Steps to Ensure Data Cleanliness in the Higher Education Sector

by | Published on Apr 1, 2021 | Data Entry Services

As business processes largely depend on data, accuracy of database is critical when it comes to succeeding in any industry. Educational establishments and their administration teams handle copious data. With an increasing number of admissions and more students graduating year by year, related data tends to pile up. Clear and concise data is critical for improved decision making. Inconsistency in data can occur due to a number of reasons like – 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 major issues in the subsequent data processing stages. Identifying and correcting inaccurate data is possible by using effective data cleansing techniques. Data cleansing services provided by reputable providers can improve the efficiency and protect the integrity of student information in the long run.

Important Steps in Data Cleansing

In simple terms, data cleansing or data scrubbing is the process of analyzing your records to find out data errors, inconsistencies and duplication. After screening the data, it is corrected through data transformations using database tools or scripting. The key to efficient data cleaning is to be organized with your processes and understand your data systems. With a clean and reliable database, the higher education sector can – improve revenue, improve student experience, select the right marketing campaigns and achieve overall operational efficiency.

Data challenges in education come in a variety of forms for colleges and schools. As it may be simple to assume that organized data means merely storing data or records digitally, this is not the real case. Today, as the data is collected and stored in a digital format, the problem of dirty data is quite common. Data that is error prone or inconsistent leads to bad outcomes, such as misguided decision making. Data needs to be streamlined, easy to access and must be available in practical and user-friendly silos. 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.

Data cleansing can effectively handle discrepancies and errors in both single source data integration and multiple source integration. Here discussed are some important steps to ensure data cleanliness in the higher education sector –

  • Prepare Resources and Data Analysis – Creating a well-organized plan and gathering resources to analyze data is one of the important steps in efficient data cleaning. Make sure to focus on coordinating key stakeholders of the data – right from those who prepare data to those who evaluate it. Often, issues tend to occur when data cleaning or transformations are done in a closed environment where groups of people are unaware of changes occurring to their data. With a coordinated group, discover how your data “looks” by evaluating the data sources and their tables. Be sure to include the records, purposes, and characteristics. Analyze the data thoroughly so that there is an understanding of the underlying structures of the sources and how well they fit their intended purposes.
  • Determine Data Quality to Identify Errors – To identify key data errors, it is important to define clean data. Use the work done in the preparation stage to follow table definitions and see how it fits in the overall database design. Clearly check whether the records are formatted for proposed use and if they make sense. After defining what clean data is, create a set of guidelines as this will make it easier to analyze and identify data that does not fit in the defined criteria. Data that does not follow the set guidelines are to be noted for correction. These guidelines will be useful for future data cleansing and for configuring your systems around data error prevention.
  • Data Cleaning – Data transformation involves the usage of tools to correct the errors identified in the previous step. In fact, technical staffs need to create these scripts and tools. Generally, data sources are moved into warehouses so that live data is not affected during this process. This type of data correction or analysis can consume a huge amount of time because this involves anything – from incorrect date formats, record duplication, typos from data entries, or misrepresentation of data. To avoid correcting the same error more than once, document all corrections and look for patterns.
  • Verification of Data Changes – Clearly inspect and validate the key changes made to the data sources. Have a clear idea of the data quality of the updated records and their accuracy. The cleaned data can be enriched with additional data for enhanced reporting, or there may be better ways to represent that data moving forward. The verification process either finishes with changes being confirmed or it repeats itself to audit the data again. Repeated cycles may be needed when certain corrections are not completed within the time allotted. In fact, more focused cycles of data cleaning can result in quicker corrections to the data and become more streamlined and efficient.
  • Preventing the Causes of Data Errors – Data cleansing helps evaluate how well your data systems handle data entry errors right from applications or other sources. The process can be used to improve these systems by identifying and correcting the causes of the errors. One of the top causes of data errors are the users themselves. Therefore, improvement may be done to update data entry applications to prevent users from entering certain data formats, to run error checking, or to enforce better integrity constraints. While it may be impossible to handle or solve every error-making situation, well-designed systems can at least ensure the data cleaning process is executed more efficiently by making it easier to identify data errors.

Data cleansing is a regular and important process to ensure better data quality, particularly in a competitive sector like education. Higher educational institutions have to constantly update their databases to maintain integrity, validity and add value to their ongoing business process. Having a well-organized plan for data cleanliness, will ensure high-quality data. Outsourcing the data cleansing task to experienced data entry service providers is a feasible way to ensure that data is as accurate and consistent as possible.

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