7 Data Cleansing Techniques Every Marketer Should Know

Data is the backbone of any data analytics of a business which sets the target of the organization in place. Bad or dirty data not only waste the time and money of a business but it can also lead them to arrive at misguided decisions. Therefore, for better data analytics, data cleansing techniques are to be put in place so that the data is ready for analysis. It is, therefore, very important to be familiar with the data cleansing process and all the other tools that are related to this process.

With the surge of big data, data cleansing has become important than ever before. The main problem in data is object identity and that affects the quality of the data. Benefits of Data cleansing tools and techniques allow you to resolve this problem effectively. Though the data cleansing steps and techniques differ from data to data, businesses can use some common steps to start cleaning data.

The first thing that you need to do is visualize the bigger picture before starting your data cleansing project. The first thing to follow while using the data cleansing techniques is to focus on your top metrics.

Let us see some of the best techniques of data cleansing that a marketer should know.

Standardizing the point of entry and checking the importance of it is equally important. You can ensure a good point of entry and reduce the risk of duplication by standardizing the data process.

For generating high-quality data, it is important to validate the accuracy of the data once you have cleaned your existing database. This can be achieved if you decide to research and invest in the data tools that help you clean the data in real-time. Some tools also use artificial intelligence and machine learning to test for better accuracy.

A simple truth about machine learning is that better data beats fancy algorithms at any time of the day. To put it simply, if you bring the garbage in, you will have to take that garbage out again.

Data cleaning is one of the things that no one talks about but everyone does it. The reason isn’t specific tips and tricks that people have to uncover with machine learning. It is one of those things that is difficult to do but needs to be done. Effective data cleaning can either make or break the project.

To break it down, we can say that even if you have a clean and proper dataset, even a simple algorithm can learn impressive insights from the data. Even if different data set needs different technique of cleaning, it can be used as a good starting point for the scrubbing.

The very first step of data cleaning is removing unwanted observations, including duplicate and irrelevant data.

Duplicate records arise mostly during data collection such as scraping data, combining datasets and receiving data from clients. You need to identify and eradicate such duplicate records.

Similarly, irrelevant observations are issues that do not fit at a particular problem. These observations can be the fields that are filled in your data but it isn’t relevant to your business, you can simply delete such records. Therefore, if you check for irrelevant observations before engineering the features, you can eliminate many headaches and dead ends down the road.

Missing data is a deceptive issue in machine learning. To be clear at the very beginning, you cannot ignore the missing values in your data and you must handle them in an effective way for a very simple reason that most of the algorithms don’t accept missing values.

There can be two ways of dealing with these missing data records where you can either delete the missing values from the observations or impute the observations from the data.

The best way of handling missing categorical data is to simply label them as ‘Missing’. However, in case of missing numerical data, you should flag and fill the values. You can flag the data with the indicator value of missed data and fill it with ‘0’.

The next step in data cleaning is to fix structural errors. Structural errors are those which arise during data transfer, data management or any other type of ‘poor housekeeping’. You can, for instance, check the typo errors or inconsistent capitalization. This becomes a concern specifically for categorical features. For errors in categorical features, you need to check for mislabelled classes, that is, separate classes which should actually be the same. All you have to do is replace the typos as well as inconsistent capitalization and make the class distribution cleaner.

In the whole process of data cleansing techniques, it is vital that you keep the communication gap at bay and inform them about the new standardized cleaning process to your team.  Now that you have cleaned your data it is important to maintain it that way. This will help you in developing and strengthening your customer segmentation and send out more targeted information to the prospects and customers, which is why keeping your team in line with the process is important.

When you have the job of managing the data, maintaining its accuracy and consistency throughout are the two underlying tasks that you will have to deal with every day. These steps prove to make the creation of daily protocol very easy. Once the process of data cleaning is done, you can move with your data forward confidently for an operational and deep insight since you can rely on the accuracy of your data now.

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