What is data cleaning and why is it important?
The primary purpose of data cleanup is to organize, update and clarify existing records. Over time, big data can clutter, duplicate, and grow challenging to manage. Data such as customer records, which are critical to providing services, need clarity and ease of access. Dirty data adds time, expense, and hassle to this necessity.
Data cleaning is the process of analyzing, identifying, and correcting dirty data from your data set.
Data cleaning is a type of data management task that minimizes business risks and maximizes business growth. It deals with missing data and validates data accuracy in your database. Also, it involves removing duplicate data and structural errors. With error-free data, you can make use of customer data correctly such as sending accurate invoices to the right customer.
Proper data cleaning can make or break your project. Professional data scientists usually spend a very large portion of their time on this step.
Better Data > Fancier Algorithms
The steps and techniques for data cleaning will vary from dataset to dataset. As a result, it’s impossible for a single guide to cover everything you might run into. However, this guide provides a reliable starting framework that can be used every time. Let’s get started!
Benefits of Data Cleaning:
- Staying organised
- Avoiding mistakes
- Improving productivity
- Avoiding unnecessary costs
- Improved mapping
How to clean your Data ?
1. Get rid of unwanted observations
The first step to data cleaning is removing unwanted observations from your dataset. Specifically, you’ll want to remove duplicate or irrelevant observations.
2. Fix structural errors
Structural errors usually emerge as a result of poor data housekeeping. They include things like typos and inconsistent capitalization, which often occur during manual data entry.
3. Standardize your data
Correcting typos is important, but you also need to ensure that every cell type follows the same rules. For instance, you should decide whether values should be all lowercase or all uppercase, and keep this consistent throughout your dataset. Standardizing also means ensuring that things like numerical data use the same unit of measurement
4. Remove unwanted outliers
Outliers can cause problems with certain types of models. For example, linear regression models are less robust to outliers than decision tree models. In general, if you have a legitimate reason to remove an outlier, it will help your model’s performance. You should never remove an outlier just because it’s a “big number.” That big number could be very informative for your model.While outliers can affect the results of an analysis, you should always approach removing them with caution. Only remove an outlier if you can prove that it is erroneous.
5. Fix contradictory data errors
Contradictory data errors are another common problem to look out for. Contradictory errors are where you have a full record containing inconsistent or incompatible data.
6. Type conversion and syntax errors
Once you’ve tackled other inconsistencies, the content of your spreadsheet or dataset might look good to go. However, you need to check that everything is in order behind the scenes, too. Type conversion refers to the categories of data that you have in your dataset. A simple example is that numbers are numerical data, whereas currency uses a currency value.
7. Deal with missing data
When data is missing, what do you do? There are three common approaches to this problem. The first is to remove the entries associated with the missing data. The second is to impute (or guess) the missing data, based on other, similar data. In most cases, however, both of these options negatively impact your dataset in other ways. One of the ways to fill the missing data is to flag the data. To do this, ensure that empty fields have the same value, e.g. ‘missing’ or ‘0’ (if it’s a numerical field). Then, when you carry out your analysis, you’ll at least be taking into account that data is missing, which in itself can be informative.
8. Validate your dataset
Once you’ve cleaned your dataset, the final step is to validate it. Validating data means checking that the process of making corrections, deduping, standardizing (and so on) is complete. This often involves using scripts that check whether or not the dataset agrees with validation rules (or ‘check routines’) that you have predefined. You can also carry out validation against existing, ‘gold standard’ datasets.
