Clean data is one of the key elements for successful business. It ensures robust performance, presents accurate results, leads to correct business insights and as a result precise recommendations. Dealing with data, it is important to know how to recognise inaccuracy and correct errors to keep the clean data.
There are some common data errors you could avoid to maintain quality data.
Incorrect and inexact Date format is one of the most common mistakes in a dataset. For example, a date of July 12, 2017 can be written as 07/12/2017. Also the same date can be mistakenly changed to December 7, 2017 mixing American format of MM/DD/YYYY with European format of DD/MM/YYYY. As a result the date of July 12, 2017 will be shown in your data set as: July 12, 2017; 07/12/2017; December 7, 2017 & 12/07/2017. The Date format will require data cleansing to avoid incorrect outcome.
Spelling mistakes is another common data entry error that you should look for when cleaning your data. It is really hard to control and define. The spell check is one thing that can be implemented to reduce the impact. This is one of the most difficult data mistake to catch. There are ways to fix it badly spelled street names using Zip code, last names using email (if last name is available) and correcting company names from websites. However, sometimes the only way to uncover this mistake is with data appending or data validation services.
Varied data range is one of the measured metrics that can present a challenge. The examples of mixed ranges might be annual revenue range, salary or age ranges. To maintain reliable and clean data it is vital to separate the high and low values in these ranges. This is typically due to historical data changes that were not cleansed when the ranges were updated.
Numerical values can be introduced differently in your data. For example, in an annual revenue field the unit can be shown as 1.6M instead of 1,600,000 for better audience perception. At the same time smaller amounts like thousands are often written in full like 500,000. The automatic report run on such a data set may read the larger number as 1.6, creating wrong results in the end. A simple solution is to have it in a set field to a specific format and of course cleaning the historical data.
Duplicates is another common error you should be aware of in your data. The duplicate definition means that same record is be entered multiple times. Very often it takes place when new data sets are appended or there is a data migration. Human data entry is also a factor that requires the deduplication. Deduplication process includes duplicate definition and duplicate removal. It is an essential part of data cleansing job.
Abbreviations is another common human time saver that can jeopardize clean data. Capital and small letters, spaces used in abbreviating terms create inconsistency that could compromise the insights and decisions. It requires standardization as a data cleansing element and should be a part of your cleaning list!
These are just some of errors that help contribute to a messy database. However, do not despair, StrategicDB was designed to help solve bad data problems. We offer data cleansing services for any issues in your CRM or database. Contact Us Today to find out more.