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What is Data Cleaning - Data Cleansing Techniques and Process

Data Cleaning
Published on Jul 19, 2024

In any organization or project that works with a dataset, some data wrangling has to be done to diagnose and fix errors as well as inaccuracies or any part of the data that is absent to make the dataset correct, complete, and trustworthy. During data preparation processes, it is very important to ensure that the data is ready for analysis, model development, or reporting purposes and is untainted with mistakes or mismatches. The aim is to produce a dataset that is as clean, structured, and consistent as possible to minimize the discrepancies from insights derived from the dataset. 

Data Cleaning

In this information era, organizations are becoming more data-driven and, therefore, prefer using data without clean for any reason that includes decision-making correction is essential in assuring the availability of quality data, which has to be used in coming up with the required information, forecasting, and decision making. For instance, businesses are able to make changes through cleaned data, make predictions, utilize their processes effectively, and enhance customer satisfaction. On the other hand, where the data is not clean or inconsistent, any data-driven cities, such as mining activities, analytics, and business intelligence, are bound to fail in countering many errors based on unfounded conclusions. There are grave ramifications, such as wrong choices, wasteful procedures, and even losing money. 

What is Data Cleaning

Data cleaning, also called data cleansing or scrubbing, is the process of rectifying a good number of data quality concerns that are likely to occur from numerous sources. These involve duplicates, gaps, incorrect formats, outliers, and erroneous data. Duplicates will likely bias the analysis by over-representing some data points, while gaps in formatted or absent values frustrate interrelations. Misformatting and wrong entry of figures create wrong interpretations and dependable models. 
 

Data Cleaning Meaning

As the number of companies and industries that rely on and adopt data-driven decision-making increases, companies in finance, healthcare, retail, and manufacturing, among many others, have progressively adopted data cleaning as part of data management, data engineering, and data strategy. Cleanup is a precondition for more advanced data activities integrating collected data from other simple sources. Critical data engineers also construct data platforms and systems to transport, interpret, and present that information. Lastly, a well-organized data cleaning procedure ensures that organizations follow proper forms of data parameters of data strategies. This contributes greatly to ensuring that data assets within the organization are utilized effectively and, most importantly, are beneficial. 

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Data Cleaning Definition 

Data cleaning can be defined as the steps to find and rectify any mistakes in the datasets. This practice maintains data integrity and consistency, which allows for data or model building and analysis. Furthermore, data cleansing facilitates better execution of data mining processes by reducing noise and making all the essential information precisely arranged and available. 

What is Your Process for Cleaning Data? 

Cleaning up the data is one of the preparatory phases before a certain set of data or information is used for analysis, machine learning, or rating purposes. Though the specific approach can vary based on the type of data, how it was obtained, and the purpose of use, a common data cleaning process involves a series of processes that follow a specific order. Every additional step carries the custom responsibility of easily enhancing the quality of the underlying dataset in terms of being correct, complete, and uniform.

Data Cleansing Process

Here is an elaborative description of the main processes carried out during the cleaning of datasets: 

  • Remove Duplicates 

Duplicate records are often a result of several factors, including the use of more than one source, human error, and system faults, resulting in unnecessary information within the data. Also, in the presence of duplicates, analysis is biased towards some data points. For example, suppose a customer is entered into a sales database twice whenever there is a transaction from him. In that case, the total number of transactions made by the company may appear incomprehensibly large and lead to erroneous estimates of sales volume.  

  • Correct Errors  

Data entry errors can be found in most raw datasets, especially when data entry is done manually, and the data is sourced from various locations. These errors can be spelling errors or incorrect formats of certain data entries. For instance, a clerk can mix the age entered for a customer, entering ‘205’ instead of ‘25.’ A clerk can also record the currency as dollars when it should have been in Euros. Rectifying such errors is crucial to ensuring that the dataset is as accurate as possible.  

  • Fill in Missing Values 

One more common problem with the data is that it might contain missing data. Missing data may happen for many reasons, such as unfulfilled questionnaires, mistakes during data entry, or a lack of focus on data gathering. Leaving these gaps unfulfilled may result in faulty analysis, as some pieces of data may be given more credit than they deserve. Such missing data can also be dealt with in several ways.  

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  • Investigate the Correctness of the Dataset 

Cleaning also comprises one critical stage, which is data accuracy. After consolidating all the changes, the next step is to confirm that the resulting version of the data in question does not deviate from the primary source. This may include original document reviews, cross-comparisons, and external evidence.  

  • Standardize Formats 

At times, the data acquired from various sources may have quite different structures. For instance, one database metric may record the date in MM/DD/YYYY format, whereas another displays the DD/MM/YYYY format. The standardizing process is the achieving completeness whereby the data points collected fit in the same structure, allowing easy manipulation and comparison of the information.  

  • Elimination of Unnecessary Information 

All of the information procured need not always be beneficial. Maintaining irrelevant data can create disorder inside the data set, which makes it impossible to extract the relevant findings easily. For instance, in a given dataset, there is a customer profile, but the analysis conducted is based on patterns of sales, such as the customer’s picture, which might be overlooked for strategy purposes.  

What is the Use of Data Cleaning? 

Due to its essential role in enhancing target operations, the application of data cleaning cuts across various sectors and processes. Here are particular uses. 

  • Better Decision-making 

Accessible data is an asset for business organizations since it leads to better decision-making analytics. In case the available data is not clean, errors and biases will be present in analytics and models, which can lead to expensive errors. 

  • Enhance Data Mining 

With correct data in place, data mining approaches will be adequately utilized to find more valuable patterns and trends. Data errors or invalid assumptions can lead to the wrong hypothesis or irrelevant results. 

  • Enhances Operational Effectiveness 

Cleaning decreases the amount of time spent correcting errors and mistakes, which leaves companies with more time for critical activities like data evaluation, categorization, and plan execution. 

  • Improved Customer Experience 

Providing accurate and consistent data enhances customer experience and satisfies needs. 

  • Allows Forecasting Models to be Validated 

The availability of all sorts of field data in a certain format is essential for predictive models, and a valid historical base is needed for forecasting. 

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What are the Steps for Data Cleaning

Data cleaning is a critical process in data analysis to ensure data quality and reliability.

Data Cleaning Steps

The steps for data cleaning help to ensure the accuracy of the dataset as it removes errors. These steps include: 

  • Remove duplicates: Repeated records, say duplicate customer information, can interfere with the data set. Identifying the duplicates and deleting them is the first very important step. 
  • Handle missing data: Some data points are simply missing, and this may hamper the interpretation of the outcomes. This treatment may involve deleting those records where the information is missing or applying other data entry techniques such as averaging. 
  • Normalize data: Data collected from various sources may have been collected in various patterns. This kind of data normalization helps the processing of the data. 
  • Remove irrelevant data: Some data will contain errors due to either typing in the information or other problems and can lead to inaccurate conclusion definitions. Such errors should be detected and corrected according to the cleaning procedure. 
  • Validate the dataset: The process of comparing the cleaned data and the credible data source can help to determine the accuracy of the data in terms of its reliability. 
  • Remove irrelevant data: Not every possession of data is worthwhile, and others are excessive and must be eliminated in order to enhance the focused material. 
  • Standardized data: This part not only includes formatting but involves more of an open consideration of data in light of regards or rules that apply to it. 

Data Cleansing Techniques

It is noteworthy that there are numerous data cleaning techniques depending on the kind of data and expected results: 

  • Parsing: Parsing is breaking up a dataset into subparts to identify errors or even to correct them easily. 
  • Validation: It is the stage of checking the data for compliance with one or a number of rules within the business. This could involve checking whether the product ID is within the allowed range, whether some other code is in some range, or if an email address is in some recognized structure. 
  • Imputation: This technique is used to interpolate or explain the reason for the missing data in a given dataset. In this case, var, various imputation methods are used: mean, median, or m, ode. 
  • Normalization: Reerefersbringing the valuative values to a certain level without changing any of the dispersion of the value ranges. 
  • Deduplication: This technique removes all the duplicate records in the dataset ensuring that the same data point is not generated.
  • Standardization: This method makes sure that similar data is treated in a manner that is uniform for all the records, for example, all the dates recorded in a certain standard. 

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Conclusion: Data Cleaning 

Data cleansing is one of the most critical components in the effective implementation of any data strategy. It ensures the integrity, validity, consistency, and reliability of information in instances such as data mining, predictive analytics, and reporting. If all organizations implemented proper cleanliness policies regarding their data, they would be more effective in deriving quality information from the data in their possession, hence making sounder decisions in managing the businesses and operating them more effectively. 

When it comes to data mining, dirty data can be the biggest hurdle. In all scenarios, be it working with consumer information, working with financial data, or dealing with machine-driven data, the quality of your data determines the quality of your data mining techniques and insights.  

Providers of data integration consulting, data engineering services, data strategy, and data processing services are thoroughly dependent on clean data to be able to offer their clients recommendations that can be implemented to improve business performance. Data visualization as a tool is also enhanced by clean, standardized data that makes the communication of the results simpler. 

A leading enterprise in Data Solutions, SG Analytics focuses on integrating a data-driven decision framework and offers in-depth domain knowledge of the underlying data with expertise in technology, data analytics, and automation. Contact us today to make critical data-driven decisions, prompting accelerated business expansion and breakthrough performance.         

About SG Analytics           

SG Analytics (SGA) is an industry-leading global data solutions company providing data-centric research, contextual, and marketing analytics services to its clients, including Fortune 500 companies, across BFSI, Technology, Media & Entertainment, and Healthcare sectors. Established in 2007, SG Analytics is a Great Place to Work® (GPTW) certified company with a team of over 1200 employees and a presence across the U.S.A., the UK, Switzerland, Poland, and India.       

Apart from being recognized by reputed firms such as Gartner, Everest Group, and ISG, SGA has been featured in the elite Deloitte Technology Fast 50 India 2023 and APAC 2024 High Growth Companies by the Financial Times & Statista. 


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