Data cleaning is an essential step in Data Science and Machine Learning. It consists in solving problems in data sets, to be able to exploit them later on. Definitions, techniques, use cases, training…
Data is essential in Data Science, Artificial Intelligence, and Machine Learning. They are the fuel of these technologies.
Therefore, it is very important to ensure data quality. Today, it is very easy to find good quality, clean and structured data on dedicated marketplaces. On the other hand, for a company to clean its internal data, a company must resort to data cleaning.
What is Data Cleaning?
Data Cleaning encompasses several processes to improve data quality. There are many tools and practices to eliminate problems in a dataset.
These processes are used to correct or remove inaccurate records in a database or dataset. Generally speaking, this means identifying and replacing incomplete, inaccurate, corrupt, or irrelevant data or records.
After a properly performed data cleaning, all data sets should be consistent and error-free. This is essential for the use and exploitation of the data.
Without cleaning, the results of the analyses are likely to be skewed. Similarly, a Machine Learning or AI model trained on bad data can be biased or deliver poor performance.
Data Cleaning is different from Data Transformation. Cleaning is about converting data from one format to another, while Transformation (also called Wrangling) is about converting the raw data into a format suitable for analysis.
What is the purpose of data cleaning?
Data is now an essential resource for companies in all sectors. In the age of Big Data, it is used to support critical decision-making.
According to a study conducted by IBM, poor data quality now costs $3.1 trillion per year in the United States. And that cost is growing exponentially.
Prevention through data cleansing is relatively affordable, but fixing existing problems can cost ten times as much. Even worse, fixing a problem in the data after it has caused an outage is 100 times more expensive.
A wide variety of problems can arise from low-quality data. For example, a marketing campaign may be poorly targeted and therefore fail.
In the medical branch, poor data can lead to inappropriate treatments and even to the failure of drug development. A study by Accenture reveals that a lack of clean data is the biggest barrier to AI adoption in this field.
In logistics, data can cause problems with inventory, delivery planning, and thus affect customer satisfaction. In the manufacturing field, factories configuring robots with bad data are exposed to serious problems.
Finally, data cleaning is required to comply with privacy regulations imposed by laws. Regardless of the sector, this practice can therefore avoid major problems.
The advantages of data cleaning
Data cleaning offers many benefits. One of the main benefits is to enable better data-driven decision-making.
Higher quality positively impacts all activities involving data. Data is becoming increasingly important in all sectors.
To take full advantage of this practice, data cleaning must be seen as an enterprise-wide effort. It not only streamlines business operations but also increases productivity as teams no longer have to waste time on incorrect data.
Sales can increase if marketing teams have access to the best data. The accumulation of these various internal and external benefits leads to increased profitability.
The different types of data problems
Companies collect a wide variety of data, from multiple sources. This information can be collected internally or from customers, or even captured from the web and social networks.
However, during this process, different problems can arise. First of all, a dataset can contain duplicate data, i.e. several identical records.
Data can also conflict. A dataset may contain several similar records with different attributes.
On the contrary, sometimes data attributes are missing. The data may also not be compliant with regulations.
These problems can be caused by different sources. It may be a synchronization issue, where data is not properly shared between two systems.
Another cause can be a software bug in data processing applications. Information may be “written” with errors, while correct data may be overwritten by accident.
Finally, the cause may simply be human. Consumers may deliberately provide incomplete or incorrect data to protect their privacy.
What are the characteristics of high-quality data?
To be considered high quality, data must meet several criteria. It must be “valid”, which means that it corresponds to the rules and constraints set by the company. This may include constraints on data types, values, or the organization of data in databases.
Quality data must also be accurate, complete, consistent, uniform, and traceable. These are the characteristics that impact data quality and that can be corrected with data cleaning.
The steps of data cleaning
To be effective, data cleaning must be considered as a step-by-step process. To begin, a data quality plan must be established.
This plan consists of identifying the main source of errors and problems, and determining how to remedy them. Corrective actions should be distributed to the appropriate managers.
In addition, metrics should be chosen to measure data quality in a clear and concise manner. This will subsequently help prioritize data-cleaning initiatives.
Finally, a set of actions and steps to be taken must be identified to start the process. These actions will be updated over time as the data quality changes and the business evolves.
The second step is to correct the data at the source before it enters the system in the wrong form. This practice saves time and energy and allows problems to be corrected before it is too late.
After that, it is important to measure the accuracy of the data in real-time. There are various tools and techniques available for this purpose.
If you, unfortunately, cannot remove duplicates at the source, it is important to detect and actively remove them afterward. You should also standardize, normalize, merge, aggregate and filter the data.
Finally, the last step is to complete the missing information. After completing this process, the data is ready to be exported to a data catalog and analyzed.
How to get trained in Data Cleaning?
Data Cleaning is essential for Data Science and Artificial Intelligence. It is therefore imperative to master the various tools and techniques that exist to work in these fields.
To acquire these skills, you can opt for DataScientest training. Our different programs Data Engineer, Data Analyst, and Data Scientist allow you to learn how to process data and especially how to clean it.
At the end of these professionalizing courses, you will be ready to work in Data Science. Among former students, 93% found a job immediately. You will also receive a degree certified by the Sorbonne University.
All our courses are offered as BootCamp or Continuing Education. The Blended Learning approach, innovative in France, reconciles distance and face-to-face learning to offer the best of both worlds. Don’t wait any longer and discover our Data Science training courses!