Which Declaration Around Business Intelligence Bi Tools Isn’t Real – In a recent post on trends in machine learning, we predicted that more no-code/low-code ML tools would become available to non-programmers. The potential market for such tools is huge given that analysts outnumber data scientists and data engineers. With improvements in AutoML tools and the availability of pre-trained models, more attention is paid to tools for model tuning, adaptation, and learning transfer, and the class of ML tools can be naturally made available to analysts and domain experts.
Business Intelligence (BI) tools allow non-programmers to transform data into insights that can be used to inform strategic and tactical business decisions. Modern BI tools allow analysts to visually analyze and interact with large data sets, and some tools include data preparation and advanced analysis and modeling capabilities.
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Given the potential role BI tools can play in expanding the use of ML to non-programmers, we wanted to understand the relative popularity of existing BI tools. For this reason, we developed an index that relies on public data and is modeled after the TIOBE programming language index. Our index consists of the following components:
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Now to the initial results. We found Microsoft Power BI to be by far the most popular BI tool, with Tableau coming in second. This is in line with anecdotal experience – discuss BI tools with users and they are most likely to mention Power BI and Tableau.
Next, we analyze the components that make up the index score of the BI tool. The chart below deconstructs the calculated index score using 100% stacked bar charts designed to show the relative percentage of the various components of the final index score. Power BI performed well on demand (number of job offers), while Tableau did well on supply (number of people who listed a particular skill in their profile).
Finally, we examine the supply and demand side of the talent pool for each of these tools. The solutions in the upper right of the diagram below are the most popular in terms of talent and therefore have more developed user ecosystems.
Figure 3: Ranking of BI tools using two talent pool metrics – [Supply (size of global talent pool)] and [Demand (number of online job postings)].
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We used several search queries to identify BI tools to include in this initial index. While the top-ranked tools in our index are long-term solutions, we believe recent advances in AI and user interfaces are likely to lead to new offerings aimed at BI users.
We would like to include emerging BI tools from startups in future iterations. Let us know (using the form below) what tools you want us to include in future releases. Considering the scoring methodology described in this post, newer solutions are likely to score much lower than established ones. But still, it will be good to start tracking the progress of some of the new and upcoming BI tools.
Use this form to suggest companies to be included in future editions of the BI index. Index Suggestions Company Name * Company Website (URL) * Leave this field blank if you are human. Submit ΔFrom NEDC to WLTP: Impact on Energy Consumption, NEV Credits and PHEV Subsidies in the Chinese Market
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Received: May 28, 2020 / Revised: July 12, 2020 / Accepted: July 13, 2020 / Published: July 17, 2020
Currently, universities are forced to change the paradigms of education, where knowledge is based mainly on the teacher’s experience. This change includes the development of quality education focused on student learning. These factors have forced universities to look for a solution that will allow them to extract data from various information systems and convert it into knowledge needed to make decisions that improve educational outcomes. Information systems managed by universities store large amounts of data on students’ socioeconomic and academic variables. In the university field, this data is generally not used to generate knowledge about its students, unlike in the business field, where the data is intensively analyzed in business intelligence in order to gain a competitive advantage. These entrepreneurial success stories can be replicated by universities through educational data analytics. This paper presents a method that combines models and data mining techniques within a business intelligence architecture to make decisions about variables that can influence the development of learning. To test the proposed method, a case study is presented in which students are identified and classified according to the data they generate in various university information systems.
Currently, the use of information and communication technologies (ICT) is part of all company activities. Universities are not far behind and include ICT in most of their processes. These processes integrate administrative management, on which the existence of universities depends, or use them to support academic management [1]. The most widespread use of ICT for academic management is the learning management system (LMS) [2], which supports online interaction between teachers and students. However, there are scenarios where specific support from ICT is needed to address common learning problems. These scenarios allow ICT to apply new models and educational methods in student education. A guide to this can be the personalization that companies have achieved with their customers through data analysis models that allow managers, executives and analysts to discover trends and improve the services and products they offer to their customers.
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A personalized service can be implemented in an educational environment, where the process is similar to that applied at the corporate level, but the goal of education is to improve the methods or activities that generate learning in students [3]. Learning environments are primarily based on a range of interactive and delivery services. Personalized learning recommendation systems can provide students with learning recommendations based on their needs [4, 5]. Companies use data analysis architectures, the results of which help them make decisions about their business. These architectures are called business intelligence (BI); their ability to extract data from various sources, process them and transform them into knowledge is a solution that can also be included in the educational management of a university [6].
As a precedent, it is important to consider that several universities use a BI platform with an administrative or operational focus, which helps them make decisions in the financial management of the institution [7]. In the same way, previous works [8, 9] analyzed dropout rates with respect to models and statistical tools using economic and academic variables, disaggregating the analysis according to whether or not students enrolled in the next semester. This formula is perfectly valid; however, it leaves aside the reasons that determined why students drop out. In contrast, our proposal differs in its ability to analyze data about students’ academic activities and focus on the learning problems they present. This analysis helps in making decisions about educational management and improving the teaching methods set by teachers [10].
Three research questions are proposed in this thesis to help align concepts and processes in their design; in addition, they try to find out the current situation of the environment where this work is carried out:
To answer each of these questions, this work includes a description of a BI framework that bases its design on a detailed review of previous works, a Unified Modeling Language (UML) diagram, and a complete method for applying academic data mining. This work extracts data from various academic sources, processes them and allows us to identify, using data mining algorithms, the strengths and weaknesses of each.
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