The Data Quality Formula details the fundamental elements to data quality; Detection, Analysis and Resolution. In order for businesses to realise success, they must understand the Rule of 3.
The document is an interactive quiz about averages: means, medians, and modes. It contains true/false statements and questions about calculating the mode, mean, and median of sets of numerical data. The mode is the most frequently occurring number, the mean is the average found by adding all values and dividing by the count, and the median is the middle number when the data is ordered from lowest to highest.
DevOps Days SLC 16: Stop running with sharp metricsJulia Wester
There are a thousand metrics floating around and it is difficult to tell what is truly important. Whether you’re the person who is being measured by something that doesn’t quite make sense or the leader that is trying to figure out just how the heck to show to others that her team is successful, there are a lot of questions out there and a lot of people that are just feeling injured by metrics.
Julia Wester will share examples of good and bad techniques for using data when coaching teams. Come, listen and learn how to avoid the pitfalls of managing by numbers, including how to identify and avoid vanity metrics, how to choose metrics that drive desired behaviors, and ways to visualize balanced team metrics that enable continuous improvement.
This document discusses machine learning methods and analysis. It provides an overview of machine learning, including that it allows computer programs to teach themselves from new data. The main machine learning techniques are described as supervised learning, unsupervised learning, and reinforcement learning. Popular applications of these techniques are also listed. The document then outlines the typical steps involved in applying machine learning, including data curation, processing, resampling, variable selection, building a predictive model, and generating predictions. It stresses that while data is important, the right analysis is also needed to apply machine learning effectively. The document concludes by discussing issues like data drift and how to implement validation and quality checks to safeguard automated predictions from such problems.
1. The document discusses four case studies of mistakes the author made in data science projects. The first case involved incorrectly predicting mail return rates without considering sample sizes. The second was backtesting a trading strategy without accounting for data leakage. The third was developing statistical software without proper testing. The fourth was incorrectly calculating an A/B test statistic without considering sample size.
2. In each case, the author explains what went wrong and the lessons learned, such as considering sample sizes, understanding where data comes from, testing software appropriately, and not compounding uncertainties when calculating statistics. The author also discusses potential pitfalls in machine learning, like incorrectly sparsifying models or using PCA before regression.
ML Drift - How to find issues before they become problemsAmy Hodler
Over time, our AI predictions degrade. Full Stop.
Whether it's concept drift where the relationships of our data to what we're trying to predict as changed or data drift where our production data no longer resembles the historical training data, identifying meaningful ML drift versus spurious or acceptable drift is tedious. Not to mention the difficulty of uncovering which ML features are the source of poorer accuracy.
This session looked at the key types of machine learning drift and how to catch them before they become a problem.
Inside the Contact Center Coaching PlaybookScott Buchanan
What's the most important job of a contact center supervisor? Coaching their team.
We've captured the best practices across the Top Places to Work, and shared their coaching principles and processes.
The document is an interactive quiz about averages: means, medians, and modes. It contains true/false statements and questions about calculating the mode, mean, and median of sets of numerical data. The mode is the most frequently occurring number, the mean is the average found by adding all values and dividing by the count, and the median is the middle number when the data is ordered from lowest to highest.
DevOps Days SLC 16: Stop running with sharp metricsJulia Wester
There are a thousand metrics floating around and it is difficult to tell what is truly important. Whether you’re the person who is being measured by something that doesn’t quite make sense or the leader that is trying to figure out just how the heck to show to others that her team is successful, there are a lot of questions out there and a lot of people that are just feeling injured by metrics.
Julia Wester will share examples of good and bad techniques for using data when coaching teams. Come, listen and learn how to avoid the pitfalls of managing by numbers, including how to identify and avoid vanity metrics, how to choose metrics that drive desired behaviors, and ways to visualize balanced team metrics that enable continuous improvement.
This document discusses machine learning methods and analysis. It provides an overview of machine learning, including that it allows computer programs to teach themselves from new data. The main machine learning techniques are described as supervised learning, unsupervised learning, and reinforcement learning. Popular applications of these techniques are also listed. The document then outlines the typical steps involved in applying machine learning, including data curation, processing, resampling, variable selection, building a predictive model, and generating predictions. It stresses that while data is important, the right analysis is also needed to apply machine learning effectively. The document concludes by discussing issues like data drift and how to implement validation and quality checks to safeguard automated predictions from such problems.
1. The document discusses four case studies of mistakes the author made in data science projects. The first case involved incorrectly predicting mail return rates without considering sample sizes. The second was backtesting a trading strategy without accounting for data leakage. The third was developing statistical software without proper testing. The fourth was incorrectly calculating an A/B test statistic without considering sample size.
2. In each case, the author explains what went wrong and the lessons learned, such as considering sample sizes, understanding where data comes from, testing software appropriately, and not compounding uncertainties when calculating statistics. The author also discusses potential pitfalls in machine learning, like incorrectly sparsifying models or using PCA before regression.
ML Drift - How to find issues before they become problemsAmy Hodler
Over time, our AI predictions degrade. Full Stop.
Whether it's concept drift where the relationships of our data to what we're trying to predict as changed or data drift where our production data no longer resembles the historical training data, identifying meaningful ML drift versus spurious or acceptable drift is tedious. Not to mention the difficulty of uncovering which ML features are the source of poorer accuracy.
This session looked at the key types of machine learning drift and how to catch them before they become a problem.
Inside the Contact Center Coaching PlaybookScott Buchanan
What's the most important job of a contact center supervisor? Coaching their team.
We've captured the best practices across the Top Places to Work, and shared their coaching principles and processes.
Video Games, Mathematical Problem Solving, and Cross-Platform Learningjamonjanight
This document summarizes research on the educational video game Cyberchase and its impact on children's mathematical problem solving abilities. The study found that children who used Cyberchase across multiple media platforms (TV, web, hands-on activities) improved more on problem solving tests than those using only one platform. Analyzing player data from the online games revealed shifts over time towards more sophisticated problem solving strategies. The researchers concluded that access to an educational world across media facilitated transfer of learning and enhanced motivation.
The document discusses the reliability of databases, noting that while databases offer accessibility, a medical database error in the US Veteran Affairs system resulted in patients receiving incorrect medications or dosages for extended periods, which in some cases was dangerous. While databases are the future, this incident shows they must be closely monitored and made more reliable through investment, as they cannot always be fully trusted due to the risk of mistakes or glitches that could potentially harm or kill people if unnoticed.
Progressive Improvement ~ A case study using NEET Weeklyquestionbang
This document presents a case study analyzing data from an app called NEET Weekly over a four week period from December 3rd to December 24th. It shows the top three scorers each week and the trend of highest scores increasing over time. It also shows the average trend of all contestants' scores was increasing. The conclusion is that using the NEET Weekly app is a smart way to prepare for medical entrance exams.
The five whys tool is used to analyze causes and effects through asking why up to five times to get to the root cause of a problem quickly. Starting with a defined problem, you ask why it exists and then why those reasons exist, tracing it back to its origin. The tool can also work in reverse, starting with a solution and tracing out potential effects to check for unwanted side effects and ways to improve the solution. The visual result is a deductive mind map that shows the hierarchical relationship between a problem and its underlying causes or a solution and its potential effects.
Summary of responses Safer Internet 2015 questionnairedoragk
The document summarizes the results of a questionnaire about internet usage among 42 respondents. It found that most respondents have internet access at home and use it daily, including for entertainment, information, and homework. Respondents reported spending varying amounts of time online per day, with over 40% spending more than 3 hours. About half like meeting people online, while over 30% think they may be addicted to the internet. Security issues like password sharing and hacking were reported by some.
The document provides guidance on problem solving techniques, with an emphasis on identifying the root cause rather than just fixing the problem. It outlines 8 keys to better problem solving, including keeping analyses simple, focusing on performance differences rather than possible causes, thoroughly documenting all steps, and maintaining discipline. The document then provides a specific methodology for problem solving, beginning with defining whether the problem is new or long-standing, writing a general and defined problem statement, considering any recent changes, establishing performance metrics for evaluation, and writing a final problem statement incorporating the metrics and comparison strategy.
The little spreadsheet overconfidently took on a complex task that was too difficult for it, despite its normal work of valuing simple projects. Spreadsheet errors often arise from overconfidence, where the creator thinks they can handle a more complex problem than they realistically can. There are three main types of spreadsheet errors: mechanical errors from mistyping numbers, logic errors from using the wrong formula, and omission errors by leaving something out. It is important to check work at the end of developing a spreadsheet to reduce errors, as error rates remain consistent even for experienced spreadsheet users. Framing the design carefully and double checking work can help recognize and avoid spreadsheet errors.
Data Analysis Goes Wrong by Microsoft Sr PMProduct School
1. The document discusses common pitfalls in data analysis projects and provides advice on how to avoid them.
2. It emphasizes starting with clearly defining the problem, finding data rooted in reality, and understanding how incentives could influence the data collection process.
3. The presentation also stresses the importance of doing a sanity check on results and recognizing that data collection can sometimes have unintended consequences by altering the system being observed.
The document discusses strategies for fostering organizational alignment through learning and growth. It emphasizes connecting an organization's mission, vision, strategies, and objectives to line operations to bridge the gap between strategy and execution. It also stresses the importance of capturing actual performance data, analyzing discrepancies, and using that data to modify strategies, set new targets, and ensure accountability across the organization.
Best machine learning algorithms for classification - infographicIntellspot
This document compares and contrasts several machine learning algorithms for classification tasks: Naive Bayes, Decision Trees, Support Vector Machines, Random Forest Classifier, and K-Nearest Neighbors (KNN). Naive Bayes is simple and fast but assumes independence between features. Decision Trees are easy to understand but can overfit. Support Vector Machines are effective in high dimensions but don't scale well to large datasets. Random Forests have high accuracy but are computationally expensive. KNN is simple to implement but performance depends on choosing the right value for k.
The document discusses several key concepts in machine learning including reinforcement learning, evolutionary learning, features, training/test/validation sets, overfitting, underfitting, and clustering. Reinforcement learning involves training an agent through rewards/punishments without being directly told what to do. Evolutionary learning follows biological evolution principles of inheritance, variation, and selection. Features represent attributes of an object encoded in a vector. Training/test/validation sets are used to develop and evaluate models. Overfitting and underfitting refer to models fitting the training data too closely or not closely enough, respectively. Clustering groups similar objects together.
Reperes Sensometrics 08 Driver Studies Using Bayesian NetworksFrançois Abiven
The document summarizes a study that used Bayesian networks to understand the consumer drivers of liking for baby food products. It analyzed survey data from 1770 mothers who tested 15 baby food products. The Bayesian network identified 5 key consumer dimensions that impact overall liking: 1) mother's sensory perception, 2) baby's perceived enjoyment, and 3) perception of health benefits. The model found that a mother's sensory evaluation, even before feeding the baby, impacts overall liking. Optimizing aspects like color could significantly increase the likelihood that mothers rate products positively. Validation tests confirmed the model's ability to understand and predict consumer behavior.
This document provides an overview of how to use Microsoft Outlook to manage business tasks and contacts more effectively. It highlights that Realtors who track contacts in a database earn more than those who don't. Outlook allows users to categorize contacts, schedule appointments, set reminders for follow-ups, and filter emails. The document reviews how to use Outlook's email, contacts, calendar, and tasks features to organize work more efficiently. It emphasizes finding a system that works for the individual user.
The document describes two coffee mixes, Mix A with a 2:3 ratio of milk to coffee and Mix B with a 3:4 ratio. It asks which mix will be more "milky" and to solve the problem in two ways, one without using fractions, percentages, or decimals and using a diagram. It then shows one student's work using percentages and another student's thinking that the mixes are equally milky because they each have one more unit of coffee than milk.
East is clinical trial design software that allows users to quickly generate multiple trial designs, assess their operating characteristics through simulation, and share designs with stakeholders through customizable reports and graphs. It has been extensively tested and validated, with designs relied upon for over 20 years in numerous pharmaceutical studies. The document provides two examples of clinical trial scenarios that could be modeled in East: a schizophrenia trial comparing a new drug to placebo on negative symptom outcomes, and an adjuvant breast cancer trial comparing Femara to Tamoxifen on disease-free survival.
How to Make Awesome SlideShares: Tips & TricksSlideShare
Turbocharge your online presence with SlideShare. We provide the best tips and tricks for succeeding on SlideShare. Get ideas for what to upload, tips for designing your deck and more.
February 16, 2017 -- Experian Data Quality hosts a fundraiser to support YouthBuild, a Boston-based nonprofit working to better the lives of low-income individuals by providing education and professional development opportunities. In partnership with renowned sales trainer, Ed Wal, the event raised over $8,000.
Atelier Lean Feedback créé par Léa Mendes Da Silva et Guillaume Bruère. Comment traiter les feedbacks de ses utilisateurs.
Atelier donné à Flupa puis à l'ECV digital pour les Master 2 UX design.
Video Games, Mathematical Problem Solving, and Cross-Platform Learningjamonjanight
This document summarizes research on the educational video game Cyberchase and its impact on children's mathematical problem solving abilities. The study found that children who used Cyberchase across multiple media platforms (TV, web, hands-on activities) improved more on problem solving tests than those using only one platform. Analyzing player data from the online games revealed shifts over time towards more sophisticated problem solving strategies. The researchers concluded that access to an educational world across media facilitated transfer of learning and enhanced motivation.
The document discusses the reliability of databases, noting that while databases offer accessibility, a medical database error in the US Veteran Affairs system resulted in patients receiving incorrect medications or dosages for extended periods, which in some cases was dangerous. While databases are the future, this incident shows they must be closely monitored and made more reliable through investment, as they cannot always be fully trusted due to the risk of mistakes or glitches that could potentially harm or kill people if unnoticed.
Progressive Improvement ~ A case study using NEET Weeklyquestionbang
This document presents a case study analyzing data from an app called NEET Weekly over a four week period from December 3rd to December 24th. It shows the top three scorers each week and the trend of highest scores increasing over time. It also shows the average trend of all contestants' scores was increasing. The conclusion is that using the NEET Weekly app is a smart way to prepare for medical entrance exams.
The five whys tool is used to analyze causes and effects through asking why up to five times to get to the root cause of a problem quickly. Starting with a defined problem, you ask why it exists and then why those reasons exist, tracing it back to its origin. The tool can also work in reverse, starting with a solution and tracing out potential effects to check for unwanted side effects and ways to improve the solution. The visual result is a deductive mind map that shows the hierarchical relationship between a problem and its underlying causes or a solution and its potential effects.
Summary of responses Safer Internet 2015 questionnairedoragk
The document summarizes the results of a questionnaire about internet usage among 42 respondents. It found that most respondents have internet access at home and use it daily, including for entertainment, information, and homework. Respondents reported spending varying amounts of time online per day, with over 40% spending more than 3 hours. About half like meeting people online, while over 30% think they may be addicted to the internet. Security issues like password sharing and hacking were reported by some.
The document provides guidance on problem solving techniques, with an emphasis on identifying the root cause rather than just fixing the problem. It outlines 8 keys to better problem solving, including keeping analyses simple, focusing on performance differences rather than possible causes, thoroughly documenting all steps, and maintaining discipline. The document then provides a specific methodology for problem solving, beginning with defining whether the problem is new or long-standing, writing a general and defined problem statement, considering any recent changes, establishing performance metrics for evaluation, and writing a final problem statement incorporating the metrics and comparison strategy.
The little spreadsheet overconfidently took on a complex task that was too difficult for it, despite its normal work of valuing simple projects. Spreadsheet errors often arise from overconfidence, where the creator thinks they can handle a more complex problem than they realistically can. There are three main types of spreadsheet errors: mechanical errors from mistyping numbers, logic errors from using the wrong formula, and omission errors by leaving something out. It is important to check work at the end of developing a spreadsheet to reduce errors, as error rates remain consistent even for experienced spreadsheet users. Framing the design carefully and double checking work can help recognize and avoid spreadsheet errors.
Data Analysis Goes Wrong by Microsoft Sr PMProduct School
1. The document discusses common pitfalls in data analysis projects and provides advice on how to avoid them.
2. It emphasizes starting with clearly defining the problem, finding data rooted in reality, and understanding how incentives could influence the data collection process.
3. The presentation also stresses the importance of doing a sanity check on results and recognizing that data collection can sometimes have unintended consequences by altering the system being observed.
The document discusses strategies for fostering organizational alignment through learning and growth. It emphasizes connecting an organization's mission, vision, strategies, and objectives to line operations to bridge the gap between strategy and execution. It also stresses the importance of capturing actual performance data, analyzing discrepancies, and using that data to modify strategies, set new targets, and ensure accountability across the organization.
Best machine learning algorithms for classification - infographicIntellspot
This document compares and contrasts several machine learning algorithms for classification tasks: Naive Bayes, Decision Trees, Support Vector Machines, Random Forest Classifier, and K-Nearest Neighbors (KNN). Naive Bayes is simple and fast but assumes independence between features. Decision Trees are easy to understand but can overfit. Support Vector Machines are effective in high dimensions but don't scale well to large datasets. Random Forests have high accuracy but are computationally expensive. KNN is simple to implement but performance depends on choosing the right value for k.
The document discusses several key concepts in machine learning including reinforcement learning, evolutionary learning, features, training/test/validation sets, overfitting, underfitting, and clustering. Reinforcement learning involves training an agent through rewards/punishments without being directly told what to do. Evolutionary learning follows biological evolution principles of inheritance, variation, and selection. Features represent attributes of an object encoded in a vector. Training/test/validation sets are used to develop and evaluate models. Overfitting and underfitting refer to models fitting the training data too closely or not closely enough, respectively. Clustering groups similar objects together.
Reperes Sensometrics 08 Driver Studies Using Bayesian NetworksFrançois Abiven
The document summarizes a study that used Bayesian networks to understand the consumer drivers of liking for baby food products. It analyzed survey data from 1770 mothers who tested 15 baby food products. The Bayesian network identified 5 key consumer dimensions that impact overall liking: 1) mother's sensory perception, 2) baby's perceived enjoyment, and 3) perception of health benefits. The model found that a mother's sensory evaluation, even before feeding the baby, impacts overall liking. Optimizing aspects like color could significantly increase the likelihood that mothers rate products positively. Validation tests confirmed the model's ability to understand and predict consumer behavior.
This document provides an overview of how to use Microsoft Outlook to manage business tasks and contacts more effectively. It highlights that Realtors who track contacts in a database earn more than those who don't. Outlook allows users to categorize contacts, schedule appointments, set reminders for follow-ups, and filter emails. The document reviews how to use Outlook's email, contacts, calendar, and tasks features to organize work more efficiently. It emphasizes finding a system that works for the individual user.
The document describes two coffee mixes, Mix A with a 2:3 ratio of milk to coffee and Mix B with a 3:4 ratio. It asks which mix will be more "milky" and to solve the problem in two ways, one without using fractions, percentages, or decimals and using a diagram. It then shows one student's work using percentages and another student's thinking that the mixes are equally milky because they each have one more unit of coffee than milk.
East is clinical trial design software that allows users to quickly generate multiple trial designs, assess their operating characteristics through simulation, and share designs with stakeholders through customizable reports and graphs. It has been extensively tested and validated, with designs relied upon for over 20 years in numerous pharmaceutical studies. The document provides two examples of clinical trial scenarios that could be modeled in East: a schizophrenia trial comparing a new drug to placebo on negative symptom outcomes, and an adjuvant breast cancer trial comparing Femara to Tamoxifen on disease-free survival.
How to Make Awesome SlideShares: Tips & TricksSlideShare
Turbocharge your online presence with SlideShare. We provide the best tips and tricks for succeeding on SlideShare. Get ideas for what to upload, tips for designing your deck and more.
February 16, 2017 -- Experian Data Quality hosts a fundraiser to support YouthBuild, a Boston-based nonprofit working to better the lives of low-income individuals by providing education and professional development opportunities. In partnership with renowned sales trainer, Ed Wal, the event raised over $8,000.
Atelier Lean Feedback créé par Léa Mendes Da Silva et Guillaume Bruère. Comment traiter les feedbacks de ses utilisateurs.
Atelier donné à Flupa puis à l'ECV digital pour les Master 2 UX design.
8 Ways a Digital Media Platform is More Powerful than “Marketing”New Rainmaker
You may have heard that “media not marketing” is the future of online business … but what does that actually mean, what can it look like?
As you’ll see in this SlideShare, examples of a media-first approach done very well are all around us, it only takes a simple shift in thinking to see them.
Can this "media not marketing" approach to building an audience have an actual effect on the bottom line revenue of your business, or is it just more philosophical wordplay?
Let's find out ...
The document provides five design principles for creating slides that effectively communicate messages to audiences:
1. Focus on the main message you want the audience to remember.
2. Keep designs simple with less text and only 1 main point per slide.
3. Use interesting fonts instead of boring standard ones to engage audiences.
4. Include high quality images that visually represent the message.
5. Choose a color scheme that fits the theme and works cohesively.
Rand Fishkin discusses why content marketing often fails and provides 5 key reasons: 1) Unrealistic expectations of how content marketing works, 2) Creating content without a community to amplify it, 3) Focusing on content creation but not amplification, 4) Ignoring search engine optimization, and 5) Giving up too soon and not allowing time for content to gain traction. He emphasizes that content marketing is a long-term process of building relationships and that most successful content took years of iteration before gaining significant reach.
The SlideShare 101 is a quick start guide if you want to walk through the main features that the platform offers. This will keep getting updated as new features are launched.
The SlideShare 101 replaces the earlier "SlideShare Quick Tour".
SlideShare now has a player specifically designed for infographics. Upload your infographics now and see them take off! Need advice on creating infographics? This presentation includes tips for producing stand-out infographics. Read more about the new SlideShare infographics player here: http://wp.me/p24NNG-2ay
This infographic was designed by Column Five: http://columnfivemedia.com/
No need to wonder how the best on SlideShare do it. The Masters of SlideShare provides storytelling, design, customization and promotion tips from 13 experts of the form. Learn what it takes to master this type of content marketing yourself.
This document provides tips to avoid common mistakes in PowerPoint presentation design. It identifies the top 5 mistakes as including putting too much information on slides, not using enough visuals, using poor quality or unreadable visuals, having messy slides with poor spacing and alignment, and not properly preparing and practicing the presentation. The document encourages presenters to use fewer words per slide, high quality images and charts, consistent formatting, and to spend significant time crafting an engaging narrative and rehearsing their presentation. It emphasizes that an attractive design is not as important as being an effective storyteller.
10 Ways to Win at SlideShare SEO & Presentation OptimizationOneupweb
Thank you, SlideShare, for teaching us that PowerPoint presentations don't have to be a total bore. But in order to tap SlideShare's 60 million global users, you must optimize. Here are 10 quick tips to make your next presentation highly engaging, shareable and well worth the effort.
For more content marketing tips: http://www.oneupweb.com/blog/
This document provides tips for getting more engagement from content published on SlideShare. It recommends beginning with a clear content marketing strategy that identifies target audiences. Content should be optimized for SlideShare by using compelling visuals, headlines, and calls to action. Analytics and search engine optimization techniques can help increase views and shares. SlideShare features like lead generation and access settings help maximize results.
How to Become a Thought Leader in Your NicheLeslie Samuel
Are bloggers thought leaders? Here are some tips on how you can become one. Provide great value, put awesome content out there on a regular basis, and help others.
Each month, join us as we highlight and discuss hot topics ranging from the future of higher education to wearable technology, best productivity hacks and secrets to hiring top talent. Upload your SlideShares, and share your expertise with the world!
Not sure what to share on SlideShare?
SlideShares that inform, inspire and educate attract the most views. Beyond that, ideas for what you can upload are limitless. We’ve selected a few popular examples to get your creative juices flowing.
Root Cause Analysis – A Practice to Understanding and Control the Failure Man...inventionjournals
International Journal of Business and Management Invention (IJBMI) is an international journal intended for professionals and researchers in all fields of Business and Management. IJBMI publishes research articles and reviews within the whole field Business and Management, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
The document discusses non-response error in survey research. It notes that high non-response rates threaten the validity and reliability of research by introducing non-response bias. It recommends several methods for handling non-response error, including comparing early to late respondents, using response speed as a variable, and comparing respondents to a sample of non-respondents. Achieving an acceptable response rate and representative sampling are important for ensuring external validity when generalizing results. The document also provides recommendations for dealing with missing data in quantitative research.
Do You Lead or Micromanage? 6 Symptoms of a MicromanagerWeekdone.com
Read the full post at: https://blog.weekdone.com/lead-or-manage-6-symptoms-micromanager/
Although we can't say where the line between management and micromanagement lies, it's quite common that employees are able to spot micromanagement behaviors from miles away. Yet, managers rarely view themselves as such.
According to statistics, 79% of employees have been micromanaged at some point or another. Some choose to put up with it, some fight back and others choose to leave. Nevertheless, 91% of managers are unaware their micromanagement behaviors might cause people to leave. Since they don't get feedback, it's difficult to change something you're not aware of.
Here are 6 common symptoms micromanagers share. It's time to reflect and see when was the last time you practices some of these behaviors. Or perhaps when was the last time your manager was reluctant to delegate or got stuck in the details?
Read the full post to discover cures and get a full description: https://blog.weekdone.com/lead-or-manage-6-symptoms-micromanager/
Problem Solving Tools and Techniques by TQMIAndrew Leong
This handy guide is for anyone involved in problem solving and improvement activities. It contains guidelines on the use of many of the tools and techniques which can be used as part of a Continuous Improvement process.
This handy guide is for anyone involved in problem solving and improvement activities. It contains guidelines on the use of many of the tools and techniques which can be used as part of a Continuous Improvement process.
Analysis of how to start thinking like a data scientist by thomas c. redmanDarpan Deoghare
The document discusses how managers can start thinking like data scientists. It emphasizes that data is becoming more important in every industry and managers who are not data savvy will be at a disadvantage. It provides insights on how to start analyzing small data, design questions based on data, visualize answers, and identify gaps to modify data collection. Managers should draw pictures to understand and communicate data, develop summary statistics, understand variation for deeper insights, and continuously ask new questions. The relevance for managers includes conducting primary and complex analysis, sharing insights with data scientists, and identifying talented data scientists to allocate significant tasks.
- Root cause analysis (RCA) seeks to identify the underlying cause of a problem by tracing actions back through their effects. This helps determine what happened, why, and how to reduce future recurrence.
- Key techniques for RCA include the five whys analysis, fishbone diagram, Pareto chart, and failure mode and effects analysis (FMEA). These help structure the analysis and identify multiple contributing causes.
- Proper RCA requires focusing on systems and processes, not just symptoms, to fully address the root causes and prevent problems from recurring.
Root cause analysis (RCA) aims to identify the underlying causes of problems in order to develop effective solutions. The key principles of RCA are to correct root causes rather than symptoms, perform analysis systematically using evidence, and identify multiple root causes that may require multiple solutions. The general RCA process involves defining the problem, gathering data, identifying contributing issues and root causes, developing recommendations to prevent recurrence, implementing changes, and ensuring effectiveness. Tools for RCA include cause mapping, 5 whys, Pareto charts, fishbone diagrams, and charts/graphs. Effective RCA requires thorough information gathering and an understanding of the sequence of events.
Invented in the 1930’s by Toyota Founder Kiichiro Toyoda’s father Sakichi and made popular in the 1970s by the Toyota Production System, the 5 Whys strategy involves looking at any problem and asking: “Why?” and “What caused this problem?”
This document provides an overview of data analytics including:
- Key topics in data analytics like popular job roles, tools, skills needed, and industries that use data analytics.
- Examples of how data analytics has been used like predicting customer churn in telecommunications, detecting fraud in energy utilities, and analyzing school performance data.
- Different analytical solutions like predictive modeling, statistical analysis, and data-driven decision making are discussed along with case studies.
- Popular skills, roles, and tools in data analytics like data scientists, data analysts, Tableau, R, Python are highlighted.
This document provides guidance on how to start thinking like a data scientist. It recommends starting with a question that interests you and developing a plan to collect relevant data to answer the question. It stresses the importance of trusting the data collected and addressing any gaps. The document advises visualizing the data through pictures to help understand and communicate findings. It also emphasizes determining the significance of the results by asking "so what?" and ensuring findings are both interesting and important before concluding the analysis.
This document discusses common problems that can occur with AB testing and experimentation. It outlines potential issues with random assignment, contamination during experiments, incorrect data logging, using improper metrics, and statistical analysis mistakes. The document also explains when experimentation may not be the appropriate method, such as when you don't have enough users, success metrics, or can only demonstrate parity rather than drive improvement. It emphasizes the importance of establishing a "metrics philosophy" and using multiple methods like UX research alongside experimentation.
Avoid organizationalmistakes by innovative thinkingSelf-employed
1. The document discusses 10 common mistakes organizations make with performance measures. These include relying solely on financial statements, only looking at monthly or quarterly results, setting goals without ways to measure them, using poor methods like brainstorming to select measures, overreliance on technology to fix measurement problems, using tables instead of graphs to report results, failing to identify relationships between measures, excluding staff from analysis and improvement, collecting too much useless data and not enough relevant data, and using measures solely to reward and punish individuals.
2. Key mistakes are focusing only on lagging financial indicators, short-term results, and goals without measurement; as well as poor methods for selecting measures, not understanding relationships between measures, and not
Workforce analytics, also called HR analytics or people analytics is getting much attention lately. And rightly so! Research has shown that companies using data to drive their decisions and actions are more succesfull than others. With (predictive) analytics an accurate view of the future requires predictions based on data rather than personal hunches or speculation.
8D Training Material From VDiversify.com | 8D Training Material PDF Free Down...VDiversify
Note: Whoever is using this Training Material on their Website shall Link back to www.vdiversify.com as the Original Author...
The 8D (Eight Disciplines) approach is a robust and systematic problem-solving process or methodology, that is widely adopted in the manufacturing, process and other industries.
This 8D training material is completely free and can be used by any organization, professionals, engineers, trainers or teachers all over the world for teaching its employees or students.
The document discusses the importance of using business intelligence and data analytics in staffing and recruiting firms. It notes that only 22% of small to mid-sized organizations currently use business intelligence solutions. It then discusses some common barriers to adopting business intelligence, such as poor data quality, not knowing what metrics to measure, not knowing where to start, and not having enough time. The document proposes focusing on one key metric per day of the workweek to help simplify getting started with business intelligence. It provides examples of metrics to track on each day of the workweek, including open job orders on Monday, sales forecast on Tuesday, etc. The overall message is that regularly analyzing metrics can help improve data quality, decision making and business performance.
Seven steps for Use Routine Information to Improve HIV/AIDS Program_Snyder_5....CORE Group
This document outlines a 7 step approach to using routine data to improve HIV/AIDS programs. The 7 steps are: 1) identify questions of interest, 2) prioritize key questions, 3) identify data needs and sources, 4) transform data into information, 5) interpret information and draw conclusions, 6) craft solutions and take action, and 7) continue to monitor key indicators. The approach aims to facilitate using existing data to answer important questions and inform decision making through collaborative work between data users and producers. Overall, the 7 step approach provides a framework to strategically use routine monitoring data to strengthen HIV/AIDS programs and policies.
We are pleased to share with you the latest VCOSA statistical report on the cotton and yarn industry for the month of May 2024.
Starting from January 2024, the full weekly and monthly reports will only be available for free to VCOSA members. To access the complete weekly report with figures, charts, and detailed analysis of the cotton fiber market in the past week, interested parties are kindly requested to contact VCOSA to subscribe to the newsletter.
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
Introduction to Jio Cinema**:
- Brief overview of Jio Cinema as a streaming platform.
- Its significance in the Indian market.
- Introduction to retention and engagement strategies in the streaming industry.
2. **Understanding Retention and Engagement**:
- Define retention and engagement in the context of streaming platforms.
- Importance of retaining users in a competitive market.
- Key metrics used to measure retention and engagement.
3. **Jio Cinema's Content Strategy**:
- Analysis of the content library offered by Jio Cinema.
- Focus on exclusive content, originals, and partnerships.
- Catering to diverse audience preferences (regional, genre-specific, etc.).
- User-generated content and interactive features.
4. **Personalization and Recommendation Algorithms**:
- How Jio Cinema leverages user data for personalized recommendations.
- Algorithmic strategies for suggesting content based on user preferences, viewing history, and behavior.
- Dynamic content curation to keep users engaged.
5. **User Experience and Interface Design**:
- Evaluation of Jio Cinema's user interface (UI) and user experience (UX).
- Accessibility features and device compatibility.
- Seamless navigation and search functionality.
- Integration with other Jio services.
6. **Community Building and Social Features**:
- Strategies for fostering a sense of community among users.
- User reviews, ratings, and comments.
- Social sharing and engagement features.
- Interactive events and campaigns.
7. **Retention through Loyalty Programs and Incentives**:
- Overview of loyalty programs and rewards offered by Jio Cinema.
- Subscription plans and benefits.
- Promotional offers, discounts, and partnerships.
- Gamification elements to encourage continued usage.
8. **Customer Support and Feedback Mechanisms**:
- Analysis of Jio Cinema's customer support infrastructure.
- Channels for user feedback and suggestions.
- Handling of user complaints and queries.
- Continuous improvement based on user feedback.
9. **Multichannel Engagement Strategies**:
- Utilization of multiple channels for user engagement (email, push notifications, SMS, etc.).
- Targeted marketing campaigns and promotions.
- Cross-promotion with other Jio services and partnerships.
- Integration with social media platforms.
10. **Data Analytics and Iterative Improvement**:
- Role of data analytics in understanding user behavior and preferences.
- A/B testing and experimentation to optimize engagement strategies.
- Iterative improvement based on data-driven insights.
Enhanced data collection methods can help uncover the true extent of child abuse and neglect. This includes Integrated Data Systems from various sources (e.g., schools, healthcare providers, social services) to identify patterns and potential cases of abuse and neglect.
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
1. DETECT ANALYSE RESOLVE
still
REACTIVELY
RESPOND
to data issues by
relying on employees,
customers and prospects
to report problems
It’s a system
that often
FAILS
the data quality equation
remains unsolved
say analysis is the
trickiest part of the
Rule of 3 to follow
Why are they so
PERPLEXED?
The result:
IT’S NO
WONDER THAT:
use every-day
(often dated)
spreadsheet
software
of those using spreadsheet
software are unsatisfied
say data errors are
only reactively
corrected after
something has
gone wrong
Ignoring data
problems doesn’t make
them go away.
Instead, they multiply
Without analysis,
the business
implications of data
problems can’t be seen
Awareness of data
issues means little if
their resolution is
left to chance
are not completely
satisfied with their
resolution strategy
Resolving your
data issues cannot
be done without
detection and analysis
JUST
conduct
AD-HOC
data cleanses
Time
consuming
Slow Incomplete
of large British
companies have a
data quality strategy
don’t include
ALL data types
in their strategy
BUT...
FAIL TO RESOLVE
data issues and the
formula just doesn’t add up
UNSURPRISING
LY
are
unsatisfied
67%
of those,
DETECTION ANALYSIS
REMEMBER
THE RULE
OF 3
Find out more
www.qas.co.uk/3
RESOLUTION
WITHOUT
ANALYSIS
64%
50%
61%
65%
All data used in this infographic is drawn from the ‘Company Data Quality Research 2014’and the ‘Global Data Quality Research
2014’two independent market research reports commissioned by Experian Data Quality and produced by Dynamic Markets.
DETECTION
ANALYSIS
RESOLUTION
These are the fundamental elements of the data quality management
equation that add up to organisational success. Our latest research
shows large British companies realise the importance of
THE RULE OF THREE.
78%
44%
63%
99%
DETECTION + ANALYSIS + RESOLUTION
28%
of those who use specialist
detection software
are still unsatisfied
DETECT, ANALYSE AND RESOLVE YOUR DATA ISSUES.
WITHOUT THE RULE OF 3,
THE DATA QUALITY FORMULA WON’T WORK!