Datasets typically contain vast amounts of data that are stored in difficult-to-use formats. As a result, data scientists must first ensure that the data is properly prepared and follows the set of standards. Furthermore, merging data from many sources can be difficult, and data scientists must ensure that the final information combination makes sense. Corrupted data will lead to inconsistent and inaccurate results for any machine learning model. Let’s understand the concern with an example. 

Consider yourself to be the General Manager of a company. The company collects consumers’ data about products they purchased from the company across regions. Now, your intention is to find out which products people are more interested in and accordingly plan your production. Now, what if the data is incomplete, incorrect or some values are missing - the results will misguide, thereby inviting trouble for you. 

Data cleaning and its necessity 

Data cleaning is nothing but ensuring that the dataset is devoid of any erroneous or incorrect data. Being the initial stage of any machine learning project, data cleaning helps to identify inaccurate, incorrect, missing values of data, modify and fix the problem - making data ready for analysis.

Indeed, one of the major challenges with data science is putting their models into production, which is generally delayed or interrupted in the process. One of the main reasons for this is the enormous amount of time required loading and cleaning the data deprives data scientists of the crucial time, which has an influence on their total output, according to research titled The State of Data Science 2020. The results are shown in the image below. 



Moreover, the inconsistencies and errors in the training data might impede algorithms from finding patterns, hence data cleaning to ensure the integrity of the training data is a critical step for sustaining model performance. The dirty data can lead to wastage of resources, investment and productivity as a whole.  

But how to clean 

It would be foolish to expect positive and accurate outcomes with dirty data, hence clean data will ensure better models. Some of the most common steps and methods of data cleansing include:

• First deal with the missing data 
• Standardising the process 
• Validating the accuracy of data 
• Removing duplicate data 
• Handling structural errors 
• Removing unwanted observations 

Let’s understand some of the crucial steps as to what is required, in detail.

• Art of dealing with missing data: It's a big mistake to ignore missing values in a data collection because most algorithms won't accept them. Some businesses solve this challenge by imputing missing values from other data or discarding observations with missing values entirely. However, these strategies lead to misinformation. The missing data should either be labelled as “missing” or filled with 0 to allow algorithms to work and come out with an output. 
• Resolving structural errors: These are errors that occur during data transfer, measurement and other issues that occur as a result of improper data management. The most prevalent issues include inconsistent punctuation, mislabeled classes, and even typos. Such errors effectively demonstrate the need for data cleaning. 
• Unwanted observations: These are some of the common problems in a dataset for companies dealing with data science. These observations could be duplicates or ones that aren't relevant to the problem they're seeking to address. Checking for unnecessary observations is a wonderful way to speed up the engineering feature development process - the development team will have an easier time creating models. 

Talking about benefits, clean data is the best way to support a decision-making process. Analytics and business intelligence are aided by accurate and up-to-date data, which provides firms with tools for better decision-making and execution. Organisations that maintain high-quality business crucial data gain a major competitive advantage in their markets by being able to quickly modify their operations to changing circumstances.