The availability of big data has opened new opportunities for the growth of data analytics. This field has promoted a culture of decision-making processes driven by data. The education sector has lately adopted decision-making techniques that exploit data analytics to support learning, evaluate efficiency, and improve feedback.
As a unit of the business sector, the education field generates large volumes of data daily. This is the very data the sector must rely on to make timely decisions. To achieve this goal, the sector needs to overcome latency and use a technology that provides speed and efficiency. In-memory data grid allows fast data access for maximum scalability and performance.
Overcoming latency by distributing data into multiple servers
The concept of in memory data grid is simple because it connects with multiple servers so that they work as a unit to help manage data and its processes within a distributed environment. Servers connected to an IMDG could be located in one place or remotely. The IMDG stores its entire data in the RAM. Despite the advantages the education sector can tap from big data, it faces one huge enemy to big data processing – latency. This is the delay time taken to generate results once a command is issued. This is a major setback due to the speed required to process data for real-time analytics.
Although it might be impossible to achieve zero latency, there is a better technology the education sector can use to improve performance. The main goal is to ensure data is processed within the least time possible to give the sector predictable insights. Latency can come from different sources like network I/O, disk I/O, operating environment, and the coding the institutions of learning use. The data environment requires reliable efficiency in terms of hardware and software on which big data processing is done. The in-memory data grid distributes data across entire clusters in a way that balances both the load and capacity to offer resilience, scalability, and speed. It gives education institutions the best solution to overcoming latency in data analytics.
Using an in-memory data grid for real-time data analytics
One of the tools that have gained popularity in data analytics is the map/reduce model that uses open-source solutions. The model can take different forms depending on the competing framework that offers it. They aim to accelerate speed when analyzing disk-based data.
As data increases to petabytes, the processing time increases too. However, the need is to reduce the time from several hours to a few minutes or seconds to benefit from big data analytics outcomes. The complexities of disk-based data analytics that use map/reduce models considerably increase the overheads in an environment where quick analytics is required due to the data scale involved. Another model has to come in place to overcome this challenge.
The IMDG has proved to be one of the life-changing technologies needed to transform map/reduce analytic engine from a scalable only memory-based data store to a parallelized computer platform. It changes the data analytics experience from a slow process to a super-fast procedure that gives educationists real-time results. It leverages the automatic load balancing on the grid to give it a minimized data movement and thereby speeds up the analytics process. Education institutions no longer need to use a longer process where data is first moved from disks into memory to begin processing. Instead, IMDG stores the data in memory, making it available in an instant. This is how it minimizes data motion for real-time processing.
Moreover, IMDG stores the results in clustered memory to minimize the I/O time required for calculating final results from files. IMDG eliminates network usage by removing the overheads, thus shortening the time taken to analyze data. IMDG doesn’t require a programmer to create keys for it to identify the specific data required for analysis. Instead, it uses the technique of object-oriented query when selecting the portion of data relevant for analysis. It is also possible to structure its map/reduce codes to make them straightforward as though the objects are being processed from a single workstation. This enables IMDG to shorten the time taken for design and analysis.
Using real-time data analytics to improve the education experience
The education sector can use big data as an asset to help them accomplish a lot of things. Incorporating data analytics within education institutions will help revolutionize the sector in terms of efficiency and performance. The classroom, admissions unit, the teaching staff, and students will experience a new wave of change in every area of the institution. Real-time data analytics can be used in various ways to improve the education experience.
The education sector is highly competitive but the greatest competition is in offering marketable courses that give students better chances in their future life. Schools have to stay updated with current trends in the course offering. Real-time data analytics will help education institutions study the patterns followed by course makers to improve on their course offerings.
The performance of instructors has a direct impact on student performance. There are many methods schools can use to evaluate instructors but most of them are manual methods that take a lot of time to process. To reduce biases in instructor evaluation, schools can use real-time data analytics to read feedback from students through freshly gathered data.
Schools need to keep up-to-date data of every student attending the institution. Sometimes the numbers are too many, which makes it hard to keep current data. Teachers and the support staff can use big data analytics to keep current student records in terms of performance, attendance, those who drop out, extracurricular activities, discipline issues, etc. It is easy to recognize poor performers or slow learners and create a way out to help them.
Education institutions go through diverse challenges too. Enrolment levels might be going down, students might be planning strikes often, and workers could be complaining. The traditional ways of finding the causes could be to seek advice or seek information from students/workers. With real-time data analytics, it is easy to get clues on root causes of challenges in the institution, such as performance, image, service level, and how the institution compares with its competitors.