and Teradata Partnership Sponsored by: Microsoft and Teradata Dan Vesset October 2008 Brian McDonough Global Headquarters:

More information

Information management software solutions White paper. Powerful data warehousing performance with IBM Red Brick Warehouse

Information management software solutions White paper. Powerful data warehousing performance with IBM Red Brick Warehouse Information management software solutions White paper Powerful data warehousing performance with IBM Red Brick Warehouse April 2004 Page 1 Contents 1 Data warehousing for the masses 2 Single step load

More information

Data Warehouse Overview. Srini Rengarajan

Data Warehouse Overview. Srini Rengarajan Data Warehouse Overview Srini Rengarajan Please mute Your cell! Agenda Data Warehouse Architecture Approaches to build a Data Warehouse Top Down Approach Bottom Up Approach Best Practices Case Example

More information

Whitepaper. Data Warehouse/BI Testing Offering YOUR SUCCESS IS OUR FOCUS. Published on: January 2009 Author: BIBA PRACTICE

Whitepaper. Data Warehouse/BI Testing Offering YOUR SUCCESS IS OUR FOCUS. Published on: January 2009 Author: BIBA PRACTICE YOUR SUCCESS IS OUR FOCUS Whitepaper Published on: January 2009 Author: BIBA PRACTICE 2009 Hexaware Technologies. All rights reserved. Table of Contents 1. 2. Data Warehouse - Typical pain points 3. Hexaware

More information

IBM Cognos Express Essential BI and planning for midsize companies

IBM Cognos Express Essential BI and planning for midsize companies Data Sheet IBM Cognos Express Essential BI and planning for midsize companies Overview IBM Cognos Express is the first and only integrated business intelligence (BI) and planning solution purposebuilt

More information

QAD Business Intelligence Data Warehouse Demonstration Guide. May 2015 BI 3.11

QAD Business Intelligence Data Warehouse Demonstration Guide. May 2015 BI 3.11 QAD Business Intelligence Data Warehouse Demonstration Guide May 2015 BI 3.11 Overview This demonstration focuses on the foundation of QAD Business Intelligence the Data Warehouse and shows how this functionality

More information

Chapter 4 Getting Started with Business Intelligence

Chapter 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 information

IBM Cognos Performance Management Solutions for Oracle

IBM Cognos Performance Management Solutions for Oracle IBM Cognos Performance Management Solutions for Oracle Gain more value from your Oracle technology investments Highlights Deliver the power of predictive analytics across the organization Address diverse

More information

W H I T E P A P E R. Deriving Intelligence from Large Data Using Hadoop and Applying Analytics. Abstract

W H I T E P A P E R. Deriving Intelligence from Large Data Using Hadoop and Applying Analytics. Abstract W H I T E P A P E R Deriving Intelligence from Large Data Using Hadoop and Applying Analytics Abstract This white paper is focused on discussing the challenges facing large scale data processing and the

More information

Business Insight Through Cloud-based Data Models. Javier Guillen, Solutions Architect - BlueGranite

Business Insight Through Cloud-based Data Models. Javier Guillen, Solutions Architect - BlueGranite Business Insight Through Cloud-based Data Models Javier Guillen, Solutions Architect - BlueGranite What we will cover The business process associated with generating undirected business insight Possible

More information

www.ijreat.org Published by: PIONEER RESEARCH & DEVELOPMENT GROUP (www.prdg.org) 28

www.ijreat.org Published by: PIONEER RESEARCH & DEVELOPMENT GROUP (www.prdg.org) 28 Data Warehousing - Essential Element To Support Decision- Making Process In Industries Ashima Bhasin 1, Mr Manoj Kumar 2 1 Computer Science Engineering Department, 2 Associate Professor, CSE Abstract SGT

More information

Vendor briefing Business Intelligence and Analytics Platforms Gartner 15 capabilities

Vendor briefing Business Intelligence and Analytics Platforms Gartner 15 capabilities Vendor briefing Business Intelligence and Analytics Platforms Gartner 15 capabilities April, 2013 gaddsoftware.com Table of content 1. Introduction... 3 2. Vendor briefings questions and answers... 3 2.1.

More information

BI4Dynamics provides rich business intelligence capabilities to companies of all sizes and industries. From the first day on you can analyse your

BI4Dynamics provides rich business intelligence capabilities to companies of all sizes and industries. From the first day on you can analyse your BI4Dynamics provides rich business intelligence capabilities to companies of all sizes and industries. From the first day on you can analyse your data quickly, accurately and make informed decisions. Spending

More information

Traditional BI vs. Business Data Lake A comparison

Traditional BI vs. Business Data Lake A comparison Traditional BI vs. Business Data Lake A comparison The need for new thinking around data storage and analysis Traditional Business Intelligence (BI) systems provide various levels and kinds of analyses

More information

Master Data Management and Data Warehousing. Zahra Mansoori

Master Data Management and Data Warehousing. Zahra Mansoori Master Data Management and Data Warehousing Zahra Mansoori 1 1. Preference 2 IT landscape growth IT landscapes have grown into complex arrays of different systems, applications, and technologies over the

More information

Data Warehousing: A Technology Review and Update Vernon Hoffner, Ph.D., CCP EntreSoft Resouces, Inc.

Data Warehousing: A Technology Review and Update Vernon Hoffner, Ph.D., CCP EntreSoft Resouces, Inc. Warehousing: A Technology Review and Update Vernon Hoffner, Ph.D., CCP EntreSoft Resouces, Inc. Introduction Abstract warehousing has been around for over a decade. Therefore, when you read the articles

More information

ENABLING OPERATIONAL BI

ENABLING OPERATIONAL BI ENABLING OPERATIONAL BI WITH SAP DATA Satisfy the need for speed with real-time data replication Author: Eric Kavanagh, The Bloor Group Co-Founder WHITE PAPER Table of Contents The Data Challenge to Make

More information

Introduction to Oracle Business Intelligence Standard Edition One. Mike Donohue Senior Manager, Product Management Oracle Business Intelligence

Introduction to Oracle Business Intelligence Standard Edition One. Mike Donohue Senior Manager, Product Management Oracle Business Intelligence Introduction to Oracle Business Intelligence Standard Edition One Mike Donohue Senior Manager, Product Management Oracle Business Intelligence The following is intended to outline our general product direction.

More information

BIG DATA: FIVE TACTICS TO MODERNIZE YOUR DATA WAREHOUSE

BIG DATA: FIVE TACTICS TO MODERNIZE YOUR DATA WAREHOUSE BIG DATA: FIVE TACTICS TO MODERNIZE YOUR DATA WAREHOUSE Current technology for Big Data allows organizations to dramatically improve return on investment (ROI) from their existing data warehouse environment.

More information

Rapid Analytics. A visual, live approach to requirements gathering and business analytic development Mark Marinelli, VP of Product Management

Rapid Analytics. A visual, live approach to requirements gathering and business analytic development Mark Marinelli, VP of Product Management Rapid Analytics A visual, live approach to requirements gathering and business analytic development Mark Marinelli, VP of Product Management Brought to you by: Agenda Why Do Traditional Analytics Projects

More information

Big Data and Healthcare Payers WHITE PAPER

Big Data and Healthcare Payers WHITE PAPER Knowledgent White Paper Series Big Data and Healthcare Payers WHITE PAPER Summary With the implementation of the Affordable Care Act, the transition to a more member-centric relationship model, and other

More information

A New Era Of Analytic

A New Era Of Analytic Penang egovernment Seminar 2014 A New Era Of Analytic Megat Anuar Idris Head, Project Delivery, Business Analytics & Big Data Agenda Overview of Big Data Case Studies on Big Data Big Data Technology Readiness

More information

CONNECTING DATA WITH BUSINESS

CONNECTING 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 information

Getting the most out of big data

Getting the most out of big data IBM Software White Paper Financial Services Getting the most out of big data How banks can gain fresh customer insight with new big data capabilities 2 Getting the most out of big data Banks thrive on

More information

Corporate Performance Management Framework

Corporate Performance Management Framework Version 1.0 Copyright 2004 Answerport, Inc. Table of Contents Table of Contents... 2 Conceptual Overview... 3 Conceptual Overview Diagram... 4 The Foundation... 4 Analytic Presentation Layer... 5 Reports...

More information

The Influence of Master Data Management on the Enterprise Data Model

The Influence of Master Data Management on the Enterprise Data Model The Influence of Master Data Management on the Enterprise Data Model For DAMA_NY Tom Haughey InfoModel LLC 868 Woodfield Road Franklin Lakes, NJ 07417 201 755-3350 tom.haughey@infomodelusa.com Feb 19,

More information

SQL Maestro and the ELT Paradigm Shift

SQL Maestro and the ELT Paradigm Shift SQL Maestro and the ELT Paradigm Shift Abstract ELT extract, load, and transform is replacing ETL (extract, transform, load) as the usual method of populating data warehouses. Modern data warehouse appliances

More information

By Makesh Kannaiyan makesh.k@sonata-software.com 8/27/2011 1

By Makesh Kannaiyan makesh.k@sonata-software.com 8/27/2011 1 Integration between SAP BusinessObjects and Netweaver By Makesh Kannaiyan makesh.k@sonata-software.com 8/27/2011 1 Agenda Evolution of BO Business Intelligence suite Integration Integration after 4.0 release

More information

TRENDS IN THE DEVELOPMENT OF BUSINESS INTELLIGENCE SYSTEMS

TRENDS 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 information

Oracle BI Standard Edition One The Right BI Foundation for the Emerging Enterprise

<Insert Picture Here> Oracle BI Standard Edition One The Right BI Foundation for the Emerging Enterprise Oracle BI Standard Edition One The Right BI Foundation for the Emerging Enterprise Business Intelligence is the #1 Priority the most important technology in 2007 is business intelligence

More information

Integrating IBM Cognos TM1 with Oracle General Ledger

Integrating IBM Cognos TM1 with Oracle General Ledger Integrating IBM Cognos TM1 with Oracle General Ledger Highlights Streamlines the data integration process for fast and precise data loads. Enables planners to drill back into transactional data for the

More information

Data Warehousing and OLAP Technology for Knowledge Discovery

Data Warehousing and OLAP Technology for Knowledge Discovery 542 Data Warehousing and OLAP Technology for Knowledge Discovery Aparajita Suman Abstract Since time immemorial, libraries have been generating services using the knowledge stored in various repositories

More information

The Principles of the Business Data Lake

The Principles of the Business Data Lake The Principles of the Business Data Lake The Business Data Lake Culture eats Strategy for Breakfast, so said Peter Drucker, elegantly making the point that the hardest thing to change in any organization

More information

The IBM Cognos Platform for Enterprise Business Intelligence

The IBM Cognos Platform for Enterprise Business Intelligence The IBM Cognos Platform for Enterprise Business Intelligence Highlights Optimize performance with in-memory processing and architecture enhancements Maximize the benefits of deploying business analytics

More information

Oracle9i Data Warehouse Review. Robert F. Edwards Dulcian, Inc.

Oracle9i Data Warehouse Review. Robert F. Edwards Dulcian, Inc. Oracle9i Data Warehouse Review Robert F. Edwards Dulcian, Inc. Agenda Oracle9i Server OLAP Server Analytical SQL Data Mining ETL Warehouse Builder 3i Oracle 9i Server Overview 9i Server = Data Warehouse

More information

Beyond the Single View with IBM InfoSphere

Beyond the Single View with IBM InfoSphere Ian Bowring MDM & Information Integration Sales Leader, NE Europe Beyond the Single View with IBM InfoSphere We are at a pivotal point with our information intensive projects 10-40% of each initiative

More information

IBM Cognos Insight. Independently explore, visualize, model and share insights without IT assistance. Highlights. IBM Software Business Analytics

IBM Cognos Insight. Independently explore, visualize, model and share insights without IT assistance. Highlights. IBM Software Business Analytics Independently explore, visualize, model and share insights without IT assistance Highlights Explore, analyze, visualize and share your insights independently, without relying on IT for assistance. Work

More information

Turning Big Data into Big Insights

Turning Big Data into Big Insights mwd a d v i s o r s Turning Big Data into Big Insights Helena Schwenk A special report prepared for Actuate May 2013 This report is the fourth in a series and focuses principally on explaining what s needed

More information

ORACLE UTILITIES ANALYTICS

ORACLE UTILITIES ANALYTICS ORACLE UTILITIES ANALYTICS TRANSFORMING COMPLEX DATA INTO BUSINESS VALUE UTILITIES FOCUS ON ANALYTICS Aging infrastructure. Escalating customer expectations. Demand growth. The challenges are many. And

More information

Blueprints for Big Data Success

Blueprints for Big Data Success Blueprints for Big Data Success Succeeding with Four Common Scenarios Copyright 2015 Pentaho Corporation. Redistribution permitted. All trademarks are the property of their respective owners. For the latest

More information

Calculating ROI for Business Intelligence Solutions in Small and Mid-Sized Businesses

Calculating ROI for Business Intelligence Solutions in Small and Mid-Sized Businesses Calculating ROI for Business Intelligence Solutions in Small and Mid-Sized Businesses Introduction Successful business intelligence implementations can unlock key information within a company s data vaults

More information

BUSINESS ANALYTICS UNVEILED

BUSINESS ANALYTICS UNVEILED BUSINESS ANALYTICS UNVEILED Presented By Thomas Wong Chief Operating Officer Cetheus MSC Berhad Presentation Agenda What is driving analytic applications? Why enterprise analytic applications? Cetheus

More information

Data Mart/Warehouse: Progress and Vision

Data Mart/Warehouse: Progress and Vision Data Mart/Warehouse: Progress and Vision Institutional Research and Planning University Information Systems What is data warehousing? A data warehouse: is a single place that contains complete, accurate

More information

Business Intelligence Data Warehousing Services

Business Intelligence Data Warehousing Services Business Intelligence Data Warehousing Services Our BI DW Services Exponential growth in volume of data and information with over 85% being unstructured, the complexity arising from disparate information

More information

POLAR IT SERVICES. Business Intelligence Project Methodology

POLAR IT SERVICES. Business Intelligence Project Methodology POLAR IT SERVICES Business Intelligence Project Methodology Table of Contents 1. Overview... 2 2. Visualize... 3 3. Planning and Architecture... 4 3.1 Define Requirements... 4 3.1.1 Define Attributes...

More information

Data Warehousing and Data Mining Introduction

Data Warehousing and Data Mining Introduction Data Warehousing and Data Mining Introduction General introduction to DWDM Business intelligence OLTP vs. OLAP Data integration Methodological framework DW definition Acknowledgements: I am indebted to

More information

Large Telecommunications Company Gains Full Customer View, Boosts Monthly Revenue, Cuts IT Costs by $3 Million

Large Telecommunications Company Gains Full Customer View, Boosts Monthly Revenue, Cuts IT Costs by $3 Million Microsoft Business Intelligence Customer Solution Case Study Large Telecommunications Company Gains Full Customer View, Boosts Monthly Revenue, Cuts IT Costs by $3 Million Overview Country or Region: United

More information

Anatomy of a Decision

Anatomy of a Decision research@bluehillresearch.com @BlueHillBoston 617.624.3600 Anatomy of a Decision BI Platform vs. Tool: Choosing Birst Over Tableau for Enterprise Business Intelligence Needs What You Need To Know The demand

More information

ElegantJ BI. White Paper. Key Performance Indicators (KPI) A Critical Component of Enterprise Business Intelligence (BI)

ElegantJ BI. White Paper. Key Performance Indicators (KPI) A Critical Component of Enterprise Business Intelligence (BI) ElegantJ BI White Paper Key Performance Indicators (KPI) A Critical Component of Enterprise Business Intelligence (BI) Integrated Business Intelligence and Reporting for Performance Management, Operational

More information

Effecting Data Quality Improvement through Data Virtualization

Effecting Data Quality Improvement through Data Virtualization Effecting Data Quality Improvement through Data Virtualization Prepared for Composite Software by: David Loshin Knowledge Integrity, Inc. June, 2010 2010 Knowledge Integrity, Inc. Page 1 Introduction The

More information

Data Mining + Business Intelligence. Integration, Design and Implementation

Data Mining + Business Intelligence. Integration, Design and Implementation Data Mining + Business Intelligence Integration, Design and Implementation ABOUT ME Vijay Kotu Data, Business, Technology, Statistics BUSINESS INTELLIGENCE - Result Making data accessible Wider distribution

More information

A business intelligence agenda for midsize organizations: Six strategies for success

A business intelligence agenda for midsize organizations: Six strategies for success IBM Software Business Analytics IBM Cognos Business Intelligence A business intelligence agenda for midsize organizations: Six strategies for success A business intelligence agenda for midsize organizations:

More information

Make the right decisions with Distribution Intelligence

Make the right decisions with Distribution Intelligence Make the right decisions with Distribution Intelligence Bengt Jensfelt, Business Product Manager, Distribution Intelligence, April 2010 Introduction It is not so very long ago that most companies made

More information

Business Intelligence Solution for Small and Midsize Enterprises (BI4SME)

Business Intelligence Solution for Small and Midsize Enterprises (BI4SME) Business Intelligence Solution for Small and Midsize Enterprises (BI4SME) Preface Not only large Enterprises can benefit from the advantages of Business Intelligence (BI) Solutions. BI4SME is a cost efficient,

More information

Practical Considerations for Real-Time Business Intelligence. Donovan Schneider Yahoo! September 11, 2006

Practical Considerations for Real-Time Business Intelligence. Donovan Schneider Yahoo! September 11, 2006 Practical Considerations for Real-Time Business Intelligence Donovan Schneider Yahoo! September 11, 2006 Outline Business Intelligence (BI) Background Real-Time Business Intelligence Examples Two Requirements

More information

Innovate and Grow: SAP and Teradata

Innovate and Grow: SAP and Teradata Partners Innovate and Grow: SAP and Teradata Lily Gulik, Teradata Director, SAP Center of Excellence Wayne Boyle, Chief Technology Officer Strategy, Teradata R&D Table of Contents Introduction: The Integrated

More information

Vertical Data Warehouse Solutions for Financial Services

Vertical Data Warehouse Solutions for Financial Services Decision Framework, M. Knox Research Note 24 July 2003 Vertical Data Warehouse Solutions for Financial Services Packaged DW financial services solutions differ in degree of and approach to verticalization,

More information