Different Sources of Data for Data Analysis

Business Analytics - Data Quality

This tutorial describes detailed concepts of Data cleaning, Data cleaning in Business Analytics, and why is Data Cleaning Important.

What is data cleaning?

Data cleaning also known as data cleansing or data scrubbing is a process of fixing errors in a dataset by removing incorrect, corrupted, partial records, incorrectly formatted, duplicate, or incomplete data within a dataset. Overall, data cleaning encompasses editing, correcting, and arranging data in a dataset to ensure its consistency and readiness for analysis.

Example − let’s assume in a dataset one column is a gender which may have text like ‘M’, ‘F’, ‘Male’, ‘Female’, ‘male’ ‘female’, ‘MALE’, ‘FEMALE’.

The main goal of data cleaning is to improve data quality and make it appropriate to find more accurate and reliable insights.

A dataset may consist of different data records gathered from one or different data sources; it may lead to duplicated or mislabelled data records. If the data is inaccurate, the outcomes and algorithms are untrustworthy, even if they appear proper. There is not a specific way to prescribe the exact process in the data cleaning process because the processes differ from dataset to dataset. Refining and optimizing datasets is an important step in ensuring optimal data analysis performance. This includes resolving and correcting entries in the data that are erroneous, inconsistent, incorrectly structured, redundant, or incomplete.

Data Cleaning in Business Analytics

During data analytics, if outcomes are not satisfactory or appropriate; then two important things can go wrong data or models. In the real world, data is not organized. This data cannot be directly used for analysis. Business Analytics requires different data cleaning methods to validate and prepare data for analysis.

Choosing appropriate data is one of the crucial steps in Business Analytics. You cannot expect your Business Analytics to be accurate unless you are certain that the data on which you did the analysis is error-free. Data cleansing is critical in data science for accurate analysis. It is a critical component of the data preparation stages for machine learning and related advanced techniques.

Data cleaning is an essential component of business analytics; it ensures the correctness of a dataset. In Business Analytics (BA), insights and predictions are derived from large and complex datasets; the quality of inputted data has a substantial impact on the validity of analytical outcomes. Data cleaning is the systematic discovery and correction of flaws, inconsistencies, and inaccuracies in a dataset, which includes tasks like handling missing values, removing duplicates, and resolving outliers. This process is critical for improving the integrity of analyses, and accurate data modelling, and supports informed decision-making based on reliable and high-quality data.

Why is Data Cleaning Important?

Inaccuracies, outliers, missing numbers, and inconsistencies in data could affect the validity of analytical results if it does not clean up properly. The importance of data cleaning can be understood by the following −

Enhances Business Decision − Data cleaning leads to more accurate and reliable decisions, reducing the risk of errors in strategic planning and operations.
Improves Business Processes − Data cleaning supports teams to identify the breakdowns in operational workflows.
Operational Efficiency − Quality data minimizes errors in processes which saves time and increases operational efficiency.
Compliance − Organizations may comply with regulations and avoid legal issues.
Competitive Advantage − Organizations with high-quality data can gain insights that lead to better strategies, product offerings, and customer experiences.
Accuracy − Data cleaning ensures error-free data that does not include inaccuracy, misspellings, incorrect numbers, or wrong classifications in data.
Completeness − Data cleaning ensures the degree to which all required data is present. It avoids missing values, incomplete fields, or records, which can lead to gaps in analysis and decision-making.
Consistency − Data cleaning ensures uniformity of data.
Standardization − Data cleaning ensures the simplicity and standardization with which authorized people can access, comprehend, and utilize data. Data that is accessible is kept in a standard format to make it simple to access and understand without unnecessary barriers.
Reliability − Data cleaning ensures data reliability which makes trustiness of data that can be utilised for analysis and its insightful results can be used to frame strategic business decisions.
Validity − Data cleaning ensures data validity to the extent to which data ensures its standards.
Data Integrity − Data cleaning ensures data integrity which shows a relationship with other data in a data source.
Uniqueness − Data cleaning ensures the degree of data devoid of redundant entries. Redundancies are avoided because unique data guarantees that each entry represents a single, distinct entity.

Data cleaning is important for organizations that rely on data quality and data-driven decision-making. Data cleaning is the process of correcting or deleting erroneous, faulty, not properly formatted, duplicated, or incomplete data from a dataset. This ensures that the findings and analytical results generated from data are consistent and accurate. When data is gathered from multiple sources and grouped in a single dataset; there is a high risk of duplication or mislabelling of data which may lead to inaccurate results or insights. Data cleansing is a feasible solution to the "garbage in, garbage out" problem by assuring data consistency within a single dataset or across multiple datasets.

Overall, data cleansing is an essential component of data preparation, laying the groundwork for datasets to be used in business intelligence (BI) and business analytics. Data cleansing improves data quality by identifying inconsistencies and modifying, updating, or removing data to correct them, resulting in more exact, coherent, and reliable information for organizational decision-making. This method is typically undertaken by data quality experts, engineers, or other data management experts; however, data scientists, data analysts, business analysts, and business users may also engage in data cleansing as per need.

Advantages of Data Cleaning

Some of the key advantages of data cleaning are as follows −

Data preparation − Data cleansing is important for data preparation; it plays a vital role in ensuring data accuracy, reliability, and quality.
Assures accurate results − Cleaned data gives accurate results which are used to frame effective business decisions.
Decision making − Cleaned data gives accurate results which help organisations to frame effective business strategies.
Data validation − Cleaned data validates data and its analytical results.
Effective for data modelling − Cleaned data enables effective data modelling and pattern recognition.
Algorithms utilisation − Algorithms perform optimally on error-free or cleaned data.
Interpretability of findings − Clean datasets improve the interpretability of findings, and facilitate the development of actionable insights.
Improves Efficiency − Cleaned data makes system performance better; the system does not suffer from the inconsistency of data due to this it gives results within the time frame.

Frequently Asked Questions (FAQs)

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

Data cleaning is the process of removing data that does not belong in a dataset. Data transformation is the conversion of data from one format or structure to another. Transformation operations, often known as data wrangling or data munging, involve changing and mapping data from one "raw" data type to another for storage and analysis. This article focuses on the techniques for cleansing that data.

2. Is data cleaning a part of Business Analytics?

In Business Analytics, data cleaning is a part of the data pre-processing which makes sure that the data is clean before it goes for any transformation or data modelling.

3. Does data cleaning ensure data quality?

Yes, data cleaning ensures data quality by removing noisy, incomplete or partial data from data sets and ensures its quality which is used for analysis and produces insightful results.

Business Analytics - Data Cleaning