This document discusses people analytics and its benefits. People analytics uses data and analytics tools to inform decisions around managing employees, such as hiring, performance, compensation, and retention. It aims to minimize human biases and replace intuitive decision making with data-driven insights. Some benefits include increased employee engagement, identifying top performers and how to compose productive teams. The future of people analytics relies on developing a responsible culture of data use to avoid perceived surveillance and maintain employee trust.
Everyone is a data scientist today, but that is impossible. How do you spot the real data scientist from the fake? Some people just lie. Don't be fooled this presentation will help find the fools
This document discusses the roles of data scientists and data analysts. It provides definitions for both from sources like Gartner and the US Bureau of Labor Statistics. A data scientist represents an evolution from the analyst role, requiring strong business and communication skills in addition to technical skills. Data scientists explore multiple data sources to discover previously hidden insights, while analysts focus on single sources and reporting. The document emphasizes that data scientists pick important business problems and communicate solutions, while cautioning against "fools with tools" being mistaken for qualified professionals.
The document discusses how HR analytics can provide insights that help optimize talent management. It explains that as companies shift from metrics to analytics, they can gain a deeper understanding of factors like retention, recruiting effectiveness, total workforce costs, and employee movement. Advanced analytics involving segmentation, predictive models, and data integration can help HR and business leaders make better decisions around people strategies that improve business outcomes. The document also notes some common challenges around HR data quality and integrating disparate data sources.
Lewis Garrad discusses how analytics is on the rise due to an exponential increase in available data. While traditionally decisions were made based on heuristics and intuition, analytics allows for evidence-based, data-driven decision making. Garrad provides examples of how various companies are using analytics to improve recruitment, performance management, leadership, and other areas. He acknowledges both the promises and pitfalls of analytics, emphasizing that it is still an experiment to determine how data can best help organizations.
This document provides an overview of an HR analytics workshop. It begins by stating the objectives of the workshop which are to explain what HR analytics is, its applications, and how to analyze data and present findings. It then defines HR analytics as applying analytical techniques to talent data to gain insights and aid decisions. The rest of the document discusses analytics maturity levels from reporting to predictive analytics. It provides examples of how analytics can be applied to talent acquisition, retention, performance and job allocation. Finally, it presents a case study on analyzing attrition at an insurance company and includes sample dashboard metrics and analysis that could be performed.
Predictive Hiring: Find Candidates Who Will Succeed in Your OrganizationHuman Capital Media
The facts are clear: Most companies today need to do a better job selecting talent. Recent survey data collected by the Corporate Executive Board indicates that nearly a quarter of all new hires leave their companies within a year, and that hiring managers wish they never extended an offer to one of every five members of their team. And a recent Gallup survey found that 52 percent of American workers were “not engaged” with their work.
Can you afford to miss an opportunity to learn how best-in-class organizations are using new technologies to scientifically assess talent before hiring, resulting in lower turnover, higher job performance and greater employee engagement?
In this presentation, you will:
Learn about new solutions that predict candidate success.
Discover how best-in-class organizations are incorporating these new solutions into their hiring process.
See the bottom-line results realized by these best-in-class practitioners.
This is a presentation that I gave recently to a group of over 300 HR recruiting professionals at a large consulting company. Thought it might be of help to others.
THINKING ABOUT THINKING
Audience: PM & BA
Level: All
Date: May 26
Time: 11:30 AM - 12:30 PM
Description
Thinking is a big part of a Project Manager’s and Business Analyst's job. But how often have you spent time thinking about thinking? This presentation looks at thinking as a critical soft skill for project managers and how a disciplined approach to thinking improves you effectiveness as a change agent for the company in the role of project manager. The presentation will discuss the Thinking Hats, Five Types of Thinking, and brush into the entire world of Business Analytics. The presentation focuses on how the skills of Strategic Analysis, Tactical Analysis, Predictive Analysis, Data mining work together for the complete business management cycle. To add to the thinking equation, the session will explore the power of Social Media sentiment and how the way people "feel" about things is an important factor in the business equation. Think about it !!!!
1. Participants will understand the relationship between planning, analysis, problem solving, decision making and thinking.
2. Students will be able to explain an "Adapting to Whats Happening Model" that includes Data Recording, Strategic Analysis, Tactical Analysis, Predictive Analysis, and Social Media Sentiment. And how it impacts the business.
3. Students will explore various factors of human bias and how that impacts thinking. The student will understand that bias cannot not be completely eliminated, but should be embraced as a human factor in any thinking exercise. The student will understand that personal perspective/bias is a factor, but not THE factor in thinking.
Everyone is a data scientist today, but that is impossible. How do you spot the real data scientist from the fake? Some people just lie. Don't be fooled this presentation will help find the fools
This document discusses the roles of data scientists and data analysts. It provides definitions for both from sources like Gartner and the US Bureau of Labor Statistics. A data scientist represents an evolution from the analyst role, requiring strong business and communication skills in addition to technical skills. Data scientists explore multiple data sources to discover previously hidden insights, while analysts focus on single sources and reporting. The document emphasizes that data scientists pick important business problems and communicate solutions, while cautioning against "fools with tools" being mistaken for qualified professionals.
The document discusses how HR analytics can provide insights that help optimize talent management. It explains that as companies shift from metrics to analytics, they can gain a deeper understanding of factors like retention, recruiting effectiveness, total workforce costs, and employee movement. Advanced analytics involving segmentation, predictive models, and data integration can help HR and business leaders make better decisions around people strategies that improve business outcomes. The document also notes some common challenges around HR data quality and integrating disparate data sources.
Lewis Garrad discusses how analytics is on the rise due to an exponential increase in available data. While traditionally decisions were made based on heuristics and intuition, analytics allows for evidence-based, data-driven decision making. Garrad provides examples of how various companies are using analytics to improve recruitment, performance management, leadership, and other areas. He acknowledges both the promises and pitfalls of analytics, emphasizing that it is still an experiment to determine how data can best help organizations.
This document provides an overview of an HR analytics workshop. It begins by stating the objectives of the workshop which are to explain what HR analytics is, its applications, and how to analyze data and present findings. It then defines HR analytics as applying analytical techniques to talent data to gain insights and aid decisions. The rest of the document discusses analytics maturity levels from reporting to predictive analytics. It provides examples of how analytics can be applied to talent acquisition, retention, performance and job allocation. Finally, it presents a case study on analyzing attrition at an insurance company and includes sample dashboard metrics and analysis that could be performed.
Predictive Hiring: Find Candidates Who Will Succeed in Your OrganizationHuman Capital Media
The facts are clear: Most companies today need to do a better job selecting talent. Recent survey data collected by the Corporate Executive Board indicates that nearly a quarter of all new hires leave their companies within a year, and that hiring managers wish they never extended an offer to one of every five members of their team. And a recent Gallup survey found that 52 percent of American workers were “not engaged” with their work.
Can you afford to miss an opportunity to learn how best-in-class organizations are using new technologies to scientifically assess talent before hiring, resulting in lower turnover, higher job performance and greater employee engagement?
In this presentation, you will:
Learn about new solutions that predict candidate success.
Discover how best-in-class organizations are incorporating these new solutions into their hiring process.
See the bottom-line results realized by these best-in-class practitioners.
This is a presentation that I gave recently to a group of over 300 HR recruiting professionals at a large consulting company. Thought it might be of help to others.
THINKING ABOUT THINKING
Audience: PM & BA
Level: All
Date: May 26
Time: 11:30 AM - 12:30 PM
Description
Thinking is a big part of a Project Manager’s and Business Analyst's job. But how often have you spent time thinking about thinking? This presentation looks at thinking as a critical soft skill for project managers and how a disciplined approach to thinking improves you effectiveness as a change agent for the company in the role of project manager. The presentation will discuss the Thinking Hats, Five Types of Thinking, and brush into the entire world of Business Analytics. The presentation focuses on how the skills of Strategic Analysis, Tactical Analysis, Predictive Analysis, Data mining work together for the complete business management cycle. To add to the thinking equation, the session will explore the power of Social Media sentiment and how the way people "feel" about things is an important factor in the business equation. Think about it !!!!
1. Participants will understand the relationship between planning, analysis, problem solving, decision making and thinking.
2. Students will be able to explain an "Adapting to Whats Happening Model" that includes Data Recording, Strategic Analysis, Tactical Analysis, Predictive Analysis, and Social Media Sentiment. And how it impacts the business.
3. Students will explore various factors of human bias and how that impacts thinking. The student will understand that bias cannot not be completely eliminated, but should be embraced as a human factor in any thinking exercise. The student will understand that personal perspective/bias is a factor, but not THE factor in thinking.
This document discusses the meaning of performance appraisals and fairness in the context of changing views in society. It notes that some now see rewards as disconnected from work effort, eroding the concept of meritocracy. This contributes to calls for eliminating annual performance reviews. The document also outlines several common performance appraisal methods, their advantages and disadvantages, including ranking, rating scales, checklists, critical incidents, essays, and behaviorally anchored rating scales. It provides additional resources on topics related to the meaning of performance appraisals.
In this file, you can ref useful information about need for performance appraisal such as need for performance appraisal methods, need for performance appraisal tips, need for performance appraisal forms, need for performance appraisal phrases … If you need more assistant for need for performance appraisal, please leave your comment at the end of file.
Different Schools Within Two Separate School DistrictsDawn Mora
The research study will use qualitative methods including direct observation, focus groups, and questionnaires to understand perceptions of healthy eating promotion programs in schools. These methods allow for an in-depth exploration of attitudes, experiences, and behaviors through observation of real-world settings and interactions with participants. By triangulating different qualitative data collection techniques, the researcher aims to develop a comprehensive understanding of the problem around lack of healthy eating education from multiple perspectives.
This document discusses forward-looking and predictive metrics that can be used for recruiting. It begins by defining key terms like historical, real-time, and predictive metrics. It then discusses reasons for using traditional metrics, such as increased business results when data-driven decision making is used. Examples of predictive recruiting metrics are also provided, such as predicting changing source effectiveness and upcoming talent availability. The document concludes by outlining elements that make a predictive metric actionable, such as listing revenue impact and recommended actions.
The Outsiders Essay Questions And AnswersAshley Mason
The Outsiders Study Guide Answers. Essay the Outsiders-Eng. The Outsiders (Papers and Projects) | PDF | Essays | Paragraph. Outsiders, Chapter Short Answer Question Sets, S.E. Hinton's The .... Year 9 English The Outsiders Essay Guide C.McDonnell Essay Questions .... The Outsiders essay. The Outsiders Essay | English - Year 11 SACE | Thinkswap.
Talent Analytics: A Systems PerspectiveSharad Verma
Describes components of Talent Analytics from a systems perspective: People, process, technology, tools, leadership, context.
Highlights difference between goals and systems.
Describes how analytics can be used to build an innovation engine.
Provides real life examples from predictive retention analysis in a Financial Technology firm.
Melinda Thielbar, Data Science Practice Lead and Director of Data Science at Fidelity Investments
From corporations to governments to private individuals, most of the AI community has recognized the growing need to incorporate ethics into the development and maintenance of AI models. Much of the current discussion, though, is meant for leaders and managers. This talk is directed to data scientists, data engineers, ML Ops specialists, and anyone else who is responsible for the hands-on, day-to-day of work building, productionalizing, and maintaining AI models. We'll give a short overview of the business case for why technical AI expertise is critical to developing an AI Ethics strategy. Then we'll discuss the technical problems that cause AI models to behave unethically, how to detect problems at all phases of model development, and the tools and techniques that are available to support technical teams in Ethical AI development.
This seminar will provide insights on analytics-based assessments of workforce diversity and guide organizations on how analytics can provide a new ‘system of record’ for workforce diversity measurements and assessments.
At the end of this seminar, participants will be able to:
a. How analytics can provide a ‘single source of truth’ for workforce diversity metrics and assessments
b. How workforce analytics can provide a conduit to organizational transformation
c. How workforce analytics can support inclusion initiatives in global workforces
d. How HR executives can message the strategic value of Diversity in the age of digital transformation
Big Data = Big Headache? Using People Analytics to Fuel ROItalent.imperative
• Interpret trend information to understand the business case for Big Data in HR.
• Examine your fears and assumptions about Big Data.
• Learn from best practice case studies how to demonstrate HR’s contributions to ROI.
• Understand how to engage key stakeholders as part of your organization’s people analytics journey.
Pre Assessment Quantitative And Qualitative Data EssayTiffany Sandoval
Here are the key factors to consider when deciding between quantitative and qualitative data:
- Sample size - Qualitative data uses smaller samples to gain an in-depth understanding of each case, while quantitative data relies on larger samples for generalizability.
- Data type - Quantitative data is numerical and can be easily grouped, compared, and analyzed statistically. Qualitative data includes text, images, and narratives that require different analysis methods.
- Research questions - Qualitative research is best for exploring a problem or generating hypotheses, while quantitative research tests hypotheses and measures outcomes.
- Resources - Qualitative data collection and analysis takes more time and resources per subject compared to quantitative methods with standardized instruments.
- Validity - It can
How to Design Research from Ilm Ideas on Slide Share ilmideas
This document discusses various aspects of research design and methodology. It addresses how to properly frame research questions, select appropriate sampling strategies, and consider challenges that may arise. Specific examples are provided on framing research on public-private partnerships in education, remedial teacher education, and the impact of a schooling program. Key points covered include how to minimize sampling error through randomization, representativeness, and accounting for clustering. The importance of statistical power in hypothesis testing and detecting real effects is also emphasized.
How to Develop and Implement Effective Research Tools from Ilm Ideas on Slide...ilmideas
This document discusses various aspects of research design and methodology. It addresses how to properly frame research questions, select appropriate sampling strategies, and consider challenges that may arise. Specific examples are provided on framing research on public-private partnerships in education, remedial teacher education, and the impact of a schooling program. Key points covered include how to minimize sampling error through randomization, representativeness, and accounting for clustering. The importance of statistical power in hypothesis testing and detecting real effects is also emphasized.
Technologies and Innovation – Digital EconomicsLee Schlenker
This document provides an overview of a workshop on digital technologies and innovation. It includes an agenda with sessions on the building blocks of innovation, digital economics, the internet of value, decision making, and data ethics. The document contains questions to prompt discussion on topics like the Fourth Industrial Revolution, how digital natives approach business, and how values are changing in a digitized world. It also includes introductory sections on data types, big data, the importance of context in data, and transforming data into actions.
Jeff Higgins: Using Talent Market Data to Create Workforce IntelligenceEdunomica
The document discusses using external talent market data and workforce analytics to inform workforce strategies. It provides examples of how organizations can use data on job demand, salary benchmarks, and talent supply across locations to make decisions around critical roles, workforce planning, and "build vs. buy" strategies. Quantitative analytics and visualizations are emphasized to translate data into actionable insights.
The high-level findings from the data analysis were:
- Turnover was comparable to industry trends both in the US and internationally.
- Better job opportunities was the main reason employees left.
- A significant number of employees who left had less than 3 years of tenure.
- Employees under 30 and specific job families, business units, and locations had higher turnover rates.
- Sunnyvale, India, and certain field operation sites had the highest attrition.
USING BIG AND LITTLE DATA TO RECRUIT THE RIGHT CANDIDATE FOR EVERY POSITIONDr. John Sullivan
This document discusses using metrics and data to improve recruiting. It recommends focusing on referrals and boomerang re-hires, which can provide 2/3 of hires. Referrals are the top source for volume and quality of hires. Boomerang re-hires, or former employees who are rehired, can provide up to 16% of hires. The document outlines selecting strategic metrics in key areas like programs, processes, and budgets. It recommends benchmarking top firms like Google that use algorithms and data to inform people decisions. The presentation provides examples of strategic "OMG metrics" that identify issues, impacts, causes, and recommended actions to drive immediate improvement.
This document provides an overview of a case study on depression. It describes the participants in the study, which included 18 patients who met the inclusion criteria of having major depressive disorder. The study aimed to assess the effectiveness of cognitive behavioral therapy (CBT) for treating depression. Patients received 8 to 16 sessions of individual CBT and completed assessments before and after treatment to measure changes in depression symptoms. The results showed that CBT was effective at reducing depressive symptoms, with the majority of patients no longer meeting the criteria for major depressive disorder after treatment. CBT was found to be a viable treatment option for depression.
Enabling Success With Big Data - Driven Talent AcquisitionDavid Bernstein
Adopting an evidence-based recruitment marketing strategy is not just reserved for large employers. In fact, a targeted sourcing strategy can in some ways have a greater impact on small and mid-size businesses who need to allocate already-limited resources to the areas that will provide the most value. Ultimately, hiring the right candidate means profitability for your business. How can talent acquisition professionals gain the insights their organizations need to make better-informed decisions about their recruitment marketing efforts?
This document discusses HR analytics and the scientific process for conducting HR analytics. It begins by defining HR analytics and explaining how it involves systematically gathering, analyzing, and reporting HR data to help improve business outcomes. It then outlines the scientific process of identifying a problem, conducting research, forming a hypothesis, testing the hypothesis through experimentation, analyzing data, and communicating results. An example is provided of using this process to analyze why employee resignations were increasing. The document also discusses the HR analytics cycle and key steps like determining stakeholder needs, defining the research agenda, identifying and gathering data sources, transforming data, communicating results, and enabling strategic decision-making. Finally, it outlines various tools that can be used for HR analytics like HRIS
The document discusses the use of predictive analytics in state government. It describes how predictive analytics has become mainstream due to decreasing technology costs, increased data availability, and the need for fairness and flexibility. The document outlines several applications of predictive analytics in the public sector, including tax delinquency modeling, Medicaid fraud detection, and unemployment insurance overpayment recovery. It provides examples of how predictive analytics could help improve outcomes in areas like child support case management and child welfare systems.
Build applications with generative AI on Google CloudMárton Kodok
We will explore Vertex AI - Model Garden powered experiences, we are going to learn more about the integration of these generative AI APIs. We are going to see in action what the Gemini family of generative models are for developers to build and deploy AI-driven applications. Vertex AI includes a suite of foundation models, these are referred to as the PaLM and Gemini family of generative ai models, and they come in different versions. We are going to cover how to use via API to: - execute prompts in text and chat - cover multimodal use cases with image prompts. - finetune and distill to improve knowledge domains - run function calls with foundation models to optimize them for specific tasks. At the end of the session, developers will understand how to innovate with generative AI and develop apps using the generative ai industry trends.
This document discusses the meaning of performance appraisals and fairness in the context of changing views in society. It notes that some now see rewards as disconnected from work effort, eroding the concept of meritocracy. This contributes to calls for eliminating annual performance reviews. The document also outlines several common performance appraisal methods, their advantages and disadvantages, including ranking, rating scales, checklists, critical incidents, essays, and behaviorally anchored rating scales. It provides additional resources on topics related to the meaning of performance appraisals.
In this file, you can ref useful information about need for performance appraisal such as need for performance appraisal methods, need for performance appraisal tips, need for performance appraisal forms, need for performance appraisal phrases … If you need more assistant for need for performance appraisal, please leave your comment at the end of file.
Different Schools Within Two Separate School DistrictsDawn Mora
The research study will use qualitative methods including direct observation, focus groups, and questionnaires to understand perceptions of healthy eating promotion programs in schools. These methods allow for an in-depth exploration of attitudes, experiences, and behaviors through observation of real-world settings and interactions with participants. By triangulating different qualitative data collection techniques, the researcher aims to develop a comprehensive understanding of the problem around lack of healthy eating education from multiple perspectives.
This document discusses forward-looking and predictive metrics that can be used for recruiting. It begins by defining key terms like historical, real-time, and predictive metrics. It then discusses reasons for using traditional metrics, such as increased business results when data-driven decision making is used. Examples of predictive recruiting metrics are also provided, such as predicting changing source effectiveness and upcoming talent availability. The document concludes by outlining elements that make a predictive metric actionable, such as listing revenue impact and recommended actions.
The Outsiders Essay Questions And AnswersAshley Mason
The Outsiders Study Guide Answers. Essay the Outsiders-Eng. The Outsiders (Papers and Projects) | PDF | Essays | Paragraph. Outsiders, Chapter Short Answer Question Sets, S.E. Hinton's The .... Year 9 English The Outsiders Essay Guide C.McDonnell Essay Questions .... The Outsiders essay. The Outsiders Essay | English - Year 11 SACE | Thinkswap.
Talent Analytics: A Systems PerspectiveSharad Verma
Describes components of Talent Analytics from a systems perspective: People, process, technology, tools, leadership, context.
Highlights difference between goals and systems.
Describes how analytics can be used to build an innovation engine.
Provides real life examples from predictive retention analysis in a Financial Technology firm.
Melinda Thielbar, Data Science Practice Lead and Director of Data Science at Fidelity Investments
From corporations to governments to private individuals, most of the AI community has recognized the growing need to incorporate ethics into the development and maintenance of AI models. Much of the current discussion, though, is meant for leaders and managers. This talk is directed to data scientists, data engineers, ML Ops specialists, and anyone else who is responsible for the hands-on, day-to-day of work building, productionalizing, and maintaining AI models. We'll give a short overview of the business case for why technical AI expertise is critical to developing an AI Ethics strategy. Then we'll discuss the technical problems that cause AI models to behave unethically, how to detect problems at all phases of model development, and the tools and techniques that are available to support technical teams in Ethical AI development.
This seminar will provide insights on analytics-based assessments of workforce diversity and guide organizations on how analytics can provide a new ‘system of record’ for workforce diversity measurements and assessments.
At the end of this seminar, participants will be able to:
a. How analytics can provide a ‘single source of truth’ for workforce diversity metrics and assessments
b. How workforce analytics can provide a conduit to organizational transformation
c. How workforce analytics can support inclusion initiatives in global workforces
d. How HR executives can message the strategic value of Diversity in the age of digital transformation
Big Data = Big Headache? Using People Analytics to Fuel ROItalent.imperative
• Interpret trend information to understand the business case for Big Data in HR.
• Examine your fears and assumptions about Big Data.
• Learn from best practice case studies how to demonstrate HR’s contributions to ROI.
• Understand how to engage key stakeholders as part of your organization’s people analytics journey.
Pre Assessment Quantitative And Qualitative Data EssayTiffany Sandoval
Here are the key factors to consider when deciding between quantitative and qualitative data:
- Sample size - Qualitative data uses smaller samples to gain an in-depth understanding of each case, while quantitative data relies on larger samples for generalizability.
- Data type - Quantitative data is numerical and can be easily grouped, compared, and analyzed statistically. Qualitative data includes text, images, and narratives that require different analysis methods.
- Research questions - Qualitative research is best for exploring a problem or generating hypotheses, while quantitative research tests hypotheses and measures outcomes.
- Resources - Qualitative data collection and analysis takes more time and resources per subject compared to quantitative methods with standardized instruments.
- Validity - It can
How to Design Research from Ilm Ideas on Slide Share ilmideas
This document discusses various aspects of research design and methodology. It addresses how to properly frame research questions, select appropriate sampling strategies, and consider challenges that may arise. Specific examples are provided on framing research on public-private partnerships in education, remedial teacher education, and the impact of a schooling program. Key points covered include how to minimize sampling error through randomization, representativeness, and accounting for clustering. The importance of statistical power in hypothesis testing and detecting real effects is also emphasized.
How to Develop and Implement Effective Research Tools from Ilm Ideas on Slide...ilmideas
This document discusses various aspects of research design and methodology. It addresses how to properly frame research questions, select appropriate sampling strategies, and consider challenges that may arise. Specific examples are provided on framing research on public-private partnerships in education, remedial teacher education, and the impact of a schooling program. Key points covered include how to minimize sampling error through randomization, representativeness, and accounting for clustering. The importance of statistical power in hypothesis testing and detecting real effects is also emphasized.
Technologies and Innovation – Digital EconomicsLee Schlenker
This document provides an overview of a workshop on digital technologies and innovation. It includes an agenda with sessions on the building blocks of innovation, digital economics, the internet of value, decision making, and data ethics. The document contains questions to prompt discussion on topics like the Fourth Industrial Revolution, how digital natives approach business, and how values are changing in a digitized world. It also includes introductory sections on data types, big data, the importance of context in data, and transforming data into actions.
Jeff Higgins: Using Talent Market Data to Create Workforce IntelligenceEdunomica
The document discusses using external talent market data and workforce analytics to inform workforce strategies. It provides examples of how organizations can use data on job demand, salary benchmarks, and talent supply across locations to make decisions around critical roles, workforce planning, and "build vs. buy" strategies. Quantitative analytics and visualizations are emphasized to translate data into actionable insights.
The high-level findings from the data analysis were:
- Turnover was comparable to industry trends both in the US and internationally.
- Better job opportunities was the main reason employees left.
- A significant number of employees who left had less than 3 years of tenure.
- Employees under 30 and specific job families, business units, and locations had higher turnover rates.
- Sunnyvale, India, and certain field operation sites had the highest attrition.
USING BIG AND LITTLE DATA TO RECRUIT THE RIGHT CANDIDATE FOR EVERY POSITIONDr. John Sullivan
This document discusses using metrics and data to improve recruiting. It recommends focusing on referrals and boomerang re-hires, which can provide 2/3 of hires. Referrals are the top source for volume and quality of hires. Boomerang re-hires, or former employees who are rehired, can provide up to 16% of hires. The document outlines selecting strategic metrics in key areas like programs, processes, and budgets. It recommends benchmarking top firms like Google that use algorithms and data to inform people decisions. The presentation provides examples of strategic "OMG metrics" that identify issues, impacts, causes, and recommended actions to drive immediate improvement.
This document provides an overview of a case study on depression. It describes the participants in the study, which included 18 patients who met the inclusion criteria of having major depressive disorder. The study aimed to assess the effectiveness of cognitive behavioral therapy (CBT) for treating depression. Patients received 8 to 16 sessions of individual CBT and completed assessments before and after treatment to measure changes in depression symptoms. The results showed that CBT was effective at reducing depressive symptoms, with the majority of patients no longer meeting the criteria for major depressive disorder after treatment. CBT was found to be a viable treatment option for depression.
Enabling Success With Big Data - Driven Talent AcquisitionDavid Bernstein
Adopting an evidence-based recruitment marketing strategy is not just reserved for large employers. In fact, a targeted sourcing strategy can in some ways have a greater impact on small and mid-size businesses who need to allocate already-limited resources to the areas that will provide the most value. Ultimately, hiring the right candidate means profitability for your business. How can talent acquisition professionals gain the insights their organizations need to make better-informed decisions about their recruitment marketing efforts?
This document discusses HR analytics and the scientific process for conducting HR analytics. It begins by defining HR analytics and explaining how it involves systematically gathering, analyzing, and reporting HR data to help improve business outcomes. It then outlines the scientific process of identifying a problem, conducting research, forming a hypothesis, testing the hypothesis through experimentation, analyzing data, and communicating results. An example is provided of using this process to analyze why employee resignations were increasing. The document also discusses the HR analytics cycle and key steps like determining stakeholder needs, defining the research agenda, identifying and gathering data sources, transforming data, communicating results, and enabling strategic decision-making. Finally, it outlines various tools that can be used for HR analytics like HRIS
The document discusses the use of predictive analytics in state government. It describes how predictive analytics has become mainstream due to decreasing technology costs, increased data availability, and the need for fairness and flexibility. The document outlines several applications of predictive analytics in the public sector, including tax delinquency modeling, Medicaid fraud detection, and unemployment insurance overpayment recovery. It provides examples of how predictive analytics could help improve outcomes in areas like child support case management and child welfare systems.
Build applications with generative AI on Google CloudMárton Kodok
We will explore Vertex AI - Model Garden powered experiences, we are going to learn more about the integration of these generative AI APIs. We are going to see in action what the Gemini family of generative models are for developers to build and deploy AI-driven applications. Vertex AI includes a suite of foundation models, these are referred to as the PaLM and Gemini family of generative ai models, and they come in different versions. We are going to cover how to use via API to: - execute prompts in text and chat - cover multimodal use cases with image prompts. - finetune and distill to improve knowledge domains - run function calls with foundation models to optimize them for specific tasks. At the end of the session, developers will understand how to innovate with generative AI and develop apps using the generative ai industry trends.
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
2. Outline
What is People Analytics?
Benefits of People Analytics
Signals & Trends
People Analytics examples
How can HR start building People Analytics
capabilities?
The future of People Analytics
3. What is People Analytics?
The use of data and analytic tools to inform
decisions about how to manage people
4. What is People Analytics?
Analytics, applied to “people issues” – hiring,
performance management, compensation, retention,
etc.
5.
6. People Analytics known as…
Touches all people-related issues in organizations to
make predictions
– Performance evaluation
– Hiring / Assessment
– Retention
– Learning and development
– Team composition
– Etc.
HR ANALYTICS TALENT MANAGEMENT
WORKFORCE ANALYTICS
7. Why is People Analytics emerging
now?
› Technical progress – HR data availability,
processing power, analytical tools, etc.
› Analytical capabilities – data analysis skills,
plenty of online courses, increasing use of popular
analytical tools in day to day work (e.g. Microsoft
Excel, Power BI, Tableau, etc.)
› Growing recognition of behavioral biases.
› Increase in ROI ($10,66 ROI per 1$ -> Nucleus
Research, 2011)
8. What does People Analytics require?
› Metrics/predictors for HR - such as attrition
rates, KPIs/performance stats, retention data, etc.
› Software for meaningful data collection that will
help to diagnose the efficiency of current HR
processes and their impact on wellbeing, happiness,
and bottom-line performance.
› Statistical modeling and machine learning methods –
to establish the probability of various scenarios and
make informed decisions that facilitate planning and
growth.
9. What about the size of data?
The size and scope of data for People Analytics
can depend on several factors:
• These can be large and complex datasets or
simpler measures such as employee surveys.
• The amount of people data used may depend on
the size of the organization and its needs.
10. People Analytics comes to minimize
human biases and replace intuitive
decision making
Use Employee Data Responsibly
12. Deloitte's 2023 Global Human Capital Trends report
https://www2.deloitte.com/us/en/insights/focus/human-
capital-trends.html#leading-in-a-boundaryless-world
56 % of respondents said
organizations have made moderate
to significant progress in
People Analytics over the past
10 years.
16. Signals: This trend applies to you if …
Your employees feel as if their every move is being
monitored, (increasing stress, job dissatisfaction, and
turnover, and leading to a lack of trust).
Employees are sharing information (data) more readily
outside of the organization (e.g., LinkedIn) but are
reticent to provide it through organizational channels due
to a perceived lack of benefit to them.
Your organization faces increased challenges and pressure
from regulators (e.g. GDPR) related to data reporting,
privacy, and protection. Source: Deloitte 2023 Global Human Capital Trends survey
Use Employee Data Responsibly
17. Barriers When asked to identify top
barriers to realizing value
from people data, 27% of
respondents cited culture,
making it the most common
barrier.
Source: Deloitte 2023 Global Human Capital Trends survey
18. Benefits When asked to identify the top
benefits from their
organization’s approach to using
people data, the top response
was increased worker engagement
and well-being of the workforce.
Source: Deloitte 2023 Global Human Capital Trends survey
19. Which performance metrics do Armenian companies collect for
PA?
How do companies make sure they don’t harbor surveillance in the
workplace and there is responsible use of worker data?
Are there legal requirements for processing data?
20. People Analytics
Examples
• Tracks the performance of
all teachers, comparing it
to evaluations when they
were hired
• Refined the most productive
steps in the hiring process,
where to allocate more
resources, etc.
21. • Systematically tracked
interview predictions about
new hires to figure out how
good they were at it
• Answer: Not very
• So dramatically reduced the #
of interviewers
Predicting Hire Success
22. People Analytics
Examples
• Believes a 1% increase in retention can save $75- $
100m/year
• 3-year study: Changing jobs increases employee
“stickiness”
• Increased internal postings of open jobs from <50%
23. Many, Many Others
• Firms in technology, financial services,
telecommunications, automotive, consumer packaged
goods, energy, not-for-profit…
…are finding:
• Better levers for retaining key employees
• More diagnostic methods for hiring
• Who their most valuable employees are
• How to compose the most productive teams
• Etc.
25. People Analytics In Use
Purpose of performance evaluation
• Feedback
• Rewards / punishment
• Performance evaluation, not talent management.
Tough to compare employees if not in identical
situations.
• Helpful starting place, for this seminar (and often for
life):
Begin by assuming all employees are equal
ability
28. NOISE
› The fundamental challenge in performance evaluation is
that performance measures are noisy (i.e., outcomes
are imperfectly related to employee effort)
› For any given level of effort, a range of outcomes can
occur due to factors outside the employee’s control:
• Competitors, team members, bosses, the economy, etc.
The challenge: Separating skill from luck
29. A Simple Model
› There are two components to performance:
• In informal terms:Real Tendency + Luck
• In more formal terms: y = x + e,
• x = true ability, and
• e = error, randomly distributed around 0.
What happens when we sample on extreme performance?
What underlies extreme success and failure?
• Extreme success = f(superior ability, positive
error)
• Extreme failure = f(inferior ability, negative
error)
• Consequences?
30. Regression to the Mean
A study was recently conducted examining the performance
of the 283 stock mutual funds that existed during the
1990s. The study divided the 1990s into an early period
(1990-1994) and a late period (1995-1999). Below are
the 10 funds that had the highest rate of return in the
early period (with their names disguised), ranked from
1 to 10. Predict their rank for the late 1990s.
31. Examples
• Officer in the Israeli Air Force— “Punishment is more
effective than praise. Whenever I punish a pilot after
a really poor flight, I see better performance the next
time. Whenever I praise a pilot after an excellent
flight, I see worse performance the next time.”
• Peters and Waterman’s book, In Search of Excellence.
They selected 43 high performing companies in the
early 1980s, and looked to see what practices they
used (some that they discovered were the
organizational equivalent of “brushing teeth”)
32.
33. Regression to the Mean
• Anytime you sample based on extreme values of one attribute, any other
attribute that is not perfectly related will tend to be closer to the mean value
• “Attributes” can be:
• Performance at different points in time
• E.g., last year’s stock returns and this year’s
• Different qualities within the same entity
• E.g., a person’s running speed and language ability
34. What Gets in the Way of Seeing This?
Among other things:
• Outcome bias
• Hindsight bias
• Narrative seeking
In short, we make sense of the past
• We find a story that connects all the dots
• Chance plays too small a role in these stories
35.
36. Extrapolating From Small Samples
• Principle: Sample means converge to the population mean as the sample
size increases. (This is known as the Central Limit Theorem.) Thus, you
will see more extreme values in small samples.
• When are you more likely to see a .400 season batting average in
baseball – May 1 or Sept. 1?
• In which hospital are you more likely to see a dramatically higher % of
boys than girls (or vice versa) born on any given day – a small
community hospital (e.g., 5 births/day) or a large city hospital (e.g., 100
births/day)?
37. Extrapolating From Small Samples
Your firm has two plants, one large and one small, which mass produce a
standard computer chip. Other than the amount they produce, the two plants
are identical in all essential regards. Both use the same technology to
produce the same product. When properly functioning, this particular
technology produces one percent (1%) defective items. Whenever the
number of defective items from one day’s production exceeds two percent
(2%), a special note is made in the quality control log to “flag” the problem.
At the end of the quarter, which plant would you expect to have more
“flagged” days in its quality control log? Please mark one.
22% A) The small plant
30% B) The large plant
48% C) The same number on average
38. “Law of Small Numbers”
• People believe small samples closely share the properties of the
underlying population
• This means they too readily infer the population’s properties (e.g., average)
from the sample’s
• That is: They neglect the role variability (aka chance) inevitably plays in
small samples
39. The Wisdom of Crowds
• The average of a large number of forecasts
reliably outperforms the average individual
forecast
• Idiosyncratic errors offset each other
• E.g., Galton’s (1906) county fair contest
• Many other examples
40. The Wisdom of Crowds
• But the value of the crowd critically depends on the independence of
their opinions
• Independent means uncorrelated
• If correlated, the value of additional opinions quickly diminishes
41. Impact of Correlation
Actual Number of Experts (n)
Equivalent Number of
Independent Experts (n*) n*=
n
1+(n- 1)r
n*®1/ r
as n ® ¥
Clemen and Winkler (1985)
42. Signal Independence
• People are bad at accounting for this effect
• Even when you tell them exactly what the correlation is, people do
not properly adjust (Enke & Zimmerman, 2015)
43. Signal Independence
• Sources of correlation between two opinions?
• They’ve discussed it already!
• They talk to the same people
• They have the same background – from the same place, trained the
same way, same historical experiences, etc.
• Need to find ways to keep opinions independent, and add independent
perspectives to experienced groups
44. Consider Broader Set of Objectives
• Organizations generally care about how a person goes about his/her job
• Most important: impact on others
• People consider too few objectives (Bond, Carlson & Keeney, 2008)
• Systematically omit nearly ½ of the objectives they later identify as
personally relevant
• Leads many firms to rely on too narrow a set of performance measures
45. Process vs. Outcome
• Famously hard-charging Dell Computers changed their performance
evaluations in the early 2000s
• Before change: 100% results
• After change:
• 50% what an employee accomplished
• 50% how he/she accomplished it, as judged those affected
46. Process vs. Outcome
• The more uncertainty in the environment, i.e., the less control an
employee has over exact outcomes, the more a firm should emphasize
process in their evaluations.
47. Process vs. Outcome
• Use analytics to better understand, and focus on, the processes that
tend to produce desired outcomes
• Key issue: Identify the fundamental drivers of value
48. Performance Evaluation Summary
1. Understand your environment
• Know you’re biased
• Account for chance
2. Ask the critical questions
51. Account for Chance
• The key issue: Persistence
• The more fundamental (skill-related) a performance measure is, the
more it will persist over time
• The more chance-related a performance measure is, the more it will
regress to the mean over time
54. Question
Which of the following methods
of evaluating job candidates
is most effective at
predicting subsequent
performance?
Which is least effective?
58. How Does Data Analysis Compare to
Human Judgment
The Bad News
• Combination of various tests and selection methods leaves much of
performance unexplained
The Worse News
• Implementation of algorithms reduced turnover in call centers
• Turnover was lower the less often managers over-ruled the algorithm