Epson has developed a toolkit to help users analyze data and make decisions. The toolkit outlines a 5-step process: 1) define the problem and data collection plan, 2) collect and clean the data, 3) interpret the data, 4) develop recommendations, and 5) monitor improvements. It also provides guidance on descriptive statistics, data relationships, grouping data, and identifying trends to analyze problems. The overall goal is to help users turn data into actionable insights and impactful decisions.
The document provides an overview of Epson's problem-solving toolkit called the Innovation Engine. It describes Epson's DMAIC problem-solving approach and provides details on the core problem-solving tools used in each phase of DMAIC. These tools include project charters, SIPOC diagrams, process maps, voice of the customer analysis, cause-and-effect diagrams, prioritization matrices, and control plans/charts. It also outlines the typical roles and responsibilities in a problem-solving project and provides links to additional learning resources. The overall toolkit is part of Epson's effort to drive innovation and performance through a structured problem-solving methodology.
This document introduces seven quality tools that can help with data collection and analysis: flowcharts, check sheets, histograms, Pareto diagrams, cause-and-effect diagrams, scatter diagrams, and control charts. Each tool is briefly described and its benefits are listed. Flowcharts map out process steps to improve understanding. Check sheets create easy-to-interpret data. Histograms show data distributions and capabilities. Pareto diagrams identify the most impactful causes. Cause-and-effect diagrams organize variable relationships. Scatter diagrams test relationships. And control charts monitor process performance over time.
This document discusses workflow and process mapping for healthcare organizations implementing health information technology (HIT). It provides an overview of workflow and process mapping, including the purpose of redesigning workflows and processes when implementing HIT. The document outlines the steps in mapping current processes and redesigning processes, including identifying processes to map, performing the mapping, validating maps, identifying problems, and determining root causes of problems. It emphasizes engaging staff in the mapping process and provides examples of process mapping tools and techniques.
The document discusses 4 of the 7 problem solving tools: Cause and Effect Diagrams, Flowcharts, Checklists, and Pareto Analysis. It provides descriptions of what each tool is, why it is used, and an example for each. Cause and Effect Diagrams help identify primary and secondary causes of a problem. Flowcharts visualize processes to detect bottlenecks. Checklists ensure standards are followed. Pareto Analysis identifies the "vital few" key causes that produce most problems.
The A3 Report poster describes the A3 problem solving process from problem identification to resolution in a fashion that fosters learning, collaboration, and personal development.
The poster comes in four themes: light, dark, color and monochrome. Formatted in PDF and in editable PPTX, the poster can be easily printed on an A3-sized paper from an office copier machine and displayed on employee workstations, or distributed together with your workshop handouts.
The A3 Report poster complements your A3 Problem Solving training presentation materials. It serves as a takeaway and summary of your process improvement presentation.
The A3 problem solving process structure includes eight elements:
1. Theme - Concise statement of what this A3 report is about.
2. Background - Relevant historical data and information.
3. Current Condition - Detailed description of the current situation (e.g. process flow, trend chart, Pareto analysis, gap identification and problem statement).
4. Goal Statement - Specific goal to address the gap or future state from the current state.
5. Analysis - Depiction of analytical techniques to uncover the root causes of the problem or factors that affect the problem in the current state.
6. Countermeasures - A summary of who will do what by when in order to resolve the problem situation or achieve the future state.
7. Check Results - Quantitative comparison of actual results versus your goal.
8. Follow Up - Summary of follow up action items (e.g. lessons learned, communication to other parties, training, standardization, or other areas).
Basic Qulaity Tools/Techniques Workshop for process improvementMouad Hourani
This material includes the easiest and most applicable quality tools that could be utilized by staff nurses at the level of direct care givers. some links cant be activated as it is PDF file.
The document discusses structured problem solving techniques including situational awareness, process mapping, identifying customer requirements, problem identification, root cause analysis, implementing changes, and control methods. It provides examples of tools that can be used at each step such as affinity diagrams, histograms, scatter plots, 5 whys, tree diagrams, and benchmarks.
The document provides an overview of Epson's problem-solving toolkit called the Innovation Engine. It describes Epson's DMAIC problem-solving approach and provides details on the core problem-solving tools used in each phase of DMAIC. These tools include project charters, SIPOC diagrams, process maps, voice of the customer analysis, cause-and-effect diagrams, prioritization matrices, and control plans/charts. It also outlines the typical roles and responsibilities in a problem-solving project and provides links to additional learning resources. The overall toolkit is part of Epson's effort to drive innovation and performance through a structured problem-solving methodology.
This document introduces seven quality tools that can help with data collection and analysis: flowcharts, check sheets, histograms, Pareto diagrams, cause-and-effect diagrams, scatter diagrams, and control charts. Each tool is briefly described and its benefits are listed. Flowcharts map out process steps to improve understanding. Check sheets create easy-to-interpret data. Histograms show data distributions and capabilities. Pareto diagrams identify the most impactful causes. Cause-and-effect diagrams organize variable relationships. Scatter diagrams test relationships. And control charts monitor process performance over time.
This document discusses workflow and process mapping for healthcare organizations implementing health information technology (HIT). It provides an overview of workflow and process mapping, including the purpose of redesigning workflows and processes when implementing HIT. The document outlines the steps in mapping current processes and redesigning processes, including identifying processes to map, performing the mapping, validating maps, identifying problems, and determining root causes of problems. It emphasizes engaging staff in the mapping process and provides examples of process mapping tools and techniques.
The document discusses 4 of the 7 problem solving tools: Cause and Effect Diagrams, Flowcharts, Checklists, and Pareto Analysis. It provides descriptions of what each tool is, why it is used, and an example for each. Cause and Effect Diagrams help identify primary and secondary causes of a problem. Flowcharts visualize processes to detect bottlenecks. Checklists ensure standards are followed. Pareto Analysis identifies the "vital few" key causes that produce most problems.
The A3 Report poster describes the A3 problem solving process from problem identification to resolution in a fashion that fosters learning, collaboration, and personal development.
The poster comes in four themes: light, dark, color and monochrome. Formatted in PDF and in editable PPTX, the poster can be easily printed on an A3-sized paper from an office copier machine and displayed on employee workstations, or distributed together with your workshop handouts.
The A3 Report poster complements your A3 Problem Solving training presentation materials. It serves as a takeaway and summary of your process improvement presentation.
The A3 problem solving process structure includes eight elements:
1. Theme - Concise statement of what this A3 report is about.
2. Background - Relevant historical data and information.
3. Current Condition - Detailed description of the current situation (e.g. process flow, trend chart, Pareto analysis, gap identification and problem statement).
4. Goal Statement - Specific goal to address the gap or future state from the current state.
5. Analysis - Depiction of analytical techniques to uncover the root causes of the problem or factors that affect the problem in the current state.
6. Countermeasures - A summary of who will do what by when in order to resolve the problem situation or achieve the future state.
7. Check Results - Quantitative comparison of actual results versus your goal.
8. Follow Up - Summary of follow up action items (e.g. lessons learned, communication to other parties, training, standardization, or other areas).
Basic Qulaity Tools/Techniques Workshop for process improvementMouad Hourani
This material includes the easiest and most applicable quality tools that could be utilized by staff nurses at the level of direct care givers. some links cant be activated as it is PDF file.
The document discusses structured problem solving techniques including situational awareness, process mapping, identifying customer requirements, problem identification, root cause analysis, implementing changes, and control methods. It provides examples of tools that can be used at each step such as affinity diagrams, histograms, scatter plots, 5 whys, tree diagrams, and benchmarks.
Practicing Structured Problem Solving MethodologySarthak Banerjee
This Presentation shows how to practice Structured Problem Solving Approach in order to identify root cause of the problem and implementing solutions for the same.
This document discusses various methodologies and tools for quality management. It describes tools like PDCA-CQI, DMAIC, and DMADV which are used for quality improvement. It also explains the seven basic quality control tools including histograms, Pareto charts, control charts, flow charts and cause-and-effect diagrams. These tools help identify and solve problems, improve processes, and ensure quality management.
This document discusses using outcomes-based measurements for quality assurance. It defines outcomes as specific changes in attitudes, behaviors, knowledge, skills, status, or functioning expected to result from program activities. Choosing meaningful outcomes is emphasized as the first step, even if they are difficult to measure. Sample evaluation questions, a timeline for implementing an outcomes-based system, and how to measure success are provided. Common myths about outcomes evaluation are addressed, emphasizing that evaluation involves ongoing, practical processes rather than a complex science.
University of Utah Health Value Improvement Leaders: MethodologyUniversity of Utah
At the University of Utah, we use a general value improvement methodology based on Lean and Six Sigma with the following phases: Project Definition, Baseline Analysis, Investigation, Design, Implement, Monitor. Problem-solving runs into challenges when an immediate solution is implemented as a reaction to the problem. Following a proven, structured, and balanced improvement methodology forces reflection on a problem.
The document discusses monitoring and evaluation (M&E) in development projects. It provides information on what M&E is, the purposes of M&E, the differences between monitoring and evaluation, and key M&E concepts like indicators, metrics, and M&E plans. Specifically, it defines monitoring as collecting routine data to track project performance over time, while evaluation measures how well objectives were achieved and the impact of the project. It also emphasizes that good indicators for M&E should be valid, reliable, precise, independent, timely, and comparable.
We believe that healthcare can be different by respecting the people on the frontlines of healthcare. For University of Utah Healthcare, respect means supporting healthcare workers through aligned tools. The Value Summary is an attempt to create a common language of value improvement in healthcare through a one-page summary document. It is more than a form; it is a planning guide, a way to share and spread ideas, and a path to earn continuing education credit. It is the currency of value improvement work at University of Utah Healthcare.
This document outlines a structured problem solving methodology called DMAIC. DMAIC stands for Define, Measure, Analyze, Improve, Control. It describes each phase of the methodology and provides examples of techniques that can be used in each phase. The goal of the methodology is to systematically solve problems by first defining the problem, measuring the current performance, analyzing the root causes, improving the process by addressing causes, and controlling the process long-term to sustain the improvements. Using this methodology and appropriate tools at each step helps ensure problems are solved thoroughly and effectively.
IBD BI MC Business Analysis Tools And Tasksbusdeve
The document discusses the role of a business analyst and the tools and tasks they use. It defines a business analyst as a liaison between stakeholders who elicits, analyzes, communicates and validates requirements to provide recommendations on business processes, policies and information systems. It outlines the scope of work for a business analyst, including requirements planning, elicitation, analysis, documentation and communication. It also discusses different methods for dividing work among a team of business analysts, including reviewing activities and deciding on a work division strategy.
Turn data into action and employees into advocates. This guide will help you discover the power of an action plan, as well as how to create and execute a plan that makes measurable improvements to your employee experience.
The Business Benefit of Root Cause AnalysisBen Linders
This paper describes how Root Cause Analysis can be implemented in a way that truly supports business targets. It explains why every RCA session should contribute to specific targets – to ensure that resolving causes found will indeed improve the performance of the organization. In the long run, the improvements also impact the efficiency of RCA sessions, and lead to better follow up for actions.
The document outlines the structured problem solving process which consists of 6 steps: 1) Describe the problem, 2) Identify potential causes and collect data, 3) Evaluate causes against facts and collect more data to identify the root cause, 4) Determine the corrective action, 5) Validate and implement the solution, and 6) Standardize the solution. Various problem solving techniques are presented for each step, such as cause-and-effect diagrams, Pareto charts, and corrective action plans. The goal of the process is to methodically analyze problems, identify root causes, and implement lasting solutions.
Quality management is the process of ensuring that all activities and outputs of an organization meet customer and user requirements. It involves establishing standards and evaluating performance against those standards to identify areas for improvement. The document provides a history of quality management, discusses its significance, and outlines some common quality management methods and tools such as control charts, Pareto charts, scatter plots, Ishikawa diagrams, and histograms. It also lists additional quality management topics and resources for further reading.
Explanation of the seven basic tools used to solve a variety of quality-related issues. They are suitable for people with little formal training in statistics.
The Agile Manager: Empowerment and AlignmentSoftware Guru
In today’s business climate, change occurs at an ever-increasing pace. Managers and executives are increasingly challenged to build an organization that is able to respond effectively to this change. This is the essence of agility.
This talk will provide the latest thinking on building the agile organization, moving beyond the command and control paradigm to one that balances employee empowerment and business alignment.
The document discusses the evolution of quality management over time. It provides an overview of key aspects of quality management including quality control, quality assurance, and quality improvement. The document also lists and describes several common quality management tools, such as check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, and histograms. These tools can be used to evaluate processes, identify issues, and ensure quality standards are met.
The document discusses quality control tools and techniques. It provides an overview of several commonly used tools, including check sheets, process flow charts, control charts, cause and effect diagrams, histograms, Pareto analysis, and scatter diagrams. It then describes the seven new quality control tools: affinity diagrams, relations diagrams, tree diagrams, matrix diagrams, arrow diagrams, process decision program charts, and prioritization matrices. The benefits of using these quality control tools include improving process performance, production efficiency, reducing costs and defects, and enhancing customer satisfaction.
This document provides an overview of statistical process control (SPC) techniques. It discusses the origins and purpose of SPC, describes the key components and interpretation of control charts, and outlines the steps involved in using SPC, including identification of problems, prioritization, data collection, and analysis using various tools. Control charts are presented as the primary analytical tool of SPC for monitoring processes over time and identifying whether processes are in control or require correction.
This document provides an overview and examples of quality management systems. It discusses implementing a quality assurance process to reduce defects and costs. It recommends keeping documentation and processes simple using visual diagrams. Several quality management tools are described, including check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, and histograms. Links are provided to download additional quality management resources.
Statistical analysis process- dr. a.amsavelAmsavel Vel
1. Statistical analysis is a problem solving tool that helps process raw data into useful information for decision making. It involves collecting, organizing, and interpreting numerical data.
2. Statistical tools like control charts, histograms, Pareto charts, cause-and-effect diagrams, and brainstorming can be used to identify problems, analyze causes, prioritize issues, monitor processes, and drive improvement.
3. Process capability analysis compares the natural variation in a process to specification limits to determine if a process is capable of meeting requirements and stable enough for improvement.
Keynote: It's More About Customers and Less About ChannelsMediaPost
The future is less about channels and more about potential customers. Cezanne Huq, online acquisition and business strategist at Intuit, will share best practices that apply to practical management of search in 2014. It's about making potential customers consider the products early in the decision process. Huq will share how Intuit turned customer data, brand value, shop/buy behavior, channel insights and analytics into tools that delight existing and potential clients.
KEYNOTE
Cezanne Huq, Online Acquisition and Business Strategist, Intuit
La tuberculosis es una enfermedad infecciosa causada por bacterias que afecta principalmente los pulmones. Se transmite de persona a persona a través del aire cuando las personas con tuberculosis en los pulmones expulsan bacterias tosiendo, estornudando o hablan. El tratamiento consiste en tomar varios medicamentos antituberculosos durante un período prolongado de tiempo generalmente de 6 meses a un año para matar las bacterias y prevenir que la enfermedad se propague.
Practicing Structured Problem Solving MethodologySarthak Banerjee
This Presentation shows how to practice Structured Problem Solving Approach in order to identify root cause of the problem and implementing solutions for the same.
This document discusses various methodologies and tools for quality management. It describes tools like PDCA-CQI, DMAIC, and DMADV which are used for quality improvement. It also explains the seven basic quality control tools including histograms, Pareto charts, control charts, flow charts and cause-and-effect diagrams. These tools help identify and solve problems, improve processes, and ensure quality management.
This document discusses using outcomes-based measurements for quality assurance. It defines outcomes as specific changes in attitudes, behaviors, knowledge, skills, status, or functioning expected to result from program activities. Choosing meaningful outcomes is emphasized as the first step, even if they are difficult to measure. Sample evaluation questions, a timeline for implementing an outcomes-based system, and how to measure success are provided. Common myths about outcomes evaluation are addressed, emphasizing that evaluation involves ongoing, practical processes rather than a complex science.
University of Utah Health Value Improvement Leaders: MethodologyUniversity of Utah
At the University of Utah, we use a general value improvement methodology based on Lean and Six Sigma with the following phases: Project Definition, Baseline Analysis, Investigation, Design, Implement, Monitor. Problem-solving runs into challenges when an immediate solution is implemented as a reaction to the problem. Following a proven, structured, and balanced improvement methodology forces reflection on a problem.
The document discusses monitoring and evaluation (M&E) in development projects. It provides information on what M&E is, the purposes of M&E, the differences between monitoring and evaluation, and key M&E concepts like indicators, metrics, and M&E plans. Specifically, it defines monitoring as collecting routine data to track project performance over time, while evaluation measures how well objectives were achieved and the impact of the project. It also emphasizes that good indicators for M&E should be valid, reliable, precise, independent, timely, and comparable.
We believe that healthcare can be different by respecting the people on the frontlines of healthcare. For University of Utah Healthcare, respect means supporting healthcare workers through aligned tools. The Value Summary is an attempt to create a common language of value improvement in healthcare through a one-page summary document. It is more than a form; it is a planning guide, a way to share and spread ideas, and a path to earn continuing education credit. It is the currency of value improvement work at University of Utah Healthcare.
This document outlines a structured problem solving methodology called DMAIC. DMAIC stands for Define, Measure, Analyze, Improve, Control. It describes each phase of the methodology and provides examples of techniques that can be used in each phase. The goal of the methodology is to systematically solve problems by first defining the problem, measuring the current performance, analyzing the root causes, improving the process by addressing causes, and controlling the process long-term to sustain the improvements. Using this methodology and appropriate tools at each step helps ensure problems are solved thoroughly and effectively.
IBD BI MC Business Analysis Tools And Tasksbusdeve
The document discusses the role of a business analyst and the tools and tasks they use. It defines a business analyst as a liaison between stakeholders who elicits, analyzes, communicates and validates requirements to provide recommendations on business processes, policies and information systems. It outlines the scope of work for a business analyst, including requirements planning, elicitation, analysis, documentation and communication. It also discusses different methods for dividing work among a team of business analysts, including reviewing activities and deciding on a work division strategy.
Turn data into action and employees into advocates. This guide will help you discover the power of an action plan, as well as how to create and execute a plan that makes measurable improvements to your employee experience.
The Business Benefit of Root Cause AnalysisBen Linders
This paper describes how Root Cause Analysis can be implemented in a way that truly supports business targets. It explains why every RCA session should contribute to specific targets – to ensure that resolving causes found will indeed improve the performance of the organization. In the long run, the improvements also impact the efficiency of RCA sessions, and lead to better follow up for actions.
The document outlines the structured problem solving process which consists of 6 steps: 1) Describe the problem, 2) Identify potential causes and collect data, 3) Evaluate causes against facts and collect more data to identify the root cause, 4) Determine the corrective action, 5) Validate and implement the solution, and 6) Standardize the solution. Various problem solving techniques are presented for each step, such as cause-and-effect diagrams, Pareto charts, and corrective action plans. The goal of the process is to methodically analyze problems, identify root causes, and implement lasting solutions.
Quality management is the process of ensuring that all activities and outputs of an organization meet customer and user requirements. It involves establishing standards and evaluating performance against those standards to identify areas for improvement. The document provides a history of quality management, discusses its significance, and outlines some common quality management methods and tools such as control charts, Pareto charts, scatter plots, Ishikawa diagrams, and histograms. It also lists additional quality management topics and resources for further reading.
Explanation of the seven basic tools used to solve a variety of quality-related issues. They are suitable for people with little formal training in statistics.
The Agile Manager: Empowerment and AlignmentSoftware Guru
In today’s business climate, change occurs at an ever-increasing pace. Managers and executives are increasingly challenged to build an organization that is able to respond effectively to this change. This is the essence of agility.
This talk will provide the latest thinking on building the agile organization, moving beyond the command and control paradigm to one that balances employee empowerment and business alignment.
The document discusses the evolution of quality management over time. It provides an overview of key aspects of quality management including quality control, quality assurance, and quality improvement. The document also lists and describes several common quality management tools, such as check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, and histograms. These tools can be used to evaluate processes, identify issues, and ensure quality standards are met.
The document discusses quality control tools and techniques. It provides an overview of several commonly used tools, including check sheets, process flow charts, control charts, cause and effect diagrams, histograms, Pareto analysis, and scatter diagrams. It then describes the seven new quality control tools: affinity diagrams, relations diagrams, tree diagrams, matrix diagrams, arrow diagrams, process decision program charts, and prioritization matrices. The benefits of using these quality control tools include improving process performance, production efficiency, reducing costs and defects, and enhancing customer satisfaction.
This document provides an overview of statistical process control (SPC) techniques. It discusses the origins and purpose of SPC, describes the key components and interpretation of control charts, and outlines the steps involved in using SPC, including identification of problems, prioritization, data collection, and analysis using various tools. Control charts are presented as the primary analytical tool of SPC for monitoring processes over time and identifying whether processes are in control or require correction.
This document provides an overview and examples of quality management systems. It discusses implementing a quality assurance process to reduce defects and costs. It recommends keeping documentation and processes simple using visual diagrams. Several quality management tools are described, including check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, and histograms. Links are provided to download additional quality management resources.
Statistical analysis process- dr. a.amsavelAmsavel Vel
1. Statistical analysis is a problem solving tool that helps process raw data into useful information for decision making. It involves collecting, organizing, and interpreting numerical data.
2. Statistical tools like control charts, histograms, Pareto charts, cause-and-effect diagrams, and brainstorming can be used to identify problems, analyze causes, prioritize issues, monitor processes, and drive improvement.
3. Process capability analysis compares the natural variation in a process to specification limits to determine if a process is capable of meeting requirements and stable enough for improvement.
Keynote: It's More About Customers and Less About ChannelsMediaPost
The future is less about channels and more about potential customers. Cezanne Huq, online acquisition and business strategist at Intuit, will share best practices that apply to practical management of search in 2014. It's about making potential customers consider the products early in the decision process. Huq will share how Intuit turned customer data, brand value, shop/buy behavior, channel insights and analytics into tools that delight existing and potential clients.
KEYNOTE
Cezanne Huq, Online Acquisition and Business Strategist, Intuit
La tuberculosis es una enfermedad infecciosa causada por bacterias que afecta principalmente los pulmones. Se transmite de persona a persona a través del aire cuando las personas con tuberculosis en los pulmones expulsan bacterias tosiendo, estornudando o hablan. El tratamiento consiste en tomar varios medicamentos antituberculosos durante un período prolongado de tiempo generalmente de 6 meses a un año para matar las bacterias y prevenir que la enfermedad se propague.
TIK memungkinkan pembelajaran jarak jauh yang lebih efektif dan efisien serta mengakses materi pembelajaran dimana saja dan kapan saja. TIK juga mendukung proses desain, produksi, dan simulasi uji coba produk industri secara cepat dan akurat. Peran TIK dalam pendidikan meliputi sebagai kompetensi, infrastruktur, sumber bahan belajar, alat bantu, pendukung manajemen, dan sistem pendukung keput
La realidad aumentada consiste en añadir información virtual a la realidad física existente en lugar de sustituirla, identificando marcadores para colocar imágenes y su orientación espacial. Se puede utilizar en campos como la medicina, la ingeniería, la educación y el marketing.
The shift towards providing a pleasant digital experience to customers has changed the business landscape forever, increasing the need to build new expertise, new marketing campaigns and creating rich and innovative data models to generate intelligence and insights, thereby attempting to personalize our response to every single customer at diverse stages of the decision making process.
A majority of marketing teams continue to complain about getting mixed results from their campaigns and find it challenging to break the silos of channels, platforms, marketing tools and automation platforms and two, majority of them complain about lack of budget, lack of adequate team sizes and are overwhelmed by the enormity of changes affecting the field on one side and inability to get a leadership buy-in on the other.
This session will provide inputs on how you can get past them and build ultra efficient marketing teams for your enterprise.
This document discusses new ways to value premium digital display advertising. It provides case studies on guarantees, viewability, and measuring success on hard-to-measure products like gaming campaigns on Facebook. For guarantees, it suggests questions to ask about audience targets and performance expectations. On viewability, it notes technical limitations and suggests minimum thresholds. The Facebook case study used a control group methodology to measure lift across key metrics from gaming ads on areas like review sites, product pages, and search.
The document contains metrics for lead generation activities in 2009, including new leads by channel, opportunities in the sales pipeline, number of contacts in the database, cost per lead, and conversion rates. Key metrics show an increase in new leads from webcast registrations and free trials over the year, with opportunities in the sales pipeline peaking in October at $785,965. The number of contacts in the database grew but fell short of goals, while cost per lead and landing page conversion rates fluctuated over the four quarters. Email campaign click-through rates ranged from 2.0% to 7.3%.
Presentation: Video killed the Radio Star. Will Mobile Kill the Desktop?MediaPost
Mobile has fundamentally changed how people interact with the Internet and influenced media companies and advertisers to shift their business models to leverage the mobile-first approaches of Google, Facebook and Twitter. The proliferation of mobile advertising and commerce will continue in 2014, but will it come at the expense of desktop search engine marketing? Join CRT Capital Analyst Neil Doshi in a look at a proprietary survey of 1,700 Twitter users. He will share recent insights gleaned from the survey that can help brands capture the attention of Twitter's mostly mobile users.
PRESENTER
Neil Doshi, Managing Director and Senior Equity, CRT
El documento describe varias técnicas didácticas como el diálogo simultáneo, la lectura comentada, el debate dirigido y la dramatización. También describe técnicas expositivas como la exposición y el método del caso, así como apoyos didácticos como la pizarra, el magnetógrafo, los materiales gráficos y audiovisuales.
El documento presenta información sobre PreparedStatement y CallableStatement en Java. PreparedStatement permite definir sentencias SQL genéricas que pueden modificarse dinámicamente cambiando los valores de los parámetros. CallableStatement permite ejecutar procedimientos almacenados e interactuar con sus resultados de forma sencilla. Ambos son sentencias SQL precompiladas que pueden ejecutarse de forma repetida para mejorar el rendimiento frente a usar sentencias normales cada vez.
Guru Gobind Singh Super Thermal Plant is a 1260MW coal-fired power plant located 12km from Roopnagar, India. It has 6 units with 210MW capacity each. Coal is sourced from Bihar, West Bengal, and Madhya Pradesh and burned in boilers to generate steam to run turbines coupled with generators. Sophisticated control systems regulate operations and pollution control systems like electrostatic precipitators control emissions to comply with environmental standards.
The document discusses how retailers like Charlotte Russe are using mobile marketing to build customer databases and engage customers. It outlines Charlotte Russe's strategies, which include collecting opt-ins through point-of-sale, in-store collateral, incentives, and website integration. Charlotte Russe has built a database of hundreds of thousands of customers this way. The future of mobile CRM is also discussed, including more personalized targeting, centralizing customer data, and using rich media like video for higher engagement.
This document discusses trends in marketing analytics from Merkle's perspective. It summarizes that marketing mix modeling and attribution are merging into a single, segment-based approach. It also discusses how cross-channel personalization is becoming more common and the need for consistent segmentation across organizations. Data is exploding in amount and complexity, and there is a shift toward data scientists who combine statistical expertise, technical skills, and business acumen.
3 Ways Brands Can Tackle the Media Transparency ChallengeOrigami Logic
Media transparency is top of mind for many marketers in the wake of the recent report from the Association of National Advertisers. Brands are investing more in digital channels than ever but getting less on a per dollar basis than expected, due to the murky state of the media supply chain. This has recently become more evident with Facebook’s admission that they have wrongly reported video view metrics. Marketers are becoming increasingly hesitant that other publishers have made the same mistakes and haven’t come clean.
Bottom line, many brands lack transparent access to performance data and are finding it increasingly difficult to manage their spend and drive effectiveness across the ever-expanding universe of marketing channels.
Join us for a webinar to discuss three important steps advertisers can take to tackle the transparency challenge and get maximum payback from their marketing investments.
- Build data transparency and accountability across your internal and external global marketing teams with a common view of cross-channel performance.
- Track media spend and progress against your plan by collecting complete, real-time data directly from media partners and publishers.
- Enable a zero-trust approach to data that enlists all parties - internal and external - in verifying performance data to establish trust along the entire data workflow.
Measure Marketing Like It's 2016: A Guided Tour of the Newest Tools & TechniquesOrigami Logic
There are currently more than 4,000 marketing technology companies worldwide, according to industry thought leader Scott Brinker. With marketers placing increasing emphasis on ROI and accountability, the solution category that focuses on cross-channel measurement and analytics is seeing explosive growth. Leading brands are investing in technology to automate campaign measurement and enhance their real-time understanding of strategies that drive marketing performance.
Join Origami Logic for an overview of the latest marketing measurement tools and techniques, and learn how these innovations will interact with your current technology stack and processes.
In this webinar, you’ll get:
- Snapshot of the fastest-growing category of marketing technology: marketing measurement and analytics
- Review of each of the major solutions in the space, and what value they provide to your business
- Guidelines for how to assess and prioritize marketing measurement investments in 2016
The document contains a name - Nataraj Pangal - and what appears to be an identification code - GAPRSG09SMM107. In just 3 words, the essential information is a name and ID code.
Marketing Measurement Mastery Webcast Slides by BECKONAmanda Roberts
Omnichannel marketing measurement doesn’t happen overnight—it’s a journey. Join Forrester Analyst Tina Moffett and Beckon VP of Data Strategy Kevin Dodson to learn how top brands measure and optimize omnichannel marketing performance.
Yext provides local cloud-computing services for marketers to manage their geodata and content to connect it everywhere. Over 200,000 businesses, including dozens of Fortune 500 companies and top retailers.
Data Processing & Explain each term in details.pptxPratikshaSurve4
Data processing involves converting raw data into useful information through various steps. It includes collecting data through surveys or experiments, cleaning and organizing the data, analyzing it using statistical tools or software, interpreting the results, and presenting findings visually through tables, charts and graphs. The goal is to gain insights and knowledge from the data that can help inform decisions. Common data analysis types are descriptive, inferential, exploratory, diagnostic and predictive analysis. Data analysis is important for businesses as it allows for better customer targeting, more accurate decision making, reduced costs, and improved problem solving.
This document provides an overview and instructions for using the 7 Quality Control tools: check sheets, stratification, Pareto charts, cause-and-effect (fishbone) diagrams, histograms, control charts, and scatter diagrams. It describes the objective, rules, background and importance of each tool. For each tool, it addresses the purpose, when to use it, procedure, and benefits. The overall goal is to present these tools to address problem solving and quality improvement through structured data collection and analysis.
Microsoft Excel is a spreadsheet program used to record and analyse numerical and statistical data. Microsoft Excel provides multiple features to perform various operations like calculations, pivot tables, graph tools, macro programming, etc.
An Excel spreadsheet can be understood as a collection of columns and rows that form a table. Alphabetical letters are usually assigned to columns, and numbers are usually assigned to rows. The point where a column and a row meet is called a cell.
SPSS (Statistical Package for the Social Sciences) is a versatile and responsive program designed to undertake a range of statistical procedures. SPSS software is widely used in a range of disciplines and is available from all computer pools within the University of South Australia.
DOE is an essential tool to ensure products and processes satisfy Quality by Design requirements imposed by regulatory agencies. Using a QbD approach to develop your testing process can help you reduce waste, meet compliance criteria and get to market faster.
DOE helps you create a reliable QbD process for assessing formula robustness, determining critical quality attributes and predicting shelf life by using a few months of historical data.
Minitab is a statistics package developed at the Pennsylvania State University by researchers Barbara F. Ryan, Thomas A. Ryan, Jr., and Brian L. Joiner in conjunction with Triola Statistics Company in 1972.
It began as a light version of OMNITAB 80, a statistical analysis program by NIST, which was conceived by Joseph Hilsenrath in years 1962-1964 as OMNITAB program for IBM 7090. The documentation for OMNITAB 80 was last published 1986, and there has been no significant development since then.
R is a language and environment for statistical computing and graphics."
"R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering) and graphical techniques, and is highly extensible."
"One of R's strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed.“
DATA ANALYSIS Presentation Computing Fundamentals.pptxAmarAbbasShah1
This document discusses data analysis and provides details on the following:
- It defines data analysis and provides examples of its use.
- It describes the four main types of data analysis: descriptive, diagnostic, predictive, and prescriptive.
- It outlines the six step data analysis process: data requirement gathering, data collection, data cleaning, analyzing data, data interpretation, and data visualization.
- It provides examples to illustrate each type and step of the analysis process.
- It also lists some commonly used data analysis tools.
This document discusses different approaches to analyzing qualitative and quantitative data from research. It addresses questions like what types of data are common, how to find meanings and patterns, and how to display results effectively. The document provides an overview of quantitative data analysis methods like statistical tests and summarizing data in tables and charts. It also discusses qualitative data analysis, including reducing and organizing text data, coding, conceptualizing, and interpreting meanings. The goal is to help researchers choose appropriate analysis methods based on their research questions, methodological approach, and type of data collected.
Data analysis involves extracting meaningful insights from raw data through visualization, organization, extraction of intelligence, and analysis. It involves the following key steps:
1) Extracting raw data from various sources and organizing it
2) Analyzing the organized data using techniques like regression analysis, time series analysis, and cluster analysis to identify patterns and relationships
3) Interpreting the analysis to derive meaningful and actionable insights that can inform business decisions
Final session in a series of four seminars presented to University of North Texas librarians. This presentation brings together some best practices for gathering, organizing, analyzing, and presenting statistics and data.
Data processing involves 5 key steps: editing data, coding data, classifying data, tabulating data, and creating data diagrams. It transforms raw collected data into a usable format through these steps of cleaning, organizing, and analyzing the data. First, data is collected from sources and prepared by cleaning errors. It is then inputted and processed using algorithms before being output and interpreted in readable formats. Finally, the processed data is stored for future use and reports.
Data processing involves 5 key steps: 1) editing data to check for errors or omissions, 2) coding data by assigning numerals or symbols to categories, 3) classifying data into groups with common characteristics, 4) tabulating data by organizing it into a table for comparison and analysis, and 5) creating data diagrams or visual representations like graphs. The goal of data processing is to transform raw collected data into a readable and interpretable format that can be analyzed and used within an organization.
The document discusses the process of data preparation for analysis. It involves checking data for accuracy, developing a database structure, entering data into the computer, and transforming data. Key steps include logging incoming data, screening for errors, generating a codebook to document the database structure and variables, entering data using double entry to ensure accuracy, and transforming data through handling missing values, reversing items, calculating scale totals, and collapsing variables into categories.
Research and Statistics Report- Estonio, Ryan.pptxRyanEstonio
Statistical tools and treatments can help researchers manage large datasets and better interpret results. Common statistical tools include measures of central tendency like the mean and measures of variability like standard deviation. Regression, hypothesis testing, and statistical software packages are also used. Determining the appropriate tools and treatments for research requires conducting a literature review, consulting experts, considering the study design, and pilot testing options.
Data science involves analyzing data to extract meaningful insights. It uses principles from fields like mathematics, statistics, and computer science. Data scientists analyze large amounts of data to answer questions about what happened, why it happened, and what will happen. This helps generate meaning from data. There are different types of data analysis including descriptive analysis, which looks at past data, diagnostic analysis, which finds causes of past events, and predictive analysis, which forecasts future trends. The data analysis process involves specifying requirements, collecting and cleaning data, analyzing it, interpreting results, and reporting findings. Tools like SAS, Excel, R and Python are used for these tasks.
This document discusses collecting and analyzing data for evaluation purposes. It defines data collection as gathering information through various means and organizing it so it can be easily worked with. Analyzing data involves examining collected information to reveal relationships, patterns, and trends. Both quantitative and qualitative data should be collected from the start of a program through completion and afterwards to evaluate effectiveness. Statistical analysis of quantitative data can show if changes were significant, while qualitative data provides insight into participants' experiences. Collecting and analyzing both types of high-quality data produces the best overall evaluation.
This document provides an overview of exploratory data analysis (EDA). It discusses the key stages of EDA including data requirements, collection, processing, cleaning, exploration, modeling, products, and communication. The stages involve examining available data to discover patterns and relationships. EDA is the first step in data mining projects to understand data without assumptions. The document also outlines the problem definition, data preparation, analysis, and result development and representation steps of EDA. Finally, it discusses different types of data like numeric, categorical, and the importance of understanding data types for analysis.
The presentation covered key steps in analyzing survey data including defining goals, designing valid and reliable survey questions, collecting data, cleaning data, conducting descriptive statistics and correlations, comparing mean differences between groups, and clearly presenting results along with conclusions and recommendations. Piloting surveys and continuously improving methods was also emphasized.
Presentation of Project and Critique.pptxBillyMoses1
This document outlines the key components that should be addressed when critiquing and defending one's own research work. It discusses re-examining the problem statement, research questions, methodology, analysis, and findings. The critique should identify strengths and weaknesses and suggest improvements. When presenting findings, the results should be logically presented and compared to prior literature. Block diagrams and short defenses of the methodology used are recommended. The research's impacts, such as on costs or operations, should also be considered.
Descriptive statistics are methods of describing the characteristics of a data set. It includes calculating things such as the average of the data, its spread and the shape it produces.
1. 1EAI Confidential
Epson Data Analysis and
Decision-Making Toolkit
An unwavering commitment to drive innovation and performance
EPSON
INNOVATION
ENGINEVersion 1.0 – January, 2016
2. Data Analysis and Decision Making Process
Define Problem and
Data Collection Plan
Collect, Validate and
Clean Data
Interpret the Data
Develop
Recommendations
or Make Decisions
• Define the business
problem
• Select exploratory or
hypothesis-driven
approach
• Define problem
statement or hypothesis
to test
• Identify sources of data /
information to explore or
test the hypothesis
• Think with the end in
mind
• Collect the data in the
format needed for
analysis
• Validate and test the
data – make sure it is
correct
• Clean data and format
as required – most
commonly as a flat file
that can be used in
Excel
• Interpret the data (e.g.
whether it supports or
does not support your
hypothesis)
• This step may be
iterative; however, it will
be insightful
• Once confident in
interpretation, brainstorm
ways to improve the
situation (further analysis
may be needed)
• Visually depict data to
support your conclusions
• Create recommendations
or alternatives if required
• Make decisions and
proceed with experiment
– fail fast and adjust
• Monitor any improvement
by continually collecting
and measuring data on
the process or problem
Alignment with problem-solving (DMAIC) phases:
Below depicts how to develop a plan to turn data into actionable or impactful results.
Define Measure Analyze Improve
3. Discovery (Exploratory) vs Hypothesis-driven Research
Past Present
Deviation
Actual Performance
Expected/Desired Performance
Investigation approaches:
• Hypothesis-based method: “I have an idea, let me verify.”
• Begins with a proposition by the user, who then seeks to validate the truthfulness of the
proposition. Click here to learn more.
• Discovery-based method: “I have no clue, let me explore.”
• Finds patterns, associations, and relationships among the data in order to uncover facts that
were previously unknown or not even contemplated by an organization. Click here to learn more.
Often, some preliminary research (discovery) is needed in order to create a hypothesis.
A problem is a deviation from a standard or expectation:
4. Hypothesis-Testing
Symptoms
- Low energy
- Headaches
- Fever
Impact
- Can’t do my job
- Can’t exercise
- Can’t take care of family
Hypotheses
- Cold?
- Flu?
- Tuberculosis?
Potential Causes
Hypothesis Testing
Virus A
Virus B
Virus C
Root Causes
2. Analysis &
Interpretation
1. Pain &
Suffering
3. Testing &
Proof
Symptoms
5. Hypothesis-Testing with Logic Trees
Epson can increase
selling time as a
proportion of total
available time
Epson can increase
sales volumes from
available selling time
How can Epson
increase sales-force
productivity?
Epson can transfer
or outsource many
non-value-added
tasks (e.g. admin)
Epson can reduce or
eliminate many non-
value-added tasks
(e.g. travel, error
correction)
Epson can improve
generation of sales
leads
Epson can improve
the proportion of
leads converted to
sales
Epson can improve
the sales conversion
skills of the sales
force
Epson can provide
the sales force with
better tools for lead
conversion
1. State the
problem
2. Generate
hypotheses
3. Keep
decomposing
them
Postulate an overall hypothesis as to the solution,
with the minimum efficient rationale to validate it.
Each hypothesis:
• Can be proven right or wrong
• Is not obvious
• Points directly to an action or actions you can take
4. Prioritize
them for
analysis
6. Descriptive Statistics Data Relationships: Scatter Plot & Correlation
Summarize large amounts of
data so that the main features
of the data can be easily
understood.
Identifies and visually displays
the relationship between two
variables.
Data Collection Techniques Data Grouping: 2x2 Matrix
Systematically gather
information to be analyzed in
order to develop a deeper
understanding of an issue.
Categorizes items into a 2x2
matrix using two variables in
order to clarify the desirability
of options and simplify
decision making.
Data Distribution: Histogram & Pareto Chart Data Trends: Trend (Run) Charts
Graphically displays data
grouped into ranges or
categories so that the
frequency/quantity of each one
can be better analyzed.
Graphically displays data over
time to identify process trends,
cycles, changes/shifts,
abnormalities, or problems.
Our Core Data Analysis and Visualization Tools
7. Descriptive Statistics
Why use these tools?
To synthesize large amounts of data so that it can be presented in a quantitative, easy to
understand manner (either numerically or graphically). The most popular descriptive statistics
show the central tendency (mean, median, and mode) and the spread of the data (standard
deviation and variance). Note that descriptive statistics only describe/summarize data. More
advanced statistics are needed for hypothesis testing.
What results you can expect?
• Make data easier to understand
and share
• Develop a deeper understanding of
the issue(s) at hand
• Point the direction for further
investigation and analysis
• Provide a basis for more advanced
statistical analysis
Further Learning
• Creating Descriptive Statistics in Excel
| LEARN MORE
8. Data Collection Techniques
Why use this tool?
To systematically gather information in order to develop a deeper understanding of an issue and
answer relevant questions. Typical data collection methods include: observations (note taking and
check sheets), surveys (questionnaires), interviews, and focus groups.
What results you can expect?
• Develop a better understanding of
the issue from new perspectives
• Identify and validate beliefs
• Generate and test hypotheses
• Discover previously unknown
factors
• Improve the probability of
developing effective solutions
Further Learning
• Surveys
• Check Sheet
• Qualitative vs Quantitative Data
| LEARN MORE
9. Data Distribution: Histogram
Why use this tool?
To graphically illustrate a distribution of numerical data by grouping data into ranges (bins) with the
frequencies shown as vertical bars. Histograms are frequently used when there is a large data set.
What results you can expect?
• Graphically display the distribution
of a data set
• Quickly identify ranges with
unusually high or low frequencies
• Point the direction for further
research
• Communicate data to stakeholders
in a simple format
Further Learning
• Dot Plots
• Box & Whisker Plots
• Comparing dot plots, histograms,
and box plots
• Creating Histograms in Excel
| LEARN MORE
10. Data Distribution: Pareto Chart
Why use this tool?
To graphically identify the key issues of a problem by following the 80/20 rule: 80% of the effects
can often be attributed to 20% of the causes. The Pareto chart is a combination histogram/bar
chart and line chart. The histogram/bar chart shows the frequency of the items/events in
descending order of magnitude while the line chart shows the cumulative frequency.
What results you can expect?
• Identify the major issues that need
to be addressed or further
investigated
• Focus analysis where it will have
the greatest impact
• Communicate data to stakeholders
in a simple format
Further Learning
• Creating Pareto Charts in Excel
| LEARN MORE
11. Data Relationships: Scatter Plot (Diagram)
Why use this tool?
To graphically display the relationship between two variables. Each variable is plotted on one axis
of an XY plot. If the variables are correlated (i.e. a relationship exists), the points will form a
pattern. The shape of the pattern indicates the type of relationship between the variables. More
well defined patterns indicate stronger relationships.
What results you can expect?
• Indicate the type and strength of
relationship between two variables
• Eliminate unimportant variables
from further analysis
• Communicate data to stakeholders
in a simple format
Further Learning
• Correlation Analysis
• Regression Analysis
• Creating Scatter Plots in Excel
| LEARN MORE
Vehicle Price vs Age
12. Data Grouping: 2x2 Matrix
Why use these tools?
To categorize items using two data variables in order to clarify the options and simplify decision
making. Generally, the matrix is structured so that the least desirable options fall into the lower left
quadrant and the most desirable options fall into the upper right quadrant.
What results you can expect?
• Rapidly sort options into categories
to facilitate decision making
• Organize data into memorable
categories or groups
• Assess the situation using more
than a single variable
• Communicate data to stakeholders
in a simple format
Further Learning
• Cluster Analysis
13. Data Trends: Trend (Run) Charts
Why use these tools?
To graphically display time series data (data sequenced over time). The horizontal axis displays
time and the vertical axis displays the values of the data. Trend/run charts are often used to
identify process trends, cycles, changes/shifts, abnormalities, or problems.
What results you can expect?
• Identify trends, changes, or
abnormalities with processes
• Increase the understanding of
processes
• Determine if a process change
resulted in improved process
performance
• Determine if improved performance
has been maintained
• Monitor and compare processes
Further Learning
• Statistical Process Control
• Control Charts
• Creating Trend/Run Charts in Excel
| LEARN MORE
14. Sources
Topic Link
Descriptive Statistics
https://www.youtube.com/watch?v=Mpl_v96dlfg
https://www.khanacademy.org/math/probability/descriptive-statistics
https://www.youtube.com/watch?v=MhDH9jsyzBA
Data Collection Techniques
http://www.sciencebuddies.org/science-fair-projects/project_ideas/Soc_survey.shtml
http://qualityamerica.com/LSS-Knowledge-
Center/qualityimprovementtools/check_sheets.php
http://regentsprep.org/regents/math/algebra/ad1/qualquant.htm
http://blog.socialcops.com/resources/4-data-collection-techniques-ones-right
https://www.youtube.com/watch?v=B2nmh_kEF98
Data Distribution: Histogram
https://www.khanacademy.org/math/cc-sixth-grade-math/cc-6th-data-statistics/dot-
plot/v/frequency-tables-and-dot-plots
https://www.khanacademy.org/math/probability/descriptive-statistics/box-and-
whisker-plots/v/reading-box-and-whisker-plots
https://www.khanacademy.org/math/cc-sixth-grade-math/cc-6th-data-statistics/cc-
7th-compare-data-displays/v/comparing-dot-plots-histograms-and-box-plots
https://www.youtube.com/watch?v=YYRkWKJIc9k
https://www.moresteam.com/toolbox/histogram.cfm
https://www.youtube.com/watch?v=gSEYtAjuZ-Y
Data Distribution: Pareto Chart
https://www.youtube.com/watch?v=i_XZzady-dQ
http://asq.org/learn-about-quality/cause-analysis-tools/overview/pareto.html
https://www.youtube.com/watch?v=GVGdtlnZ7xM
15. Sources
Topic Link
Data Relationships: Scatter Plot (Diagram)
https://explorable.com/statistical-correlation
https://www.moresteam.com/toolbox/regression-analysis.cfm
https://www.youtube.com/watch?v=uvJNfRmfAys
http://asq.org/learn-about-quality/cause-analysis-tools/overview/scatter.html
https://www.youtube.com/watch?v=CWnfwZRAuaY
Data Grouping: 2x2 Matrix
https://www.youtube.com/watch?v=zqKFH7WNmfE
https://www.youtube.com/watch?v=PLr3CT79pSc
Data Trends: Trend (Run) Charts
https://www.moresteam.com/toolbox/statistical-process-control-spc.cfm
http://asq.org/learn-about-quality/data-collection-analysis-tools/overview/control-
chart.html
https://www.youtube.com/watch?v=jWlM9z8iFZI
https://www.moresteam.com/toolbox/trend-chart.cfm
https://www.youtube.com/watch?v=YQd1QoMHYwU