The education system is becoming increasingly dependent on information technology to maintain competitiveness and adapt to an ever-changing business environment . The industry, which is essentially becoming a higher order service industry, must rely on technology to keep pace with the global economy that technology has opened up. Say no to plagiarism. Get a tailor-made essay on “Why Violent Video Games Should Not Be Banned”? Get an original essay How Data and Analytics Can Improve Education George Siemens on the applications and challenges of educational data. Universities have a long history of collecting data: tracking grades, attendance, textbook purchases, test scores, cafeteria meals, and more. But little has been done with this information – whether for reasons of privacy or technical capabilities – to improve student learning. With the adoption of technology in more colleges and the push to make government data more open, there are clearly many opportunities for better data collection and analysis in education. But what will it look like? This is undoubtedly a politically charged issue, as some states look to things like standardized test score data in order to evaluate teacher effectiveness and, therefore, their retention and promotion. What types of data do colleges traditionally track? Colleges and universities have long tracked a wide range of learner data, often drawn from applications (universities) or registration forms (colleges). This data includes any combination of: location, previous learning activities, health conditions (physical and emotional/mental), attendance, grades, socio-economic data (parental income), parental status, etc. Most universities will store and aggregate this data under the umbrella of institutional statistics. Privacy laws differ from country to country, but generally prohibit academics from accessing data that is not relevant to a particular class, course, or program. Unfortunately, most colleges and universities do very little with this wealth of data, other than possibly producing an annual institutional profile report. Even a simple analysis of existing institutional data could raise the profile of potentially at-risk students or reveal patterns in attendance or assignment submission that indicate the need for additional support. What new types of educational data can now be captured and used? In terms of learning analytics or educational data mining, the increasing outsourcing of learning activity (i.e. capturing how learners interact with content and discourse they have around the learning material as well as the social networks they form during the process) is driven by the increased attention to online learning. For example, a learning management system like Moodle or Desire2Learn captures a significant amount of data, including time spent on a resource, posting frequency, number of logins, and more. This data is quite similar to what Google Analytics or Piwik collects regarding website traffic. . A new generation of tools, like SNAPP, uses this data to analyze social networks, levels of connectivity and learnersperipherals. Discourse analysis tools, such as those developed at the Knowledge Media Institute at the Open University in the United Kingdom, are also effective in assessing the qualitative attributes of speech and discussions and evaluating each learner's contributions based on their depth and substance in relation to the subject of discussion. Day in and day out, mountains of data are produced directly as a result of educational activities and as a byproduct of various procedures. A large amount of information concerns their students. Yet most of this data remains locked in archive systems that must be coupled with operational systems to generate the information needed to support strategic decision-making. Various approaches to computer-assisted decision-making systems have emerged over time under different terms like Management Information Systems (MIS), Executive Information Systems (EIS), and Decision Support Systems (DSS). . The term management information system is not new in the education sector. Colleges and universities use management information systems to generate various reports which are used for analysis of admissions, exams, results, etc. for their decision-making for their own use as well as for their transmission to the authorities in charge of regulation. Often these reports are computer generated and can be generated at any time. However, the use of the terms Data Warehousing and Data Mining is relatively new. These terms have gained importance with the increasing sophistication of technology and the need for predictive analysis with what-if simulations. Finally, data warehousing and mining tools are essential components in the education sector. management support systems are characterized by cyclical ups and downs of buzzwords. Model-based decision support and executive information systems have always been limited by the lack of consistent data. Nowadays, the data warehouse attempts to fill this gap by providing real, decision-relevant information to enable control of critical success factors. A data warehouse integrates large amounts of enterprise data from multiple, independent data sources consisting of operational databases into a common repository for querying and analysis. Data warehousing will assume crucial importance in the presence of data mining and generation of several types of analytical reports which are generally not available in the original transaction processing systems. Since education is an information-intensive industry, building a management information system is a mammoth task. This is even more true for public sector colleges and universities, which have a vast network of colleges or universities and branches spread across the country. This becomes more difficult due to the prevalence of varying degrees of computerization. Currently, colleges and universities generate MIS reports largely from periodic paper reports/statements submitted by branches and regional/zonal offices. Except for a few colleges and universities, which use technology extensively, MIS reports are available with a considerable period of time. The reports thus generated also have a high margin of error due to the fact that data entry is carried out at different levels and the likelihood of varying interpretations at different levels. Although the computerization of college or university branches.