Business analytics applies analytical techniques to create insightful resolutions to business issues, driving cost savings and innovation. It provides frameworks for decision-making, improving performance and anticipating change while managing risk. By going beyond apparent features to better understand customer behavior, business analytics enhances decision-making and surfaces hidden insights from large amounts of data.
The document discusses challenges facing the financial services industry and how analytics can help address them. It outlines five trends creating difficulties: 1) slowing growth, 2) increased regulation, 3) shifting customer behavior, 4) heightened risk management needs, and 5) new competitors. It then presents a methodology using causal clarity and five profit engines to help companies adapt: 1) customer lifetime value analysis, 2) customer retention, 3) cross-selling, 4) marketing ROI, and 5) new product design. Finally, it stresses the importance of integrating these analytical approaches rather than using them in isolation.
Ast 0127927 anatomy-of_an_analytic_enterprise_107111_0514Luqman Hakim
This document discusses the key characteristics of an analytic enterprise. It argues that simply collecting large amounts of data is not enough - organizations must learn to use data effectively to drive decisions. Becoming truly analytic requires developing an "analytic mindset" by overcoming inherent biases. Memories, both semantic knowledge of concepts and episodic memories of past experiences, can act as barriers unless an organization fosters a collaborative culture of challenging assumptions and following evidence. Developing analytic skills and processes enables data-driven decision making across the organization.
Accenture: Outlook What C Suite Should Know About Analytics 2011Brian Crotty
This article discusses how analytics will fundamentally change business decision making in the future. It argues that analytics will establish an empirical, data-driven basis for decisions at all levels of an organization, replacing many decisions currently made based on assumptions without evidence. The article outlines five dimensions along which companies of the future will operate differently due to sophisticated use of analytics: high analytical literacy, volatility, fine-grained decision making, integrated awareness across data silos, and responsibility for outcomes informed by all available data.
1. Competitive intelligence is highly specific and timely information about a corporation that has been analyzed to the point of making decisions.
2. Competitive intelligence is a tool that provides early warnings about threats and opportunities to alert management.
3. Competitive intelligence involves gathering information from a variety of primary and secondary sources.
Analytics applications are designed to measure, predict, and optimize business performance; they are used to analyze specific data related to particular aspects of a business. This paper discusses how Pivotal CRM Analytics can help companies across a range of industries improve their effectiveness through practical, low-cost CRM analytics applications.
Giving Organisations new capabilities to ask the right business questions 1.7OReillyStrata
This presentation takes the seminal work structured analytic techniques work pioneered within US intelligence, and proposes adaptions and simplifications for use within commercial enterprises
This document provides guidance to finance executives on leveraging advanced analytics. It begins with an overview of the rise of advanced analytics and its potential value of over $1 trillion for businesses worldwide. It then describes the different stages companies can be at in their analytics adaptation - from laggard to adopter to leader. The rest of the document details the six key components of a holistic analytics strategy and provides a checklist of recommendations for each stage of adaptation to help companies advance their analytics capabilities.
The document discusses challenges facing the financial services industry and how analytics can help address them. It outlines five trends creating difficulties: 1) slowing growth, 2) increased regulation, 3) shifting customer behavior, 4) heightened risk management needs, and 5) new competitors. It then presents a methodology using causal clarity and five profit engines to help companies adapt: 1) customer lifetime value analysis, 2) customer retention, 3) cross-selling, 4) marketing ROI, and 5) new product design. Finally, it stresses the importance of integrating these analytical approaches rather than using them in isolation.
Ast 0127927 anatomy-of_an_analytic_enterprise_107111_0514Luqman Hakim
This document discusses the key characteristics of an analytic enterprise. It argues that simply collecting large amounts of data is not enough - organizations must learn to use data effectively to drive decisions. Becoming truly analytic requires developing an "analytic mindset" by overcoming inherent biases. Memories, both semantic knowledge of concepts and episodic memories of past experiences, can act as barriers unless an organization fosters a collaborative culture of challenging assumptions and following evidence. Developing analytic skills and processes enables data-driven decision making across the organization.
Accenture: Outlook What C Suite Should Know About Analytics 2011Brian Crotty
This article discusses how analytics will fundamentally change business decision making in the future. It argues that analytics will establish an empirical, data-driven basis for decisions at all levels of an organization, replacing many decisions currently made based on assumptions without evidence. The article outlines five dimensions along which companies of the future will operate differently due to sophisticated use of analytics: high analytical literacy, volatility, fine-grained decision making, integrated awareness across data silos, and responsibility for outcomes informed by all available data.
1. Competitive intelligence is highly specific and timely information about a corporation that has been analyzed to the point of making decisions.
2. Competitive intelligence is a tool that provides early warnings about threats and opportunities to alert management.
3. Competitive intelligence involves gathering information from a variety of primary and secondary sources.
Analytics applications are designed to measure, predict, and optimize business performance; they are used to analyze specific data related to particular aspects of a business. This paper discusses how Pivotal CRM Analytics can help companies across a range of industries improve their effectiveness through practical, low-cost CRM analytics applications.
Giving Organisations new capabilities to ask the right business questions 1.7OReillyStrata
This presentation takes the seminal work structured analytic techniques work pioneered within US intelligence, and proposes adaptions and simplifications for use within commercial enterprises
This document provides guidance to finance executives on leveraging advanced analytics. It begins with an overview of the rise of advanced analytics and its potential value of over $1 trillion for businesses worldwide. It then describes the different stages companies can be at in their analytics adaptation - from laggard to adopter to leader. The rest of the document details the six key components of a holistic analytics strategy and provides a checklist of recommendations for each stage of adaptation to help companies advance their analytics capabilities.
The document discusses the business value of predictive analytics based on research from IBM and IDC. Some key points:
1) Predictive analytics can significantly increase business metrics like marketing offer acceptance rates by 300% and identify fraudulent claims 30 days faster.
2) The ROI of predictive analytics projects is around 250%, significantly higher than non-predictive projects. Predictive analytics provide sustained benefits over time.
3) Case studies show how predictive analytics helped an asset manager increase revenues, an insurer reduce costs and fraud, and a communications company improve customer satisfaction.
Analytics in Financial Services: Keynote Presentation for TDWI and NY Tech Co...Fitzgerald Analytics, Inc.
Keynote Presentation Given in New York City on March 30th, at a joint event of The Data Warehousing Institute (TDWI) and the New York Technology Council. This keynote presentation by Jaime Fitzgerald focused on "Bridging the Gap" between business goals in the data and analytic enablers of achieving these goals.
A new white paper from Forte Consultancy Group company Forte Wares, about a new and wholly unique approach to business reporting – “Pro-active Reporting" – enabled by the Wares solution, CIWare.
Customer Intelligence & Analytics - Part IVivastream
This document discusses how the field of marketing analytics is evolving due to the explosion of available data and increased use of analytics. It describes how companies now use analytics throughout their operations to gain insights from data and make better decisions. The document also outlines some common areas where companies apply analytics, such as customer acquisition, pricing, and forecasting. It cautions that analytics must be implemented carefully and should be guided by the data rather than preconceptions to avoid bias.
Ontonix Complexity Measurement and Predictive Analytics WP Oct 2013Datonix.it
Breakthrough analytics for your business. Ontonix model-free and patented technology is used for advanced BI, Risk and Business Governance Management. Discover the big picture from all structured business process and discover the hidden fragility an what your options are to fix it.
Do not measure the wrong KPI - we automatically discover the native and intrinsic key performance indicators for you.
What is Competitive Intelligence (CI) and What It Should IncludeTrainOurTroops.org
* Online course: https://www.voiceofthebusinessacademy.com/course/what-competitive-intelligence-ci-and-what-it-should-include
While market research often focuses on fulfilling specific information needs, Competitive Intelligence is an ongoing process of developing a holistic analysis of your organizational environment. “Competitive” Intelligence is not “Competitor” Intelligence. Competitive Intelligence can focus on a specific aspect of Competitor Intelligence, but it can also focus on other disciplines such as products, customers, employees, lost prospects, marketing, sales or environmental aspects. Competitive Intelligence provides organizations with a competitive advantage in any of those disciplines when a structured program is properly implemented, managed and maintained.
Competitive Intelligence is not market research, nor is it espionage, nor is it simply doing internet searches. It is a completely legal and an ever increasingly essential element in allowing organizations to make more effective decisions on both strategic and tactical initiatives. Competitive Intelligence also provides vital insights and serves as an early warning of future events, which uncovers positive or negative impacts to your organization.
One of the most important aspects of implementing any intelligence program is the ability to convert data and information into actionable intelligence. It is often said that information costs you money, while intelligence makes you money. In order for that to happen the data and information obtained has to be strategic, unbiased, measurable, actionable, and above all, repeatable. Without that foundation in-place, intelligence programs are much more susceptible to failure.
This webinar will uncover and discuss all aspects an organization should understand and consider when it comes to what Competitive Intelligence is comprised of and all the different facets that it can be leveraged for.
A brief introduction to Competitive IntelligenceNeharica Sharma
Competitive intelligence (CI) involves ethically gathering information about competitors to make strategic business decisions. CI data provides context about a company's performance and industry trends for insights. It allows companies to anticipate rather than just react to market changes. CI is done by collecting information, analyzing it alongside other data, communicating findings, and countering competitors. While many companies utilize CI, some are ignorant of its benefits or overconfident in their own strategies. Regular CI analysis of competitors is important for any organization's long-term success.
Turning Customer Interactions Into Money White PaperJeffrey Katz
This white paper discusses how companies can use predictive analytics to achieve stellar returns on investment by turning customer interactions into increased profits. It finds that companies successfully using predictive analytics have well-defined goals, measure both direct and indirect ROI benefits, and make analytic results easy for all employees to access and act on. The paper profiles several companies that have improved customer retention and profits through predictive analytics approaches.
Tighten up the Ship or Build an Airplane? - How to decide?Robert Brown
Dr. Bush and I describe our experience applying the principles of decision analysis to small business, which typically do not have access to as many informed resources as larger organizations where decision analysis is more routinely applied.Published in "Decision Analysis Today," newsletter of the INFORMS Decision Analysis Society, Volume 29, No. 1, April 2010, pg. 16.
IBM Business Analytics and Optimization - Introduktion till Prediktiv AnalysIBM Sverige
This document introduces predictive analytics and how it can improve decision making. It discusses how predictive analytics uses historical data patterns to make accurate predictions about current conditions and future events. This allows decisions to be made based on evidence rather than intuition. Examples are given of how predictive analytics has been used to reduce costs, increase sales and reduce customer churn. The document also outlines how IBM SPSS predictive software links different data sources into intelligence that can be used to target marketing campaigns, optimize product mix decisions and conduct proactive customer retention efforts.
Business Process Analytics Unlocking the Power of Data and Analytics: Transfo...Capgemini
According our 2012 survey with the the Economist Intelligence Unit, one of the biggest impediments to effective decision making using big data is a shortage of skilled analysts. Capgemini’s BPO Analytics solution can help fill this gap with a centrally-operated managed service that provides the expertise and tools to analyze and interpret trends within and across business processes. We have a team of professionals with both analytical skills and domain expertise along with a flexible, multi-purpose technology platform to convert multiple sources of unstructured data into one structured view. This combination provides clients with a centralized function for transforming vast amounts of data into meaningful insights.
This document provides an overview of data analytics including:
- The basics of data analytics including analytics definitions and the need for data analytics due to increasing data volumes.
- Descriptions of different types of analytics including descriptive, diagnostic, predictive, and prescriptive analytics and their purposes.
- An overview of the data analytics lifecycle including phases such as data preparation, model planning, model building, and communication of results.
Lean Analytics for Intrapreneurs (Lean Startup Conf 2013)Lean Analytics
This document provides an introduction to Lean Analytics for intrapreneurs. It begins with two key lessons: companies die when they fail to adopt new business models, and the difference between a rogue agent and special operative is permission. It then discusses Lean Analytics fundamentals like good metrics being understandable, comparative, ratios or rates, and behavior changing. It covers qualitative vs quantitative data, exploratory vs reporting analytics, and examples of leading metrics. The document emphasizes focusing on one metric that matters for a given business model and stage. It provides examples of analytics baselines for growth, engagement, churn, and calculating customer lifetime value.
CUSTOMER JOURNEY
Der Weg des Kunden zum Produkt umspannt Touchpoints in Online, Mobile, In-Store, Kundencenter, sozialen Netzen, Werbung im TV, Print, auf Plakaten, im Radio – einfach gesagt: Er sieht alles und das überall. Wie man dem Konsumenten trotzdem ein sinnvolles Bild der Marke gibt und ein unverwechselbares Angebot schafft. Wir begleiten den Konsumenten auf seinem Weg zum Kauf.
Matthew has a consistent record of under-achievement. His professional incompetence, intellectual torpor and general sloth are complemented by appalling communication skills. He is widely regarded as the most dysfunctional and unpopular member of any team he has ever worked in.
Through a combination of incompetence and corruption on the part of the admissions board, he was elected to a scholarship at Magdalen College Oxford, where he read English and Modern Languages. His undistinguished academic career was a surprise and disappointment to all except those who knew him.
His initial career in the textile industry was marked by a series of over-hyped marketing transformation initiatives, which delivered no business value and alienated customers and employees alike. On the back of these disastrous projects, there was nowhere to hide but the software industry.
His subsequent career in software has been one of spectacular failure and meteoric descent. In a series of ill-judged and unimaginative moves, he has helped destroy the value of marketing applications right across the industry. It is difficult to gauge which has been less impressive, his time in product management or in presales consulting. At every level, he has exuded arrogance and incompetence.
His promotion to Vice President at Oracle in December 2011 was welcomed by close colleagues as ‘an utter travesty’ and hailed as ‘a clear mistake’. With his new broader focus on Customer Experience Management solutions, across a wide range of industries – encompassing all of Oracle applications and technologies – he has the opportunity to wreak havoc on a truly global scale. It is a worrying new juncture in a dismal career.
Matthew’s self-knowledge is his only redeeming feature.
Analytics, business cycles and disruptionsMark Albala
The digital economy is different. Depending on platforms and a much more malleable set of methods to interact with consumers, an accelerated rate of disruptions compromises the orderly business experience of most market participants. A well-honed analytics program facilitates understanding these accelerated disruptions. With a platform based digital marketplace, obtaining the information necessary to decipher unexpected outcomes and prescribe suitable actions is difficult because the information required Both of these facts are important to analytics. First, platforms. Platform based activity is hard to decipher, not because it is more complex but because the information needed to decipher activity is not contained within your four walls.
Once deciphered, the next challenge facing organizations deciphering unexpected outcomes is a determination of whether the unexpected outcome is truly a disruptive event or simply a phase change in a regularly occurring business cycle. There are significant differences in the suitable reactions to disruptions and business cycle phase changes. Unfortunately, many organizations are ill equipped to discern between these two classes of unexpected business outcomes and consistently find their business plans fall victim to the actions of others within the marketplace.
Luckily, many of the activities of governmental and regulatory bodies are focused on predicting phase changes to the business cycles likely to impact the economic forces within the next fiscal year and describe their economic policies and agendas in publicly available documents and analysis. Understanding where to find these documents and how to use the published to discern between the likely business cycle phase changes and true disruptions as one of the vehicles available within your arsenal of analytics will lessen the occurrence of falling victim in the marketplace by misreading the clues available from unexpected outcomes. This document will address the sources most likely to assist and the actions to be taken to utilize the information attained from these documents.
Et si dans dix ans les agences remplaçaient les planneurs stratégiques par des logiciels de « génération de langage naturel » ? La question est encore un peu extrapolée certes, mais la production robotique de rapport est déjà une réalité. La preuve aux Etats-Unis avec Quill.
Business is running ever faster—generating, collecting and using increas-ing volumes of data about every aspect of the interactions between sup-pliers, manufacturers, retailers and customers. Within these mountains of data are seams of gold—patterns of behavior that can be interpreted, classified and analyzed to allow predictions of real value. Which treat-ment is likely to be most effective for this patient? What can we offer that this particular customer is more likely to buy? Can we identify if that transaction is fraudulent before the sale is closed?
Business Intelligence: Realizing the Benefits of a Data-Driven JourneyRob Williams
1) The document discusses how adopting a data-driven approach and embracing business intelligence (BI) tools and analytics can help improve decision-making, safety performance, and quality. It outlines a three-stage maturity model for developing BI capabilities for environmental, health, safety, and quality (EHSQ) functions.
2) Stage 1 involves basic, compliance-focused data collection and reporting. Stage 2 incorporates more systematic data analysis to understand why issues occur. Stage 3 advances to predictive analytics using machine learning and embedded insights. The document provides examples of how data-driven insights can predict failures and optimize processes.
Data science and data analytics professionals enable organizations to utilize the potential of predictive analytics to make informed decisions & help in transforming analytics maturity model of the organization.
Visual and wizard-driven paradigms for analytics can empower more business users to explore data and develop analytic workflows without extensive coding expertise. The webinar demonstrated how SAS solutions provide intuitive visual discovery of data, visual programming to develop analytic workflows through a drag-and-drop interface, and guided wizards for model development. These capabilities make analytics more accessible, help spread capabilities across organizations, and free quantitative experts to focus on more complex issues.
The document discusses the business value of predictive analytics based on research from IBM and IDC. Some key points:
1) Predictive analytics can significantly increase business metrics like marketing offer acceptance rates by 300% and identify fraudulent claims 30 days faster.
2) The ROI of predictive analytics projects is around 250%, significantly higher than non-predictive projects. Predictive analytics provide sustained benefits over time.
3) Case studies show how predictive analytics helped an asset manager increase revenues, an insurer reduce costs and fraud, and a communications company improve customer satisfaction.
Analytics in Financial Services: Keynote Presentation for TDWI and NY Tech Co...Fitzgerald Analytics, Inc.
Keynote Presentation Given in New York City on March 30th, at a joint event of The Data Warehousing Institute (TDWI) and the New York Technology Council. This keynote presentation by Jaime Fitzgerald focused on "Bridging the Gap" between business goals in the data and analytic enablers of achieving these goals.
A new white paper from Forte Consultancy Group company Forte Wares, about a new and wholly unique approach to business reporting – “Pro-active Reporting" – enabled by the Wares solution, CIWare.
Customer Intelligence & Analytics - Part IVivastream
This document discusses how the field of marketing analytics is evolving due to the explosion of available data and increased use of analytics. It describes how companies now use analytics throughout their operations to gain insights from data and make better decisions. The document also outlines some common areas where companies apply analytics, such as customer acquisition, pricing, and forecasting. It cautions that analytics must be implemented carefully and should be guided by the data rather than preconceptions to avoid bias.
Ontonix Complexity Measurement and Predictive Analytics WP Oct 2013Datonix.it
Breakthrough analytics for your business. Ontonix model-free and patented technology is used for advanced BI, Risk and Business Governance Management. Discover the big picture from all structured business process and discover the hidden fragility an what your options are to fix it.
Do not measure the wrong KPI - we automatically discover the native and intrinsic key performance indicators for you.
What is Competitive Intelligence (CI) and What It Should IncludeTrainOurTroops.org
* Online course: https://www.voiceofthebusinessacademy.com/course/what-competitive-intelligence-ci-and-what-it-should-include
While market research often focuses on fulfilling specific information needs, Competitive Intelligence is an ongoing process of developing a holistic analysis of your organizational environment. “Competitive” Intelligence is not “Competitor” Intelligence. Competitive Intelligence can focus on a specific aspect of Competitor Intelligence, but it can also focus on other disciplines such as products, customers, employees, lost prospects, marketing, sales or environmental aspects. Competitive Intelligence provides organizations with a competitive advantage in any of those disciplines when a structured program is properly implemented, managed and maintained.
Competitive Intelligence is not market research, nor is it espionage, nor is it simply doing internet searches. It is a completely legal and an ever increasingly essential element in allowing organizations to make more effective decisions on both strategic and tactical initiatives. Competitive Intelligence also provides vital insights and serves as an early warning of future events, which uncovers positive or negative impacts to your organization.
One of the most important aspects of implementing any intelligence program is the ability to convert data and information into actionable intelligence. It is often said that information costs you money, while intelligence makes you money. In order for that to happen the data and information obtained has to be strategic, unbiased, measurable, actionable, and above all, repeatable. Without that foundation in-place, intelligence programs are much more susceptible to failure.
This webinar will uncover and discuss all aspects an organization should understand and consider when it comes to what Competitive Intelligence is comprised of and all the different facets that it can be leveraged for.
A brief introduction to Competitive IntelligenceNeharica Sharma
Competitive intelligence (CI) involves ethically gathering information about competitors to make strategic business decisions. CI data provides context about a company's performance and industry trends for insights. It allows companies to anticipate rather than just react to market changes. CI is done by collecting information, analyzing it alongside other data, communicating findings, and countering competitors. While many companies utilize CI, some are ignorant of its benefits or overconfident in their own strategies. Regular CI analysis of competitors is important for any organization's long-term success.
Turning Customer Interactions Into Money White PaperJeffrey Katz
This white paper discusses how companies can use predictive analytics to achieve stellar returns on investment by turning customer interactions into increased profits. It finds that companies successfully using predictive analytics have well-defined goals, measure both direct and indirect ROI benefits, and make analytic results easy for all employees to access and act on. The paper profiles several companies that have improved customer retention and profits through predictive analytics approaches.
Tighten up the Ship or Build an Airplane? - How to decide?Robert Brown
Dr. Bush and I describe our experience applying the principles of decision analysis to small business, which typically do not have access to as many informed resources as larger organizations where decision analysis is more routinely applied.Published in "Decision Analysis Today," newsletter of the INFORMS Decision Analysis Society, Volume 29, No. 1, April 2010, pg. 16.
IBM Business Analytics and Optimization - Introduktion till Prediktiv AnalysIBM Sverige
This document introduces predictive analytics and how it can improve decision making. It discusses how predictive analytics uses historical data patterns to make accurate predictions about current conditions and future events. This allows decisions to be made based on evidence rather than intuition. Examples are given of how predictive analytics has been used to reduce costs, increase sales and reduce customer churn. The document also outlines how IBM SPSS predictive software links different data sources into intelligence that can be used to target marketing campaigns, optimize product mix decisions and conduct proactive customer retention efforts.
Business Process Analytics Unlocking the Power of Data and Analytics: Transfo...Capgemini
According our 2012 survey with the the Economist Intelligence Unit, one of the biggest impediments to effective decision making using big data is a shortage of skilled analysts. Capgemini’s BPO Analytics solution can help fill this gap with a centrally-operated managed service that provides the expertise and tools to analyze and interpret trends within and across business processes. We have a team of professionals with both analytical skills and domain expertise along with a flexible, multi-purpose technology platform to convert multiple sources of unstructured data into one structured view. This combination provides clients with a centralized function for transforming vast amounts of data into meaningful insights.
This document provides an overview of data analytics including:
- The basics of data analytics including analytics definitions and the need for data analytics due to increasing data volumes.
- Descriptions of different types of analytics including descriptive, diagnostic, predictive, and prescriptive analytics and their purposes.
- An overview of the data analytics lifecycle including phases such as data preparation, model planning, model building, and communication of results.
Lean Analytics for Intrapreneurs (Lean Startup Conf 2013)Lean Analytics
This document provides an introduction to Lean Analytics for intrapreneurs. It begins with two key lessons: companies die when they fail to adopt new business models, and the difference between a rogue agent and special operative is permission. It then discusses Lean Analytics fundamentals like good metrics being understandable, comparative, ratios or rates, and behavior changing. It covers qualitative vs quantitative data, exploratory vs reporting analytics, and examples of leading metrics. The document emphasizes focusing on one metric that matters for a given business model and stage. It provides examples of analytics baselines for growth, engagement, churn, and calculating customer lifetime value.
CUSTOMER JOURNEY
Der Weg des Kunden zum Produkt umspannt Touchpoints in Online, Mobile, In-Store, Kundencenter, sozialen Netzen, Werbung im TV, Print, auf Plakaten, im Radio – einfach gesagt: Er sieht alles und das überall. Wie man dem Konsumenten trotzdem ein sinnvolles Bild der Marke gibt und ein unverwechselbares Angebot schafft. Wir begleiten den Konsumenten auf seinem Weg zum Kauf.
Matthew has a consistent record of under-achievement. His professional incompetence, intellectual torpor and general sloth are complemented by appalling communication skills. He is widely regarded as the most dysfunctional and unpopular member of any team he has ever worked in.
Through a combination of incompetence and corruption on the part of the admissions board, he was elected to a scholarship at Magdalen College Oxford, where he read English and Modern Languages. His undistinguished academic career was a surprise and disappointment to all except those who knew him.
His initial career in the textile industry was marked by a series of over-hyped marketing transformation initiatives, which delivered no business value and alienated customers and employees alike. On the back of these disastrous projects, there was nowhere to hide but the software industry.
His subsequent career in software has been one of spectacular failure and meteoric descent. In a series of ill-judged and unimaginative moves, he has helped destroy the value of marketing applications right across the industry. It is difficult to gauge which has been less impressive, his time in product management or in presales consulting. At every level, he has exuded arrogance and incompetence.
His promotion to Vice President at Oracle in December 2011 was welcomed by close colleagues as ‘an utter travesty’ and hailed as ‘a clear mistake’. With his new broader focus on Customer Experience Management solutions, across a wide range of industries – encompassing all of Oracle applications and technologies – he has the opportunity to wreak havoc on a truly global scale. It is a worrying new juncture in a dismal career.
Matthew’s self-knowledge is his only redeeming feature.
Analytics, business cycles and disruptionsMark Albala
The digital economy is different. Depending on platforms and a much more malleable set of methods to interact with consumers, an accelerated rate of disruptions compromises the orderly business experience of most market participants. A well-honed analytics program facilitates understanding these accelerated disruptions. With a platform based digital marketplace, obtaining the information necessary to decipher unexpected outcomes and prescribe suitable actions is difficult because the information required Both of these facts are important to analytics. First, platforms. Platform based activity is hard to decipher, not because it is more complex but because the information needed to decipher activity is not contained within your four walls.
Once deciphered, the next challenge facing organizations deciphering unexpected outcomes is a determination of whether the unexpected outcome is truly a disruptive event or simply a phase change in a regularly occurring business cycle. There are significant differences in the suitable reactions to disruptions and business cycle phase changes. Unfortunately, many organizations are ill equipped to discern between these two classes of unexpected business outcomes and consistently find their business plans fall victim to the actions of others within the marketplace.
Luckily, many of the activities of governmental and regulatory bodies are focused on predicting phase changes to the business cycles likely to impact the economic forces within the next fiscal year and describe their economic policies and agendas in publicly available documents and analysis. Understanding where to find these documents and how to use the published to discern between the likely business cycle phase changes and true disruptions as one of the vehicles available within your arsenal of analytics will lessen the occurrence of falling victim in the marketplace by misreading the clues available from unexpected outcomes. This document will address the sources most likely to assist and the actions to be taken to utilize the information attained from these documents.
Et si dans dix ans les agences remplaçaient les planneurs stratégiques par des logiciels de « génération de langage naturel » ? La question est encore un peu extrapolée certes, mais la production robotique de rapport est déjà une réalité. La preuve aux Etats-Unis avec Quill.
Business is running ever faster—generating, collecting and using increas-ing volumes of data about every aspect of the interactions between sup-pliers, manufacturers, retailers and customers. Within these mountains of data are seams of gold—patterns of behavior that can be interpreted, classified and analyzed to allow predictions of real value. Which treat-ment is likely to be most effective for this patient? What can we offer that this particular customer is more likely to buy? Can we identify if that transaction is fraudulent before the sale is closed?
Business Intelligence: Realizing the Benefits of a Data-Driven JourneyRob Williams
1) The document discusses how adopting a data-driven approach and embracing business intelligence (BI) tools and analytics can help improve decision-making, safety performance, and quality. It outlines a three-stage maturity model for developing BI capabilities for environmental, health, safety, and quality (EHSQ) functions.
2) Stage 1 involves basic, compliance-focused data collection and reporting. Stage 2 incorporates more systematic data analysis to understand why issues occur. Stage 3 advances to predictive analytics using machine learning and embedded insights. The document provides examples of how data-driven insights can predict failures and optimize processes.
Data science and data analytics professionals enable organizations to utilize the potential of predictive analytics to make informed decisions & help in transforming analytics maturity model of the organization.
Visual and wizard-driven paradigms for analytics can empower more business users to explore data and develop analytic workflows without extensive coding expertise. The webinar demonstrated how SAS solutions provide intuitive visual discovery of data, visual programming to develop analytic workflows through a drag-and-drop interface, and guided wizards for model development. These capabilities make analytics more accessible, help spread capabilities across organizations, and free quantitative experts to focus on more complex issues.
Business is running ever faster—generating, collecting and using increas-ing volumes of data about every aspect of the interactions between sup-pliers, manufacturers, retailers and customers. Within these mountains of data are seams of gold—patterns of behavior that can be interpreted, classified and analyzed to allow predictions of real value. Which treat-ment is likely to be most effective for this patient? What can we offer that this particular customer is more likely to buy? Can we identify if that transaction is fraudulent before the sale is closed?
This document provides an overview of business analytics concepts. It defines business analytics as the skills, technologies, and practices used to explore past business performance and gain insights to drive planning. The document outlines a business analytics model with layers moving from strategic goals to technical data sources. It describes types of analytics including descriptive, predictive, and prescriptive. Finally, it discusses the importance of business analytics in decision making, understanding data, providing results, and managing data.
This document provides an introduction to data literacy for beginners. It defines key terms like data science, data analytics, and data literacy. It explains that data science involves building and structuring datasets, while data analytics refers to analyzing data to gain insights. The document then covers foundational concepts like the data ecosystem and lifecycle, data privacy and ethics, and data integrity. Finally, it discusses seven skills needed for data and analytics success, such as critical thinking, data visualization, and machine learning, and how readers can improve their skills. The overall document aims to give beginners a foundational understanding of data concepts to build their data literacy.
The purpose of diagnostic analytics is to give a business more actionable information than descriptive analytics alone. It enables you to find out why something is working (or not working), allowing you to correct any wrong assumptions you may have had. The benefits of diagnostic analytics include:
Greater insights: It allows for deeper insights into your data when used in conjunction with other types of data analytics.
Forming and testing hypotheses: Having evidence of what has previously happened helps businesses to form and test new hypotheses more easily.
Identifying anomalies: Diagnostic analytics helps you determine whether data outliers were one-off anomalies or useful, significant findings.
Avoiding future mistakes: It helps you identify when and where something didn’t perform well, enabling you to improve efficiency, reduce waste, and avoid costly mistakes.
Ease of understanding: Diagnostic findings are generally simple to understand, and once they have been turned into data visualizations, they can be easily shared with stakeholders.
Limitations of diagnostic analytics
One limitation of diagnostic analytics is that it is easy to mistake correlation for causation. For example, there is a correlation between ice cream sales and bee stings, but that doesn’t mean that one caused the other. They are in fact both dependent on a third factor (warm temperatures). So any correlations in your data must always be fully investigated before assuming a causal link.
Diagnostic analytics can’t predict the future, or make suggestions about what should be done — it can only explain why something happened, and any further information can only be gained either from a knowledgeable person making educated guesses or from predictive or prescriptive analytics. Nor does it answer the question “What should we do?” — this is answered by the field of prescriptive analytics.
Diagnostic analytics doesn’t give definitive answers. It can’t tell you that A definitely caused B, only that a certain percentage of people who encountered event A did (or did not) encounter event B. The accuracy of outcomes can be improved, however, with better-quality data, larger data sets, and the involvement of domain experts in interpreting the data.
How to use diagnostic analytics in your business
The first step in diagnostic analytics is deciding on the questions you want answers to. These may include questions like:
"What causes customers to cancel their subscriptions to our online product?"
"Why has web traffic decreased by so much this month?"
"Why are so many of our employees quitting their jobs this year?"
"Why do sales always increase in November?"
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The synthesis journal 2011 business analytics sas
1. Section Two 21
Business Analytics; Unleashing Latent Potential
Despite the availability of raw information such as scorecards and metrics, gut-feel is
still, often, the basis for important and, sometimes critical decisions by senior executives
and managers. Business Analytics applies analytical techniques to create insightful and efficient resolutions to
everyday business issues which translate into effective cost savings for your corporation. This is achieved through
the simultaneous provision of frameworks for decision making, improving performance, driving sustainable growth
through innovation, anticipating and planning for change while managing and balancing risk. Business Analytics
drives innovation and improves your business’s response time to market and environmental changes.
Dr David R. Hardoon
Principal, Analytics
SAS Singapore
1 INTRODUCTION
Society, through its multiple facets, has been engulfed with the accumulation of data for as long as
information has been generated. Hordes of data include emails, documents, customer loyalty transaction,
sensor and financial information, to list a few. Taking a step back to understand our fascination with data, we
pose the question “What is data?”. Simply put, the term data refers to qualitative or quantitative attributes
of an item or set of items, whatever they may be. Most importantly data are often viewed as the lowest
level of abstraction from which information and then knowledge and ‘intelligence’ can be derived. Thus,
our unrelenting pursuit of data is now easily explained. Data is synonymous to information which in turn is
synonymous to knowledge and ‘intelligence’.
“Knowledge is Power”
- Sir Francis Bacon (1561 – 1626), Religious Meditations of Heresies, 1597
Despite the availability of raw information, gut-feel is still, often the most widely used basis for important
and sometimes critical decisions by senior executives and managers. How does one currently gauge which
items should be placed on the shelves of various retail or grocery shops? What is the underlying commonality
amongst customers or patrons? How do business analysts go beyond the apparent and traditional features,
2. 22 Section Two
to better understand the customers behavioural and buying drivers? How does one answer the question of
why a certain event had occurred, in a prompt and timely manner, and more importantly how does one pre-
emptively get alerts to the possibility of a future event occurring?
Gut-feel entwined with domain knowledge has, and still is, an instrumental tool in driving businesses forward.
Notwithstanding the abundance of information available, the goal of achieving an even better understanding
of a business problem, driver, and behaviour – if left to standard practice – is simply overwhelming. Humans
excel in the detection of patterns, given it is provided in bite size. For example, information on customers’
credit card transactions provided in a few hundred rows and a dozen or so columns of excel would pose no
challenge for the domain expert to uncover a trend. Although, given the same information, on a granular
level, spanning several years – possibly thousands to millions of rows and hundreds to thousands of columns
is practically impossible to digest. Thus, it is always important to emphasise that such Business Intelligence
and/or Business Analytics tools and capabilities are intended to enhance the domain-knowledge rather than
replace it, surfacing latent information in a digestible and succinct format.
Given all of the above, and perhaps at times the challenge of establishing a framework to sort through
the deluge of data, one could dive even deeper into the realms of data by asking “Do you know what you
do not know?”. “Is there any insight, knowledge, information that is currently hidden within the wealth of
accumulated data that would improve business processes, uncover new growth potential, etc.?”. This is the
birth of Business Analytics.
2 BUSINESS ANALYTICS
Despite the recent buzz and focus on the Business Analytics paradigm shift, there is still an on-going
confusion as to the fundamental difference between Business Intelligence and Business Analytics. What is it
and what is it not? How do the two terms compare? It is not uncommon to have Business Intelligence and
Business Analytics assumed to refer to the same concept. There are also organisations which believe that
historical trending and analysis is akin to business analytics. Therefore prior to any in-depth discussion on
how Business Analytics can aid organisations to improve their decision making process – we first address the
issue of terminology.
The degree of “intelligence”, i.e. sophistication of technology, and the potential value returned to an
organisation can be illustrated as a two-dimensional graph of analytical maturity, ‘intelligence vs. business
value’. We address the terminology question through an eight levels of “Intelligence through Analytics”
(Illustrated in Figure 1).
1) Standard Reports
The first level of the analytical ladder focuses on understanding what had happened. For example,
imagine reviewing a company’s annual report and pinpointing the various events that had occurred.
“What was sold?”, “What was bought?”, “What was the volume of fraudulent cases detected?” etc.
3. Section Two 23
2) Ad-Hoc Report
Now that we had understood and captured the events what had happened, secondary questions may
surface. Such as, “When did it happen?”, “How many times did it occur during a particular period of
time?”.
3) Query Drilldowns - Online Analytical Processing (OLAP)
Diving deeper into the event, answering questions such as, “Where did the event happen?”, “Where
exactly was the problem?”.
4) Alerts
Finally, the concluding stage of the first four levels is alerts. Given the previous three levels, we are
keen to act/re-act on the uncovered information by triggering various business specific alerts. What
are the actions needed when threshold is breached?
These four levels are common practice in almost all organisations as the foundation of standard business
operations. The four levels are known as part of Business Intelligence. That is, the presentation and reporting
of historical data. Furthermore, a secondary attribute of Business Intelligence system is that the user knows
what they are looking and/or the basic analysis required to produce it. The subsequent levels are where
analytical processes kick in.
5) Statistical Analysis
In this level we aim to go beyond the realm of ‘what’ and ‘where’, to dive into the hidden gems of the
data to understand why, the event had happened. Such knowledge is the foundation of understanding
how to identify, prevent, exploit, and so forth.
6) Forecasting
Until this stage we had focused entirely on deriving insight from historical data, one of the key
elements of analytics is the capability of statistical forecasting. Will this observed trend continue
and for how long? Furthermore, this stage is analogous to driving a car – one needs to have a front
windscreen in order to know what is likely to happen.
7) Predictive Modelling
This stage is focused on uncovering the unknown from the data at hand to surface new insights that
may not have been previously known, also to provide the foundation for predicting future events,
“What will happen next?” and “Why will it happen?”, such as the likelihood of events occurring.
8) Optimisation
The cherry on top of the pie, optimisation or also referred to as Operation Research (OR), combines all
the previous levels to optimise business processes/objectives given operational and other constraints.
How to maximise profit, minimise cost? How to optimally allocate resources?
4. 24 Section Two
These four final levels construe Business Analytics, the uncovering of insights from historical data and the
projections into future, using analytical processes in alignment to business requirements. To place the full
eight levels into a more relatable context, assume the case of customer complaints. The type of questions and
insights generated at each of the stages could be the following:
OPTIMISATION
Business Value
What’s the best that can happen?
Business PREDICTIVE MODELLING
What will happen next?
Analytics
FORECASTING
What if these trends continue?
STATISTICAL ANALYSIS
Why is this happening?
ALERTS
What actions are needed?
QUERY DRILLDOWN (OLAP)
Where exactly is the problem?
AD-HOC REPORTS Business
How many, how often, where? Intelligence
STANDARD REPORTS
What happened?
Degree of Intelligence
Figure 1: ‘Intelligence Through Analytics’ - The eight levels of analytics from Business
Intelligence to Business Analytics (Source: SAS Eight Levels of Analytics)
1) Standard Report:
How many complaints by product and/or channel (phone call, email, forum, etc.)?
2) Ad-Hoc Report:
Compare complaint volume of the brand concerned with another brand.
3) Query Drilldown:
Which channel is causing us the greatest problem?
4) Alerts:
Highlight variance against targets and or budget.
5. Section Two 25
5) Statistical Analysis:
What are the most significant factors causing complaints?
6) Forecasting:
What will be the level of complaints in the coming fourth quarter?
7) Predictive Modelling:
Which complaints from which customer segments are most likely to escalate?
8) Optimisation:
What is the best deployment of available resources to maximise complaint resolution?
There are those who would argue that Business Analytics is merely a part of Business Intelligence, and
this is indeed quite plausible as the analytical capability sits as a layer (hidden at most times) between
Data Management and Reporting. Although as numorous Business Intelligence systems do not include the
analytics layer, it is therefore simpler to keep the two concepts distinct, especially as ‘reports’ are not Business
Analytics, but an outcome of the application of the latter. Secondly, the boundries between the different
layers are thinning – given the recent advancement in technological abilities to ‘push’/’embed’ analytical
processing into the database, or surface them directly within dashboard and interactive portals. The anchor
that differentiates Business Intelligence and Business Analytics, is the latter’s ability to accommodate any, if
not all, of the levels 5 to 8 in Figure 1.
Therefore, expecting a Business Intelligence system to go beyond the realm of ‘What if, How and Where else’
is akin to running around in a circle expecting to find a corner.
“Insanity: Doing the same thing over and over again and expecting different results.”
– Albert Einstein (1879 – 1955)
To lead organisations in today’s challenging economic climate, one needs fact-based answers to drive a
business forward and stay ahead of competitors and continuously improve the way in which work is done. We
need to understand ‘why’, and have foresight as to what is going to and what is likely to happen next.
3 ADOPTION
Most organisations still rely on spreadsheets as the most commonly used tool for some form of analysis, with
dashboards/KPIs and forecasting as secondary and third in use. In fact, currently most organisations mainly
use analytical tools at their most fundamental level.
The adoption of Business Analytics tools usually begins with the idenfication of a business challenge to be
addressed or a potential business benefit that can be achieved. These two core instigators are traditionally
followed with an internal assessment of readiness, setup and required skillset.
6. 26 Section Two
Despite common perception, Business Analytics tools do not require statisticians or PhD holders to man and
operate. Although they do require individuals who are analytically inclined – there is a growing requirement
to have right analytical talent in the right place. The increase in adoption of Business Analytics tools is
mirrored by the increase in demand for analytical talent, where the current demand far exceeds the supply.
In the Bloomberg Businessweek Research Service report, it was mentioned that “Nearly half of survey
respondents say their organisations place a premium on workers with analytical skills”, while continuing to
state that the “Inability to use analytics to make decisions and lack of appropriate analytical talent are two of
the main issues inhibiting companies in their Business Analytics initiatives”.
Organisations have varied entry points for their Business Analytics as well as Business Intelligence, journey.
The application of business analytics could be driven by an individual, a departmental or enterprise wide
requirement. There is no one-size-fits-all formula, and thus no definitive right or wrong stance. The
exponential growth of information demands, data volumes and audience populations show a growing need
for an organisation wide business analytic adoption to achieve the maximum business outcome. As the
demand increases so does the imperative for a sound strategy that meets short-term needs and provides the
foundation to meet an organisation’s long-term vision.
4 CONCLUSION
Organisations aim to optimise processes and improve existing operations and decision making, desiring to
go beyond the realm of static reporting of historical data. The limitation of static reporting of historical data
can be remedied through the interjection of Business Analytics by, for example, the uncovering of insights
from historical data and the forecast of future events using analytical processes aligned with business
requirements. Business Analytics is usually a bespoke process as no two business problems are identical.
While roadblocks and challenges are to be expected in the analytical journey, Business Analytics is
fundamentally constrained by two factors - technology and imagination. With appropriate choice of
technology to enable the analytical journey, organisations have successfully reduced their risk, grown
their revenue, realised greater profits, and detected fraudulent transactions. Imagination or even mindset
is irrefutably the more elusive constraint to address. Business Analytics’ true potential can be elegantly
summarised with the one line elevator pitch, “Do you know what you do not know?”. To conclude this
introductory piece with an entertaining quote, it is apt to note that Mark Twain said a century ago:
“Most people use statistics the way a drunkard uses a lamp post, more for support than illumination.”
– Mark Twain (1835 – 1910)
Research conducted by Bloomberg Businessweek suggests that “The more an organisation relies on analytics
in the decision-making process, the more effective it will be”.
7. Section Two 27
5 REFERENCES
[1] “Analytics at Work” by Thomas H. Davenport, Jeanne G. Harris and Robert Morison.
Harvard Business Press, 2010.
[2] “Competing on Analytics” by Thomas H. Davenport and Jeanne G. Harris. Harvard Business Press, 2007.
[3] “Data Mining and Predictive Analysis” by Collen McCue. Elsevier, 2007.
[4] “Business Intelligence for Dummies” by Swain Scheps. 2008.
[5] “Consolidations in the BI industry” by Nigel Pendse. The OLAP Report, 2008.
[6] “The Current State of Business Analytics: Where Do We Go From Here?”, Bloomberg Businessweek
Research Services, 2011.
<www.sas.com/bbw1>
8. 28 Section Two
BIOGRAPHY OF AUTHOR
Dr David R. Hardoon
Principal, Analytics
SAS Singapore
Dr David R. Hardoon is the Principal of Analytics at SAS Singapore. He is the in-house Analytics subject matter
expert and Business Analytics evangelist. He has established expertise in developing and applying computational
analytical models for business knowledge discovery and analysis through his involvement in a number of research
projects aimed at developing new principles, methods, and prototypes for the next generation of interfaces to
data. He has completed extensive research in, but not limited to, these fields; data mining, information retrieval,
knowledge discovery, pattern recognition and machine learning. These have been applied across a wide cross-
disciplinary scope including problems/applications in music, medical analysis, retail, time sequence analysis,
aerospace, taxonomy, content based information retrieval, vision and finance. His findings have been published in
international conferences and journals.
David received a B.Sc. in Computer Science and Artificial Intelligence from Royal Holloway, University of
London and a Ph.D. in Computer Science in the field of Machine Learning from the University of Southampton.
He is currently an Adjunct Assistant Professor at the School of Computing, National University of Singapore
and a Honorary Senior Research Associate at the Centre for Computational Statistics & Machine Learning,
University College London. David regularly tutors, advises and provided consulting support in Analytics and
Business Analytics.
More can be found on <www.davidroihardoon.com>.