The document discusses web analytics solutions and DDWeb Web Analytics in particular. It states that DDWeb allows organizations to measure website performance and effectiveness by identifying unique visitors precisely without data duplication. It also integrates digital and offline data to provide a 360-degree view of web visitors. This helps organizations optimize their online channels, detect issues, and better anticipate needs.
“Big Picture”: Mixed-Initiative Visual Analytics of Big Data (VINCI 2013 Keyn...Michelle Zhou
Information graphics have been used for thousands of years to help illustrate ideas and communicate information. However, it requires skills and time to hand craft high-quality, customized information graphics for specific situations (e.g., data characteristics and user tasks). The problem becomes more acute when we must deal with big data. To address this problem, we are researching and developing mixed-initiative visual analytic systems that leverage both the intelligence of humans and machines to aid users in deriving insights from massive data. On the one hand, such a system automatically guides users to perform their data analytic tasks by recommending suitable visualization and discovery paths in context. On the other hand, users interactively explore, verify, and improve visual analytic results, which in turn helps the system to learn from users' behavior and improve its quality over time. In this talk, I will present key technologies that we have developed in building mixed-initiative visual analytic systems, including feature-based visualization recommendation and optimization-based approaches to dynamic data transformation for more effective visualization. I will also use concrete applications to demonstrate the use and value of mixed-initiative visual analytic systems, and discuss existing challenges and future directions in this area.
The document discusses setting up a Scalable Metrics Model (SMM) for a data lake. The SMM uses pre-aggregated metrics and dimension keys to provide a flexible and scalable approach. An example model from retail is described, including defining customer-level dimensions, metrics, and value-added metrics. The document outlines the ETL process, including writing queries to populate metrics for different granularities and integrating them into a framework. Reporting options are also discussed, including using views, tables, and reporting tools to provide accessibility and analysis capabilities. Finally, the extensibility of the model by adding new dimensions like sellers and transactions is highlighted.
Raymond Chau discusses the importance and benefits of digital analytics. He explains that digital analytics involves collecting data from websites, apps, social media, and other online sources to better understand user behavior and make improvements. Some key benefits include optimizing marketing spending, fixing issues on websites to reduce bounce rates, running A/B tests to optimize pages, and personalizing the user experience through recommendations and targeted advertising. While traditional marketing can be difficult to measure and optimize, digital analytics allows for real-time data collection, targeted messaging, and faster iteration to improve performance.
Many retailers are stymied by the complex, multi-channel world of today's consumer. In today’s era of one-to-one personalized relationships, you need a way to link customer interactions, visits, purchases and the like from multiple touch points to fill those gaps and capture the 360° customer view needed to improve the customer experience, target offers and generate better returns.
Cross Channel Attribution Modeling In ActioniCrossing GmbH
iCrossing Capabilities Report - Cross-Channel Attribution Modeling in Action
Many brands use a last-click attribution model for their marketing efforts online because they do not know that they have other options...
The document discusses a survey of 300 enterprise organizations about data ownership and big data initiatives. It finds that marketing and sales are most involved in purchase decisions, but sales, business development, and insights/analytics have the most influence. Most functions see their involvement peaking late in the purchase process. Organizations need strategies to align functional areas and determine influence. Data initiatives are being driven by needs for better analytics, marketing intelligence, and predictive capabilities rather than just data quality issues.
Teradata Integrated Web Intelligence (IWI) allows companies to integrate online and offline customer data to gain new insights. This can help turn missed opportunities into profits by better targeting customers. For example, seeing that a customer abandoned an online cart, a company could remarket the item to them offline. IWI provides a 360-degree view of each customer to inform marketing decisions. Large companies that have implemented IWI report increased campaign response rates, revenue, and the ability to rapidly test multiple offers. IWI helps companies get ahead of their competition through more effective use of customer data.
Organizations across diverse industries are in pursuit of Customer 360, by integrating customer information across multiple channels, systems, devices and products. Having a 360-degree view of the customer enables enterprises to improve the interaction experience, drive customer loyalty and improve retention. However delivering a true Customer 360 can be very challenging.
“Big Picture”: Mixed-Initiative Visual Analytics of Big Data (VINCI 2013 Keyn...Michelle Zhou
Information graphics have been used for thousands of years to help illustrate ideas and communicate information. However, it requires skills and time to hand craft high-quality, customized information graphics for specific situations (e.g., data characteristics and user tasks). The problem becomes more acute when we must deal with big data. To address this problem, we are researching and developing mixed-initiative visual analytic systems that leverage both the intelligence of humans and machines to aid users in deriving insights from massive data. On the one hand, such a system automatically guides users to perform their data analytic tasks by recommending suitable visualization and discovery paths in context. On the other hand, users interactively explore, verify, and improve visual analytic results, which in turn helps the system to learn from users' behavior and improve its quality over time. In this talk, I will present key technologies that we have developed in building mixed-initiative visual analytic systems, including feature-based visualization recommendation and optimization-based approaches to dynamic data transformation for more effective visualization. I will also use concrete applications to demonstrate the use and value of mixed-initiative visual analytic systems, and discuss existing challenges and future directions in this area.
The document discusses setting up a Scalable Metrics Model (SMM) for a data lake. The SMM uses pre-aggregated metrics and dimension keys to provide a flexible and scalable approach. An example model from retail is described, including defining customer-level dimensions, metrics, and value-added metrics. The document outlines the ETL process, including writing queries to populate metrics for different granularities and integrating them into a framework. Reporting options are also discussed, including using views, tables, and reporting tools to provide accessibility and analysis capabilities. Finally, the extensibility of the model by adding new dimensions like sellers and transactions is highlighted.
Raymond Chau discusses the importance and benefits of digital analytics. He explains that digital analytics involves collecting data from websites, apps, social media, and other online sources to better understand user behavior and make improvements. Some key benefits include optimizing marketing spending, fixing issues on websites to reduce bounce rates, running A/B tests to optimize pages, and personalizing the user experience through recommendations and targeted advertising. While traditional marketing can be difficult to measure and optimize, digital analytics allows for real-time data collection, targeted messaging, and faster iteration to improve performance.
Many retailers are stymied by the complex, multi-channel world of today's consumer. In today’s era of one-to-one personalized relationships, you need a way to link customer interactions, visits, purchases and the like from multiple touch points to fill those gaps and capture the 360° customer view needed to improve the customer experience, target offers and generate better returns.
Cross Channel Attribution Modeling In ActioniCrossing GmbH
iCrossing Capabilities Report - Cross-Channel Attribution Modeling in Action
Many brands use a last-click attribution model for their marketing efforts online because they do not know that they have other options...
The document discusses a survey of 300 enterprise organizations about data ownership and big data initiatives. It finds that marketing and sales are most involved in purchase decisions, but sales, business development, and insights/analytics have the most influence. Most functions see their involvement peaking late in the purchase process. Organizations need strategies to align functional areas and determine influence. Data initiatives are being driven by needs for better analytics, marketing intelligence, and predictive capabilities rather than just data quality issues.
Teradata Integrated Web Intelligence (IWI) allows companies to integrate online and offline customer data to gain new insights. This can help turn missed opportunities into profits by better targeting customers. For example, seeing that a customer abandoned an online cart, a company could remarket the item to them offline. IWI provides a 360-degree view of each customer to inform marketing decisions. Large companies that have implemented IWI report increased campaign response rates, revenue, and the ability to rapidly test multiple offers. IWI helps companies get ahead of their competition through more effective use of customer data.
Organizations across diverse industries are in pursuit of Customer 360, by integrating customer information across multiple channels, systems, devices and products. Having a 360-degree view of the customer enables enterprises to improve the interaction experience, drive customer loyalty and improve retention. However delivering a true Customer 360 can be very challenging.
Bloomerangs Audience Relationship Management is a set of Strategies, Solutions and Services that assists decision makers in better understanding and serving their Audience.
Audience Loyalty is part of Bloomerangs' Audience Relationship Management Framework (http://www.bloomerangs.com)
Digital & Analytics Dialogue UK event, 26 Apr. 2018
Pestana Chelsea Bridge - London, UK
Website: http://goo.gl/kbDfkW
Sjaun goes through the engineering projects to build a framework and infrastructure to overcome the most frustrating issues his data science teams experience on marketing campaign projects (data preparation and automating activated data across 3rd party sites).
Agenda:
• Define and compare - Marketing Mix, Attribution Modelling &
Customer 360 degree view strategy aka Customer Journey
Analytics.
• Outline the value Customer 360 degree view strategy engineering brings to both models by
improving data quality matching off-site web data.
• The complexity of tracking customer journeys in Customer 360 degree view strategy.
• Explain the engineering solution and a quick example.
Thinking Small: Bringing the Power of Big Data to the MassesFlutterbyBarb
Thinking Small: Bringing the Power of Big Data to the Masses via Adobe with the results of improved access to insights, better user experiences, and greater productivity in the enterprise.
As the strategic importance of data has increased, new approaches to customer analytics have emerged as well. As customer interactions with companies grow and diversify, the need to integrate data faster and deliver real-time insights is critical. This presentation explores the underlying trends driving companies to become more data-driven and invest in customer analytics. And, it outlines three types of approaches to capturing, managing, analyzing, and activating customer knowledge and insights.
Addressing the challenges and issues with businesses struggling to deliver successful Data Science in environments - Measure Camp, Bucharest (2 Nov. 2019)
This document discusses compliant digital marketing practices under GDPR regulations. It covers topics like lawful bases for processing personal data, obtaining consent, use of cookies, and the importance of user experience design. The presentation emphasizes the need for transparency, accountability, and putting privacy at the core of marketing systems and processes. It acknowledges that while compliance can be complex, the regulations should not prevent effective marketing. The future of e-Privacy regulations is also addressed.
XtremeLogics provides a solution for real-time social media monitoring and data categorization. Their platform automatically categorizes social media data to transform it into useful information for applications. It can answer questions about customer satisfaction and interest in programs. The platform extracts data from sources like Twitter, Facebook, and LinkedIn and performs sentiment analysis, behavioral mapping, and parameterization.
Why Do Banks Need A Customer Data Platform?Lemnisk
Banks traditionally have been known to amass customer information across both online and offline data channels. However, a lot of this data resides in silos and marketers have been unable to leverage this data to run targeted marketing campaigns. Here are the top four reasons why a Customer Data Platform would be best suited for Banks.
The document discusses how marketers can better leverage customer data to improve the customer experience. It provides tips from various experts on developing a robust data strategy, asking the right questions of data to uncover insights, owning customer data to stay compliant with regulations, and how IoT can be used to inform and deploy customer experience solutions. The overall message is that marketers need to stop data from being fragmented and better connect customer touchpoints to deliver personalized experiences.
1. The document discusses how contact center managers currently struggle to track customer interactions across multiple vendor systems storing proprietary data.
2. It proposes using a big data platform like Esgyn to ingest real-time and historical data from various systems into a single "customer data lake" to better analyze customer journeys and behavior.
3. Esgyn provides a vendor-agnostic platform for building a contact center data lake to gain insights into customer experiences across different touchpoints.
Artificial Intelligence (AI) has the potential to completely revolutionize the ecommerce experience. However, most organizations are still struggling with harmonizing data standards, streamlining information flows and optimizing upstream processes to improve the customer experience with the tools they have today. AI can enable customer service and sales reps to be significantly more productive and improve the self-service experience; but areas of product on boarding, content optimization, marketing automation and process orchestration have to be improved in order to support this vision. This workshop will demystify AI and show how companies can re-think processes for the product and content lifecycle from initial onboarding to end user experience. Outline how to build the infrastructure to machine-intelligence-enable content to create product associations and personalized experiences not possible without AI.
Automated Trading Summit 2012, Amsterdam
Big Data impacts the way we think about managing, processing and analyzing marketing data. It is the foundational element for building Digital Marketing solutions such as Audience Optimization, Channel Optimization, Content Optimization and Yield Optimization.
Recent research and studies provides some fascinating insights into how
(a) CMO's view Big Data as their biggest areas of "under-preparedness",
(b) Organizations view Advanced Analytics as a competitive advantage and
(c) Digital Marketers view Big Data as an enabling platform for all their future initiatives
Creating Insightful Reports with Data from Sugar and Other Critical SaaS SourcesSugarCRM
Murray Dunn, The Connection Cloud
Today, users of SaaS applications struggle to access their data for advanced reporting and BI. Many users need reports combine data from multiple applications such as SugarCRM, Intacct and Zuora. This typically means manually merging data from static spreadsheets to generate reports that stale, the minute they’re created. Users are addressing these challenges by turning to cloud data virtualization which provides instant connection to cloud application data to access exactly the data needed, from where it resides, into their preferred reporting tool. Join us to seehow these best practices can help you easily access your SugarCRM and other SaaS data for your advanced reporting needs.
Overview of major factors in big data, analytics and data science. Illustrates the growing changes from data capture and the way it is changing business beyond technology industries.
Accelerating Personalization to Cut Through Digital NoisePrecisely
Your customers’ inboxes are overflowing with unread email. Even social media and text messages are ignored. There is simply too much digital content and too little time to consume it.
So how do you stand out and get your customers’ attention? You need to go beyond traditional Customer 360 efforts. To get your customers’ attention, you need to maximize personalization based on context.
This document provides an overview of the enterprise customer data platform (CDP) market and guidance for choosing a CDP. It finds that the CDP market is growing rapidly due to increasing complexity in customer data and journeys. CDPs collect, normalize and build unified customer profiles from all data sources to share across marketing systems. The report describes key CDP capabilities, vendors, and recommends a multi-step process for selecting a CDP that includes determining needs, identifying vendors, scheduling demos and checking references. Nineteen leading CDP vendors are profiled.
Learn how retailers can leverage their own Big Data. Go from data sources to increasing profits, margins and market share at a fraction of the time and cost.
Target architecture: Overcoming barriers to effective Enterprise ArchitectureDave Hornford
Target architecture, and the resulting roadmap, is the fast path to effective business engagement. Change leaders are looking for help in effecting transformation. Dave will explore the real and self-imposed barriers to developing Target Architecture. Why most ‘Targets’ look more like a first Transition Architecture?
This document provides a template for an architecture roadmap for Project XXXX. It includes sections for the purpose of the document, a list of projects required to achieve the target architecture, a time-oriented migration plan showing the sequencing of projects, and recommendations for implementation. The migration plan section provides example views categorizing projects by characteristics like business objective. The document is intended to guide progression from the current to target architecture through incremental changes outlined in the projects.
Bloomerangs Audience Relationship Management is a set of Strategies, Solutions and Services that assists decision makers in better understanding and serving their Audience.
Audience Loyalty is part of Bloomerangs' Audience Relationship Management Framework (http://www.bloomerangs.com)
Digital & Analytics Dialogue UK event, 26 Apr. 2018
Pestana Chelsea Bridge - London, UK
Website: http://goo.gl/kbDfkW
Sjaun goes through the engineering projects to build a framework and infrastructure to overcome the most frustrating issues his data science teams experience on marketing campaign projects (data preparation and automating activated data across 3rd party sites).
Agenda:
• Define and compare - Marketing Mix, Attribution Modelling &
Customer 360 degree view strategy aka Customer Journey
Analytics.
• Outline the value Customer 360 degree view strategy engineering brings to both models by
improving data quality matching off-site web data.
• The complexity of tracking customer journeys in Customer 360 degree view strategy.
• Explain the engineering solution and a quick example.
Thinking Small: Bringing the Power of Big Data to the MassesFlutterbyBarb
Thinking Small: Bringing the Power of Big Data to the Masses via Adobe with the results of improved access to insights, better user experiences, and greater productivity in the enterprise.
As the strategic importance of data has increased, new approaches to customer analytics have emerged as well. As customer interactions with companies grow and diversify, the need to integrate data faster and deliver real-time insights is critical. This presentation explores the underlying trends driving companies to become more data-driven and invest in customer analytics. And, it outlines three types of approaches to capturing, managing, analyzing, and activating customer knowledge and insights.
Addressing the challenges and issues with businesses struggling to deliver successful Data Science in environments - Measure Camp, Bucharest (2 Nov. 2019)
This document discusses compliant digital marketing practices under GDPR regulations. It covers topics like lawful bases for processing personal data, obtaining consent, use of cookies, and the importance of user experience design. The presentation emphasizes the need for transparency, accountability, and putting privacy at the core of marketing systems and processes. It acknowledges that while compliance can be complex, the regulations should not prevent effective marketing. The future of e-Privacy regulations is also addressed.
XtremeLogics provides a solution for real-time social media monitoring and data categorization. Their platform automatically categorizes social media data to transform it into useful information for applications. It can answer questions about customer satisfaction and interest in programs. The platform extracts data from sources like Twitter, Facebook, and LinkedIn and performs sentiment analysis, behavioral mapping, and parameterization.
Why Do Banks Need A Customer Data Platform?Lemnisk
Banks traditionally have been known to amass customer information across both online and offline data channels. However, a lot of this data resides in silos and marketers have been unable to leverage this data to run targeted marketing campaigns. Here are the top four reasons why a Customer Data Platform would be best suited for Banks.
The document discusses how marketers can better leverage customer data to improve the customer experience. It provides tips from various experts on developing a robust data strategy, asking the right questions of data to uncover insights, owning customer data to stay compliant with regulations, and how IoT can be used to inform and deploy customer experience solutions. The overall message is that marketers need to stop data from being fragmented and better connect customer touchpoints to deliver personalized experiences.
1. The document discusses how contact center managers currently struggle to track customer interactions across multiple vendor systems storing proprietary data.
2. It proposes using a big data platform like Esgyn to ingest real-time and historical data from various systems into a single "customer data lake" to better analyze customer journeys and behavior.
3. Esgyn provides a vendor-agnostic platform for building a contact center data lake to gain insights into customer experiences across different touchpoints.
Artificial Intelligence (AI) has the potential to completely revolutionize the ecommerce experience. However, most organizations are still struggling with harmonizing data standards, streamlining information flows and optimizing upstream processes to improve the customer experience with the tools they have today. AI can enable customer service and sales reps to be significantly more productive and improve the self-service experience; but areas of product on boarding, content optimization, marketing automation and process orchestration have to be improved in order to support this vision. This workshop will demystify AI and show how companies can re-think processes for the product and content lifecycle from initial onboarding to end user experience. Outline how to build the infrastructure to machine-intelligence-enable content to create product associations and personalized experiences not possible without AI.
Automated Trading Summit 2012, Amsterdam
Big Data impacts the way we think about managing, processing and analyzing marketing data. It is the foundational element for building Digital Marketing solutions such as Audience Optimization, Channel Optimization, Content Optimization and Yield Optimization.
Recent research and studies provides some fascinating insights into how
(a) CMO's view Big Data as their biggest areas of "under-preparedness",
(b) Organizations view Advanced Analytics as a competitive advantage and
(c) Digital Marketers view Big Data as an enabling platform for all their future initiatives
Creating Insightful Reports with Data from Sugar and Other Critical SaaS SourcesSugarCRM
Murray Dunn, The Connection Cloud
Today, users of SaaS applications struggle to access their data for advanced reporting and BI. Many users need reports combine data from multiple applications such as SugarCRM, Intacct and Zuora. This typically means manually merging data from static spreadsheets to generate reports that stale, the minute they’re created. Users are addressing these challenges by turning to cloud data virtualization which provides instant connection to cloud application data to access exactly the data needed, from where it resides, into their preferred reporting tool. Join us to seehow these best practices can help you easily access your SugarCRM and other SaaS data for your advanced reporting needs.
Overview of major factors in big data, analytics and data science. Illustrates the growing changes from data capture and the way it is changing business beyond technology industries.
Accelerating Personalization to Cut Through Digital NoisePrecisely
Your customers’ inboxes are overflowing with unread email. Even social media and text messages are ignored. There is simply too much digital content and too little time to consume it.
So how do you stand out and get your customers’ attention? You need to go beyond traditional Customer 360 efforts. To get your customers’ attention, you need to maximize personalization based on context.
This document provides an overview of the enterprise customer data platform (CDP) market and guidance for choosing a CDP. It finds that the CDP market is growing rapidly due to increasing complexity in customer data and journeys. CDPs collect, normalize and build unified customer profiles from all data sources to share across marketing systems. The report describes key CDP capabilities, vendors, and recommends a multi-step process for selecting a CDP that includes determining needs, identifying vendors, scheduling demos and checking references. Nineteen leading CDP vendors are profiled.
Learn how retailers can leverage their own Big Data. Go from data sources to increasing profits, margins and market share at a fraction of the time and cost.
Target architecture: Overcoming barriers to effective Enterprise ArchitectureDave Hornford
Target architecture, and the resulting roadmap, is the fast path to effective business engagement. Change leaders are looking for help in effecting transformation. Dave will explore the real and self-imposed barriers to developing Target Architecture. Why most ‘Targets’ look more like a first Transition Architecture?
This document provides a template for an architecture roadmap for Project XXXX. It includes sections for the purpose of the document, a list of projects required to achieve the target architecture, a time-oriented migration plan showing the sequencing of projects, and recommendations for implementation. The migration plan section provides example views categorizing projects by characteristics like business objective. The document is intended to guide progression from the current to target architecture through incremental changes outlined in the projects.
Privacy Regulations and Your Digital SetupPiwik PRO
How Will the New Privacy Regulations Affect Your Digital Set-up? In less than 2 years from now, Europe’s new data privacy law will come into effect, changing the way organizations handle information of their users. General Data Protection Regulation will heavily impact usage of digital tools for customer insights and analytics.
This presentation was created by the Piwik PRO Team for a webinar session with Aurelie Pols. Webinar recording is available on: https://youtu.be/dPOvbbZ3vdo
Building enterprise advance analytics platformHaoran Du
Raymond Fu gave a presentation on building an enterprise analytics platform at the SoCal Data Science Conference. He has over 16 years of experience in big data, business intelligence, and enterprise architecture. He discussed how big data disrupts traditional architecture and requires new skills. Advanced analytics involves creating predictive models through machine learning to enable strategic and operational decisions. An enterprise analytics strategy involves data management, modernizing data platforms, and operationalizing advanced analytics models. Fu outlined the key capabilities needed for data management, analytics creation, and analytics operationalization. He provided examples of reference architectures and services that can be used to build an enterprise analytics platform.
The document discusses how to mitigate data risks with web analytics in light of evolving privacy legislation. It outlines that if a business collects and processes personal data, it needs to follow strict privacy laws to avoid fines. It then discusses key topics around privacy for web analytics, including evolving privacy legislation like the General Data Protection Regulation (GDPR), distinguishing between personal data and personally identifiable information (PII), and using methods like the RACI model and privacy impact assessments to classify risks and assign responsibilities for data use. Color coding of green, orange and red is also suggested to classify risk levels associated with different types of customer data processing.
Javascript Tracking or Web Log Analytics? Piwik PRO
JavaScript tracking method is used to collect data by the majority of analytics solutions today. It is valued for delivering high-quality insights, but also for extensive customization capabilities. However, for a variety of reasons, JS Tracking may not be accurate for everyone. The most popular alternative solution is Web Log Analytics, which allows you to collect web server log files and import them into your platform for viewing, analyzing, and reporting. Log analytics and JS tracking differ a lot when it comes to the processing and storage of data. See how they compare and choose the right tracking method for your organization.
2016 05 technology roadmapping update for u mass (1)Karen Ali
The document discusses a technology roadmapping initiative led by BioPhorum Operations Group Ltd. It establishes a steering committee of 18 companies to guide the roadmapping. The roadmapping aims to develop an industry technology strategy by determining critical needs, identifying technology targets, and assessing potential solutions. It will focus on key areas like continuous processing, single-use technologies, and process intensification. The roadmapping process will engage stakeholders, define future industry needs and challenges, and communicate requirements to guide innovation. Substantial benefits are possible through this cross-industry collaboration, like reduced capital costs and faster speed to market.
The Government of New Brunswick Enterprise Architecture RoadmapTamim Rahman
This document can be downloaded as a PDF for a better viewing experience; click on "Save". This roadmap was shared in a Symposium held on September 25th. A summary of this event can be found on http://www.qrs3e.com/gnb_ocio_togaf/
When IT consultants only respond to technical emergencies, it is inefficient, costly, and a disservice to clients. Consultants should help clients understand issues before emergencies occur to prevent downtime and its associated costs. The document discusses costs and downtime reductions from migrating clients from aging Windows 2003 servers to newer Windows 2008 servers with new hardware and consolidated consulting.
Building Business & IT Architecture Roadmaps with ArchiMate & TOGAFCorso
Building effective Business and IT Architecture Roadmaps and aligning your business strategy and IT capability with current and future-state architectures with ArchiMate and TOGAF.
Learn about:
• Transition planning
• Milestones with visual status
• Heatmaps and gap analysis
• Lifecycle states
• And more...
Corso webinar slides. Presented on Thursday 25th September 2014 by Larry Wallendorf, Global Pre-Sales Manager Enterprise Architecture at Corso.
A Comparison of Analytics and Tag Management Suites by Piwik PRO and GooglePiwik PRO
A combination of Google Tag Manager and Google Analytics has become the standard in the everyday activities of a data-driven marketer. But what if you’re looking for a more secure and privacy-compliant alternative? Welcome to Piwik PRO Tag Manager, which comes with built-in templates and integrations for Piwik along with a range of popular marketing and web analytics tags. Self-hosted and robust, it complies with the strictest privacy laws. See how the Piwik PRO analytics and tag management suite compares to Google and how you can make it work for your business.
What comes to your mind when you hear the word Analytics?
What exactly does it mean?
How it is that the Web Analytics is done & why use it?
What for & to what Capacity is it used?
The document discusses various topics related to e-commerce including online marketing strategies, website development, technical aspects, project design, and business intelligence. Some key points include:
- It lists several online marketing strategies such as affiliate marketing, lead generation, content marketing, and email marketing campaigns.
- Website development should follow standard practices like a catchy URL, easy navigation, security features, and mobile responsiveness.
- Technical aspects of an e-commerce system include security, scalability, availability, and fault tolerance.
- Project design can follow agile or waterfall methodologies and includes defining requirements, designing the database, and developing the web application.
- Business intelligence is important for analyzing online transactions
Web analytics involves analyzing data from websites and web pages. It can identify areas of risk, fraud, or ineffectiveness and influence business decisions. Web 2.0 analytics analyzes quantitative and qualitative data from websites to improve customers' online experiences and achieve expected results. Metrics like bounce rate, traffic sources, and abandonment rate are important to analyze. Popular web analytics tools include Google Analytics, True Social Metrics, and Kissmetrics. Mobile analytics differs from web analytics due to differences in how users interact with mobile apps versus websites.
Web analytics involves analyzing data from websites and web pages. It can identify areas of risk, fraud, or ineffectiveness and influence business decisions. Web 2.0 analytics analyzes quantitative and qualitative data from websites to improve customers' online experiences and achieve expected results. Metrics like bounce rate, traffic sources, and abandonment rate are important to analyze. Popular web analytics tools include Google Analytics, True Social Metrics, and Kissmetrics. Mobile analytics differs from web analytics due to differences in how users interact with mobile apps versus websites.
This document provides an introduction to web analytics. It defines web analytics as the measurement, collection, analysis and reporting of internet data to understand and optimize web usage. The main goals of web analytics are to measure traffic, understand user behavior on websites, and optimize websites. It discusses different categories of web analytics including on-site and off-site analytics. Common web analytics tools like Google Analytics use page tagging to collect data as users interact with websites. Key metrics that web analytics measures are discussed like visits, time on site, bounce rate, and conversion rate. The document also covers common web analytics products, how data is collected and stored, and factors to consider when selecting a web analytics tool.
This presentation covers methods to set up Google Analytics on your website. It also includes basic terminologies / jargons / concepts that are used throughout Google Analytics
5 Essential Practices of the Data Driven OrganizationVivastream
The document discusses five essential practices of data-driven organizations: 1) defining key performance indicators, 2) deploying analytics tools expertly across channels, 3) analyzing results and making recommendations, 4) creating changes based on data, and 5) measuring results continuously. It emphasizes the importance of standardization, governance, accuracy, and having a repeatable process for using data to optimize digital properties and drive business goals.
This document discusses web analytics and how it can be used to improve websites and customer experience. It presents a web analytics process consisting of defining goals and key performance indicators (KPIs), collecting data, analyzing the data, and taking action. The process aims to understand customer behavior and improve website performance and profitability. It describes common data collection methods like web logs, JavaScript tagging, web beacons, and packet sniffing. It emphasizes defining relevant and timely KPIs aligned with business goals and analyzing basic metrics as the starting point.
This document provides an introduction to web analytics. It begins with explaining why web analytics is needed by discussing how offline marketing lacks accountability and measurability. It then defines web analytics as the measurement, collection, analysis and reporting of internet data to understand and optimize web usage. The document outlines different types of web analytics including on-site and off-site. It also discusses the history and context of web analytics within decision support systems and business intelligence. Finally, it covers the main website data collection methods of server log file analysis and page tagging.
This document provides an introduction to web analytics. It discusses why web analytics is needed to measure the success of digital marketing efforts. Web analytics involves measuring, collecting, analyzing and reporting internet data to understand and optimize web usage. There are two main methods for collecting web analytics data: server log file analysis and page tagging. Server log files record information from a website's server, while page tagging involves including tracking code on website pages that collects user interaction data. The document outlines the advantages and considerations of each data collection method.
This document provides an agenda for a Google Analytics training session. The agenda includes topics such as getting started with Google Analytics, navigating the interface, audience, acquisition, behavior, and conversion reports. It also covers account administration, advanced tracking implementations, measuring content, importing and extracting data, and common applications of Google Analytics. The training emphasizes using Google Analytics for analysis rather than just reporting, and how to tell data-driven stories to different audiences. It provides best practices for setting up views and segments, understanding users, tracking campaigns and visitor engagement, and setting up conversion goals.
This document provides an overview of web analytics and its application to five of Singapore's most popular websites. It discusses how web analytics can help each type of website meet its objectives by analyzing acquisition, behavior, and conversion metrics. The types of websites examined are e-commerce (Groupon), e-service (OCBC), classifieds (sgCarMart), news (The Straits Times), and information (NTU). Limitations of Google Analytics are also identified.
The document provides an overview of tools for social media marketing and web analytics. It discusses Buffer, HootSuite, Canva, Trello and Hotjar as tools for social media marketing and communication. It also discusses Google Analytics, audience analytics, acquisition analytics, behaviour analytics and conversion analytics as tools for web analytics. The purpose of web analytics is to understand and improve the online user experience and optimize digital marketing campaigns. Data is collected using these various analytics tools and analyzed to gain insights.
5 Essential Practices for the Data Driven OrganizationVivastream
The document discusses the essential practices of a data-driven organization. It outlines five practices: 1) defining metrics to measure, 2) deploying analytics tools expertly, 3) analyzing results and making recommendations, 4) creating changes based on data, and 5) measuring again to see what works. It also discusses establishing a repeatable process like the eBusiness 5 Step Optimization process and knowing how to categorize digital properties, define conversions, measure campaigns, and ensure data quality. The goal of a data-driven organization is to be organized around customer intelligence gained from data.
Data analytics can immensely impact and improve a business’s decision-making processes. From better strategies to profits, explore the full scope of analytics.
Data analytics can immensely impact and improve a business’s decision-making processes. From better strategies to profits, explore the full scope of analytics.
This document discusses web usage mining and related processes. It begins with an introduction to web usage mining and its goal of analyzing user behavioral patterns on websites. It then covers topics like data collection and pre-processing, including cleaning, fusion, transformation, and reduction. Specific pre-processing techniques are described, such as sessionization, pageview identification, and user identification. The document also discusses data modeling and discovery of patterns, including various pattern types like decision trees, paths, groups, and associations. Finally, it covers potential applications and conclusions about web usage mining.
MarketView Marketing Database Platform | Data Services, Inc.Data Services, Inc.
Data Services' MarketView Data Management & Analytics Platform provides direct & data-driven marketers with a 360 degree view of their US & int'l marketing databases with advanced tools for database segmentation, customer/data analytics, data visualization, business intelligence, campaign management, cross-product analysis, marketing channel affinity reporting as well as a seamless connection to Data Services DSIemail Broadcasting Platform as well as integration with 3rd party platforms for CRM, ESP, eCommerce, Marketing Automation and more, all in a seamless online platform requiring no software or application to download.
Designing Outcomes For Usability Nycupa Hurst FinalWIKOLO
MarkoHurst.com :: My topic of discussion at the Feb 17 2009 NYC UPA.
Even as the pace of society, business, and the Internet continue to increase, many budgets and time lines continue to decrease. To compound this issue, there is a serious disconnect between business goals, user goals, and what visitors actually do on your site. UX practitioners need a simple and efficient way to reconcile these diverse needs while taking action on their data. Join us to learn about a new method for incorporating quantitative data such as web analytics and business intelligence into your qualitative user experience deliverables: personas, wireframes, and more. This presentation will include discussions of online business models, feedback loops for ensuring cross-discipline collaboration, and ongoing revisions.
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Customer analytics. Turn big data into big valueJosep Arroyo
BIRT Analytics is a customer analytics solution that allows companies to gain valuable insights from big data. It integrates data from multiple sources, analyzes large volumes of data, and provides clear and granular customer information. Tools allow users to explore data, identify patterns, profile customers, and forecast trends. Advanced analytics help optimize marketing, identify cross-sell opportunities, and understand customer behavior. The solution aims to help companies understand customer needs and adapt strategies based on real customer data.
Quite4me.com customer analytics all in oneJosep Arroyo
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The document describes the key features of a customer analytics platform called Quiterian Analytics. It allows users to integrate customer data from multiple sources, explore and visualize the data, enrich and cleanse the data, perform advanced analytics and data mining, create dashboards, and automate marketing campaigns. The platform aims to provide a complete view of customers and help companies gain insights, improve strategic decision making, and anticipate customer behavior.
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Brochure if you are a user or technician engJosep Arroyo
Quiterian allows users to ask questions and receive instant answers from large amounts of data without technical skills. It provides advanced analytics that help users understand how and why things happen, investigate hypotheses, and discover hidden patterns. The system also reduces the workload for technicians by eliminating the need for data preparation. It enables self-sufficient and secure access to data for faster, more in-depth analysis while maintaining compliance.
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Today's bi and data mining ecosystem v2Josep Arroyo
Visualization tools provide predefined reports and dashboards based on a data model to distribute to users, allowing flexible visualization of data through tools like sales per region. Visual Data Mining analyzes raw data to obtain immediate insights through intuitive techniques like customer churn prediction. Data Mining develops accurate predictive models for issues like fraud and risks that are managed by expert statisticians using algorithms and data engineering.
Visualization tools provide flexible reports and dashboards based on predefined data models for occasional and business users. Data mining discovers immediate key business insights through intuitive techniques by analyzing raw data for analysts and power users. Expert mathematicians and statisticians develop accurate predictive models for core business issues using statistical algorithms.
This document provides tips for software salespeople to focus on selling the benefits of the product rather than features in order to increase sales. It recommends highlighting how the software can help customers sell more, save money, work faster, and work more efficiently, then asking for the order.
Companies are facing a very competitive marketplace where decisions need to be made faster to keep up with rapid changes. Quiterian provides samples that can generate insights like predicting customer churn, recommending products, detecting fraud, and identifying customer segments in seconds or minutes, helping companies make smarter and faster decisions without depending on experts. Quiterian complements traditional BI and data mining by delivering quick results that have high impact on the business and provide a competitive advantage, as validated by leading companies.
Flexibility and stability in bi systemsJosep Arroyo
The document discusses factors to consider for decision making, including market dynamics which can be speed/competitiveness focused or predictable/stable. Corporate culture can prioritize flexibility, agility and just-in-time approaches which value innovation, customers and empowerment, or stability, predefined processes and scalability which focus on variability, speed and customization. Operations are also examined in terms of these factors.
The document discusses Quiterian, a data mining and predictive analysis platform that helps companies get more value from data sooner, anticipate the future to react earlier, and empower users while reducing IT costs. It provides fast data loading and exploration without limits, dynamic analysis and predictive modeling techniques, and easy report publishing and distribution. A typical implementation takes less than a month and requires minimal IT resources. Quiterian has been used by leading organizations in various industries.
Quiterian Ksf on advanced analytics for banking april 2011 engJosep Arroyo
This document discusses how advanced analytics can help banks and savings banks sell off real estate assets acquired from mortgage defaults. It outlines a 6-step strategy using the Quiterian DDWeb analytics platform: 1) Identify target customer segments, 2) Determine profiles of past buyers, 3) Use profiling to identify important variables, 4) Predict potential buyers using decision trees, 5) Automate workflows to apply lessons, and 6) Plan targeted marketing campaigns. The platform provides tools to help financial institutions better understand customers, detect fraud, and make predictive, data-driven decisions.
Traditional BI focuses on predefined reporting and distribution of reports while visualization and dynamic reporting allow for more dynamic exploration of data. Advanced analytics and predictive analytics focus on getting more value from data through easy self-service analysis techniques and big data to provide insights, anticipate outcomes, and gain a competitive advantage through better understanding why things occurred and what will happen in the future. Advanced analytics complement existing BI approaches by providing more predictive capabilities beyond what, when, and how much questions to answer why, what's next, and who questions.
Quiterian is a company that provides advanced analytics services to help improve results for clients in the health and pharmaceutical industries. Their analytics tools can be used to gain insights from large datasets and help optimize processes. They are focused on delivering valuable analytics to help clients in these sectors.
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Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
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In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
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Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots.
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Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
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Monitoring and Managing Anomaly Detection on OpenShift
Overview
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Key Topics Covered
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12. Jupyter Notebooks with Code Examples
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2. Marketing Advanced Analytics
DDWeb Web Analytics
DDWeb Web Analytics
The callenge of having a total knowledge of your website’s navigators
Every time more companies and institutions are conscious of the important role that websites play
as a mean to project the corporate image. At the same time, the huge amount of information that
organizations generate –through business establishments and physical offices or through their
webs-, requires a common place that joins all the business information, in order to interconnect that
information and to analyze it.
This is why organizations don’t hesitate to acquire Web Analytics tools which offer them qualitative
information about their web visitors’ behaviour, with the aim of measuring their website’s profitability
and effectiveness and therefore, to optimize the online channel.
“Organizations equip themselves with Web Analytics tools that allow them to measure their
website’s performance and effectiveness and to optimize the online channel ”
In the current market of Web Analytics solutions, it is absolutely necessary to choose the most suitable
solution. That one which, as Quiterian’s DDWeb Web Analytics, allows its users to cross the information
from navigation with transactional sources from the organization, as well as to obtain a very accurate
and comprehensive knowledge of the web visitor, with no duplicates, inmediately and in a simple
way. Only this way, organizations will have a global vision of all the information they manage and will
be able to anticipate to deviations in order to be more efficient and competitive.
www.quiterian.com info@quiterian.com
3. Marketing Advanced Analytics
DDWeb Web Analytics
The market and
Web Analytics Solutions
Disconnection between navigation data and the own information
Web Analytics’ market is nowadays very suppliers is, therefore, unique. As a result, there
consolidated; so is it that eight suppliers hoard are various Web Analytics solutions in this so
more than 90% of the market’s incomes. However, consolidated market. Most of them, more than 80%
its is a sector in constant evolution that must face of the total incomes of the market come from a SaaS
the arising challenges: user’s maturity, intelligent suscription model. The others –a minority-, are in-
terminals, social networking, independent analysis, house products –stored in the own organization-.
detailed and reliable visitors’ identification, etc. This This means that almost all Web Analytics solutions
means there are still many gaps between suppliers that can be found in the market have in common
(those who make the most from the products’ the fact that information is treated as an island
skills and stretch them) and customers (those who regarding the rest of the information the company
believe and invest in their use). manages. In other words, many reports and
graphics explaining the use given to websites are
At a technical level, the market still lacks of open provided, but there is no connection with the rest of
instrumentation standards or correctly adopted the information the organization generates.
metrics, and every proposal from each one of the
“Most part of Web Analytics solutions which can be found in the market come from a SaaS
suscription model; they provide reports and graphics on the website’s use, but there is no
connection with the rest of the information the organization generates”
Some solutions, on the other hand, solve this data referent to purchases in the orders files (which
problem facilitating the placement of additional is their natural placement) and the web analytics
information in specific actions that are carried out files. This situation occurs due to two possible
in a website. For example: to store information reasons: Maybe because we don’t have access to
about products, amounts and unit prices, that have enough information about the visits our websites
been selected by the user in the shopping basket. generate, or maybe because although having access
But with this, what is really being generated is an to them, that information is not related with the
information duplication; this means, we will have business information.
*Source: GASSMAN, Bill; Web Analytics, from
‘Hype Cycle for Business Intelligence’, 2010
www.quiterian.com info@quiterian.com
4. Marketing Advanced Analytics
DDWeb Web Analytics
DDWeb Web Analytics
Solution
Web visitors identification and analysis of their behaviour
Web Analytics enables access to information in This way, it makes a qualitative analysis in detail of
its maximum granularity. With no need of making each web visitor’s behaviour, beyond quantitative
aggregates or previous calculations, it identifys data on visits, visited sites, average length of
unique visitors clearly and precisely, with the certain visits, etc., being able to respond in a personalized
of data not being duplicated, and it also makes a way questions such as the following: how many
tracking (tracking_in) of them all along the route users visit you, where do they come from, how do
they follow when navigating through the tool called they behave, how do they respond to marketing
Analytic Behavior Recorder. campaigns, how many users abandon you and how
many are you still retaining, etc.
“DDWeb Web Analytics identifies unique visitors clearly and precisely, and it makes a perso-
nalized tracking of them as they navigate the web; it allows therefore to make a qualitative
analysis in detail on each visitor’s behaviour ”
Actions Visit Visit Conversions
Web Analytics’ Data Model
The Data Model is organized in a unique database, which
is called Web Analytics. This database contains tables
with information about Visits, Actions made in each Visit,
Goal
Conversions and Goals.
DDWeb’s technology, on the other hand, cubes are created in order to answer an information
responds to current needs of the marketing and analysis need, but when those are ready for the final
communication departments –focused on online user, that need has evolved and those cubes are not
strategies-, and it is perfectly adaptable to a useful anymore.
changeable and in constant evolution environment As it doesn’t require cubes, DDWeb connects all
such as the Internet. Most of the techniques which variables between each other, answering any
IT departments put into practice use a based-on- information analysis need from users immediately,
cubes technology, that is very useful for predefined so that the final user can make any necessary
or financial analysis, but insufficient for an analysis crossing at any time, the same way he creates the
involving the Internet. The cause of this is basically metrics needed for analysis or reports.
the continuous evolution of the own channel:
www.quiterian.com info@quiterian.com
5. Marketing Advanced Analytics
DDWeb Web Analytics
360º Knowledge on the web visitor: digital and offline integration
Web Analytics allows to condense all the then the most part of Web Analytics solutions must
organization’s information in one only place, identify the user previously. It often occurs that it
identifying a link between the information is not possible to identify the user, and in that case
compiled by web analytics and the organization’s the temporal link is always available, being therefore
transactional data (databases, products, sales,…) necessary to relate different relevant facts (web
This way, appart from providing a common place visits, sales, telephone calls, etc.) through the date
for the storage and analysis of all the business when they do take place.
information, Web Analytics enables a 360º
knowlegde on the web visitor, as it connects However, DDWeb Web Analytics always provides
information from the digital media with off line data. detailed information on each visitor’s interaction
with the website –including the non registered
This link between web and transactional data is ones- through creating behavioral profiles.
usually located in the user who visits the website;
“As it integrates web information and offline data, Web Analytics offers a 360º knowledge of
web visitor and puts all the information the organization manages together”
Increase of the capability to anticipate
At the time it obtains operating information of of website and to detect which features to modify
users when they acceed to web applications and with the aim of achieving the business goals.
navigate them, Web Analytics brings information
on if the goals –previously defined- from campaigns As it puts Data Mining and Predictive Analysis
launching, e-mailings, etc., are or not reached. techniques into practice –on which Dynamic
Data Web is based- the organization can identify
This way, it permits to detect improvement flanks; deviations easily regarding the goals to reach and,
this means, to know what works and what does not as a result, it takes measures soon, anticipates and
–or does not as it was expected to do-, in order to acts with a clear competitive advantage.
obtain steps that increase effectiveness of any kind
“Through Data Mining & Predictive techniques, the organization can detect deviations and
improvement flanks soon, and it increases its capability to anticipate”
Independent analysis, secure information
With Web Analytics, the information is stored inside
the organization; this means, data are located in
own recources and they remain under control. Its
installation in the organization’s server guarantees If you want to read more about Marketing Advanced
reliable information, which has not been disturbed Analytics Solutions, visit www.quiterian.com
or slanted in their way through third parties’ servers.
www.quiterian.com info@quiterian.com
6. Marketing Advanced Analytics
DDWeb Web Analytics
Optimum visits classification and instant segmentation
As it offers information in detail on each visitor’s segment instantly from the extracted data. For
interaction with web applications, Web Analytics example, it provides correctly the number of unique
makes an optimum visits classification, obtaining users from last quarter or from last three days.
behavioral profiles that improve and speed up Furthermore -and appart from offering
segmentation. predetermined reports-, it enables to create own
and personalized reports and dashboards, with
On the other hand, DDWeb Web Analytics allows to unlimited segmentations immediately, without
create advanced segments which are applicable to waiting and completely independent from IT
current and historic data, and creates any complex department.
“Web Analytics generates behavioral profiles, allows to create advanced segments which
are applicable to current and historic data, and creates any kind of complex segment
from the extracted data”
Web Analytics’ Predefined Reports
Web Analytics includes the following predefined reports:
Visits: Length, Origin, Average time, Day of the week,
Browser, Local time, Country, Key word of search,
Provider, Date, Month, Seen sites, External
entrance,...
Actions: Of the visit, For each type of visit, Average actions
per visit,...
Visitors: Visitor’s country, Visits per country,...
Report on Average time per visit
Sites: Landing pages, Visited pages,...
Report on Visits per day of the week Report on Origin of visits
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7. Marketing Advanced Analytics
DDWeb Web Analytics
Dynamic Data Web: Depth, Self-government and Immediacy
Dynamic Data Web is a 2.0 Business Intelligence platform, based on Data Mining and Predictive
Analysis techniques, that allows to extract implicit information from data, to identify hidden relations and
to discover patterns and trends in order to build predictive models. It helps organizations to take more value
from data soon, so they can anticipate and become more agile, more competitive and more efficient.
With a technology conceived and developed to give answer to any kind
of information request with no need to predict this request in advance
(it doesn’t require modellings, it does not work with dimensional cubes
nor metadata); Dynamic Data Web is the information exploitation
solution which provides a dynamic environment to obtain knowledge
from data and to generate added value information.
Depth and Business Knowledge
It allows to explore, to visualize, to filter and to select data in their
maximum granularity to obtain the information knowledge in all their
detail.
In an intuitive way (without previous programming, using the mouse),
it permits to use crossing techniques, comparisons, profiles, clustering
which bring a high degree of business knowledge. It helps to obtain
profiles, behaviour patterns, multidimensional crosses, comparison
diagrams, etc.
Adding Value to Data
It includes the tools for creating data as you move along in order to
add value to native data, with self-government and with no need of
reloading the database. The creation of stretches, ranks, quantiles,
decodings, metrics; which add new perspectives and enrich the vision
of the information.
Dynamism and Immediate Response
Dynamic Data Web does not require modeling, nor dimensional cubes
nor metadata.
Its distinctive logistics allows an immediate starting-up process. All the
loaded data are ready for their exploitation just because of them being
loaded.
Ergonomics and Users Self-government
User is self-governing in the data exploitation. He can put all his
business knowledge into practice when exploiting, exploring,
investigating and simulating with full self-government.
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