Data Cleaning is a very important step in Data Science and Machine Learning. Its challenges? Solving the various problems in the data sets in order to exploit them, providing error-free and complete data. What errors can be encountered? How can the cleaning of this data be carried out? All the tips and information in this complete article!
To achieve their objectives, more and more data is collected and used by companies, which also increases the risk of errors. The solution is to clean up the data in order to optimise the data management processes. Data cleaning consists of identifying and correcting data that is inaccurate, altered or irrelevant. It is an essential step in data processing to improve the consistency, reliability and value of the information to be used.
By reducing errors, cleansing enhances the integrity and relevance of the data, allowing for accurate and more informed decisions. What is an 'unfit' dataset? What are the benefits of data cleansing? What are the basic practices of data cleansing?
When is a dataset considered "unsuitable"?
The scientific data set is a multitude of data that are organised to form a coherent whole. They must be communicable, interpretable and suitable for computerised processing. However, the data stored in a database can be described as unfit for use, as they contain errors such as typing mistakes, inaccuracies, missing information, etc. These are identified during the data processing process. These are identified during the cleaning process and corrected automatically with a computer programme or modified by an expert in the field.
What is the role of the Data Scientist?
The Data Scientist is the professional who collects, processes, analyses and makes sense of massive data, also known as "Big Data". He is a specialist in statistics, IT and marketing whose mission is to bring relevant information to the surface. The idea is to respond to the company's problems, to support strategic decision-making and to optimise the customer experience.
To become a Data Analyst, the most traditional training courses are those of engineering schools and university courses such as Masters in statistics, computer science or mathematics. With the ever-increasing demand for specialists capable of studying and transforming raw data into actionable information, more and more people are turning to bootcamp-type training courses, which have the advantage of offering much more practical learning with experts in the field. This type of training is to be preferred, especially in the context of a retraining to work in Big Data. You will be able to become a Data Scientist in a short period of time and master Data Cleaning as well as all the other related concepts.
The different types of errors encountered
An improper data set may refer to the presence of the following data:
- unsecured data
- obsolete data
- incorrect data
- duplicate data
- inaccurate data
- data that do not follow company rules
- non-integrated data
Companies are well advised to follow data security and privacy laws or risk facing increasing fines for non-compliance. Insecure data is thusone of the most dangerous types of improper data. A record is incomplete when it does not contain all the elements you need to process the information.
Incorrect data is data that is stored in an inappropriate location, for example numerical values inserted into a text field. Inaccurate data is when the information is not correct, such as a false email address. The most common duplicate data are contacts, accounts and leads. They reduce the effectiveness of CRM and marketing automation systems.
Using information and making decisions based on incorrect data can have disastrous consequences for a business. This can result in poor targeting or segmentation, untimely or non-existent emails, or a lack of competitive intelligence depending on the type of error recorded.
Why is data cleansing important?
Making a good decision depends largely on the quality of the data being reviewed. Considering the large amounts of data that companies use from a multitude of sources, the use of an effective data cleansing tool is essential to ensure the accuracy of the information and to keep your company competitive in the marketplace. Here are the key benefits of data cleansing.
Improved decision making capacity
As mentioned earlier, data cleansingimproves the quality of the information processed, which ensures good analysis and more interesting business intelligence. Overall, you will make better decisions and execute better to achieve your goals. This is one of the main reasons to implement a sophisticated data cleansing process.
Accelerate customer acquisition
With high quality data, companies can dramatically increase their efficiency in customer acquisition. With a proven data cleansing strategy, you have more accurate information that you can use not only to acquire new customers, but also to better target old ones. This is directly in line with the operating principles of CRM platforms.
Cleaning up data by removing duplicate and inaccurate items can save companies valuable resources. This can affect processing time and storage space. The presence of inaccurate data can be a significant drain on a company's resources, especially if it is a data-driven operation. The clean-up process is also time consuming and costly for organisations, especially without the tools and techniques to do it effectively.
Clean data is a great help to workers who now make the most of their working hours. Using poor quality data means spending a lot of time cleaning and reanalysing because of the presence of errors. Poor quality data can lead employees to make incorrect decisions. This results in significant inefficiencies or in the worst case catastrophic error situations. On the other hand, making competent and timely decisions will boost the morale of the entire team. Your employees will be more confident in their working methods, which obviously leads to higher productivity.
Increase your income
Companies that take the necessary steps to ensure they have good quality data by adopting an effective data cleansing strategy can maximise their return on investment. They will perform better in their business.
When should data cleansing be done?
A company usually has a multitude of applications, databases and many other sources of information that need to be exploited. This is where the data pipeline comes in. It is a series of actions that begin with the ingestion of all raw data, regardless of its source, with the aim of quickly converting it into data that is ready to be exploited. The data pipeline thus comprises a number of steps:
- collection or extraction of raw data
- data governance
- data transformation
Collection is the gathering of data sets from various sources and in various formats. At this stage, it is still unstructured and unclassified information. Data governance refers to the discipline that is applied by companies to organise data at their scale. The security of the data and its quality are also controlled for massive consumption. During the data transformation stage, data cleaning and conversion into the appropriate reporting formats are carried out.
Python programming is one of the most powerful and widely used tools for data scientists. During the data cleansing process, this language is very effective in exploring and manipulating complex data in order to prepare it for analysis.
How to carry out data cleansing?
While several tools help automate most aspects of a data cleaning programme, they are only part of the solution. In practice, to get the data clean, the data scientist must go through a few steps:
- identification of essential data
- collection of data
- elimination of duplicates
- resolution of empty values
- standardisation of the cleaning process
- examination and adaptation
With the rise of big data, companies have access to more data, but its level of relevance remains highly variable. The first step in data cleaning is to identify which types of data are essential for a specific project. The relevant data fields are then collected, sorted and structured.
The clean-up process can then properly start with the resolution of inconsistencies and errors. This usually starts with the removal of values that are present in multiple copies. Missing values are also searched for with the possibility of adding fields so as to create a complete dataset with no gaps in information. To increase efficiency, the data cleansing process should be standardised to facilitate replication. Companies should define a cleaning frequency and take the time to re-evaluate the process as necessary to implement any improvements.
If you want to acquire the skills and master the entire Data pipeline, take a look at the Data courses that Jedha Bootcamp offers.