Top 3 Data Engineering Challenges in the Edtech Sector
Our last blog talked about how best Edtech companies use Data Analytics to improve student performance but every coin has two faces. Since Data Engineering is still budding, it does face a few challenges in the Edtech sector as of now.
So, here we will pick up the top challenges faced by Data Engineering in the Edtech sector.
Challenges the Edtech Sector is Trying to Figure Out in Relation with Data Analytics
1. Storage Issues
Since Data Engineering is relatively new and still blossoming, it is sometimes possible that the rate at which data is analyzed and collected runs outside the processing capabilities of the data machines available. This leads to slowdowns and might affect the outcome.
Why Storage Issues?
There are more learners today than there had been at any point in the past due to obvious reasons such as population surge and higher awareness of the need for education. A lot of students prefer online studying of some sort because of the additional benefits it offers in terms of flexibility.
Thus, the data is huge and it does get a bit tedious to store and manage all this humongous data all at once.
The Solution!
Developers are continuously working on coming up with better, more scalable storage systems to offer higher support for both current and expected requirements.
2. Safety Concerns
Education stakeholders and regulatory authorities always have data safety concerns on their minds. The existing security protocols are somewhat lagging in handling huge volumes of data. The cost is high and the management process is continuous and dynamic.
Why Safety Concerns?
The demographics of these online learners is not restricted to a certain gender or age bracket. While the data might not lie entirely in the category of super sensitivity, it does have a lot of personal data including academic records which one may not want to disclose to everyone.
The Solution!
The one thing keeping everything from falling apart is the fact that academic data is not as highly sensitive as health or financial data.
Furthermore, researchers are working day and night to come up with more reliable data protection methods and are soon expected to offer some security related relief.
3. Data Errors
Data engineering requires dealing with huge volumes of data, which can sometimes lead to errors. These errors are more common in cloud storage systems and countering the errors can be quite an expensive affair too.
Why Data Errors?
Let's say an institute has thousands of students. Their information would lead to multiple datasets and in the process of keeping it all, mistakes are common. This leads to loss of data, sometimes even irrevocable.
The Solution!
The issue has been navigated and experts are already working on trying to come up with a system that will lead to better data management, storage and processing, leading to lesser errors.
Conclusion
Data analytics holds the prowess to become a game changer. While the sector does face some challenges, it also has the potential to overcome them because there is no deficiency of experts.
Edtech is a sector looking on the brighter side and with the right incorporation of data analytics, it will definitely grow to become an even bigger picture in the coming times.