No matter what kind of data analytics you use, the quality of your analysis and any other subsequent steps depend on the initial data.
Before you start your analysis, the majority of raw data—whether text, images, video, or even data stored in spreadsheets—needs to be correctly cleaned and structured because it is frequently insufficient, improperly formatted, or downright dirty.
You can use a variety of data cleansing, “data cleaning,” or “data scrubbing” methods to make sure your data is correctly set up for analysis. As a business owner, you have other important activities that require your time and attention rather than focusing on data entry requirements. Data entry outsourcing to a reputable data cleansing company is the best solution. Read on to learn about the different stages involved in data cleansing.
1. Simply remove irrelevant data
Determine the analyses you’ll perform first and your downstream requirements. What are the issues you hope to resolve?
Look carefully at your data to determine what is important and what you might not need. Remove information or observations that are not pertinent to your needs later on.
2. Eliminate duplicate data
You’ll frequently get data duplicates if you’re gathering data from multiple sources or departments, using scraped data for analysis, or receiving multiple surveys or client responses.
Duplicate records increase storage needs and slow down analysis. More importantly, if you train a machine learning model on a dataset that contains duplicate results, the model will probably give the duplicates more weight, depending on how frequently they have been duplicated. For well-balanced results, they must be eliminated.
3. Correct structural issues
Misspellings, inconsistent naming conventions, incorrect capitalization, misuse of certain words, etc., are examples of structural errors. These can skew analyses because, even though they may be obvious to humans, the majority of machine learning programs wouldn’t catch the errors.
4. Address any missing data
To find empty text boxes, missing cells, unanswered survey questions, etc., scan your data or put it through a cleansing program. This might be the result of inaccurate or incomplete data. You must decide whether everything associated with this missing data—a complete column or row, a complete survey, etc.—should be completely discarded, individual cells should be manually entered, or everything should be left as is.
The analysis you want to conduct and the way you intend to preprocess your data will determine the best course of action to handle missing data. In some cases, you can even restructure your data to ensure that your analysis is unaffected by the missing values.
6. Data validation
The final step in data cleansing is data validation, which means verifying your data’s accuracy, consistency, and format to ensure that it is suitable for use in subsequent steps.
Ensure that your data is consistently structured and clean enough for your needs. Make sure all relevant data points are cross-checked to ensure there are no errors or omissions.
You can use machine learning and artificial intelligence (AI) tools to check that your data is accurate and suitable for use. And after you’ve followed the right procedures for data cleansing, you can use tools and techniques for data wrangling to help automate the procedure. A data cleansing company invests significantly in automation solutions such as RPA for data wrangling or data munging.
Don’t overlook data cleansing if you have the responsibility of managing data. It’s essential to stay on top of accurate and consistent inputs on a daily basis. Making a daily protocol should be simpler as a result of the above-described steps. Once your data cleansing process is finished, you can confidently use the data moving forward for insightful operational analysis because it is now accurate and dependable. Data entry outsourcing can prove more effective than pushing in-house teams to do this tedious job. So, if you are looking for prospect data entry outsourcing, you will have a good bunch of options—finding the right data cleansing company is important.