Data cleansing is essentially the process of removing or amending data that is incomplete, incorrect, duplicated, or improperly formatted in a database. Organizations in data-intensive fields such as insurance, banking, retailing, transportation, and telecommunications might turn to cleansing of data to examine data systematically for flaws using look-up tables, algorithms, and rules. Typically, database cleansing makes use of programs that can correct specific kinds of mistakes like completing zip codes or zeroing in on duplicates. Routine data cleaning can also save database administrators a whole load of time and resources compared to manually fixing errors that arise from incomplete, incorrect, duplicated, or improperly formatted data.
Simple versus complex data cleansing
At its simplest form, cleansing of data can involve just one person reading through records and checking to verify for accuracy. Spelling errors and typographical mistakes are corrected, alongside data being properly filed and labeled and missing entries completed.
Simple data cleaning is utilized in order to purge unrecoverable or out-of-date records to make room for correct data and ensure the efficiency of operations. More complex data cleansing though is handled by computer programs, checking and verifying data as instructed. Depending on what you need, you can have mistakes corrected, data deleted, and records updated to properly reflect on databases automatically as cleansing is done.
Benefits of data cleansing
Cleansing of data is very important because it affects how efficient a data-intensive business can be. If clients in a database, for instance, don’t have accurate details, then employees won’t be able to contact them easily. In fact, they might not be able to contact clients at all. Automated services like mailing lists will fail if email addresses are not properly formatted because then recipients won’t be able to get what they signed up for. Cleansing data will ensure that all data within systems are correct in order to allow systems to actually use the data. Incomplete or inaccurate data is useless so they are removed to streamline operations. If you are working with two systems, cleansing of data is even more important because you need your two systems to be synergistic with each other.
All data must be compatible for use with either of the systems, ensuring that whichever system is used data will be accessed. Where a lot of data is involved, errors can be expected to happen in a system or database. Data cleansing can keep these errors at bay by weeding out useful from useless data.
When cleansing data is done regularly, the errors don’t add up so you don’t have to worry about your system blowing in your face in the most inopportune of times. Think of it going to your doctor regularly versus coming in only if you are sick. Routine checkups will detect illnesses while they are small so they are easier to deal with compared to finding out you’re in the severe stages and there’s very little you can do. Prognosis is not good. If only you went to the doctor sooner. Your data is your business so you need to take care of it if you want to stay in business. Cleansing data can help you with that.
Julian Hartley is a data analyst for DinaliC.com, a talent management company.