PoT - probeer de mogelijkheden van datamining zelf uit 30-10-2014Daniel Westzaan
This document outlines an agenda for an IBM SPSS Data Mining Workshop. It includes introductions, an overview of predictive analytics and data mining, exercises demonstrating the use of IBM SPSS Modeler software, and a discussion of data mining methodology and applications. The objectives are to introduce predictive analytics and data mining, demonstrate the ease of use of IBM SPSS Modeler, and provide hands-on experience applying data mining techniques and models.
The document provides advice on successfully managing predictive analytics programs. It discusses the importance of having an open organizational mindset that embraces new ideas and change. It also emphasizes having a clear business strategy and objectives when developing predictive models. Regularly testing and updating models is key to ensuring optimal predictive accuracy over time as business needs and available data evolve.
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.
Modak Analytics provides predictive modeling solutions to help companies analyze customer data and make reliable decisions. Predictive modeling involves [1] analyzing piled up customer data to derive useful insights, [2] designing a predictive model using various techniques like clustering, decision trees, regression, and scorecards, and [3] implementing the model to better understand customers and make profitable decisions. Predictive analysis allows companies to segment markets, rank products, predict customer responses, and reduce fraud. Modak Analytics' customized solutions leverage different modeling techniques to create ensemble models that extract the strengths of each technique.
Predictive Analytics: An Executive PrimerRyan Withop
This document is an executive primer on predictive analytics by Ryan Withop of YouSendIt.com. It discusses how predictive analytics can be used to determine which users might react to certain messages and what actions lead to paid subscriptions, unlike reporting which only shows what already happened. It provides examples of how YouSendIt has used predictive analytics to increase conversions and lifetime value. Finally, it recommends technologies and resources for further research into predictive analytics.
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.
PoT - probeer de mogelijkheden van datamining zelf uit 30-10-2014Daniel Westzaan
This document outlines an agenda for an IBM SPSS Data Mining Workshop. It includes introductions, an overview of predictive analytics and data mining, exercises demonstrating the use of IBM SPSS Modeler software, and a discussion of data mining methodology and applications. The objectives are to introduce predictive analytics and data mining, demonstrate the ease of use of IBM SPSS Modeler, and provide hands-on experience applying data mining techniques and models.
The document provides advice on successfully managing predictive analytics programs. It discusses the importance of having an open organizational mindset that embraces new ideas and change. It also emphasizes having a clear business strategy and objectives when developing predictive models. Regularly testing and updating models is key to ensuring optimal predictive accuracy over time as business needs and available data evolve.
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.
Modak Analytics provides predictive modeling solutions to help companies analyze customer data and make reliable decisions. Predictive modeling involves [1] analyzing piled up customer data to derive useful insights, [2] designing a predictive model using various techniques like clustering, decision trees, regression, and scorecards, and [3] implementing the model to better understand customers and make profitable decisions. Predictive analysis allows companies to segment markets, rank products, predict customer responses, and reduce fraud. Modak Analytics' customized solutions leverage different modeling techniques to create ensemble models that extract the strengths of each technique.
Predictive Analytics: An Executive PrimerRyan Withop
This document is an executive primer on predictive analytics by Ryan Withop of YouSendIt.com. It discusses how predictive analytics can be used to determine which users might react to certain messages and what actions lead to paid subscriptions, unlike reporting which only shows what already happened. It provides examples of how YouSendIt has used predictive analytics to increase conversions and lifetime value. Finally, it recommends technologies and resources for further research into predictive analytics.
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.
The document discusses simplifying analytics by focusing on important data and how to use it to improve business outcomes, rather than complex analytics. It recommends building an environment to accelerate data processing for faster insights and decisions. Companies should leverage business intelligence, data visualization, and data discovery tools, as well as machine learning models, to automate analysis and gain insights from large data sets. Different problems may require hypothesis-based or discovery-based approaches. The key is to identify important data, delegate analysis to tools when possible, visualize data for better understanding, uncover hidden patterns, and customize the approach to the specific problem and data.
Data analytics refers to the broad field of using data and tools to make business decisions, while data analysis is a subset that refers to specific actions within the analytics process. Data analysis involves collecting, manipulating, and examining past data to gain insights, while data analytics takes the analyzed data and works with it in a meaningful way to inform business decisions and identify new opportunities. Both are important, with data analysis providing understanding of what happened in the past and data analytics enabling predictions about what will happen in the future.
Five Pitfalls when Operationalizing Data Science and a Strategy for SuccessVMware Tanzu
Enterprise executives and IT teams alike know that data science is not optional, but struggle to benefit from it because the process takes too long and operationalizing models in applications can be hairy.
Join guest speaker, Forrester Research’s Mike Gualtieri and Pivotal’s Jeff Kelly and Dormain Drewitz for an interactive discussion about operationalizing data science in your business. In this webinar, the first of a two-part series, you will learn:
- The essential value of data science and the concept of perishable insights.
- Five common pitfalls of data science teams.
- How to dramatically increase the productivity of data scientists.
- The smooth hand-off steps required to operationalize data science models in enterprise applications.
Presenter : Guest Speakers Mike Gualtieri, Forrester, Dormain Drewitz and Jeff Kelly, Pivotal
The document outlines an approach called "Descriptive-Prescriptive" for better problem solving. It involves first describing a problem by connecting elements and details to understand it fully ("Analysis"), then prescribing rules and conditions to formulate a solution ("Synthesis"). This approach can be applied to test baselining, strategy formulation, test design, and reporting. Diagrams and examples are provided to illustrate applying description and prescription at different stages. The approach forms the basis of a personal test methodology called HBT, which uses six stages and eight disciplines of thinking.
Business Data Analytics Powerpoint Presentation SlidesSlideTeam
Enthrall your audience with this Business Data Analytics Powerpoint Presentation Slides. Increase your presentation threshold by deploying this well crafted template. It acts as a great communication tool due to its well researched content. It also contains stylized icons, graphics, visuals etc, which make it an immediate attention grabber. Comprising twenty nine slides, this complete deck is all you need to get noticed. All the slides and their content can be altered to suit your unique business setting. Not only that, other components and graphics can also be modified to add personal touches to this prefabricated set. https://bit.ly/3d4gdzY
This document discusses how predictive analytics can help drive smarter business outcomes. It summarizes the challenges facing decision makers with large amounts of data in various formats and the need for faster decisions. Predictive analytics is presented as a way to analyze patterns in data to predict future outcomes and provide unique insights. The document outlines SPSS predictive analytics software capabilities in data collection, statistics, modeling, and deployment to help organizations capture data, predict behaviors, and act on predictions to improve business performance. Case studies demonstrate how SPSS solutions have helped customers increase revenue, reduce costs and improve customer retention.
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.
Operationalizing Data Science: The Right Architecture and ToolsVMware Tanzu
In part one of this two-part series, you learned some of the common reasons enterprises struggle to turn insights into actions as well as a strategy for overcoming these challenges to successfully operationalize data science. In part two, it’s time to fill in the architectural and technological details of that strategy.
Pivotal Data Scientist Megha Agarwal will share the key ingredients to successfully put data science models in production and use them to drive actions in real-time. In this webinar, you will learn:
- Adopting extreme programming practices for data science
- Importance of working in a balanced team
- How to put and maintain machine learning models in production
- End-to-end pipeline design
Presenter: Megha Agarwal, Data Scientist
Gather the required information from the data and predict future outcomes and trends. Use content-ready Predictive Analysis PowerPoint Presentation Slides to forecast future probabilities. Majorly applied in the business field, predictive analysis PPT templates will help you evaluate current data and historical facts to understand customers, products, services, partners, and to identify potential risks and opportunities for an organization. This deck comprises of templates such as research methodology, consumer insights consumption, need for consumer insights, key stats, data collection and processing, consumer insight capabilities, These templates are completely customizable. You can edit the templates as per your need. Change color, text, icon and font size as per your requirement. Add or remove the content, if needed. Get access to the predictive analysis PowerPoint presentation slideshow to predict future outcomes for various business topics such as customer relationship management, health care, collection analytics, fraud detection, risk management, direct marketing, industry applications, etc. Get access to the professionally designed ready-made predictive analysis PowerPoint presentation slides for your business to interpret big data for your benefit. Maintain your demeanour with our Predictive Analysis Powerpoint Presentation Slides. They will help you keep your cool.
Business Analytics, "Second Edition teaches the fundamental concepts of the emerging field of business analytics and provides vital tools in understanding how data analysis works in today s organizations. Students will learn to apply basic business analytics principles, communicate with analytics professionals, and effectively use and interpret analytic models to make better business decisions. Included access to commercial grade analytics software gives students real-world experience and career-focused value. Author James Evans takes a balanced, holistic approach and looks at business analytics from descriptive, and predictive perspectives.
Watson Analytics provides self-service data analytics capabilities including data acquisition, cleansing, insights discovery, outcome prediction, visualization, and action without requiring data expert assistance. It handles large volumes of rapidly accessible data and automates data preparation, refinement, management, and analysis from the cloud. Statistical analysis, correlations, and predictions help users gain a deeper understanding of their business to see relevant information, take action, and anticipate opportunities.
Data analytics involves analyzing data to extract useful information. It is used to identify risks, improve business processes, verify effectiveness, and influence decisions. There are five categories: data analytics of transactions and operations; web analytics of website traffic; social analytics of social media; mobile analytics of device data; and big data analytics. Companies obtain user data from GPS, sensors, and social media to perform analyses that benefit organizations.
Data mining is the process of automatically discovering patterns and trends in large datasets. It involves defining problems, gathering and preparing data, building and evaluating models, and deploying knowledge. Common data mining techniques include association, classification, clustering, prediction, sequential patterns, and decision trees. These techniques can be combined and applied to domains like marketing, banking, healthcare, and more to analyze customer behavior, identify fraud, and make predictions. While data mining can find hidden patterns, it requires domain expertise and cannot determine the value of information or replace the need to understand business needs and data.
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.
1) Data analytics is the process of examining large data sets to uncover patterns and insights. It involves descriptive, predictive, and prescriptive analysis.
2) Descriptive analysis summarizes past events, predictive analysis forecasts future events, and prescriptive analysis recommends actions.
3) Major companies like Facebook, Amazon, Uber, banks and Spotify extensively use big data and data analytics to improve customer experience, detect fraud, personalize recommendations and gain business insights.
CRISP-DM: a data science project methodologySergey Shelpuk
This document outlines the methodology for a data science project using the Cross-Industry Standard Process for Data Mining (CRISP-DM). It describes the 6 phases of the project - business understanding, data understanding, data preparation, modeling, evaluation, and deployment. For each phase, it provides an overview of the key steps and asks questions to determine readiness to move to the next phase of the project. The overall goal is to successfully apply a standard data science methodology to gain business value from data.
Predictive analytics uses data from the past to predict future outcomes. It allows organizations to measure customer lifetime value, make product recommendations, and forecast sales. By understanding predictive analytics, managers can make better decisions and feel more comfortable working with predictive analytics results and recommendations in their organizations, though a lack of quality data remains a key barrier.
Advanced analytics uses sophisticated techniques beyond traditional business intelligence to discover deeper insights from data. It includes techniques like machine learning, data mining, and neural networks. While many major companies invest in analytics, some hesitate due to a lack of structured data or past failures. The document provides suggestions for effective advanced analytics, including choosing the right data sources, building models to optimize business outcomes, and embedding analytics in tools to generate maximum profit. However, companies must set boundaries on data use and consider ethics to avoid illegal or reputation-damaging practices.
This document discusses breaking through the "analysis barrier" in web analytics. It describes the difference between reporting and analysis, with analysis involving deeper study of problems and recommendations for change. The document outlines a 5-stage model of web analytics maturity and provides examples of real customer analyses, showing how they identified issues and made recommendations. It introduces Semphonic as a consultancy that helps clients overcome the analysis barrier through an analytic roadmap and ongoing deep-dive analysis projects.
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.
Optimizely building your_data_dna_e_booktthhciciedeng
This document provides guidance on how to build a company's data DNA by establishing key metrics, gathering both quantitative and qualitative data, and using that information to optimize business performance through experimentation and A/B testing. It emphasizes the importance of identifying a single "guiding light" metric that defines business goals and can be used to prioritize optimization efforts. The document also outlines how to map customer journeys and core conversion funnels in order to determine high-value areas of a website or product to test. It recommends using qualitative user research to identify major roadblocks or weaknesses before developing hypotheses for A/B tests aimed at improving conversion rates and the guiding metric.
The document discusses simplifying analytics by focusing on important data and how to use it to improve business outcomes, rather than complex analytics. It recommends building an environment to accelerate data processing for faster insights and decisions. Companies should leverage business intelligence, data visualization, and data discovery tools, as well as machine learning models, to automate analysis and gain insights from large data sets. Different problems may require hypothesis-based or discovery-based approaches. The key is to identify important data, delegate analysis to tools when possible, visualize data for better understanding, uncover hidden patterns, and customize the approach to the specific problem and data.
Data analytics refers to the broad field of using data and tools to make business decisions, while data analysis is a subset that refers to specific actions within the analytics process. Data analysis involves collecting, manipulating, and examining past data to gain insights, while data analytics takes the analyzed data and works with it in a meaningful way to inform business decisions and identify new opportunities. Both are important, with data analysis providing understanding of what happened in the past and data analytics enabling predictions about what will happen in the future.
Five Pitfalls when Operationalizing Data Science and a Strategy for SuccessVMware Tanzu
Enterprise executives and IT teams alike know that data science is not optional, but struggle to benefit from it because the process takes too long and operationalizing models in applications can be hairy.
Join guest speaker, Forrester Research’s Mike Gualtieri and Pivotal’s Jeff Kelly and Dormain Drewitz for an interactive discussion about operationalizing data science in your business. In this webinar, the first of a two-part series, you will learn:
- The essential value of data science and the concept of perishable insights.
- Five common pitfalls of data science teams.
- How to dramatically increase the productivity of data scientists.
- The smooth hand-off steps required to operationalize data science models in enterprise applications.
Presenter : Guest Speakers Mike Gualtieri, Forrester, Dormain Drewitz and Jeff Kelly, Pivotal
The document outlines an approach called "Descriptive-Prescriptive" for better problem solving. It involves first describing a problem by connecting elements and details to understand it fully ("Analysis"), then prescribing rules and conditions to formulate a solution ("Synthesis"). This approach can be applied to test baselining, strategy formulation, test design, and reporting. Diagrams and examples are provided to illustrate applying description and prescription at different stages. The approach forms the basis of a personal test methodology called HBT, which uses six stages and eight disciplines of thinking.
Business Data Analytics Powerpoint Presentation SlidesSlideTeam
Enthrall your audience with this Business Data Analytics Powerpoint Presentation Slides. Increase your presentation threshold by deploying this well crafted template. It acts as a great communication tool due to its well researched content. It also contains stylized icons, graphics, visuals etc, which make it an immediate attention grabber. Comprising twenty nine slides, this complete deck is all you need to get noticed. All the slides and their content can be altered to suit your unique business setting. Not only that, other components and graphics can also be modified to add personal touches to this prefabricated set. https://bit.ly/3d4gdzY
This document discusses how predictive analytics can help drive smarter business outcomes. It summarizes the challenges facing decision makers with large amounts of data in various formats and the need for faster decisions. Predictive analytics is presented as a way to analyze patterns in data to predict future outcomes and provide unique insights. The document outlines SPSS predictive analytics software capabilities in data collection, statistics, modeling, and deployment to help organizations capture data, predict behaviors, and act on predictions to improve business performance. Case studies demonstrate how SPSS solutions have helped customers increase revenue, reduce costs and improve customer retention.
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.
Operationalizing Data Science: The Right Architecture and ToolsVMware Tanzu
In part one of this two-part series, you learned some of the common reasons enterprises struggle to turn insights into actions as well as a strategy for overcoming these challenges to successfully operationalize data science. In part two, it’s time to fill in the architectural and technological details of that strategy.
Pivotal Data Scientist Megha Agarwal will share the key ingredients to successfully put data science models in production and use them to drive actions in real-time. In this webinar, you will learn:
- Adopting extreme programming practices for data science
- Importance of working in a balanced team
- How to put and maintain machine learning models in production
- End-to-end pipeline design
Presenter: Megha Agarwal, Data Scientist
Gather the required information from the data and predict future outcomes and trends. Use content-ready Predictive Analysis PowerPoint Presentation Slides to forecast future probabilities. Majorly applied in the business field, predictive analysis PPT templates will help you evaluate current data and historical facts to understand customers, products, services, partners, and to identify potential risks and opportunities for an organization. This deck comprises of templates such as research methodology, consumer insights consumption, need for consumer insights, key stats, data collection and processing, consumer insight capabilities, These templates are completely customizable. You can edit the templates as per your need. Change color, text, icon and font size as per your requirement. Add or remove the content, if needed. Get access to the predictive analysis PowerPoint presentation slideshow to predict future outcomes for various business topics such as customer relationship management, health care, collection analytics, fraud detection, risk management, direct marketing, industry applications, etc. Get access to the professionally designed ready-made predictive analysis PowerPoint presentation slides for your business to interpret big data for your benefit. Maintain your demeanour with our Predictive Analysis Powerpoint Presentation Slides. They will help you keep your cool.
Business Analytics, "Second Edition teaches the fundamental concepts of the emerging field of business analytics and provides vital tools in understanding how data analysis works in today s organizations. Students will learn to apply basic business analytics principles, communicate with analytics professionals, and effectively use and interpret analytic models to make better business decisions. Included access to commercial grade analytics software gives students real-world experience and career-focused value. Author James Evans takes a balanced, holistic approach and looks at business analytics from descriptive, and predictive perspectives.
Watson Analytics provides self-service data analytics capabilities including data acquisition, cleansing, insights discovery, outcome prediction, visualization, and action without requiring data expert assistance. It handles large volumes of rapidly accessible data and automates data preparation, refinement, management, and analysis from the cloud. Statistical analysis, correlations, and predictions help users gain a deeper understanding of their business to see relevant information, take action, and anticipate opportunities.
Data analytics involves analyzing data to extract useful information. It is used to identify risks, improve business processes, verify effectiveness, and influence decisions. There are five categories: data analytics of transactions and operations; web analytics of website traffic; social analytics of social media; mobile analytics of device data; and big data analytics. Companies obtain user data from GPS, sensors, and social media to perform analyses that benefit organizations.
Data mining is the process of automatically discovering patterns and trends in large datasets. It involves defining problems, gathering and preparing data, building and evaluating models, and deploying knowledge. Common data mining techniques include association, classification, clustering, prediction, sequential patterns, and decision trees. These techniques can be combined and applied to domains like marketing, banking, healthcare, and more to analyze customer behavior, identify fraud, and make predictions. While data mining can find hidden patterns, it requires domain expertise and cannot determine the value of information or replace the need to understand business needs and data.
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.
1) Data analytics is the process of examining large data sets to uncover patterns and insights. It involves descriptive, predictive, and prescriptive analysis.
2) Descriptive analysis summarizes past events, predictive analysis forecasts future events, and prescriptive analysis recommends actions.
3) Major companies like Facebook, Amazon, Uber, banks and Spotify extensively use big data and data analytics to improve customer experience, detect fraud, personalize recommendations and gain business insights.
CRISP-DM: a data science project methodologySergey Shelpuk
This document outlines the methodology for a data science project using the Cross-Industry Standard Process for Data Mining (CRISP-DM). It describes the 6 phases of the project - business understanding, data understanding, data preparation, modeling, evaluation, and deployment. For each phase, it provides an overview of the key steps and asks questions to determine readiness to move to the next phase of the project. The overall goal is to successfully apply a standard data science methodology to gain business value from data.
Predictive analytics uses data from the past to predict future outcomes. It allows organizations to measure customer lifetime value, make product recommendations, and forecast sales. By understanding predictive analytics, managers can make better decisions and feel more comfortable working with predictive analytics results and recommendations in their organizations, though a lack of quality data remains a key barrier.
Advanced analytics uses sophisticated techniques beyond traditional business intelligence to discover deeper insights from data. It includes techniques like machine learning, data mining, and neural networks. While many major companies invest in analytics, some hesitate due to a lack of structured data or past failures. The document provides suggestions for effective advanced analytics, including choosing the right data sources, building models to optimize business outcomes, and embedding analytics in tools to generate maximum profit. However, companies must set boundaries on data use and consider ethics to avoid illegal or reputation-damaging practices.
This document discusses breaking through the "analysis barrier" in web analytics. It describes the difference between reporting and analysis, with analysis involving deeper study of problems and recommendations for change. The document outlines a 5-stage model of web analytics maturity and provides examples of real customer analyses, showing how they identified issues and made recommendations. It introduces Semphonic as a consultancy that helps clients overcome the analysis barrier through an analytic roadmap and ongoing deep-dive analysis projects.
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.
Optimizely building your_data_dna_e_booktthhciciedeng
This document provides guidance on how to build a company's data DNA by establishing key metrics, gathering both quantitative and qualitative data, and using that information to optimize business performance through experimentation and A/B testing. It emphasizes the importance of identifying a single "guiding light" metric that defines business goals and can be used to prioritize optimization efforts. The document also outlines how to map customer journeys and core conversion funnels in order to determine high-value areas of a website or product to test. It recommends using qualitative user research to identify major roadblocks or weaknesses before developing hypotheses for A/B tests aimed at improving conversion rates and the guiding metric.
The document outlines steps for marketers to create and use dashboards to better monitor marketing progress and facilitate decision making. It discusses the benefits of dashboards, including helping address poor data organization, biases, accountability demands, and cross-department integration. Case studies show how dashboards can inform decisions across various industries. The book provides guidance on assembling teams, gaining IT support, building databases, designing effective visualizations, and cultivating a data-driven culture.
The document discusses how businesses need the right data and tools to make fast, data-driven decisions during uncertain times. It outlines some common issues that prevent businesses from realizing a data-driven agenda, such as long delays in accessing and preparing data from different siloed sources. Modern unified analytics platforms can help solve these problems by providing quick access to raw integrated data from multiple sources and empowering business users to independently explore and analyze the data.
The document discusses the importance of using business intelligence and data analytics in staffing and recruiting firms. It notes that only 22% of small to mid-sized organizations currently use business intelligence solutions. It then discusses some common barriers to adopting business intelligence, such as poor data quality, not knowing what metrics to measure, not knowing where to start, and not having enough time. The document proposes focusing on one key metric per day of the workweek to help simplify getting started with business intelligence. It provides examples of metrics to track on each day of the workweek, including open job orders on Monday, sales forecast on Tuesday, etc. The overall message is that regularly analyzing metrics can help improve data quality, decision making and business performance.
This document discusses a new approach to business intelligence called "rapid-fire BI" that aims to provide faster and more self-service analytics capabilities. The key attributes of rapid-fire BI outlined in the document are:
1) Speed - It allows users to access, analyze, publish, and share data and insights 10 to 100 times faster than traditional BI solutions.
2) Self-reliance - It enables business users rather than IT to independently access data, build reports and dashboards, and answer their own questions without waiting for developer support.
3) Visual discovery - It uses intuitive visual interfaces rather than complex queries, allowing users to easily explore data visually and gain insights through interaction with various chart types
To effectively leverage the power of rich visualizations in making data-driven decisions, you must significantly reduce front-end data preparation time.
In order to create visualizations that lead to answers quickly, you need to prepare your data in the right way. Together, Alteryx and Tableau can help. This paper will show you how.
The document discusses the process of spend analysis, which involves gathering an organization's spend data from various sources, consolidating it into a central database, cleaning the data by removing errors and inconsistencies, clustering similar suppliers under consistent names, categorizing the cleaned data into predefined spend categories, analyzing the categorized data to identify opportunities to reduce costs and streamline operations, and periodically refreshing the analysis with new data. The goal is to provide businesses with improved spend visibility and insights to help optimize operations and maximize profits through better sourcing and procurement decisions.
How much time do you spend mashing up web analytics data vs. looking for data insights? Your Analytics Site automates the data extraction form multiple marketing channels, including WebTrends, Google Analytics, Twitter, YouTube, Slideshare and Flickr with more be added. Each dashboard is customized to satisfy each clients specific business needs. What you get, one cohesive, actionable and visually interactive reporting mechanism for your all your analytics.
Trying to figure out if embedded analytics are for you?
According to Gartner Research, more than 90% of business leaders view content information as a strategic asset, yet fewer than 10% can quantify its economic value. Read this guide to learn why you should be leveraging an asset you already own--data--to build relationships, increase retention, and drive revenue.
Now companies are in the middle of a renovation that forces them to be analytics-driven to
continue being competitive. Data analysis provides a complete insight about their business. It
also gives noteworthy advantages over their competitors. Analytics-driven insights compel
businesses to take action on service innovation, enhance client experience, detect irregularities in
process and provide extra time for product or service marketing. To work on analytics driven
activities, companies require to gather, analyse and store information from all possible sources.
Companies should bring appropriate tools and workflows in practice to analyse data rapidly and
unceasingly. They should obtain insight from data analysis result and make changes in their
business process and practice on the basis of gained result. It would help to be more agile than
their previous process and function.
The document provides an overview and introduction to "The Analytics Setup Guidebook". It discusses how the guidebook aims to give readers a high-level framework for building a modern analytics setup by explaining the components and best practices for consolidating, transforming, modeling, and using data. The guidebook is intended for those who need guidance in setting up their first analytics stack, such as junior data analysts, product managers, or engineers tasked with building a data stack from scratch.
- Traditional data warehousing projects are expensive and time-consuming but often still result in managers not having access to the information they need when they need it. Common excuses include bad or inconsistent data, difficulty accessing data across multiple systems, and requiring technical expertise.
- CXAIR is a next generation business intelligence tool that uses search technology to index and query data across multiple sources. It allows users to perform fast ad-hoc queries and build their own reports without technical expertise or dealing with data quality issues.
- By indexing both internal data sources and other corporate assets, CXAIR provides a single access point for all information. It addresses many of the common problems with traditional BI and removes bad data as an excuse for not being able
Business intelligence environments involve collecting data from various sources, transforming and organizing it using tools like ETL, and storing it in data warehouses or marts. This data is then analyzed using OLAP and reporting tools to provide useful information for business decisions. Setting up an effective BI environment requires understanding business requirements, defining processes, determining data needs, integrating data sources, and selecting appropriate tools and techniques. Careful planning and skilled people are needed to ensure the BI environment supports organizational goals.
Most companies have data in various sources. Often, they do nothing but store the data because it takes too much time to make sense of it all. Taking control of the data is a process, but once the building blocks are in place a true Demand Signal Management Process will support an enterprise with reliable business insights.
Tips --Break Down the Barriers to Better Data AnalyticsAbhishek Sood
1) Analytics executives face challenges in collecting, analyzing, and delivering insights from data due to a lack of skills, cultural barriers, IT backlogs, and productivity drains.
2) Legacy systems and complex analytics platforms also impede effective data use. Modular solutions that integrate with existing systems and empower self-service are recommended.
3) The document promotes the Statistica software as addressing these challenges through its ease of use, integration capabilities, and support for big data analytics.
How to successfully implement Business Intelligence into your organisation.
A completely agnostic and independent view from a market leader in delivering technology transformation.
Details on how to build a strategy to successfully execute on and more importantly how to get the business to adopt Business Intelligence into their day to day role.
Essential tool kit for any organisation looking to invest in Business Intelligence.
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KETL Quick guide to data analytics
1. Quick guide to
data analytics
How to turn your data assets into customer
insight to add value to your buiness
2. Quick guide to data
analytics
1
“Generate insight from
your data with 6 top
tips plus a case study:
start thinking like a
data scientist.
1. You already know more than you think
You probably already have a good idea of what
you think is right and wrong with key areas of your
business. You might even have something
specific that you want to investigate. Just be
prepared to learn as you go.
2. Get to know your data sources
Start with a single data source that your business
already knows really well. Check for obvious
errors in your data. If you have a realtively small
amount of data you can do this simply by
exporting to a spreadsheet. The most efficient
way is to profile you data. You can use a free data
profiler tool to create a report on how well your
data source rates on different data quality scales.
You can then make a value based decision on
how much to invest in correcting the poor data.
See our free data quality fact sheet for more
information on data profiling.
You want to get your data to work harder
for you and to be able to use the ‘data
lake’ of cusotmer information that you
have stored; but you don’t know where to
start or what questions to ask. These tips
will help you to consider where to start
gathering that valuable insight.
”
“Profile the data from
each new source
before you introduce
it into your analytics
reporting structure. As
your understanding
improves across each
data source you can
start to consider
blending the data
between the data
sources.
”
3. Quick guide to data
analytics
2
This might sound
strange but it is
important to be
prepared to get
things wrong. A
scientist creates a
hypothesis that is then
tested through
experimentation.
“
3. Keep it simple when you can
If you already have reports from separate
systems and you can compare the report outputs
easily then you don’t need to integrate the data at
source you could perhaps just produce an Excel
spreadsheet.
Gaining an understanding of just what you can
glean from the data available with the tools at
hand is important and controls the scope of
demands for reports from the wider business.
Using systems that are already in place is the
best way to start. The business may well learn so
much from these first inroads into data analytics
that it decides to invest further to gain more
insight.
4. Think like a scientist
IWhen you fail you learn more than when you
succeed. Fact based evidence leads to a
working theory that can then be used to create a
conceptual framework.
As a data scientist your aims are to understand
the relationships between the data in your
organisation. You may start off with a hunch
about a particular business issue; so consider
what data sets surround the business issue
process and then test your theories. Just
remember to document the entire journey. ”
It isn’t always
necessary to merge
data sources and
sometimes it just isn’t
possible.
“
”
4. Quick guide to data
analytics
3
5. Fail fast, fail cheap
Analytics is a fast moving process and it is all
about experimenting, documenting, learning and
then moving on. Once the learning has taken
place, the analyst can share the findings with the
wider business, then move on to the next
analytics project.
6. Data specialists need to get out more
Get the analytics team out to the different
business departments and out to the customer so
that they can be aware of data related issues and
witness impact. Always remember that your data
is your competitive advantage – it is a key asset of
your business.
“By using your own
customer data you
will be able to
create more
accurate models
that provide
meaningful insight
to your own
business processes.
”
Next step data analytics
Here at KETL we are a data integration partner
with TIBCO Spotfire - a powerful data analytics
tool. Each week the Spotfire team provide a new
demo for visitors to explore. The advantage of a
tool like Spotfire is that you can have a central
analyst that creates the analytics environment that
can then be used by multiple business teams who
are not trained analysts.
http://spotfire.tibco.com/solutions/technology/big-
data
5. Quick guide to data
analytics
4
Case study
An online retail call centre based in South
Wales. The call centre can easily track call
volumes to establish the busy periods for their
Customer Service Agents (CSAs) so then they
decide to develop their reporting by measuring
call volume by length of call and start to track if
there are patterns developing on length of calls at
particular times of day.
They use date and time, as these data elements
will be constant in each of their data source
systems. The telephone software they use also
has a good reporting system that the business is
comfortable using.
Now the business decides it can match the stock
inventory against the call centre volumes to get
an impression of the number of calls per sale, the
number of items per sale and the value of each
sale.
So even though the two systems are not
integrated they are able compare the data from
each source to plot productivity over different
departments over one day. With this information
the business is then able to establish measures
of activities against each department.
6. Quick guide to data
analytics
5
Immediate gain
1. The business insight that has been gained
allows the business to plot trends across its
departments.
2. Once the business identifies these daily
measures it can then make progress on how to
make improvements by assigning Key
Performance Indicators (KPIs).
3. Putting in an analytical process that is making
use of systems already in place is almost always
less expensive than creating new data
warehouses.
Learning
The business realises through the data profiling
exercise that there are frequent input errors made
by the call centre CSAs. Although the codified
data input errors can be resolved quite easily, it is
not so straightforward with free text.
If the customer service CSAs have a data entry
screen to input free text but then they forget to
code the complaint it will be difficult to analyse
and learn from this vital customer interaction.
Data input errors can be rectified through better
entry code design in consultation with the CSAs.
What you can do next
is download your own
copy of The Essential
Guide to Better Data
from ketl.co.uk
We also can offer
small workshops or
information evenings
to help you and your
team to learn more
about data analytics.
please email
helen@ketl.co.uk for
more information.
“
”
7. Quick guide to data
analytics
6
Get in touch
For further information or help with your
data analytics project speak to Helen to see
how we can help >
Helen Woodcock
helen@ketl.co.uk
Illustration www.thirteen.co.uk