A picture is worth a thousand points of data. The power of data is transformative when the analytics is displayed visually enabling for faster decision making.
The document discusses research on big data and business analytics. It aims to understand how firms derive business value from big data analytics and identify key requirements. A literature review develops a framework to classify big data articles. A case study analyzes an emergency service using big data. Requirements for effective analytics are identified. A survey examines business value across countries. The research conceptualizes big data, assesses benefits to business units and organizations, and provides recommendations for senior management to maximize value from big data and analytics.
Digital Pragmatism with Business Intelligence, Big Data and Data VisualisationJen Stirrup
Contact details:
Jen.Stirrup@datarelish.com
In a world where the HiPPO’s (Highest Paid Person’s Opinion) is final, how can we use technology to drive the organisation towards data-driven decision making as part of their organizational DNA? R provides a range of functionality in machine learning, but we need to expose its richness in a world where it is made accessible to decision makers. Using Data Storytelling with R, we can imprint data in the culture of the organization by making it easily accessible to everyone, including decision makers. Together, the insights and process of machine learning are combined with data visualisation to help organisations derive value and insights from big and little data.
SQLBits Module 2 RStats Introduction to R and StatisticsJen Stirrup
SQLBits Module 2 RStats Introduction to R and Statistics. This is a 90 minute segment of a full preconference workshop, focusing on data analytics with R.
Statistics is all about facts and ratio, well it is a lot more than that and understanding every bit of it requires right statistics assignment help from some expert sources. We at helpmeinhomework are one such homework helping source providing adequate help whenever necessary.
Statistics Assignment Help from the Statistics Assignment Experts. Statistics assignment help is the most common assignment that students are mostly demand for. Statistics is the branch of mathematics, comprises the collection, summarizing, analysis, interpretation, and presentation of data.
I am Joshua M. I am a Statistics Assignment Expert at statisticsassignmenthelp.com. I hold a Masters in Statistics from, Michigan State University, USA
I have been helping students with their homework for the past 5 years. I solve assignments related to Statistics.
Visit statisticsassignmenthelp.com or email info@statisticsassignmenthelp.com.
You can also call on +1 678 648 4277 for any assistance with Statistics Assignments.
Data Processing & Explain each term in details.pptxPratikshaSurve4
Data processing involves converting raw data into useful information through various steps. It includes collecting data through surveys or experiments, cleaning and organizing the data, analyzing it using statistical tools or software, interpreting the results, and presenting findings visually through tables, charts and graphs. The goal is to gain insights and knowledge from the data that can help inform decisions. Common data analysis types are descriptive, inferential, exploratory, diagnostic and predictive analysis. Data analysis is important for businesses as it allows for better customer targeting, more accurate decision making, reduced costs, and improved problem solving.
The document discusses research on big data and business analytics. It aims to understand how firms derive business value from big data analytics and identify key requirements. A literature review develops a framework to classify big data articles. A case study analyzes an emergency service using big data. Requirements for effective analytics are identified. A survey examines business value across countries. The research conceptualizes big data, assesses benefits to business units and organizations, and provides recommendations for senior management to maximize value from big data and analytics.
Digital Pragmatism with Business Intelligence, Big Data and Data VisualisationJen Stirrup
Contact details:
Jen.Stirrup@datarelish.com
In a world where the HiPPO’s (Highest Paid Person’s Opinion) is final, how can we use technology to drive the organisation towards data-driven decision making as part of their organizational DNA? R provides a range of functionality in machine learning, but we need to expose its richness in a world where it is made accessible to decision makers. Using Data Storytelling with R, we can imprint data in the culture of the organization by making it easily accessible to everyone, including decision makers. Together, the insights and process of machine learning are combined with data visualisation to help organisations derive value and insights from big and little data.
SQLBits Module 2 RStats Introduction to R and StatisticsJen Stirrup
SQLBits Module 2 RStats Introduction to R and Statistics. This is a 90 minute segment of a full preconference workshop, focusing on data analytics with R.
Statistics is all about facts and ratio, well it is a lot more than that and understanding every bit of it requires right statistics assignment help from some expert sources. We at helpmeinhomework are one such homework helping source providing adequate help whenever necessary.
Statistics Assignment Help from the Statistics Assignment Experts. Statistics assignment help is the most common assignment that students are mostly demand for. Statistics is the branch of mathematics, comprises the collection, summarizing, analysis, interpretation, and presentation of data.
I am Joshua M. I am a Statistics Assignment Expert at statisticsassignmenthelp.com. I hold a Masters in Statistics from, Michigan State University, USA
I have been helping students with their homework for the past 5 years. I solve assignments related to Statistics.
Visit statisticsassignmenthelp.com or email info@statisticsassignmenthelp.com.
You can also call on +1 678 648 4277 for any assistance with Statistics Assignments.
Data Processing & Explain each term in details.pptxPratikshaSurve4
Data processing involves converting raw data into useful information through various steps. It includes collecting data through surveys or experiments, cleaning and organizing the data, analyzing it using statistical tools or software, interpreting the results, and presenting findings visually through tables, charts and graphs. The goal is to gain insights and knowledge from the data that can help inform decisions. Common data analysis types are descriptive, inferential, exploratory, diagnostic and predictive analysis. Data analysis is important for businesses as it allows for better customer targeting, more accurate decision making, reduced costs, and improved problem solving.
Decision support systems (DSS) are computer-based systems that analyze data and help decision-makers solve semi-structured or unstructured problems. DSS provide access to internal and external data, models, and documents to help identify problems and solutions. There are different types of DSS including data-driven, model-driven, communication-driven, document-driven, and knowledge-driven systems. DSS have benefits like improved efficiency, faster decision-making, and competitive advantages. They are used in various applications including clinical decision support, banking, and analyzing business performance.
The document discusses the analytics life cycle and its key phases. It describes the business understanding phase which ensures projects align with objectives and identifies relevant data. The data understanding phase involves gaining familiarity with available data. Data preparation aims to assess and improve data quality for analysis through tasks like cleaning, integration, transformation and reduction. Modeling techniques are selected and models are generated, built and assessed. The primary goal of data understanding is to gain a comprehensive view of available data to inform subsequent phases.
This document discusses Synchronoss' journey in developing their data pipeline and profiling capabilities. It describes:
1) Their initial ETL-based pipeline (V1) that had long batch processes and could not handle large, unstructured data.
2) An upgraded version (V2) using a MPP appliance that improved performance but had high costs.
3) Their adoption of Spark (V4) to build a flexible, scalable pipeline that profiles data in the data lake using RDDs and built-in transformations.
4) This approach improved their data analysis time from weeks to hours and identified data quality issues earlier.
Tips for Effective Data Science in the EnterpriseLisa Cohen
Data Science is an evolving field, that requires a diverse skill set. From Career Advice to steps for how to approach your Data Science Workflow, this talk is full of practical tips that you can apply immediately to your job.
What is data mining? The process of analyzing data to discover hidden patterns and relationships that can help you manage and improve your business.
Check out: www.eleaderstochange.com
Follow #eleaders2change
This document discusses how information systems can support management decision making at different levels of the organization. It describes decision support systems (DSS) that help individual and group decision making, as well as executive support systems. DSS combine data, tools and models to support semi-structured and unstructured decision making. Types of DSS include model-driven, data-driven, communications-driven, and document-driven systems. Group decision support systems use software tools to facilitate collaborative group problem solving. Executive support systems provide customized access and analysis of internal and external data to support strategic decision making by senior executives.
This document discusses how information systems can support management decision making through decision support systems (DSS). It describes different types of DSS including data-driven, model-driven, communications-driven, and group DSS. Examples are provided of how various organizations have implemented DSS to improve pricing decisions, supply chain management, and customer analysis. Executive support systems are discussed which integrate internal and external data to support strategic decision making by senior executives.
Building New Data Ecosystem for Customer Analytics, Strata + Hadoop World, 2016Caserta
Caserta Concepts Founder and President, Joe Caserta, gave this presentation at Strata + Hadoop World 2016 in New York, NY. His session covers path-to-purchase analytics using a data lake and spark.
For more information, visit http://casertaconcepts.com/
The data architecture of solutions is frequently not given the attention it deserves or needs. Frequently, too little attention is paid to designing and specifying the data architecture within individual solutions and their constituent components. This is due to the behaviours of both solution architects ad data architects.
Solution architecture tends to concern itself with functional, technology and software components of the solution
Data architecture tends not to get involved with the data aspects of technology solutions, leaving a data architecture gap. Combined with the gap where data architecture tends not to get involved with the data aspects of technology solutions, there is also frequently a solution architecture data gap. Solution architecture also frequently omits the detail of data aspects of solutions leading to a solution data architecture gap. These gaps result in a data blind spot for the organisation.
Data architecture tends to concern itself with post-individual solutions. Data architecture needs to shift left into the domain of solutions and their data and more actively engage with the data dimensions of individual solutions. Data architecture can provide the lead in sealing these data gaps through a shift-left of its scope and activities as well providing standards and common data tooling for solution data architecture
The objective of data design for solutions is the same as that for overall solution design:
• To capture sufficient information to enable the solution design to be implemented
• To unambiguously define the data requirements of the solution and to confirm and agree those requirements with the target solution consumers
• To ensure that the implemented solution meets the requirements of the solution consumers and that no deviations have taken place during the solution implementation journey
Solution data architecture avoids problems with solution operation and use:
• Poor and inconsistent data quality
• Poor performance, throughput, response times and scalability
• Poorly designed data structures can lead to long data update times leading to long response times, affecting solution usability, loss of productivity and transaction abandonment
• Poor reporting and analysis
• Poor data integration
• Poor solution serviceability and maintainability
• Manual workarounds for data integration, data extract for reporting and analysis
Data-design-related solution problems frequently become evident and manifest themselves only after the solution goes live. The benefits of solution data architecture are not always evident initially.
FOCUS AREA:
- Identify data requirements and goals.
- IT solution (data) design.
- Focus on data development and configuration (solutions/projects).
- Develop data standards.
- Ensure data integration.
- Ensure correct data testing.
- Maintain and optimize data solutions.
RELATION TO STRATEGY:
- Develop data solutions based on business/IT requirements
Develop data solutions and goals based on operational objectives.
- Link business KPI’s to system KPI’s.
- Ensure correct data reporting in terms of system reports, cockpits, dashboards and scorecards.
A presentation covers how data science is connected to build effective machine learning solutions. How to build end to end solutions in Azure ML. How to build, model, and evaluate algorithms in Azure ML.
The document discusses business analytics and data visualization. It defines business analytics as the iterative and methodical exploration of an organization's data using statistical analysis to support data-driven decision making. It describes the main areas of business analytics techniques as business intelligence and statistical analysis. It also outlines the four main types of business analytics: descriptive, predictive, prescriptive, and diagnostic. The document further discusses data visualization, consumption of analytics, tools for data visualization, examples of data visualizations, and characteristics of effective graphical displays.
In-Database Analytics Deep Dive with Teradata and RevolutionRevolution Analytics
Teradata and Revolution Analytics worked together to develop in-database analytical capabilities for Teradata Database. Teradata v14.10 provides a foundation for in-database analytics in Teradata. Revolution Analytics has ported its Revolution R Enterprise (RRE) Version 7.1 to use the in-database capabilities of version 14.10. With RRE inside Teradata, users can run fully parallelized algorithms in each node of the Teradata appliance to achieve performance and data scale heretofore unavailable. We'll get past the market-ecture quickly and dive into a “how it really works” presentation, review implications for system configuration and administration, and then take questions from Teradata users who will be charged with deploying and administering Teradata systems as platforms for big data analytics inside the database engine.
Big Data Warehousing Meetup: Dimensional Modeling Still Matters!!!Caserta
Joe Caserta went over the details inside the big data ecosystem and the Caserta Concepts Data Pyramid, which includes Data Ingestion, Data Lake/Data Science Workbench and the Big Data Warehouse. He then dove into the foundation of dimensional data modeling, which is as important as ever in the top tier of the Data Pyramid. Topics covered:
- The 3 grains of Fact Tables
- Modeling the different types of Slowly Changing Dimensions
- Advanced Modeling techniques like Ragged Hierarchies, Bridge Tables, etc.
- ETL Architecture.
He also talked about ModelStorming, a technique used to quickly convert business requirements into an Event Matrix and Dimensional Data Model.
This was a jam-packed abbreviated version of 4 days of rigorous training of these techniques being taught in September by Joe Caserta (Co-Author, with Ralph Kimball, The Data Warehouse ETL Toolkit) and Lawrence Corr (Author, Agile Data Warehouse Design).
For more information, visit http://casertaconcepts.com/.
Technical Documentation 101 for Data Engineers.pdfShristi Shrestha
This document discusses metadata and data documentation best practices. It begins by defining metadata as data that describes other data, such as author, file size, and date for text files. It recommends documenting the table or database last documented, documenter, business case, tools used, and data quality. Good documentation practices include knowing your audience and purpose, keeping documentation minimal but effective, and building user documentation. Common data documentation templates include CRISP-DM, which outlines phases for documentation like business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Thorough data documentation is important for project understanding, reuse, and governance.
In today's competitive market, many organizations are unaware of the quantity of poor-quality data in their systems. Some organizations assume that their data is of adequate quality, although they have conducted no metrical or statistical analysis to support the assumption. Others know that their performance is hampered by poor-quality data, but they cannot measure the problem.
What Is SAS | SAS Tutorial For Beginners | SAS Training | SAS Programming | E...Edureka!
The document discusses SAS (Statistical Analytics System), a software for data management, analytics and visualization. It provides an overview of SAS framework, programming and applications. SAS allows users to access, manage and analyze data, and then present results. It discusses key SAS concepts like data sets, variables, formats and linear regression modeling using SAS procedures. Common applications of SAS mentioned are in domains like stock prediction, drug discovery, fraud detection and workflow optimization.
Data Management for High Performance AnalyticsMary Snyder
High-performance analytics is only as good as the data management supporting it.
In fact, high-performance data management plays a key role when it comes to in-database, in-memory and in-stream analytics.
In this webinar Dan Socenau from SAS explores:
•The data management building blocks needed to succeed with high-performance analytics.
•Assessing, planning and executing these bedrock data management capabilities.
•How to deploy a modern data analysis practice.
View the on-demand webinar: http://www.sas.com/en_us/webinars/data-management-high-performance-analytics.html
This webinar talks about data collection in political polling and expanding your reach by using a multimode platform.
We will talk about the use of phone, online, and SMS to invite voters and let their voice be heard.
We also talk about techniques to measure ad messaging satisfaction before these media spots go public so that they can be refined before hitting the airwaves.
Lastly, we will talk about reporting the results in your polling project.
This presentation discusses post transaction surveys, which can be done via integrations.
We talk about the reasons for a post transaction surveys. We talk about how to approach customers to get them to take your surveys and how to communicate to your customers. We talk about how to plan out the methodology and sampling.
We also talk about the various scores in research that are the right tools to calculate various scores to measure Customer Satisfaction Score, Net Promoter Score, and Customer Experience Score. We go over how to use these scores and when to use them.
Decision support systems (DSS) are computer-based systems that analyze data and help decision-makers solve semi-structured or unstructured problems. DSS provide access to internal and external data, models, and documents to help identify problems and solutions. There are different types of DSS including data-driven, model-driven, communication-driven, document-driven, and knowledge-driven systems. DSS have benefits like improved efficiency, faster decision-making, and competitive advantages. They are used in various applications including clinical decision support, banking, and analyzing business performance.
The document discusses the analytics life cycle and its key phases. It describes the business understanding phase which ensures projects align with objectives and identifies relevant data. The data understanding phase involves gaining familiarity with available data. Data preparation aims to assess and improve data quality for analysis through tasks like cleaning, integration, transformation and reduction. Modeling techniques are selected and models are generated, built and assessed. The primary goal of data understanding is to gain a comprehensive view of available data to inform subsequent phases.
This document discusses Synchronoss' journey in developing their data pipeline and profiling capabilities. It describes:
1) Their initial ETL-based pipeline (V1) that had long batch processes and could not handle large, unstructured data.
2) An upgraded version (V2) using a MPP appliance that improved performance but had high costs.
3) Their adoption of Spark (V4) to build a flexible, scalable pipeline that profiles data in the data lake using RDDs and built-in transformations.
4) This approach improved their data analysis time from weeks to hours and identified data quality issues earlier.
Tips for Effective Data Science in the EnterpriseLisa Cohen
Data Science is an evolving field, that requires a diverse skill set. From Career Advice to steps for how to approach your Data Science Workflow, this talk is full of practical tips that you can apply immediately to your job.
What is data mining? The process of analyzing data to discover hidden patterns and relationships that can help you manage and improve your business.
Check out: www.eleaderstochange.com
Follow #eleaders2change
This document discusses how information systems can support management decision making at different levels of the organization. It describes decision support systems (DSS) that help individual and group decision making, as well as executive support systems. DSS combine data, tools and models to support semi-structured and unstructured decision making. Types of DSS include model-driven, data-driven, communications-driven, and document-driven systems. Group decision support systems use software tools to facilitate collaborative group problem solving. Executive support systems provide customized access and analysis of internal and external data to support strategic decision making by senior executives.
This document discusses how information systems can support management decision making through decision support systems (DSS). It describes different types of DSS including data-driven, model-driven, communications-driven, and group DSS. Examples are provided of how various organizations have implemented DSS to improve pricing decisions, supply chain management, and customer analysis. Executive support systems are discussed which integrate internal and external data to support strategic decision making by senior executives.
Building New Data Ecosystem for Customer Analytics, Strata + Hadoop World, 2016Caserta
Caserta Concepts Founder and President, Joe Caserta, gave this presentation at Strata + Hadoop World 2016 in New York, NY. His session covers path-to-purchase analytics using a data lake and spark.
For more information, visit http://casertaconcepts.com/
The data architecture of solutions is frequently not given the attention it deserves or needs. Frequently, too little attention is paid to designing and specifying the data architecture within individual solutions and their constituent components. This is due to the behaviours of both solution architects ad data architects.
Solution architecture tends to concern itself with functional, technology and software components of the solution
Data architecture tends not to get involved with the data aspects of technology solutions, leaving a data architecture gap. Combined with the gap where data architecture tends not to get involved with the data aspects of technology solutions, there is also frequently a solution architecture data gap. Solution architecture also frequently omits the detail of data aspects of solutions leading to a solution data architecture gap. These gaps result in a data blind spot for the organisation.
Data architecture tends to concern itself with post-individual solutions. Data architecture needs to shift left into the domain of solutions and their data and more actively engage with the data dimensions of individual solutions. Data architecture can provide the lead in sealing these data gaps through a shift-left of its scope and activities as well providing standards and common data tooling for solution data architecture
The objective of data design for solutions is the same as that for overall solution design:
• To capture sufficient information to enable the solution design to be implemented
• To unambiguously define the data requirements of the solution and to confirm and agree those requirements with the target solution consumers
• To ensure that the implemented solution meets the requirements of the solution consumers and that no deviations have taken place during the solution implementation journey
Solution data architecture avoids problems with solution operation and use:
• Poor and inconsistent data quality
• Poor performance, throughput, response times and scalability
• Poorly designed data structures can lead to long data update times leading to long response times, affecting solution usability, loss of productivity and transaction abandonment
• Poor reporting and analysis
• Poor data integration
• Poor solution serviceability and maintainability
• Manual workarounds for data integration, data extract for reporting and analysis
Data-design-related solution problems frequently become evident and manifest themselves only after the solution goes live. The benefits of solution data architecture are not always evident initially.
FOCUS AREA:
- Identify data requirements and goals.
- IT solution (data) design.
- Focus on data development and configuration (solutions/projects).
- Develop data standards.
- Ensure data integration.
- Ensure correct data testing.
- Maintain and optimize data solutions.
RELATION TO STRATEGY:
- Develop data solutions based on business/IT requirements
Develop data solutions and goals based on operational objectives.
- Link business KPI’s to system KPI’s.
- Ensure correct data reporting in terms of system reports, cockpits, dashboards and scorecards.
A presentation covers how data science is connected to build effective machine learning solutions. How to build end to end solutions in Azure ML. How to build, model, and evaluate algorithms in Azure ML.
The document discusses business analytics and data visualization. It defines business analytics as the iterative and methodical exploration of an organization's data using statistical analysis to support data-driven decision making. It describes the main areas of business analytics techniques as business intelligence and statistical analysis. It also outlines the four main types of business analytics: descriptive, predictive, prescriptive, and diagnostic. The document further discusses data visualization, consumption of analytics, tools for data visualization, examples of data visualizations, and characteristics of effective graphical displays.
In-Database Analytics Deep Dive with Teradata and RevolutionRevolution Analytics
Teradata and Revolution Analytics worked together to develop in-database analytical capabilities for Teradata Database. Teradata v14.10 provides a foundation for in-database analytics in Teradata. Revolution Analytics has ported its Revolution R Enterprise (RRE) Version 7.1 to use the in-database capabilities of version 14.10. With RRE inside Teradata, users can run fully parallelized algorithms in each node of the Teradata appliance to achieve performance and data scale heretofore unavailable. We'll get past the market-ecture quickly and dive into a “how it really works” presentation, review implications for system configuration and administration, and then take questions from Teradata users who will be charged with deploying and administering Teradata systems as platforms for big data analytics inside the database engine.
Big Data Warehousing Meetup: Dimensional Modeling Still Matters!!!Caserta
Joe Caserta went over the details inside the big data ecosystem and the Caserta Concepts Data Pyramid, which includes Data Ingestion, Data Lake/Data Science Workbench and the Big Data Warehouse. He then dove into the foundation of dimensional data modeling, which is as important as ever in the top tier of the Data Pyramid. Topics covered:
- The 3 grains of Fact Tables
- Modeling the different types of Slowly Changing Dimensions
- Advanced Modeling techniques like Ragged Hierarchies, Bridge Tables, etc.
- ETL Architecture.
He also talked about ModelStorming, a technique used to quickly convert business requirements into an Event Matrix and Dimensional Data Model.
This was a jam-packed abbreviated version of 4 days of rigorous training of these techniques being taught in September by Joe Caserta (Co-Author, with Ralph Kimball, The Data Warehouse ETL Toolkit) and Lawrence Corr (Author, Agile Data Warehouse Design).
For more information, visit http://casertaconcepts.com/.
Technical Documentation 101 for Data Engineers.pdfShristi Shrestha
This document discusses metadata and data documentation best practices. It begins by defining metadata as data that describes other data, such as author, file size, and date for text files. It recommends documenting the table or database last documented, documenter, business case, tools used, and data quality. Good documentation practices include knowing your audience and purpose, keeping documentation minimal but effective, and building user documentation. Common data documentation templates include CRISP-DM, which outlines phases for documentation like business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Thorough data documentation is important for project understanding, reuse, and governance.
In today's competitive market, many organizations are unaware of the quantity of poor-quality data in their systems. Some organizations assume that their data is of adequate quality, although they have conducted no metrical or statistical analysis to support the assumption. Others know that their performance is hampered by poor-quality data, but they cannot measure the problem.
What Is SAS | SAS Tutorial For Beginners | SAS Training | SAS Programming | E...Edureka!
The document discusses SAS (Statistical Analytics System), a software for data management, analytics and visualization. It provides an overview of SAS framework, programming and applications. SAS allows users to access, manage and analyze data, and then present results. It discusses key SAS concepts like data sets, variables, formats and linear regression modeling using SAS procedures. Common applications of SAS mentioned are in domains like stock prediction, drug discovery, fraud detection and workflow optimization.
Data Management for High Performance AnalyticsMary Snyder
High-performance analytics is only as good as the data management supporting it.
In fact, high-performance data management plays a key role when it comes to in-database, in-memory and in-stream analytics.
In this webinar Dan Socenau from SAS explores:
•The data management building blocks needed to succeed with high-performance analytics.
•Assessing, planning and executing these bedrock data management capabilities.
•How to deploy a modern data analysis practice.
View the on-demand webinar: http://www.sas.com/en_us/webinars/data-management-high-performance-analytics.html
This webinar talks about data collection in political polling and expanding your reach by using a multimode platform.
We will talk about the use of phone, online, and SMS to invite voters and let their voice be heard.
We also talk about techniques to measure ad messaging satisfaction before these media spots go public so that they can be refined before hitting the airwaves.
Lastly, we will talk about reporting the results in your polling project.
This presentation discusses post transaction surveys, which can be done via integrations.
We talk about the reasons for a post transaction surveys. We talk about how to approach customers to get them to take your surveys and how to communicate to your customers. We talk about how to plan out the methodology and sampling.
We also talk about the various scores in research that are the right tools to calculate various scores to measure Customer Satisfaction Score, Net Promoter Score, and Customer Experience Score. We go over how to use these scores and when to use them.
Diversity & Inclusion in Data Collection.pptxDaniel Rangel
This presentation talks about how to bring D&I into your survey research. We explore using multimode software to expand your reach in bringing in diversity in terms of age, ethnicity, income, and more in your data collection.
We take a deep dive in statistics in various demographics across the United States and talk about how to have them participate in the survey.
We also discuss eye-catching invitations to take a survey by grabbing certain demographics' attention.
1) The document discusses different options for integrating customer satisfaction surveys with a call center, including transferring callers to an automated survey, outbound callbacks in real-time or the next day, and passing customer and agent data.
2) It provides pros and cons of the different integration methods and recommends designing surveys with fewer than 10 questions that measure customer satisfaction, net promoter score, and customer effort score.
3) The surveys can be administered through IVR with question skip patterns, response lists, languages selection, and can reference customer data to personalize the experience.
A discussion about multi-mode survey data collection. The presentations talks about best practices and the pros and cons of various modes and when to use them.
This document provides tips for market researchers on branding themselves through social media, websites, presentations, and networking. It recommends optimizing profiles on LinkedIn and Twitter to highlight experience and generate leads. Website content should be updated regularly with news, resources, testimonials and team bios. Presentations and webinars are suggested topics to raise your profile at conferences and associations. In-person networking at events and volunteering allows face-to-face relationship building. Checking in on clients during and after projects helps develop long-term relationships and repeat business.
Ways to bring up your response rates in survey research. Give your audience the preferred method in which they want to be approach. This includes online, phone, IVR, gamification, the invite letter and other techniques.
Marketing Your Data Collection CapabilitiesDaniel Rangel
This document discusses marketing data collection capabilities using Survox. It provides information on Dan Rangel as the Research Solutions Director, highlights various data collection methodologies and modes available through Survox including phone, online, mobile, IVR, and multimode. It discusses using quotas to target audiences and maximize response rates across modes and vendors. The document also provides tips on selling the benefits of Survox's capabilities to clients.
In this presentation, we will get you to think about methodologies to get the public to engage the way they want to be approached. Today there is the internet of things, there is voice, there are people who want to be heard. Pulling together various channels and taking a holistic approach to collecting data is the new standard to communicating with the public and the consumer of corporate America.
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.
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
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
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
15. • Key metrics in one dashboard
• Incorporate multiple sources of
MR data
• Dynamic data filters and drill
down to more detailed views
• Table Tool and Open-end tools
• Support MR requirements:
– Weighting & Sig Testing
– Correlations & benchmarks
– Computation of variables & indexes
– Moving averages
– Etc.
Transform your reporting
31. Must support Market Research process
Analytics| Significance test (Z-test, Norm test) | Correlation test (Pearson) | Numeric mean calculation |
Categorized mean calculation | Numeric percent share | Conversion rate | Categorized percent share | Mean of Mean |
Index calculation | Benchmarking on time, compare sets and hierarchical relations |Compare series |Filters
|Hierarchical filters |Weights vs Filters| Moving Averages
Data Management | Compute Variables with Operators like [AND, OR, NOT, +, -. LIKE, LESS, MORE] and Functions like
[Count responses, Count, Mean, DIFF] and more | Compute Answers (Nets) | Define filters | Sort Variables | Sort Answers |
Sort Filter | Transform Value Labels into Values | Define or Create Data & Time variables | Create Weighting definitions |
Weight cases | Transforms Strings to Categorical | Define Variable definitions | Identify Multi response sets
(propriety version) | Identify Duplicate Cases | Identify Duplicate of Answers | Replace or update existing datasets |Merge
data | Add Cases | Add Variables | Recode into Same variable | Automatic Recode values| Replace missing values |
Supports the common variable types; Numeric, Categorized scale/non-scale, Multiple choice, String, Global, Date |
Renaming of Variable labels | Renaming of Value labels | Color management | Factor average manipulation for Average
calculations | Exclude Answers from Average calculations | Question-block logic | Answer-blocks logic | Changing
Variable type | Pos/Neg/Neu administration for POS/NEG charting | Variable Subsets
53. Michael DeNitto | MarketSight Founder & CEO | mdenitto@marketsight.com
Introducing
MarketSight
54. Michael DeNitto | MarketSight Founder & CEO | mdenitto@marketsight.com
Speaker
Michael DeNitto
MarketSight Founder & CEO
Passion for delivering information
and software for decision support
Before MarketSight
Monitor Group
Consumer Reports
Revenio
Cahners/Reed-Elsevier
IndustryNet
AT&T
Ziff-Davis
Open Software Foundation
55. Michael DeNitto | MarketSight Founder & CEO | mdenitto@marketsight.com
Overview
Cloud-based software solution
Data analysis, visualization, & reporting
Designed by Market Researchers for Market
Researchers
Thousands of users worldwide at MR Firms and
Client organizations
Introducing MarketSight
56. Michael DeNitto | MarketSight Founder & CEO | mdenitto@marketsight.com
How we’re different
100% Software as a Service (SaaS) solution
Private cloud or behind your firewall
Designed for collaboration
Easier to learn and use
Less expensive than traditional tools
Work with any dataset, regardless of source
Industry-leading integration with PowerPoint
Integrated online sharing portal Key Findings
Introducing MarketSight
57. Michael DeNitto | MarketSight Founder & CEO | mdenitto@marketsight.com
Major features
Data Upload
Data Cleaning
Editing & Creating Variables
Crosstabs
Automatic Statistical Significance Testing
Advanced Analytics
Charts
PowerPoint & Excel Export
Dashboards
Key Findings sharing platform
Introducing MarketSight
58.
59.
60.
61.
62.
63.
64.
65.
66.
67.
68.
69.
70. Michael DeNitto | MarketSight Founder & CEO | mdenitto@marketsight.com
Thanks for joining us today
Michael DeNitto
mdenitto@marketsight.com
617.580.3550
www.marketsight.com/trial