The education system is constantly evolving, which means the importance of good data quality in the academic setting is more important and crucial than ever.
Clean data should drive district decision making instead of bad data grounding decisions to a halt. Good and bad quality data can affect the education system in more ways than you might realize.
Data analysis has become more and more necessary as district technology evolves. The reason is because accurate data replaces much of the guesswork involved by using hard numbers and facts, which can improve the ways the schools and districts operate. However, data analytics and data entry are only helpful when accurate data is collected.
Poor Data Quality Risks and Examples
Data collected by schools can have inaccuracies or incompatibilities due to no fault of the administration. Inaccurate or incompatible data can affect the school’s education system and even taint its reputation. Examples of data issues can be seen all around us. As the Hechinger Report indicates it’s like data systems in schools all speak different languages and translating the data takes time and risks error.
Furthermore, The Hechinger Report goes on to say that data in schools today is siloed and in many cases should be connected to form a unified picture of the student. One attempt at making this a reality is Project Unicorn, which is trying to unite both districts and vendors in interoperability thereby creating a clean and unified data picture.
Backing both The Hechinger Report and Project Unicorn’s findings, Experian has also indicated that data residing in silos coupled with manual data entry lead to a data lag and can be prone to errors.
Poor data quality can affect the entire district. Data Quality concerns are why data publishers (both automated and manual) and users need to work together to interpret data more effectively. Districts spend significant amounts of funding each year on manual processes to consolidate data rather than addressing the root cause of data inaccuracies in the first place. Instead of wasting this funding on manipulating poor data quality, that funding can used more efficiently in a two-step process:
1. Identify and implement methods of automating and interconnecting disparate data sources.
2. Re-allocate the traditional spend to improve processes identified through step 1.
Ways to Improve Data Quality
The first step in improving data quality is recognizing the problem. All too often this problem is very apparent. Simple things like returned mail, bad email addresses or phone number are key indicators that can lead to identifying bigger issues such as misidentified grade reports and divulged information. While these may seem like small issues, they identify the bigger issues, such as bad addresses leading to incorrect Free and Reduced Lunch funding which will cost your district significantly. This is simply one example of where clean data can both increase district funding and improve educational quality.