International Journal of Data Mining and Big Data (IJDMBD) is an open access peer-reviewed journal that publishes articles which contribute new results in all the areas of Data Mining and Big Data. Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in this area.
This document discusses data quality and provides facts about the high costs of poor data quality to businesses and the US economy. It defines data quality as ensuring data is "fit for purpose" by measuring it against its intended uses and dimensions of quality. The document outlines best practices for measuring data quality including profiling data to understand metadata and trends, using statistical process control, master data management to create standardized "gold records", and implementing a data governance program to centrally manage data quality.
This document discusses data analytics and related concepts. It defines data and information, explaining that data becomes information when it is organized and analyzed to be useful. It then discusses how data is everywhere and the value of data analysis skills. The rest of the document outlines the methodology of data analytics, including data collection, management, cleaning, exploratory analysis, modeling, mining, and visualization. It provides examples of how data analytics is used in healthcare and travel to optimize processes and customer experiences.
This document provides an introduction to data analytics. It defines data, analytics, and data analytics. The main types of analytics described are descriptive, predictive, and prescriptive. Applications of data analytics discussed include self-driving cars, recommendation engines, and decision making. Key activities in data analytics include data extraction, analysis, manipulation, modeling, and visualization. Various roles and careers in data analytics are also outlined, along with example use cases and common tools used.
A brief introduction to Data Quality rule development and implementation covering:
- What are Data Quality Rules.
- Examples of Data Quality Rules.
- What are the benefits of rules.
- How can I create my own rules?
- What alternate approaches are there to building my own rules?
The presentation also includes a very brief overview of our Data Quality Rule services. For more information on this please contact us.
Data Quality Analytics: Understanding what is in your data, before using itDomino Data Lab
Analytics and data science are ever growing fields, as business decision makers continue to use data to drive decisions. The pinnacle of these fields are the models and their accuracy/fit,; what about the data? Is your data clean, and how do you know that? Our discussion will focus on best practices for data preprocessing for analytic uses. Beginning with essential distributional checks of a dataset to a propose method for automated data validation process during ETL for transactional data.
Data quality - The True Big Data ChallengeStefan Kühn
The document discusses data quality challenges, especially with big data. It notes that data quality starts at data creation and production, and that both data producers and consumers play a role. With big data, quality issues like redundancy, lack of resolution, and noise are exacerbated due to diverse sources of data, lack of documentation and standards, and increasing volumes of data. The document recommends treating data as a product and implementing quality standards, detection of problems, and root cause analysis to improve quality rather than just collecting more raw data. A shared responsibility approach between business and IT is needed to develop a common understanding of data.
It is an introduction to Data Analytics, its applications in different domains, the stages of Analytics project and the different phases of Data Analytics life cycle.
I deeply acknowledge the sources from which I could consolidate the material.
The document discusses how companies that are leading in analytics use data and analytics to gain competitive advantages and innovate. It profiles "Analytical Innovators" - companies that rely on analytics to compete and innovate. These companies share a belief that data is a core asset, make effective use of more data for faster results, and have senior management support for data-driven decision making. The document provides examples of companies in different industries that are successfully using analytics and a framework for other companies to also become more analytical.
This document discusses data quality and provides facts about the high costs of poor data quality to businesses and the US economy. It defines data quality as ensuring data is "fit for purpose" by measuring it against its intended uses and dimensions of quality. The document outlines best practices for measuring data quality including profiling data to understand metadata and trends, using statistical process control, master data management to create standardized "gold records", and implementing a data governance program to centrally manage data quality.
This document discusses data analytics and related concepts. It defines data and information, explaining that data becomes information when it is organized and analyzed to be useful. It then discusses how data is everywhere and the value of data analysis skills. The rest of the document outlines the methodology of data analytics, including data collection, management, cleaning, exploratory analysis, modeling, mining, and visualization. It provides examples of how data analytics is used in healthcare and travel to optimize processes and customer experiences.
This document provides an introduction to data analytics. It defines data, analytics, and data analytics. The main types of analytics described are descriptive, predictive, and prescriptive. Applications of data analytics discussed include self-driving cars, recommendation engines, and decision making. Key activities in data analytics include data extraction, analysis, manipulation, modeling, and visualization. Various roles and careers in data analytics are also outlined, along with example use cases and common tools used.
A brief introduction to Data Quality rule development and implementation covering:
- What are Data Quality Rules.
- Examples of Data Quality Rules.
- What are the benefits of rules.
- How can I create my own rules?
- What alternate approaches are there to building my own rules?
The presentation also includes a very brief overview of our Data Quality Rule services. For more information on this please contact us.
Data Quality Analytics: Understanding what is in your data, before using itDomino Data Lab
Analytics and data science are ever growing fields, as business decision makers continue to use data to drive decisions. The pinnacle of these fields are the models and their accuracy/fit,; what about the data? Is your data clean, and how do you know that? Our discussion will focus on best practices for data preprocessing for analytic uses. Beginning with essential distributional checks of a dataset to a propose method for automated data validation process during ETL for transactional data.
Data quality - The True Big Data ChallengeStefan Kühn
The document discusses data quality challenges, especially with big data. It notes that data quality starts at data creation and production, and that both data producers and consumers play a role. With big data, quality issues like redundancy, lack of resolution, and noise are exacerbated due to diverse sources of data, lack of documentation and standards, and increasing volumes of data. The document recommends treating data as a product and implementing quality standards, detection of problems, and root cause analysis to improve quality rather than just collecting more raw data. A shared responsibility approach between business and IT is needed to develop a common understanding of data.
It is an introduction to Data Analytics, its applications in different domains, the stages of Analytics project and the different phases of Data Analytics life cycle.
I deeply acknowledge the sources from which I could consolidate the material.
The document discusses how companies that are leading in analytics use data and analytics to gain competitive advantages and innovate. It profiles "Analytical Innovators" - companies that rely on analytics to compete and innovate. These companies share a belief that data is a core asset, make effective use of more data for faster results, and have senior management support for data-driven decision making. The document provides examples of companies in different industries that are successfully using analytics and a framework for other companies to also become more analytical.
Hello everyone! Data is required for every organisation in every field in today's world, and personal life. so, I am here to introduce how about What is Data and What is large scale computing.
When Big Data and Predictive Analytics Collide: Visual Magic HappensInfini Graph
Big data is useless data unless you have a way to handle and perform meaningful analysis that drives a business outcome. Data visualization has transformed complex data sets into patterns now being used to constructed predictive models. In the massive exploding world of social data and content engagement the need for intelligent data mining and pattern prediction is required to realize data driving marketing. In this presentation, we will explore techniques, key takeaways and examples behind this fast growing market of predictive analysis.
Supporting innovation in insurance with randomized experimentationDomino Data Lab
Recent technological advances, a dynamic competitive landscape, and an evolving regulatory environment have led to a period of rapid innovation for many insurance providers. Here, we’ll explore how data scientists may use randomized experiments to rigorously assess the causal impact of innovations on business outcomes. Particular emphasis will be placed on experimentation in “offline” channels, with some of the challenges and mitigation strategies highlighted.
Predictive modeling is a process used in predictive analytics to create statistical models that can forecast future outcomes based on historical data. Predictive modeling uses techniques from data mining, statistics, and machine learning to analyze current data to make predictions. The predictive modeling process involves collecting data, creating a model, testing and validating the model, and evaluating the model's performance. Predictive models are commonly used to predict customer behavior, risk levels, product performance, and more. Industries like retail, healthcare, finance, and telecommunications frequently use predictive modeling techniques.
Data Analytics For Beginners | Introduction To Data Analytics | Data Analytic...Edureka!
Data Analytics for R Course: https://www.edureka.co/r-for-analytics
This Edureka Tutorial on Data Analytics for Beginners will help you learn the various parameters you need to consider while performing data analysis.
The following are the topics covered in this session:
Introduction To Data Analytics
Statistics
Data Cleaning and Manipulation
Data Visualization
Machine Learning
Roles, Responsibilities and Salary of Data Analyst
Need of R
Hands-On
Statistics for Data Science: https://youtu.be/oT87O0VQRi8
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This document outlines topics related to data analytics including the definition of data analytics, the data analytics process, types of data analytics, steps of data analytics, tools used, trends in the field, techniques and methods, the importance of data analytics, skills required, and benefits. It defines data analytics as the science of analyzing raw data to make conclusions and explains that many analytics techniques and processes have been automated into algorithms. The importance of data analytics includes predicting customer trends, analyzing and interpreting data, increasing business productivity, and driving effective decision-making.
The document discusses data science and data analytics. It provides definitions of data science, noting it emerged as a discipline to provide insights from large data volumes. It also defines data analytics as the process of analyzing datasets to find insights using algorithms and statistics. Additionally, it discusses components of data science including preprocessing, data modeling, and visualization. It provides examples of data science applications in various domains like personalization, pricing, fraud detection, and smart grids.
Predictive Analytics enables organisations to forecast future events, analyse risks and opportunities, and automate decision making processes by analysing historic data.
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.
This document discusses several myths about big data and data science. It addresses that most of the work in a typical data science project is spent cleaning and preparing data rather than actual analysis. It also notes that while AWS has good support services, they do not provide detailed infrastructure information. Additionally, it states that data lakes should not be viewed as direct replacements for data warehouses due to differences in maturity. The document advocates focusing presentations on what insights can be gained rather than technical details, and that combining multiple sources of information is important for analytics. Basic regression methods are also noted as commonly sufficient for predictive tasks.
Big Data Expo 2015 - Trillium software Big Data and the Data QualityBigDataExpo
Successful Big Data initiatives rely on accurate, complete data, but the information they draw on is often not validated when it enters an organization. In this session we will look at the challenges big data brings to an organization, and how data quality principles are adapting to ensure business goals and return on investments in big data are realised. We will cover:
- Challenges of big data
- Turning data lakes into reservoirs
- How data quality tools are adapting
- Why data governance disciplines remain crucial
Data mining-implementation-to-predict-sales-using-time-series-method By Raiha...raihansikdar
This document discusses using data mining techniques to predict sales using time series analysis. It outlines the research method, which involves collecting transaction data, constructing a moving average model, forecasting values, and evaluating the results. The document also includes a flowchart of the data mining process, showing the steps of clustering, classification, prediction, and association. It presents a graph comparing actual transaction values to forecasted values, showing the highest forecasting accuracy was 99.68% while the lowest was over 50%. The conclusion is that while forecasts may not be 100% accurate, they can still provide useful estimates to assist business processes.
Thought leaders in big data ulf mattsson, cto of protegrity (part 4)Ulf Mattsson
Protegrity CTO and data security expert Ulf Mattsson featured in recent post on One Million by One Million blog. In her recent post, “Thought Leaders in Big Data: Ulf Mattsson, CTO of Protegrity,” Sramana Mitra profiles Protegrity CTO Ulf Mattsson and gets his thoughts on the state of Big Data security today.
This document discusses data quality and its importance for business decision making. It defines data quality as ensuring information is fit for its intended purpose and helps data consumers make the right decisions. Poor data quality can significantly impact business performance, with 75% of companies reporting financial losses due to low quality data. The document outlines different data quality needs and metrics for various use cases and decision makers. It also presents examples of companies that have benefited financially from implementing thorough data quality management programs.
every business needs a data analytics to get a detailed value of cost and profits. we will study the importance in detail in this particular presentation.
Dynamic Talks: "Implementing data quality automation with open source stack" ...Grid Dynamics
The quality of business decisions, machine learning insights, and executive reports depend on the quality and integrity of the underlying data. There are many ways that data can get corrupted in an analytical data platform from de-synchronization with the system-of-record to defects in data pipelines. We will show how to detect and prevent data corruption with automation, open source tools, and machine learning.
Differential Privacy for Information RetrievalGrace Hui Yang
This document summarizes a tutorial on differential privacy for information retrieval. The tutorial covered early attempts at privacy-preserving information retrieval using techniques like log deletion, hashing queries and identifiers, and scrubbing query content. However, these naive techniques were shown to not adequately protect privacy. More advanced techniques like k-anonymity, l-diversity, and t-closeness were discussed, but also have limitations. The main topic of the tutorial was introducing differential privacy and how it can be applied to typical information retrieval applications and tasks.
This document discusses using community feedback to improve software engineering data quality over time. It proposes using a dynamic data model where human feedback enhances static data sets, similar to how Wikipedia evolves. Key challenges are how to collect feedback, integrate it with AI, and motivate user participation through incentives, usability and trust. The goal is to lower costs while building a stronger evidence base that adapts to changing conditions through a synergistic human-AI approach.
On this slides, we tried to give an overview of advanced Data quality management (ADQM). To understand about DQ why important, and all those steps of DQ management.
This document describes a grievance redressal system developed for a college to allow students, faculty, and staff to easily file complaints online in a secure manner. The system was created to address issues with the previous paper-based complaint system, such as lack of acknowledgment, inability to track complaints, and lack of environmental friendliness. The new system uses registration and login for security and allows complaints to be filed, tracked, and resolved by competent authorities. It aims to improve user satisfaction and encourage feedback to enhance service quality. The system was implemented using a web interface and database. An evaluation found it encouraged complaints without fear and provided transparency, timeliness, and ease of use compared to the prior manual system. Future enhancements could
1) The document discusses best practices in complaints handling within public organizations. Effective complaints handling provides benefits to both the organization and the public it serves.
2) For a complaints system to operate effectively, people must be able to easily access it and have confidence in the system. Organizations should address any barriers to access and reassure complainants that they will not face retaliation.
3) Proper management practices are needed for complaints handling within an organization. Risk assessment, clear roles and responsibilities, and support for frontline staff are important. Periodic reviews of the system help ensure continuous improvement.
4) A system-wide perspective is also valuable to categorize complaints and spread lessons learned across organizations. An effective complaints system
Hello everyone! Data is required for every organisation in every field in today's world, and personal life. so, I am here to introduce how about What is Data and What is large scale computing.
When Big Data and Predictive Analytics Collide: Visual Magic HappensInfini Graph
Big data is useless data unless you have a way to handle and perform meaningful analysis that drives a business outcome. Data visualization has transformed complex data sets into patterns now being used to constructed predictive models. In the massive exploding world of social data and content engagement the need for intelligent data mining and pattern prediction is required to realize data driving marketing. In this presentation, we will explore techniques, key takeaways and examples behind this fast growing market of predictive analysis.
Supporting innovation in insurance with randomized experimentationDomino Data Lab
Recent technological advances, a dynamic competitive landscape, and an evolving regulatory environment have led to a period of rapid innovation for many insurance providers. Here, we’ll explore how data scientists may use randomized experiments to rigorously assess the causal impact of innovations on business outcomes. Particular emphasis will be placed on experimentation in “offline” channels, with some of the challenges and mitigation strategies highlighted.
Predictive modeling is a process used in predictive analytics to create statistical models that can forecast future outcomes based on historical data. Predictive modeling uses techniques from data mining, statistics, and machine learning to analyze current data to make predictions. The predictive modeling process involves collecting data, creating a model, testing and validating the model, and evaluating the model's performance. Predictive models are commonly used to predict customer behavior, risk levels, product performance, and more. Industries like retail, healthcare, finance, and telecommunications frequently use predictive modeling techniques.
Data Analytics For Beginners | Introduction To Data Analytics | Data Analytic...Edureka!
Data Analytics for R Course: https://www.edureka.co/r-for-analytics
This Edureka Tutorial on Data Analytics for Beginners will help you learn the various parameters you need to consider while performing data analysis.
The following are the topics covered in this session:
Introduction To Data Analytics
Statistics
Data Cleaning and Manipulation
Data Visualization
Machine Learning
Roles, Responsibilities and Salary of Data Analyst
Need of R
Hands-On
Statistics for Data Science: https://youtu.be/oT87O0VQRi8
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
This document outlines topics related to data analytics including the definition of data analytics, the data analytics process, types of data analytics, steps of data analytics, tools used, trends in the field, techniques and methods, the importance of data analytics, skills required, and benefits. It defines data analytics as the science of analyzing raw data to make conclusions and explains that many analytics techniques and processes have been automated into algorithms. The importance of data analytics includes predicting customer trends, analyzing and interpreting data, increasing business productivity, and driving effective decision-making.
The document discusses data science and data analytics. It provides definitions of data science, noting it emerged as a discipline to provide insights from large data volumes. It also defines data analytics as the process of analyzing datasets to find insights using algorithms and statistics. Additionally, it discusses components of data science including preprocessing, data modeling, and visualization. It provides examples of data science applications in various domains like personalization, pricing, fraud detection, and smart grids.
Predictive Analytics enables organisations to forecast future events, analyse risks and opportunities, and automate decision making processes by analysing historic data.
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.
This document discusses several myths about big data and data science. It addresses that most of the work in a typical data science project is spent cleaning and preparing data rather than actual analysis. It also notes that while AWS has good support services, they do not provide detailed infrastructure information. Additionally, it states that data lakes should not be viewed as direct replacements for data warehouses due to differences in maturity. The document advocates focusing presentations on what insights can be gained rather than technical details, and that combining multiple sources of information is important for analytics. Basic regression methods are also noted as commonly sufficient for predictive tasks.
Big Data Expo 2015 - Trillium software Big Data and the Data QualityBigDataExpo
Successful Big Data initiatives rely on accurate, complete data, but the information they draw on is often not validated when it enters an organization. In this session we will look at the challenges big data brings to an organization, and how data quality principles are adapting to ensure business goals and return on investments in big data are realised. We will cover:
- Challenges of big data
- Turning data lakes into reservoirs
- How data quality tools are adapting
- Why data governance disciplines remain crucial
Data mining-implementation-to-predict-sales-using-time-series-method By Raiha...raihansikdar
This document discusses using data mining techniques to predict sales using time series analysis. It outlines the research method, which involves collecting transaction data, constructing a moving average model, forecasting values, and evaluating the results. The document also includes a flowchart of the data mining process, showing the steps of clustering, classification, prediction, and association. It presents a graph comparing actual transaction values to forecasted values, showing the highest forecasting accuracy was 99.68% while the lowest was over 50%. The conclusion is that while forecasts may not be 100% accurate, they can still provide useful estimates to assist business processes.
Thought leaders in big data ulf mattsson, cto of protegrity (part 4)Ulf Mattsson
Protegrity CTO and data security expert Ulf Mattsson featured in recent post on One Million by One Million blog. In her recent post, “Thought Leaders in Big Data: Ulf Mattsson, CTO of Protegrity,” Sramana Mitra profiles Protegrity CTO Ulf Mattsson and gets his thoughts on the state of Big Data security today.
This document discusses data quality and its importance for business decision making. It defines data quality as ensuring information is fit for its intended purpose and helps data consumers make the right decisions. Poor data quality can significantly impact business performance, with 75% of companies reporting financial losses due to low quality data. The document outlines different data quality needs and metrics for various use cases and decision makers. It also presents examples of companies that have benefited financially from implementing thorough data quality management programs.
every business needs a data analytics to get a detailed value of cost and profits. we will study the importance in detail in this particular presentation.
Dynamic Talks: "Implementing data quality automation with open source stack" ...Grid Dynamics
The quality of business decisions, machine learning insights, and executive reports depend on the quality and integrity of the underlying data. There are many ways that data can get corrupted in an analytical data platform from de-synchronization with the system-of-record to defects in data pipelines. We will show how to detect and prevent data corruption with automation, open source tools, and machine learning.
Differential Privacy for Information RetrievalGrace Hui Yang
This document summarizes a tutorial on differential privacy for information retrieval. The tutorial covered early attempts at privacy-preserving information retrieval using techniques like log deletion, hashing queries and identifiers, and scrubbing query content. However, these naive techniques were shown to not adequately protect privacy. More advanced techniques like k-anonymity, l-diversity, and t-closeness were discussed, but also have limitations. The main topic of the tutorial was introducing differential privacy and how it can be applied to typical information retrieval applications and tasks.
This document discusses using community feedback to improve software engineering data quality over time. It proposes using a dynamic data model where human feedback enhances static data sets, similar to how Wikipedia evolves. Key challenges are how to collect feedback, integrate it with AI, and motivate user participation through incentives, usability and trust. The goal is to lower costs while building a stronger evidence base that adapts to changing conditions through a synergistic human-AI approach.
On this slides, we tried to give an overview of advanced Data quality management (ADQM). To understand about DQ why important, and all those steps of DQ management.
This document describes a grievance redressal system developed for a college to allow students, faculty, and staff to easily file complaints online in a secure manner. The system was created to address issues with the previous paper-based complaint system, such as lack of acknowledgment, inability to track complaints, and lack of environmental friendliness. The new system uses registration and login for security and allows complaints to be filed, tracked, and resolved by competent authorities. It aims to improve user satisfaction and encourage feedback to enhance service quality. The system was implemented using a web interface and database. An evaluation found it encouraged complaints without fear and provided transparency, timeliness, and ease of use compared to the prior manual system. Future enhancements could
1) The document discusses best practices in complaints handling within public organizations. Effective complaints handling provides benefits to both the organization and the public it serves.
2) For a complaints system to operate effectively, people must be able to easily access it and have confidence in the system. Organizations should address any barriers to access and reassure complainants that they will not face retaliation.
3) Proper management practices are needed for complaints handling within an organization. Risk assessment, clear roles and responsibilities, and support for frontline staff are important. Periodic reviews of the system help ensure continuous improvement.
4) A system-wide perspective is also valuable to categorize complaints and spread lessons learned across organizations. An effective complaints system
1) The document discusses best practices in complaints handling within public organizations. Effective complaints handling provides benefits to both the organization and the public it serves.
2) For a complaints system to operate effectively, people must be able to easily access it and have confidence in the system. Organizations should address any barriers to access and reassure people they will not face retaliation for complaining.
3) Proper management practices are needed for complaints handling within each organization, and oversight is required from a system-wide perspective to ensure lessons are shared. Risk assessment, clear roles and responsibilities, and support for staff are important.
1) The document discusses best practices in complaints handling within public organizations. Effective complaints handling provides benefits to both the organization and the public it serves.
2) For a complaints system to operate effectively, people must be able to easily access it and have confidence in the system. Organizations should address any barriers to access and reassure complainants that they will not face retaliation.
3) Proper management practices are needed for complaints handling within an organization. Risk assessment, clear roles and responsibilities, and support for frontline staff are important. Periodic reviews of the system help ensure continuous improvement.
4) A system-wide perspective is also valuable to categorize complaints and spread lessons learned across organizations. An effective complaints system
Design and implementation of a web based student complaint systemResearchWap
The main aims and objectives of the system are:
• To design a web based complaint system record and information system to replace some extent of human role in cases of unavailability of job.
• To provide quick responds to students complains on campus.
Also, the purpose of this software is to model a computerized complain system to enable proper complain submitting and control.
The document describes a web portal for an effective student grievance support system. The system allows students to lodge complaints online about various issues like admission irregularities, schedule conflicts, and financial issues. The complaints are forwarded to a grievance redressal committee and then to relevant departments. The departments can update the status of the complaints for students to view, providing transparency. The proposed system aims to streamline the grievance redressal process and ensure issues are addressed in a timely manner without students having to visit the administration in-person.
The document provides guidance on developing an effective complaints resolution system with six key principles:
1. A positive complaints culture where complaints are seen as opportunities for improvement rather than criticism.
2. Accountability where the organization follows its complaints policy and procedure, keeps accurate records, and informs users of improvements from complaints.
3. A fair system where all parties' views are represented impartially and decisions are based on facts.
4. Confidentiality where complaint information and records are kept private and only discussed with those directly involved.
5. Accessibility where the process is responsive to all users and interpreters are available to help those who need assistance.
6. Responsiveness where complaints are acknowledged
Why You Should Go To A Community College Free EsMichelle Wilson
Social media has a significant impact on consumer purchasing behavior and implications for organizations. It allows organizations to directly target and engage with customers on platforms like Facebook, Twitter and YouTube. The growing number of social media users provides organizations an effective way to promote products, interact with consumers, and analyze customer behavior to better align their marketing strategies. The research aims to understand how social media influences consumers and how organizations can best utilize social media to their advantage.
Performance improvement through mobile devicesSawad thotathil
This document discusses how mobile apps can help organizations improve performance. It faces 3 types of complexity: structural, mission creep, and process. Mobile devices connect knowledge workers and embed continuous improvement by enabling seamless data collection, access to metrics, and knowledge sharing. They overcome hurdles to change like difficult process deployment and rigid information structures. Apps allow testing solutions without costly failures. The case study describes a provider group using mobile apps to integrate information systems, level provider availability, coordinate workflows, implement performance measurement through data collection, and make protocols accessible. Mobile devices can expedite data collection and decision making to implement plan-do-study-act cycles.
Write An Essay On The Dog Essay Writing - YouTubeKatrina Duarte
This document discusses perspectives on school security measures outlined in a textbook. It argues that two specific measures - limiting student access to school mail and reviewing procedures for handling envelopes with powder substances - are not as important as other measures. For the school mail measure, students are unlikely to have access to mail in the first place. For the envelope measure, a focus on suspicious packages found on school grounds would be more practical than creating paranoia about letters. It also questions whether field trips should be cancelled during times of possible threat to take a precautionary approach to security.
This document summarizes an industrial training report on developing a Smart Reminder App. The report acknowledges those who provided guidance and support for the project. It then provides background on the company where the training took place, Agile Softech Pvt. Ltd., which develops customized software solutions. The report abstract introduces the Smart Reminder App, which allows patients to set medication alarms and search for doctors. Finally, the document discusses system analysis conducted for the app, including identifying user needs, feasibility analysis, and technical requirements.
Online job placement system project report.pdfKamal Acharya
Our project Expert.Com Job Placement System has been designed to help the millions of unemployed youth to get in touch with the major companies which would help them in getting the right kind of jobs and would also help the companies to get the appropriate candidates for appropriate jobs.
International best practice approaches to complaints handling. The document discusses defining complaints, why effective complaints handling is important, ways to encourage complaints, establishing a complaints handling system, dealing with difficult complaints, quality assuring complaints handling processes, and ensuring lessons are learned. The key aspects covered are defining complaints, the importance of complaints for improving services, establishing transparent multi-stage complaints procedures, and promoting organization-wide learning from complaints.
Essay On A Train Journey That You Have MadeMelissa Ford
The document provides instructions for creating an account on a writing assistance website and submitting requests for papers to be written. It outlines a 5-step process: 1) Create an account with an email and password. 2) Complete an order form with instructions, sources, and deadline. 3) Review bids from writers and select one. 4) Review the completed paper and authorize payment. 5) Request revisions until satisfied. The purpose is to help students obtain original, high-quality papers through this online writing service.
HAP - Measuring Effectiveness in international developmentNIDOS
The document discusses benchmarks for humanitarian organizations to measure effective accountability. It outlines 6 benchmarks: 1) establishing a quality management system, 2) sharing information publicly, 3) enabling beneficiary participation, 4) ensuring competent staff, 5) handling complaints, and 6) continual improvement. Each benchmark is described in 1-2 sentences that identify its key aspects or requirements for humanitarian organizations.
This document outlines a proposed conflict resolution management system design for the Raleigh Boys and Girls Club. It was developed based on a pilot study that found the club lacked effective processes for addressing conflicts between members. The proposed system includes appointing an ombudsperson to oversee training peer mediators to help resolve conflicts. It describes training the peer mediators would receive and the referral process staff would use to direct conflicts to mediation. The goals of the system are to help members develop skills to prevent, manage, and resolve conflicts in a cooperative manner.
The document discusses the development and implementation of management information systems (MIS). It covers various approaches to developing an MIS, including prototyping and the life cycle approach. It also discusses implementing the MIS as a management process that brings about organizational change and affects people. The successful implementation of an MIS requires handling human factors carefully and addressing any resistance to change.
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.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
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."
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
2. 2 Mohammad Shahnawaz et al.
legitimacy may be challenged when results do not meet popula-
tion’s expectations or people working in that firm is not satisfied
to overcome this e-grievance redressal systems should be intro-
duced relevant channels to sustain inclusion, as promised.
Based on the study of the existing system available for com-
plaint collection in various colleges and as a consequence of
meeting with the competent authority of college a small sur-
vey is conducted with students in order to know the gray areas
and the areas of improvement in the existing system. The survey
is based on the parameters like Which Problem Do You Likely
Face? (Financial or Non financial?), What Else You Expect To
Be In Redressal Window, any Suggestions. Following concerns
are identified as a result of survey
(i) After submitting the complaint, users are not acknowl-
edged properly and timely.
(ii) They don’t have the copy of their complaint for future
reference.
(iii) They can’t track their complaint or check its status.
Study of various website which are providing relevant services
revealed in depth details about e-complaint booking systems.
3 Methodology
Grievances redressal system address the issues known the
present system, by providing a user friendly and secure interface
to lodge the criticism in conjunction with necessary document
and track an equivalent.
3.1 Analysis
As an outcome of the present research it is suggested that there
must be a special grievance handling cell or a system for the all
the stakeholders. The manager in-charge should be capable to
identify all grievances and must take appropriate steps to elim-
inate the causes of such grievances so that the end-user remain
loyal and committed to their work. For effective grievance man-
agement the managers should adopt the following approach to
manage grievance effectively:-
(1) Fast Action:-
As before long as a result of the grievance arises; it ought to be
known and resolved. coaching should tend to the managers to
effectively and timely manage a grievance. this may lower the
harmful effects of grievance on the user and their performance.
(2) Acknowledging Grievance:-
The member and heads should acknowledge the grievance sug-
gests by the user as manifestation of true and real feelings.
Acknowledgement by the manager implies that the manager is
keen to look into the criticism impartially and with none bias,
this may produce contributive work setting with instances of
grievance reduced.
(3) Gathering Facts:-
The member and heads ought to gather acceptable and sufficient
facts explaining the grievance’s nature. A record of such facts
Figure 1. Mechanism for Grievances Redressal System.
should be maintained so as that these square measure usually
used in later stage of grievance redressal.
(4) Examining the causes of grievance:-
The actual reason behind grievance ought to be known. Conse-
quently, remedial actions ought to be taken to prevent repetition
of the grievance.
(5) Call Making:-
In this system we tend to distinctive the grievance, once distinc-
tive the grievance various course of actions ought to be taken to
manage the grievance. The result of every action on the man-
agement policies and procedure ought to be analysed and so call
ought to be taken by the Committee.
An effective grievance procedure ensures associate well-
meaning Work setting as a result of it redresses the grievance
to mutual satisfaction of each the users and also the man-
agers. It conjointly helps the management to frame policies
and procedures acceptable to the schools. It becomes asso-
ciate effective medium for the schools to precise the emotions,
discontent and discontentedness in an exceedingly systematic
method.
3.2 Design
The aim of the proposed system is to deal with the problems
present within the current system, implement validation tech-
niques which will help reduce the margin of error in operations,
providing adequate data backup facilities in order to ensure sys-
tem restart even after a calamity and ensures consistency.
The Steps for Student Service Request Redressal Window
handling procedure:-
Step 1 – User should initiate the complaint registration
Step 2 – User will receive a confirmation of submission along
their form submitted in XML format.
Step 3 – Grievances redressal system investigates the complaint
and reverts back with action.
Step 4 – Once the issue is addressed Grievances redressal system
Window will be closed.
3. Grievance Redressal System 3
Figure 2. A Prototype for Grievance Redressal System.
4 Implementation & Result
The system consists of an administrator, user and a committee
member who is efficient in handling the given task, and thus the
system works smoothly without further delays. Victim’s authen-
tication is done beforehand in order to avoid the nuisance which
might arise in the manual system. The entire system is automated
and is based on the principles of Confidentiality, Transparency,
and Sensitivity of the issue. Emphasis is on Timely and appropri-
ate action on the registered concern.
• Opening the web-server at http://iilmredressalwin.epizy.
com. You will see the login page as shown in Fig. 3. Enter
the required details to login or signup.
• Once you login, you will be at our home page, toggle
between the tabs to go to your required page. If you click
on RAISE YOUR VOICE or http://iilmredressalwin.epizy.
com/raise-your-voice/ here, you can submit your problem.
• You can also track your problem using the given link http:
//iilmredressalwin.epizy.com/raise-your-voice/
• You can also visit STUDENT WELFARE page either
through home or by using this link, http://iilmredressalwin.
epizy.com/student-welfare/
The automated system ensures
• Encourages users to raise grievances without fear
• Provides a good and speedy means of grievance handling.
Figure 3. A screenshot for the login-page of the Grievance
Redressal System.
• Transparency in handling grievance of a student.
• Save time of students.
• Helps to build harmonious atmosphere in campus with
openness and trust
5 Conclusion
Innovation:-
• Data Collection:- Once a user complete the registration
process, data related to user will be saved in dashboard and
those who submit the form will also provide important data
in terms of data collection.
• Data Analysis:- Once the data is collected, we can analyse
data for following purposes:-
◦ College Point of view:- Administration can identify
from which section most of the problem are being
arises and how they can improve them.
College can also use the submitted form data to
analyse their performance in last few years and know
how much students are satisfied with them.
◦ Student Point of view:- When a student complaint is
not resolved, and didn’t get a fruitful reply from the
college, they can go back directly to the government
organisation mentioned in our website.
This system mainly designed to reduce the manual efforts,
receive all complaints about college, track complaint status man-
aging data of complaints and make the work easier for users and
the competent authorities. It leads to
• Reduced paperwork, operational time
• Reduced human efforts.
• Increased accuracy and reliability
• Easy maintenance of Data.
• Data security
• Instant access
• Improved productivity
• Optimum utilization of resources,
• Efficient management of resources.
6 Future Scope
• Time Based Tracker:- In future time based tracker will be
introduced for limiting the delay in reply of complaint from
the organization.
• Open Dashboard for all:- A open dashboard will be created
which can be visible to all visitor. A visitor can see number
of complaint filed and number of cases resolved. This can
give a detail idea to student to look while they are searching
college for admission. This will also give college to create
a trust worthy image by minimizing the number of cases
filed and solving those which are filed.
4. 4 Mohammad Shahnawaz et al.
References
[1] Brohman, M.k., et al., Data completeness: A key to effective net-based cus-
tomer service systems. 2003.
[2] Customer Complaint. [cited 2017 2]; Available from: http://www.
financepractitioner.com/dictionary/customer-complaint.
[3] Subhash, C., Ashwani K.: Assessing grievances redressing mechanism in
India. Int.J. Comput. Appl. 52(5), 12–19 (2012).
[4] Hardeman, T., 2004. Complaint, Grievance, WhistleBlowing Administrative
Regulation. Personnel Management, Vol. 18, No. 3.
[5] Jackson, Tricia., 2000. Handling Grievances, Institute of Personnel and
Development.
[6] Gianluca, M., Karin, P., Javier, M., Rahul, D.: Openness might not
MeanDemocratization-e-Grievance Systems Their Consequences.
14th N-AERUS and GISDECO, University of Twente, Netherlands
(2013).