What is Data Cleaning? A Simple Guide (2021)

Introduction

Data cleaning also denoted as data scrubbing, and data cleaning is amongst the most crucial features needed to build an entity containing the art of quality decision-making. There is no doubt an analysis can only be good if the data used for that analysis is of superior quality. Data cleaning means the process of developing data for stipulation by eliminating or reshape data that is incomplete, incorrect, irrelevant, improper or duplicated.

1. What is Data Cleaning?
2. What is the difference between data cleaning and data transformation?
3. How to clean data?
4. Components of quality data
5. Benefits of data cleaning
6. Data cleaning tools and software for efficiency

1. What is Data Cleaning?

Data cleaning is the process of changing or eliminating incorrect, duplicate, corrupted or incomplete data inside a database. Algorithms and outcomes are unreliable if data is inaccurate, even though it seems to be correct. The data cleaning process isn’t merely concerned about erasing data to increase space for new data, but rather find a method of maximizing a data set’s authenticity without having to erase information.

Data cleaning is more than just eliminating data but also includes rectifying syntax and spelling errors, amending mistakes such as missing codes, empty fields, identifying duplicate data points and standardizing data sets. Data cleaning plays a crucial part in developing reliable answers and in the analytical process and is observed to be a basic feature of the data science basics. The motive of data cleaning services is to construct uniform and standardized data sets that enable data analytical tools and business intelligence easy access and perceive accurate data for each problem.

2. What is the difference between data cleaning and data transformation?

Data warehouses assist in analysing data, creating reports, visualising data and making valuable business resolutions. Data transformation and data cleaning are two methods which are utilised in data warehousing. Data cleansing means to remove incoherent information from the database to boost data uniformity, whereas data transformation is the conversion of data from one structure to another to make processing easier.

3. How to clean data?

A data cleaning tool will alter most aspects of an entity’s general data cleansing program, but this data cleaning tool is just a part of an ongoing remedy for data cleaning. An outline of the data cleaning steps are as below: 

1. Identify critical data fields: The primary step is to identify which types of data fields are crucial for the intended project.
2. Data collection: The data contained in the short-listed data fields is collected, classified and organised.
3. Discard duplicate values: Duplicate figures are recognised, eliminated, and inaccuracies are resolved.
4. Resolve Empty Values: Data cleansing tools search and fill up those missing values to complete the data set and evade gaps in information.
5. Standardised Cleaning Process: The data cleaning process must be standardized as per repeated testings and methods which proved to produce quality data, which later on helps in easy replication and consistency. The procedure and also the frequency of data cleaning must be standardized, considering the most often used data, it’s a requirement and the person responsible for the maintaining process.
6. Review, Adapt, Repeat: Specific time must be set aside every week or month carefully analysing the faults, methods working well, room for improvement, bugs and glitches that are occurring. 

4. Components of quality data

Ascertaining the standard of information requires scrutiny of its characteristics, thereafter measuring such characteristics in order of its importance and their application in the organization. The five characteristics of quality data must possess are:

1. Validity: The extent of conformity to defined business constraints and rules that the data provides.
2. Accuracy: The data must be capable of portraying the true and best values.
3. Completeness: The extent to which all the required data is familiar.
4. Consistency: The consistency in data within the same database and across different data sets.
5. Uniformity: The degree to which the data is conformed to the same units of measurement.

5. Benefits of data cleaning

Procuring clean and quality data will eventually and for sure will increase overall productivity and enables high-quality information for quick and right decision-making. 

• When multiple sources of data are at play, the errors are removed for smooth functioning.
• Little to no errors make for happy and satisfied clients and less stress on employees.
• Ability to map the different functions and what your data is intended to do.
• Keeping track of errors and a higher standard of reporting and pinpointing the origin of errors makes it easier to debug incorrect data for future application.
• Data cleaning tools makes for more efficient and effective business operations and allows for quick and easy decision making.
• Revenue booster: Smooth functioning of business operations means more flexibility and efficiency, which leads to better performance and increased growth in the organisation, eventually leading to rising in revenues.
• Cost-effective: Working with the right database for marketing will help save costs spent on ineffective marketing practices.
• Increases productivity:  Employees spend lesser time contacting expired contacts or customers with useless information.
• Reputation: Boost in trust and reputation is bound to happen, especially for companies that involve sharing data with the public. 

6. Data cleaning tools and software for efficiency

Software like Tableau Prep is a data cleaning tool that can help in providing quality data by offering visual and direct methods to clean and combine the data. The two products are Tableau Prep Builder for constructing data flows and Tableau Prep Conductor for monitoring, scheduling, and managing flow across an institution. A database administrator can save lots of time by helping analysts begin their analyses faster and have confidence in the data by using a data scrubbing tool.

Conclusion

The startling rise in digitisation has lead to data being one of the most valuable possessions of modern mankind. The easy accessibility of data online through search engines, social media, websites, television, etc. is one of the fascinating features of data. However, the downfall with that is that the data is full of inaccuracies or irrelevancies. Therefore, we need to take our time to clean the easily accessible huge amounts of data. Data cleaning is inarguably the most important step towards acquiring extraordinary results from the data analysis process. 

Data cleansing and migration are very much needed in today’s busy life which is encircling the data possessed by an individual. So to conclude the answer to the question “What is data cleaning?” is rectifying all errors and creating quality data for superior analysis and decision-making.

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