Dirty Data Vs. Clean Data
Dirty data refers to data that is inaccurate, incomplete, inconsistent or duplicate data in a database. Dirty data severely hurts your bottom line, wastes a lot of marketing dollars and time. But, where does dirty data originate? Dirty data mainly comes from two places, systems, and people. Data becomes dirty in a system when errors and mistakes occur during setup, maintenance, integrations, and duplicated fields. Data becomes dirty in a database when people don’t understand, make mistakes when manually entering data, become lazy, are lacking time, or do not have a good system for entering data. But have no fear, there are many ways to clean your dirty data!
What Is Clean Data?
Clean data is data that is complete, meaning there are no missing data fields for a particular record in the database. Cleaning data increases efficiency, cuts costs, reduces risk and makes the most out of your information. Clean data is unique, timely, valid, accurate, and consistent. When there are no duplications in your data, your data is easily accessible when needed, the right information is in the right place, the data is correct, and the data is consistent when located in multiple locations your data can be classified as clean.
How To Clean Your Dirty Data
Everyone knows that cleaning data is an essential part of any company. Cleaning data provides a more successful marketing campaign and enhances sales efforts. A few ways to clean your data include comparing your data across all sources and databases in which it is stored. This will help to ensure that your data is consistent. Check for completeness. Is your data filtered in the system you are collecting it and then again when entered into excel? If not, you should apply this practice to ensure your data is complete and consistent. You can also utilize features in excel to remove duplicate information to have unique data. To focus on timely data, you can calculate the average response time to optimize your collection process to speed it up. To clean your dirty data in terms of validity and accuracy, one must check for consistency in the respondent’s answer choices. Do you have a respondent who selected all answer choices that include “strongly agree,” “Strongly disagree,” or “neither agree nor disagree”? If so, delete these responses because these respondents are skewing your data by putting the same answer for all answer choices.
Using these tactics to clean your dirty data will be time-consuming but incredibly worth it in the end. Clean data will produce better metrics and drive better results. Be sure to perform these steps regularly to ensure your dirty data stays clean and consistent. If you have any questions or you are interested in cleaning your dirty data, contact us for a consultation!