Knowledge management solutions serve an important role in the successful management of an educational institution. Among the current analytics tools, machine learning and the soon-to-be-released Microsoft Fabric are worth paying attention to right now.
An educational institution benefits from AI-enabled analytics in many different ways
In education, many of the same laws apply as in business: demand and supply must be matched, production must be rational, and the available resources should be used in an optimal way. It is also important to monitor results – in educational institutions, not only the monetary value, but also and above all the learning outcomes.
Advanced analytics can help educational institutions, for example, to orient degree and course offerings in the desired direction, monitor progress, identify pupils in need of additional support, and optimize practical teaching arrangements, such as the use of space.
The basic requirement for all knowledge management is that the data used is of high quality, collected, and centralized in one place.
Increasingly, the benefits of artificial intelligence can also be used in monitoring. Artificial intelligence and machine learning can be used in the management of educational institutions, for example to predict demand, the number of students, student dropout rate, and even the illness rate. It can also be used to classify current or potential students into different segments. AI can also be used to recommend study choices in order to achieve specific goals.
Microsoft Fabric makes analytics easily accessible to all
Many educational institutions, companies, and other organizations already rely on Microsoft products, such as Power BI and Azure, for their data and analytics needs. In spring 2023, Microsoft published for preview a new AI-based data and analytics solution called Microsoft Fabric. Microsoft Fabric combines existing and new components of Power BI, Azure Synapse and Axure Data Factory into a single, integrated environment.
The core idea behind Microsoft Fabric is to provide users with a complete analytics package in one place. It covers the OneLake storage architecture, data migration and integration, data science and real-time analytics services, as well as business intelligence, among others. The environment is based on an SaaS model with scalable capacity according to the needs of the organization.
Another important development for users is the fact that Microsoft Fabric enables even more data collection, the use of machine learning models, and customizable self-service reporting. As such, it improves the accessibility of information for different user groups in educational institutions, such as teaching staff, finance, HR, and management.
A data strategy and knowledge management roadmap provide the big picture
The basic requirement for all knowledge management is that the data used is of high quality, collected, and centralized in one place. For information to be truly useful in everyday work and decision-making, it must be accurate, real-time, and easy to use. Users also need to be aware of the existence of the data, and be able to use it for their needs.
A strong backbone for achieving these goals is the data strategy and the knowledge management roadmap.
A data strategy is a plan for how an organization collects, manages and uses data to achieve its goals. Its guiding idea is to move the organization from seeking knowledge to acting on it. Systematizing data management can, for example, automate many steps, and eliminate unnecessary redundancy in work tasks. For example, a typical starting point is that several people collect the same data from different sources, and produce different reports.
In a nutshell, a knowledge management roadmap means identifying the current state of knowledge management, defining the target state, and drawing a roadmap between the two. The end result is a clear and practical plan, in which the development measures for knowledge management are also phased and scheduled.
As important as it is to understand the present and learn from the past, advanced analytics and generative artificial intelligence also enable educational institutions to look to the future. This contributes to the success factors identified at the outset: matching supply and demand, keeping production rational, and optimizing the use of resources. Positive developments then start to show up in results – not just in euros, but also in learning outcomes.
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