The Data Futures transformation programme will facilitate in-year data collection, assurance, and dissemination of HE data. There will be 3 data collections throughout the year, meaning that institutions will need to ensure the validity of their data on an ongoing basis.  Coding and data structures have changed HESA Coding Manual with some fields no longer in use and some remapped. HESA have provided a model to assist with the process model

The new data collection platform, the HESA Data Platform (HDP) will deliver a more efficient system for HE providers to submit and quality assure their data leading to operational efficiencies.

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Data Quality

Higher Education establishments and their administration teams handle large volumes of data. With an ever-increasing number of admissions and more students graduating each year, it is essential that processes are in place to collect accurate data that is compatible to the requirements of the HESA Data Futures platform.

Clear and concise data is critical for improved decision making. Inconsistency in data can occur due to several reasons – wrong manual data entry, misspelling, missing information, and presence of redundant data in different accounts. If these errors are not corrected promptly, it can lead to issues in the succeeding data processing stages. Identifying and correcting inaccurate data is possible by using effective data cleansing techniques and will improve the efficiency and protect the integrity of student information for future HESA Data Future submissions.

With a clean and dependable database, the higher education sector can improve revenue, improve student experience, select the right marketing campaigns, and achieve overall operational efficiency.

As data analysts continue to spend considerable time in preparing data prior to analysis or reporting, it becomes critical to clean your higher education data.

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Data Cleansing Steps

Incorrect data can inform poor business strategy and decision making and potentially cost institutions significant amounts in lost revenue.

We can help you with the following: -

• Structural Data Errors - including misspellings, inconsistent naming conventions, incorrect word use, capitalization errors, and more. While these errors may seem obvious to humans, databases or applications won’t be able to decipher these errors and will skew results. This can cause several challenges with data if such fields like dates, address, or phone numbers have no standardisation.

•  Remove duplicate data - makes it difficult to accurately analyse information, causes confusion and bottlenecks, and creates wasted space within your database. Collecting large amounts of data from various sources is often the cause of duplications.

• Search for missing values - is a problem for several reasons, including distorting analysis, hindering communication, and loss of quality insights. 

• Remove unwanted outliers - are pieces of information that fall significantly outside the normal range. Though this extreme data is generally less common, it still has the potential to negatively affect reporting and analysis. 

 

Data cleansing is a regular and important process to ensure better data quality, particularly in a competitive sector like higher education. University’s must constantly update their databases to maintain integrity, validity and add value to their ongoing business process. Ensuring appropriate data quality for national statistics products and providing fit for purpose data for regulation and statutory purposes. Having a well-organized plan for data cleanliness, will ensure high-quality data. Outsourcing the data cleansing task to experienced providers is a feasible way to ensure that data is as accurate and consistent as possible.

It’s all about ensuring that your data is validated and quantifiable to load onto the HESA Data Futures platform (HDP)