Why Master Data Management Projects Fail and what this means for Big DataSam Thomsett
This document discusses why Master Data Management (MDM) projects often fail and the implications for big data initiatives. Some key reasons for MDM project failures include a lack of enterprise thinking and executive sponsorship, weak business cases, treating MDM as an IT solution rather than business solution, unrealistic roadmaps, and poor communications planning. The document argues that establishing a data governance strategy, enterprise reference architecture, and prioritized project roadmap are important for MDM and big data success.
This document summarizes a case study presentation on how a firm's competitive environment and digital strategic posture influence digital business strategy. It discusses how digital technologies are reshaping traditional business strategies and structures. The summary defines digital business strategy as using digital resources to create value beyond traditional views of IT. It examines how a firm's industry turbulence, concentration, and growth impact its digital investments and outsourcing. The presentation concludes that a firm's digital strategic posture has convergent effects on general IT investment but divergent effects on outsourcing, and that understanding variations in a competitive environment can explain differences in digital strategic posture.
This document provides an overview of business intelligence (BI). It defines BI as a technology infrastructure that analyzes available data to improve business processes and make effective decisions. The main components of a BI infrastructure include software to gather, clean, integrate, analyze and share data. The purpose of BI is to help executives and managers make better informed decisions to cut costs, find new opportunities and improve inefficient processes. Key benefits of BI include accelerating decision making, optimizing processes, increasing efficiency, and gaining competitive advantages. BI differs from business analytics in that BI describes past and current states, while business analytics uses software to predict future outcomes or prescribe optimal approaches.
Several factors will affect innovation in the manufacturing industry across processes, product development, outsourcing engagements and even IT services. Among these factors are digital consumers in the automotive industry; and decision-making using Business Intelligence (BI). This list is in no way complete. But it certainly is a good starting point to explore further.
Building a Content Marketing Engine to Accelerate Revenue Growth - BreakingPointOnline Marketing Summit
Building a Content Marketing Engine to Accelerate Revenue Growth
Learn how BreakingPoint drove triple digit revenue during the "great recession" by creating a content marketing engine. Pam O'Neal will discuss the "not so obvious" roles content plays in accelerating demand generation and how to develop the right content marketing strategy, trigger buyer pressure points and continue to fuel your engine.
* Pam O'Neal, VP Marketing, BreakingPoint (@poneal)
Metrics social crm - myths and realities - march 2011 finalDean Westervelt
This document discusses social customer relationship management (social CRM). It begins with definitions of social CRM from various experts and the presenter. It then covers key components of social CRM including strategy, technology, and data. For each component, it contrasts the current reality with the future state. Specifically, it discusses how social CRM strategies are beginning to focus on customer service and product development rather than just marketing. It also notes the need for more advanced social CRM technology and data management platforms to fully realize the future state of social CRM.
The document discusses how emerging technologies are creating new sources of data and how analyzing this data can provide businesses a competitive advantage. It identifies key trends like cloud computing, social media, mobile devices, and big data that are fueling data growth. To leverage this "nexus of forces", companies need strategies to innovate using new types of information and analytics. This includes assessing business needs, understanding new possibilities, and adopting technologies like analytics, databases, and Hadoop to access diverse data sources and gain insights.
Why Master Data Management Projects Fail and what this means for Big DataSam Thomsett
This document discusses why Master Data Management (MDM) projects often fail and the implications for big data initiatives. Some key reasons for MDM project failures include a lack of enterprise thinking and executive sponsorship, weak business cases, treating MDM as an IT solution rather than business solution, unrealistic roadmaps, and poor communications planning. The document argues that establishing a data governance strategy, enterprise reference architecture, and prioritized project roadmap are important for MDM and big data success.
This document summarizes a case study presentation on how a firm's competitive environment and digital strategic posture influence digital business strategy. It discusses how digital technologies are reshaping traditional business strategies and structures. The summary defines digital business strategy as using digital resources to create value beyond traditional views of IT. It examines how a firm's industry turbulence, concentration, and growth impact its digital investments and outsourcing. The presentation concludes that a firm's digital strategic posture has convergent effects on general IT investment but divergent effects on outsourcing, and that understanding variations in a competitive environment can explain differences in digital strategic posture.
This document provides an overview of business intelligence (BI). It defines BI as a technology infrastructure that analyzes available data to improve business processes and make effective decisions. The main components of a BI infrastructure include software to gather, clean, integrate, analyze and share data. The purpose of BI is to help executives and managers make better informed decisions to cut costs, find new opportunities and improve inefficient processes. Key benefits of BI include accelerating decision making, optimizing processes, increasing efficiency, and gaining competitive advantages. BI differs from business analytics in that BI describes past and current states, while business analytics uses software to predict future outcomes or prescribe optimal approaches.
Several factors will affect innovation in the manufacturing industry across processes, product development, outsourcing engagements and even IT services. Among these factors are digital consumers in the automotive industry; and decision-making using Business Intelligence (BI). This list is in no way complete. But it certainly is a good starting point to explore further.
Building a Content Marketing Engine to Accelerate Revenue Growth - BreakingPointOnline Marketing Summit
Building a Content Marketing Engine to Accelerate Revenue Growth
Learn how BreakingPoint drove triple digit revenue during the "great recession" by creating a content marketing engine. Pam O'Neal will discuss the "not so obvious" roles content plays in accelerating demand generation and how to develop the right content marketing strategy, trigger buyer pressure points and continue to fuel your engine.
* Pam O'Neal, VP Marketing, BreakingPoint (@poneal)
Metrics social crm - myths and realities - march 2011 finalDean Westervelt
This document discusses social customer relationship management (social CRM). It begins with definitions of social CRM from various experts and the presenter. It then covers key components of social CRM including strategy, technology, and data. For each component, it contrasts the current reality with the future state. Specifically, it discusses how social CRM strategies are beginning to focus on customer service and product development rather than just marketing. It also notes the need for more advanced social CRM technology and data management platforms to fully realize the future state of social CRM.
The document discusses how emerging technologies are creating new sources of data and how analyzing this data can provide businesses a competitive advantage. It identifies key trends like cloud computing, social media, mobile devices, and big data that are fueling data growth. To leverage this "nexus of forces", companies need strategies to innovate using new types of information and analytics. This includes assessing business needs, understanding new possibilities, and adopting technologies like analytics, databases, and Hadoop to access diverse data sources and gain insights.
The document discusses three approaches to business intelligence (BI) that organizations can take to improve decision making:
1. IT-centric - Focuses on analyzing historical data to understand what happened in the past. Asks "What happened?"
2. Information management - Enables real-time decision making by integrating data sources. Asks "How are we doing and what can we tweak now?"
3. Predictive insight - Adds advanced analytics to anticipate the future and identify opportunities. Asks "What will happen next and how can we optimize outcomes?" More advanced organizations use this approach.
McKinsey Big Data Trinity for self-learning cultureMatt Ariker
The document discusses building a "test and learn" capability at scale by creating a "big data trinity" consisting of a 3D-360 degree understanding of the customer, an analytics roadmap, and a self-learning ecosystem. It emphasizes the importance of combining both structured and unstructured customer data to develop a comprehensive customer view, planning analytics strategies and requirements, and integrating systems to allow insights to continuously feed back into the learning process.
IBM is redefining business intelligence (BI) by combining reporting, analysis, modeling, planning, and collaboration capabilities into a single enterprise solution. This allows for better decision-making cycles while also removing barriers to being an analytics-driven organization. Specifically, IBM's solution balances user freedom with IT control by infusing traditional BI with personal analytics and performance management capabilities. The goal is to provide users with the freedom to explore and analyze data while also maintaining management and governance from IT.
1) Data management is crucial for financial firms to manage risk and generate returns, but new regulations have increased the amount of data firms must handle.
2) The document discusses challenges financial firms face in data management, including legacy systems, changing a focus to data quality, and establishing consistent data definitions across business units and regulations.
3) Interviewees note key processes like risk management, compliance, and reporting require clean, consistent data without room for error, but data transformations across systems introduce reconciliation issues and inconsistencies.
This document provides an excerpt from an IDC MarketScape report on smart multifunction peripherals (MFPs) in the US market in 2013. It discusses key factors for success with smart MFPs, including a complete product and services portfolio. It also outlines IDC's vendor assessment methodology and positions major vendors as leaders, major players, contenders or participants based on their strategies and capabilities. The excerpt highlights Lexmark as a leader, noting its broad MFP lineup, software acquisition strategy and emphasis on managed print services.
The document discusses using a cybernetics approach to understand sales incentive compensation management (ICM) as a business system. It describes cybernetics as the study of communication and control in living organisms and organizations. Applying this approach allows visualizing the ICM system and its sub-systems and feedback loops. Positive feedback loops reinforce the system, while negative loops regulate it. A cybernetics influence diagram can represent the ICM system and feedback relationships to analyze system dynamics.
Improving Organizational Performance Through Pervasive Business IntelligenceFindWhitePapers
Explore the growing body of evidence suggesting a direct link between investment in business analytics solutions and organizational performance. This white paper highlights market trends that point toward more pervasive use of BI solutions. The recommendations presented are based on ongoing IDC coverage of the BI and analytics solutions market.
Intelligence 2.0 Keynote Presentation to the 1st China Competitive Intelligen...Arik Johnson
The document provides an overview of next generation priorities for competitive intelligence (CI) software. It discusses trends like increased organizational collaboration, corporate governance priorities around reliable earnings forecasts, and sustainable innovation. It outlines the traditional CI process and 12 key application areas. It also discusses tools for identifying strategic issues, key players, and early warnings through conducting interviews and developing integrated watch lists. The document emphasizes the importance of analysis in transforming data into useful intelligence to support better strategic, operational, and tactical decision-making.
Driving Value Through Data Analytics: The Path from Raw Data to Informational...Cognizant
As organizations gather and process colossal amounts of data, analytics is essential for operational and strategic excellence. We offer a guide to the phases of the data analytics journey, from descriptive to diagnostic to predictive to prescriptive, covering intentions, tools and people considerations.
This document discusses organizational and entrepreneurial factors related to effective customer information systems practices in B2B firms. It finds that:
1) Effective CIS requires coordination across functions to actively manage customer information as the core of marketing decision-making.
2) One exemplary firm was found to develop customer-centric strategies interactively through dialogue between middle and upper management using customer data and trends.
3) While CIS promises closer customer relationships, many firms fail due to lack of information sharing across functions, requiring cultural and structural changes to facilitate sharing.
Radical innovations in technology are increasing the importance of IT in achieving core business objectives, shifting the role of CIOs to be more strategic. Chief Information Officers now operate as business executives first and technology experts second, speaking the language of the business. They are seen as the principal strategists for emerging areas like big data, mobile apps, social media, and online learning. CIOs also target technology budgets towards innovation in analytics, cloud computing, mobile and social technologies.
Big Data for Marketing: When is Big Data the right choice?Swyx
Chief Marketing Officers (CMOs) without plans for Big Data may be putting themselves and
their companies at a competitive disadvantage. Big Data is already being widely deployed to enhance marketing responsibilities, although the small number of widely-touted success stories might be masking a significant number of failed implementations. When correctly planned and implemented, however, Big Data can create significant value for CMOs and their organisations. In this paper, we focus on describing specific examples of how Big Data can support CMO responsibilities and developing frameworks for identifying Big Data opportunities.
The CDO and the Delivery of Enterprise ValueMark Albala
The document discusses the role of the Chief Data Officer (CDO) and how they can help deliver enterprise value through effective use of data and information. The key points are:
1) The CDO is responsible for treating data/information as valuable assets and ensuring their optimal use to support business strategies and value propositions.
2) Information flows through an organization's business model and influences the success of value propositions. The CDO aims to maximize this value by addressing issues like data quality, accessibility, and understanding.
3) The effectiveness of the CDO is measured by their influence on how information is used strategically in the business, and by improving the "information value levers" that can restrict
Analysis of Sales and Distribution of an IT Industry Using Data Mining Techni...ijdmtaiir
The goal of this work is to allow a corporation to
improve its marketing, sales, and customer support operations
through a better understanding of its customers. Keep in mind,
however, that the data mining techniques and tools described
here are equally applicable in fields ranging from law
enforcement to radio astronomy, medicine, and industrial
process control. Businesses in today’s environment
increasingly focus on gaining competitive advantages.
Organizations have recognized that the effective use of data is
the key element in the next generation is to predict the sales
value and emerging trend of technology market. Data is
becoming an important resource for the companies to analyze
existing sales value with current technology trends and this
will be more useful for the companies to identify future sales
value. There a variety of data analysis and modeling techniques
to discover patterns and relationships in data that are used to
understand what your customers want and predict what they
will do. The main focus of this is to help companies to select
the right prospects on whom to focus, offer the right additional
products to company’s existing customers and identify good
customers who may be about to leave. This results in improved
revenue because of a greatly improved ability to respond to
each individual contact in the best way and reduced costs due
to properly allocated resources. Keywords: sales, customer,
technology, profit.
Today’s fast-paced modern business landscape demands that businesses deliver responsive, engaging experiences across multiple touchpoints and at all stages of a customer’s journey. To be able to meet these needs, organizations must now make Digital Asset Management (DAM) a strategic business priority.
Since the outset of digital publishing in the 1990s, digital libraries have housed brand images, text and graphics accessible through basic search features and early metadata indexing. But now everything is digital; online buyer research and digital marketing dominates, and eCommerce rules. So digital libraries have exploded in volume and usage. But enterprise can no longer organize their vast libraries of content using ad hoc point solutions or simple tools like email and spreadsheets.
This presentation will discuss how the management of digital assets has now become mission-critical to most organizations. It explains how to set up a strategic DAM project and how to consider the right technology partner.
Like “optimization” before it, “social CRM” (sCRM) is the latest catchphrase that has marketing and customer intelligence professionals abuzz.
Broadly, social CRM is the application of emerging social technologies, strategy, and data to traditional customer relationship marketing (CRM) practices. Core sCRM components to consider when extending traditional CRM approaches are strategy, data, and underlying technology used (e.g., text mining).
Learn to separate the myths about sCRM from the realities. This presentation will highlight current realities and challenges facing each of these three foundational aspects of sCRM.
This document discusses strategies for simplifying analytics. It recommends focusing on insights that are important for customers, stakeholders, and employees rather than trying to analyze all possible data. Specific strategies include using next-gen business intelligence and data visualization to improve decision making; applying data discovery techniques to uncover patterns; deploying analytics applications to simplify advanced analytics; and harnessing techniques like machine learning to reduce human effort and produce predictions. The goal is to generate insights that lead to tangible outcomes through a faster, simpler analytical approach.
G12.2012 magic quadrant for data masking technologySatya Harish
The document discusses the data masking technology market and provides details on several vendors that offer static data masking (SDM) solutions. It defines SDM as a technology that masks nonproduction databases to deter misuse of sensitive data by users. The market is expanding from SDM into dynamic data masking and data redaction. Several vendors are profiled, including their strengths and cautions. Leaders in the market like IBM offer comprehensive portfolios while smaller vendors may be more limited in capabilities and market presence.
Sit717 enterprise business intelligence 2019 t2 copy1NellutlaKishore
This document discusses data mining techniques and business intelligence. It begins with an introduction to different data mining techniques like clustering, statistical analysis, visualization, classification, neural networks, rules, and decision trees. It then provides more detail on statistical techniques, explaining that they help analyze large datasets. The document evaluates how big data and business intelligence are related, concluding that while they are different concepts, they need to work together to effectively analyze data and make smart business decisions. Big data provides the large datasets, while business intelligence extracts useful information from those datasets.
The Softer Skills Analysts need to make an impactPaul Laughlin
25 min presentation given at London Business School, to the OR Society's Analytics Network. Summarising Laughlin Consultancy's 9 step model of Softer Skills for Analysts.
Value proposition of analytics in P&C insuranceGregg Barrett
The document provides an overview of the value of analytics in the property and casualty (P&C) insurance industry. It discusses the challenges facing the industry and how analytics can help insurers address these challenges. The document is divided into six sections that cover topics such as the impact of analytics across the insurance life cycle, the value of data and analytics, and considerations for implementing analytics and managing big data. Organizations that effectively adopt analytics are shown to achieve greater growth and returns. While analytics provides opportunities, the document also notes challenges such as developing a data-driven culture and addressing privacy and ethical issues that can arise from certain data collection and analytic practices.
The document discusses three approaches to business intelligence (BI) that organizations can take to improve decision making:
1. IT-centric - Focuses on analyzing historical data to understand what happened in the past. Asks "What happened?"
2. Information management - Enables real-time decision making by integrating data sources. Asks "How are we doing and what can we tweak now?"
3. Predictive insight - Adds advanced analytics to anticipate the future and identify opportunities. Asks "What will happen next and how can we optimize outcomes?" More advanced organizations use this approach.
McKinsey Big Data Trinity for self-learning cultureMatt Ariker
The document discusses building a "test and learn" capability at scale by creating a "big data trinity" consisting of a 3D-360 degree understanding of the customer, an analytics roadmap, and a self-learning ecosystem. It emphasizes the importance of combining both structured and unstructured customer data to develop a comprehensive customer view, planning analytics strategies and requirements, and integrating systems to allow insights to continuously feed back into the learning process.
IBM is redefining business intelligence (BI) by combining reporting, analysis, modeling, planning, and collaboration capabilities into a single enterprise solution. This allows for better decision-making cycles while also removing barriers to being an analytics-driven organization. Specifically, IBM's solution balances user freedom with IT control by infusing traditional BI with personal analytics and performance management capabilities. The goal is to provide users with the freedom to explore and analyze data while also maintaining management and governance from IT.
1) Data management is crucial for financial firms to manage risk and generate returns, but new regulations have increased the amount of data firms must handle.
2) The document discusses challenges financial firms face in data management, including legacy systems, changing a focus to data quality, and establishing consistent data definitions across business units and regulations.
3) Interviewees note key processes like risk management, compliance, and reporting require clean, consistent data without room for error, but data transformations across systems introduce reconciliation issues and inconsistencies.
This document provides an excerpt from an IDC MarketScape report on smart multifunction peripherals (MFPs) in the US market in 2013. It discusses key factors for success with smart MFPs, including a complete product and services portfolio. It also outlines IDC's vendor assessment methodology and positions major vendors as leaders, major players, contenders or participants based on their strategies and capabilities. The excerpt highlights Lexmark as a leader, noting its broad MFP lineup, software acquisition strategy and emphasis on managed print services.
The document discusses using a cybernetics approach to understand sales incentive compensation management (ICM) as a business system. It describes cybernetics as the study of communication and control in living organisms and organizations. Applying this approach allows visualizing the ICM system and its sub-systems and feedback loops. Positive feedback loops reinforce the system, while negative loops regulate it. A cybernetics influence diagram can represent the ICM system and feedback relationships to analyze system dynamics.
Improving Organizational Performance Through Pervasive Business IntelligenceFindWhitePapers
Explore the growing body of evidence suggesting a direct link between investment in business analytics solutions and organizational performance. This white paper highlights market trends that point toward more pervasive use of BI solutions. The recommendations presented are based on ongoing IDC coverage of the BI and analytics solutions market.
Intelligence 2.0 Keynote Presentation to the 1st China Competitive Intelligen...Arik Johnson
The document provides an overview of next generation priorities for competitive intelligence (CI) software. It discusses trends like increased organizational collaboration, corporate governance priorities around reliable earnings forecasts, and sustainable innovation. It outlines the traditional CI process and 12 key application areas. It also discusses tools for identifying strategic issues, key players, and early warnings through conducting interviews and developing integrated watch lists. The document emphasizes the importance of analysis in transforming data into useful intelligence to support better strategic, operational, and tactical decision-making.
Driving Value Through Data Analytics: The Path from Raw Data to Informational...Cognizant
As organizations gather and process colossal amounts of data, analytics is essential for operational and strategic excellence. We offer a guide to the phases of the data analytics journey, from descriptive to diagnostic to predictive to prescriptive, covering intentions, tools and people considerations.
This document discusses organizational and entrepreneurial factors related to effective customer information systems practices in B2B firms. It finds that:
1) Effective CIS requires coordination across functions to actively manage customer information as the core of marketing decision-making.
2) One exemplary firm was found to develop customer-centric strategies interactively through dialogue between middle and upper management using customer data and trends.
3) While CIS promises closer customer relationships, many firms fail due to lack of information sharing across functions, requiring cultural and structural changes to facilitate sharing.
Radical innovations in technology are increasing the importance of IT in achieving core business objectives, shifting the role of CIOs to be more strategic. Chief Information Officers now operate as business executives first and technology experts second, speaking the language of the business. They are seen as the principal strategists for emerging areas like big data, mobile apps, social media, and online learning. CIOs also target technology budgets towards innovation in analytics, cloud computing, mobile and social technologies.
Big Data for Marketing: When is Big Data the right choice?Swyx
Chief Marketing Officers (CMOs) without plans for Big Data may be putting themselves and
their companies at a competitive disadvantage. Big Data is already being widely deployed to enhance marketing responsibilities, although the small number of widely-touted success stories might be masking a significant number of failed implementations. When correctly planned and implemented, however, Big Data can create significant value for CMOs and their organisations. In this paper, we focus on describing specific examples of how Big Data can support CMO responsibilities and developing frameworks for identifying Big Data opportunities.
The CDO and the Delivery of Enterprise ValueMark Albala
The document discusses the role of the Chief Data Officer (CDO) and how they can help deliver enterprise value through effective use of data and information. The key points are:
1) The CDO is responsible for treating data/information as valuable assets and ensuring their optimal use to support business strategies and value propositions.
2) Information flows through an organization's business model and influences the success of value propositions. The CDO aims to maximize this value by addressing issues like data quality, accessibility, and understanding.
3) The effectiveness of the CDO is measured by their influence on how information is used strategically in the business, and by improving the "information value levers" that can restrict
Analysis of Sales and Distribution of an IT Industry Using Data Mining Techni...ijdmtaiir
The goal of this work is to allow a corporation to
improve its marketing, sales, and customer support operations
through a better understanding of its customers. Keep in mind,
however, that the data mining techniques and tools described
here are equally applicable in fields ranging from law
enforcement to radio astronomy, medicine, and industrial
process control. Businesses in today’s environment
increasingly focus on gaining competitive advantages.
Organizations have recognized that the effective use of data is
the key element in the next generation is to predict the sales
value and emerging trend of technology market. Data is
becoming an important resource for the companies to analyze
existing sales value with current technology trends and this
will be more useful for the companies to identify future sales
value. There a variety of data analysis and modeling techniques
to discover patterns and relationships in data that are used to
understand what your customers want and predict what they
will do. The main focus of this is to help companies to select
the right prospects on whom to focus, offer the right additional
products to company’s existing customers and identify good
customers who may be about to leave. This results in improved
revenue because of a greatly improved ability to respond to
each individual contact in the best way and reduced costs due
to properly allocated resources. Keywords: sales, customer,
technology, profit.
Today’s fast-paced modern business landscape demands that businesses deliver responsive, engaging experiences across multiple touchpoints and at all stages of a customer’s journey. To be able to meet these needs, organizations must now make Digital Asset Management (DAM) a strategic business priority.
Since the outset of digital publishing in the 1990s, digital libraries have housed brand images, text and graphics accessible through basic search features and early metadata indexing. But now everything is digital; online buyer research and digital marketing dominates, and eCommerce rules. So digital libraries have exploded in volume and usage. But enterprise can no longer organize their vast libraries of content using ad hoc point solutions or simple tools like email and spreadsheets.
This presentation will discuss how the management of digital assets has now become mission-critical to most organizations. It explains how to set up a strategic DAM project and how to consider the right technology partner.
Like “optimization” before it, “social CRM” (sCRM) is the latest catchphrase that has marketing and customer intelligence professionals abuzz.
Broadly, social CRM is the application of emerging social technologies, strategy, and data to traditional customer relationship marketing (CRM) practices. Core sCRM components to consider when extending traditional CRM approaches are strategy, data, and underlying technology used (e.g., text mining).
Learn to separate the myths about sCRM from the realities. This presentation will highlight current realities and challenges facing each of these three foundational aspects of sCRM.
This document discusses strategies for simplifying analytics. It recommends focusing on insights that are important for customers, stakeholders, and employees rather than trying to analyze all possible data. Specific strategies include using next-gen business intelligence and data visualization to improve decision making; applying data discovery techniques to uncover patterns; deploying analytics applications to simplify advanced analytics; and harnessing techniques like machine learning to reduce human effort and produce predictions. The goal is to generate insights that lead to tangible outcomes through a faster, simpler analytical approach.
G12.2012 magic quadrant for data masking technologySatya Harish
The document discusses the data masking technology market and provides details on several vendors that offer static data masking (SDM) solutions. It defines SDM as a technology that masks nonproduction databases to deter misuse of sensitive data by users. The market is expanding from SDM into dynamic data masking and data redaction. Several vendors are profiled, including their strengths and cautions. Leaders in the market like IBM offer comprehensive portfolios while smaller vendors may be more limited in capabilities and market presence.
Sit717 enterprise business intelligence 2019 t2 copy1NellutlaKishore
This document discusses data mining techniques and business intelligence. It begins with an introduction to different data mining techniques like clustering, statistical analysis, visualization, classification, neural networks, rules, and decision trees. It then provides more detail on statistical techniques, explaining that they help analyze large datasets. The document evaluates how big data and business intelligence are related, concluding that while they are different concepts, they need to work together to effectively analyze data and make smart business decisions. Big data provides the large datasets, while business intelligence extracts useful information from those datasets.
The Softer Skills Analysts need to make an impactPaul Laughlin
25 min presentation given at London Business School, to the OR Society's Analytics Network. Summarising Laughlin Consultancy's 9 step model of Softer Skills for Analysts.
Value proposition of analytics in P&C insuranceGregg Barrett
The document provides an overview of the value of analytics in the property and casualty (P&C) insurance industry. It discusses the challenges facing the industry and how analytics can help insurers address these challenges. The document is divided into six sections that cover topics such as the impact of analytics across the insurance life cycle, the value of data and analytics, and considerations for implementing analytics and managing big data. Organizations that effectively adopt analytics are shown to achieve greater growth and returns. While analytics provides opportunities, the document also notes challenges such as developing a data-driven culture and addressing privacy and ethical issues that can arise from certain data collection and analytic practices.
Business intelligence (BI) is the strategies, processes, technologies and architectures used to support the collection, analysis, presentation and dissemination of business information. BI technologies are capable of handling large amounts of structured and unstructured data to help identify new strategic opportunities. Common functions of BI technologies include reporting, analytics, data mining and predictive analytics. The goal of BI is to allow for the easy interpretation of large volumes of data to provide businesses with a competitive advantage.
Thinking Small: Bringing the Power of Big Data to the MassesFlutterbyBarb
Thinking Small: Bringing the Power of Big Data to the Masses via Adobe with the results of improved access to insights, better user experiences, and greater productivity in the enterprise.
Social-Media-Analytics-Enabling-Intelligent-Real-Time-Decision-MakingAmit Shah
This document discusses how organizations can use social media analytics to gain business insights from social media data. It recommends a framework called LAEI (Listen, Analyze, Engage, Integrate) for organizations to effectively harness social media data. The framework involves listening to social media conversations, analyzing the data to understand customer sentiment, engaging customers by responding to feedback, and integrating social insights with enterprise data to build customer profiles. Advanced analytics tools are needed to make sense of large amounts of unstructured social media data and gain actionable insights for improved decision making.
The document provides an overview of HR shared services and how it enables HR transformation. It discusses how HR shared services involves transitioning administrative HR roles to a specialist function in order to allow HR to focus more on strategic activities. Setting up an HR shared services center aims to reduce costs, increase quality, and drive efficiency through process standardization, centralization, and leveraging expertise. Outsourcing transactional HR activities to a shared services center can deliver cost savings, access to skills and technology not otherwise available, improved governance, risk management and operating performance with commitments to on-time delivery.
This document discusses how to develop a data-driven marketing strategy. It recommends building a data-driven culture, leveraging data to act at the right time, optimizing for key data points, integrating customer data, and developing a vision for how to use data. Case studies show how data-driven approaches helped companies increase sales by 34% and better target audiences. The conclusion states that transitioning to data-driven marketing will enhance data collection and analysis to continuously improve performance.
The document discusses IBM's strategic information technology plan. It outlines IBM's mission to innovate in ways that solve major business and societal problems through responsible practices. The plan focuses on managing technology and innovation through resource allocation, understanding competitors and technical developments, promoting entrepreneurship, assessing sustainability and ethics, communicating with stakeholders, and maintaining social responsibility and profitability. IBM aims to continue leading through innovative actions that improve its reputation.
What’s Your Data Strategy· Leandro DalleMule· Thomas H. Daven.docxhelzerpatrina
This document provides an overview of a framework for developing a robust data strategy. It discusses the importance of distinguishing between information and data, and between information architecture and data architecture. The key innovation of the framework is the concept of having both a single source of truth (SSOT) and multiple versions of the truth (MVOTs). The SSOT provides a standardized set of core data, while MVOTs allow flexible transformation and interpretation of data into customized information to meet different business needs. This balancing of standardization and control via the SSOT with flexibility through MVOTs supports both defensive goals like compliance and offensive goals like customer insights.
Abdulrahman Ibrahim, the Chief Data and Innovation Officer at Al-Madinah Region Development Authority, embodies transformative leadership in the vibrant urban landscape. As a guiding light of innovative thinking and progressive vision, Abdulrahman creates a vibrant metropolis teeming with life and opportunity.
Organizational change is closely tied to digitization. Economists have not appreciated how firms are employing AI besides employing it to deploy robots. In fact, many firms have innovated by creating intelligent analytic systems that change their ability to create new products and manage processes and production.
In these firms, AI helps firms understand complex operations, connect with customers on a personalized level, and obtain a far more sophisticated analysis of processes and services. The result is that AI and ML are creating many new economic advantages at the firm level and for customers.artifici
barcoo ist eine App für Mobiltelefone, mit der Scan-Codes gelesen werden und Informationen über die gescannten Produkte an Verbraucher kommuniziert werden können.
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[To download this presentation, visit:
https://www.oeconsulting.com.sg/training-presentations]
This presentation is a curated compilation of PowerPoint diagrams and templates designed to illustrate 20 different digital transformation frameworks and models. These frameworks are based on recent industry trends and best practices, ensuring that the content remains relevant and up-to-date.
Key highlights include Microsoft's Digital Transformation Framework, which focuses on driving innovation and efficiency, and McKinsey's Ten Guiding Principles, which provide strategic insights for successful digital transformation. Additionally, Forrester's framework emphasizes enhancing customer experiences and modernizing IT infrastructure, while IDC's MaturityScape helps assess and develop organizational digital maturity. MIT's framework explores cutting-edge strategies for achieving digital success.
These materials are perfect for enhancing your business or classroom presentations, offering visual aids to supplement your insights. Please note that while comprehensive, these slides are intended as supplementary resources and may not be complete for standalone instructional purposes.
Frameworks/Models included:
Microsoft’s Digital Transformation Framework
McKinsey’s Ten Guiding Principles of Digital Transformation
Forrester’s Digital Transformation Framework
IDC’s Digital Transformation MaturityScape
MIT’s Digital Transformation Framework
Gartner’s Digital Transformation Framework
Accenture’s Digital Strategy & Enterprise Frameworks
Deloitte’s Digital Industrial Transformation Framework
Capgemini’s Digital Transformation Framework
PwC’s Digital Transformation Framework
Cisco’s Digital Transformation Framework
Cognizant’s Digital Transformation Framework
DXC Technology’s Digital Transformation Framework
The BCG Strategy Palette
McKinsey’s Digital Transformation Framework
Digital Transformation Compass
Four Levels of Digital Maturity
Design Thinking Framework
Business Model Canvas
Customer Journey Map
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3. Executive Summary
Data Driven Marketing
Behavior is a consequence of attitude together with
individual norms and values.
Attitudes and strategies of apology have statistically
significant influence to human behavior.
Given the possibility to extract measurable and
influenceable factors of behavior this information
would help to predict future activities, needs and
wishes of individuals.
Agenda
4. Theory of Planned Behavior
attitude
Intention Behavior
Individual
norms & values
Agenda
Copyright I.C.A.R.O.S. GmbH 2011
5. Statistics
Statistical studies* have found evidence that there is a
statistically significant influence of attitude to behavior.
The same evidence was found for past behavior.
An extension of the model to strategies of apology for
past behavior is another significant variable for current
behavior.
* Beispiel: http://www.uns.ethz.ch/res/crp/sustlearn/wadis_dt/erg3/Liz_arbeit_littering-verhalten.pdf
Agenda
Copyright I.C.A.R.O.S. GmbH 2011
6. SUMMARY
The theory behind Data Driven Marketing delivers a
framework for data driven Business Intelligence by
extraction of measurable and influenceable factors of
attitudes
strategies of apology
explaining human behavior.
Agenda
Copyright I.C.A.R.O.S. GmbH 2011
7. Executive Summary
Technology of DDM
New Marketing-Tools like Smartphone Ads are sources of
information at the same time and so could be used for
Business Intelligence.
Reporting of daily behavior, together with field studies and
interpretation of social interactions will be the basis of
future marketing actions.
The identification of most valuable customers and their
retention will succeed even without knowing the
personalized data of individuals - only by knowing habits.
Agenda
8. Data sources for DDM*
Data sources for DDM could be segmented into 3
groups:
Activities of customers
General field studies about behavior
Interpretation of social interactions
Combining these sources could be managed
technically and/or by intuition of the marketer.
Agenda * See also Market Research 2.0 http://www.slideshare.net/viktorriemer
Copyright I.C.A.R.O.S. GmbH 2011
9. Data sources for DDM*
Examples
barcoo (Smartphone used as product scanner)
productfinder (virtual sales assistant)
theSteward (mobile service platform)
With these tools an integration between marketing
activity and market research measurement could
easily be installed.
Agenda * See also Market Research 2.0 http://www.slideshare.net/viktorriemer
Copyright I.C.A.R.O.S. GmbH 2011
10. Technique of DDM 1
Records about activities and behavior of customers will
be generated by electronical marketing tools and/or
CRM sources.
The questions WHY and HOW an attitude is built will be
generated by individually styled fiels studies.
Knowledge and interpretation of social interactions will
be won from social platforms, blogs, forums out of the
Internet.
Copyright I.C.A.R.O.S. GmbH 2011
11. Technique of DDM 2
With cluster and factor analysis the most valuable
target group can be identified by combining the three
sources above.
Even more effective is the structured observation of
behavior and its anomalies. In-time interpretation of
behavior anomalies will avoid losses of customers,
dissatisfaction and building a bad reputation.
Indicated actions could be planned and executed in-
time.
Copyright I.C.A.R.O.S. GmbH 2011
12. Technique of DDM 3
DDM is to aim for:
Prevention of customer losses and winning new
clients
Identifying most valuable target groups
Adressing attitudes and values of these customers
Agenda
Copyright I.C.A.R.O.S. GmbH 2011
13. Challenges for DDM 1
Biggest challenge for DDM is defining and identifying
the right and most influential parameters of attitude
and strategies of apology.
Particularly the needed granularity of informationen its
link attitude of customers is the huge question mark
behind Data Driven marketing and Business
Intelligence.
The second crucial topic is in-time delivery of the above
information.
Copyright I.C.A.R.O.S. GmbH 2011
14. Challenges of DDM 2
Jim Novo, the most important professional behind
DDM (1), answers these challenges by a rather simple
advice:
„I think social folks would find more success by not
focussing so much on measuring engagement, but
instead by measuring „dis-engagement“ (2).
(1) http://www.jimnovo.com (2) http://marktamis.com/2010/04/15/data-driven-social-crm/
Agenda
Copyright I.C.A.R.O.S. GmbH 2011
15. DDM implementation
To successfully implement DDM in an organization you
need the following:
Strategic focus of top management
Existence of structured data sources and marketing
tools
Clear vision, what performance indicators measure
aiming the business targets best AND can be
influenced by company management
Agenda
Copyright I.C.A.R.O.S. GmbH 2011
16. Services I.C.A.R.O.S. GmbH
Assessment of relevance and effectiveness of a
DDM strategy for the client company
Consultancy, conception and implementation of
DDM frameworks including marketing tools
Support for market research data generation
Selection of tools and business partner for
implementation of DDM strategy
Agenda
Copyright I.C.A.R.O.S. GmbH 2011
17. More Informationen
I.C.A.R.O.S. GmbH
Viktor Riemer
mobile +491715422715
v_riemer@icaros.de
www.icaros.de
Agenda
Copyright I.C.A.R.O.S. GmbH 2011