Data cleaning: the key to precise analyses 

Data has become a valuable asset for companies in almost every industry. It serves as the basis for strategic decisions, customer analyses, trend forecasts and much more. This is where data cleaning comes into play.

Data cleaning involves identifying and correcting errors, inconsistencies and irregularities in data sets to ensure that they are reliable and accurate.

In this blog article, you will learn everything you need to know about data cleansing, the various steps and tools that automate this work. 

data cleaning definition

Data Cleaning - Definition

Data cleaning, also known as data cleansing, is the process of removing or correcting unwanted or inaccurate information from a data set in order to improve data quality. 

This includes the Identification and rectification of errorssuch as missing values, duplicates, typos and inconsistencies. 

Data cleaning is crucial to ensure reliable and accurate data for analysis, reporting and decision making.

It often includes the following procedures:

  • Removal of outliers
  • Normalization of data
  • Inclusion of missing values
  • Standardization of data formats
  • Consolidation of data records from different sources

This is a iterative processwhich requires care to ensure that the cleansed data meets the desired quality standards.

Data cleaning is an essential step in any data analysis process that serves to cleanse data of inaccuracies, inconsistencies and redundancies. Two important techniques that are used for this are Data Mapping and Data Wrangling. They ensure that the data is interpreted correctly and prepared for analysis and visualization.

You can perform data cleaning manually or automatically, using technologies such as Machine Learning and specialized software tools are becoming increasingly relevant. 

Data cleaning helps to gain reliable insights from data and to optimize the Efficiency of business processes to increase.

data cleaning goals

Objectives of data cleaning

The objectives of data cleaning are diverse and serve to improve the quality of data and increase its usefulness in various areas of application. 

Improve data quality

The basic aim of data cleansing is to increase the quality of data. 

This includes the removal of errors, such as missing values, typos and inconsistencies, to ensure that the data is reliable and accurate.

Increasing data consistency

Data from different sources or points in time can be inconsistent. Data cleaning ensures that data is made consistent by eliminating any inconsistencies.

Elimination of duplicates

Removing duplicates helps to reduce the amount of data and ensure that analyses and reports access non-redundant information.

Normalization and standardization

Data cleaning can normalize data by converting it into a standardized format. This makes it easier to compare and analyze the data.

Standardization of data formats

Different data sources often use different formats. Data Cleaning standardizes these formats to facilitate integration and analysis.

Removal of outliers

Data Cleaning identifies and eliminates outliers that could have a negative impact on analyses and models.

Inclusion of missing values

If there are gaps in the data, data cleansing offers strategies for dealing with these gaps, such as imputing missing values.

Optimization for analyses

Cleaned data is better suited for statistical analysis and modeling as it provides reliable and meaningful results, which ultimately improves the company's competitiveness.

Reduction of data quality problems

Data cleaning helps to reduce or prevent data quality problems. This helps you avoid costly errors or incorrect conclusions.

Increasing the efficiency of business processes

In companies, data cleansing helps to increase the efficiency of business processes by ensuring that data bases are reliable and enable better decisions.

The goals of data cleaning are relevant for various industries and application areas and help to protect data as a valuable asset and optimize its use for better decision-making and analysis.

Steps in the data cleaning process

The data cleaning process consists of several steps aimed at identifying and eliminating data errors and irregularities in order to improve data quality. 

Below you will find a breakdown of the manual data cleaning process. If you are using software, this will take care of the procedure for you.

The basic data cleaning process is as follows:

  1. Data collection and understanding:

    Collect the raw data from different sources and understand the structure, format and context of the data.

  2. Data profiling

    Perform data profiling to get an overview of the data, including the number of records, the number of columns, the distribution of values and possible errors or inconsistencies.

  3. Identification of data errors:

    Search for data errors such as missing values, typos, inconsistent formats, duplicates and outliers.

  4. Adjustment of missing values

    Decide how to deal with missing values by deleting, replacing or imputing them to fill data gaps.

  5. Removal of duplicates

    Identify and remove duplicates to ensure that each row contains unique information.

  6. Correct inconsistencies

    Correct inconsistent data by standardizing formats, correcting spelling errors and bringing values into a consistent form.

  7. Outlier treatment

    Identify and decide how to handle outliers that lie outside the expected value range.

  8. Normalization and standardization:

    Convert data into a standardized format to facilitate comparison and analysis.

  9. Validation and quality control

    Validate the cleansed data to ensure that it meets quality standards and document the cleansing process.

  10. Documentation

    Document all changes and decisions made in the data cleaning process to ensure transparency and traceability.

  11. Automation

    Automate as many steps as possible using software tools or scripts to make the process more efficient and repeatable.

    One such software is the IDP platform Konfuziowhich not only supports you in cleansing the data, but also automates the entire document management process individually for each company.

  12. Repetition and monitoring

    Data cleaning is often an iterative process. It is important to repeat the process if necessary and to monitor data quality regularly.

  13. Data archiving

    Keep a copy of the original raw data and the cleansed data to ensure the integrity of the data and to keep it available for future analysis.

The data cleaning process requires care, accuracy and a structured approach to ensure that the cleaned data is reliable and suitable for analysis and decision making.

green box with eight triangle in the center

Attention - Common mistakes

When cleansing data in companies, you should avoid various common mistakes:

  1. Insufficient documentation: It is important to carefully document the entire data cleaning process. If changes are made to the data, it should be clear which steps were carried out and why. The lack of adequate documentation can impair traceability.
  2. Incomplete data cleansing: A common mistake is overlooking important areas of the data or not cleaning them up sufficiently. It is important to consider all relevant aspects of the data in order to completely eliminate errors and inconsistencies.
  3. Lack of quality control: Data cleansing without quality control can lead to new errors or problems. It is important to check the cleansed data to ensure that it meets the desired quality standards.
  4. Overcleaning: Removing data too aggressively or changing values leads to data loss and renders the data unusable. You should therefore use data cleaning precisely and moderately.
  5. Missing backup of the original data: Companies should always keep copies of the original raw data before performing data cleansing to ensure that they can fall back on the original data in the event of problems or errors. For example, the Snapshot feature.
  6. Lack of data validation: Data should not only be cleansed, but also validated to ensure that it is meaningful and correct. Without validation, incorrect data will go unnoticed.
  7. Lack of integration of specialist knowledge: It is important to include the expertise of people who are familiar with the data in the data cleaning process. They can provide context and help identify inconsistencies or errors. This is also known as Human-In-The-Loop labeled.
  8. Ignoring data protection regulations: Companies should comply with data protection laws and guidelines when purging data. Removing data without complying with legal regulations often has legal consequences.

Avoid these errors and ensure that the data cleaning process delivers the desired results and maintains or improves data quality.

Best practices & further tips

Is the data cleaning process in your company complicated and demanding?

Then the following best practices are worth considering:

  1. Machine learning for data cleaning: Advanced machine learning models support you in identifying and correcting data errors and anomalies. However, this requires extensive expertise and specialized resources.
  2. Entity Resolution: This technique helps to identify and merge data that relates to the same entity but is inconsistent in different data sets. This is useful when integrating data from different sources.
  3. Text analysis and Natural Language Processing (NLP): With unstructured text data, such as customer ratings or comments, NLP helps to recognize and correct patterns and errors.
  4. Regression and imputation: Advanced statistical models such as regression analyses help with the imputation of missing values. These models use existing data to predict missing values.
  5. Data AugmentationData enrichment techniques are used with limited data sets to increase the amount of available data and improve the accuracy of the analysis.
  6. Data quality frameworks: Use specialized data quality frameworks or tools that provide advanced data cleansing and monitoring capabilities.
  7. Involvement of experts: In complex domains, you should work with experts in the relevant field to gain valuable insights and assistance with data cleansing.
  8. User-defined scripts and rules: Create custom scripts and rules that are specifically tailored to the needs of your organization and your data.
  9. Visualization for error detection: Use Data visualization techniquesto make it easier to identify errors and inconsistencies in the data.
  10. Automation and Workflow-orchestration: Implement automated data cleansing workflows that regularly cleanse and monitor data.

These advanced techniques and considerations are useful when companies work with complex and large data sets or have specific requirements.

However, you should note that not all of these techniques are relevant or necessary for every use case, and their implementation often requires additional expertise and resources.

data cleaning konfuzio

Automation with Konfuzio

Data cleaning is an important part of the document management process. 

Software that completely customizes this area for the company with the help of its IDP platform automated, is Konfuzio. This is a versatile tool for the automatic processing of documents. 

The application stands out in particular due to the following advantages:

  • Optimized document management through extensive interfaces
  • Easily configurable and integrable AI software
  • Individual customization and training options of the AI
  • Many integrations for seamless work
  • Partner ecosystem to support the implementation of AI solutions (in the cloud or on-premise)

Quality control

Below you will find the 5 most important tips for ensuring that quality control meets your high standards: 

  1. The most important aspect of quality control in data cleansing is the Clear definition of quality objectives and criteriato ensure that the cleansed data meets the requirements. 
  2. You should also enter the data Regularly check for patterns, trends and deviationsto detect errors at an early stage. 
  3. Benchmarking and comparison with the original data provide important reference points for evaluating data quality. 
  4. The Multiple checks of the data by different people and the use of automated validation tests further improve quality assurance. 
  5. Continuous training and awareness in the team promote an awareness of the importance of data quality and enable continuous improvements.

Data Cleaning - Use Cases

Data cleaning is of crucial importance in various industries and business areas. 

These are five use cases for data cleaning in the corporate context in a wide range of industries:

E-commerce company

E-commerce platforms must regularly cleanse product information, customer ratings and transaction data. 

An online marketplace removes duplicates from product listings to ensure that each product is only listed once, and corrects product attributes such as sizing to ensure consistency.

Public health

Data quality and consistency are crucial in the healthcare sector. 

A hospital validates patient data to ensure that medical records are correctly attributed and removes or corrects incorrect or incomplete patient information.

Financial services

Financial institutions need accurate data for risk assessments and regulatory compliance. 

Ehe bank cleans transaction data to detect and correct erroneous or duplicate transfers to ensure accurate billing and account statements.

Retail

In retail, clean data is crucial to manage inventory and better understand customer needs. 

A retailer removes duplicates in the customer database to create more accurate customer profiles and corrects product data to ensure that product information such as pricing and availability is up to date.

Telecommunications

Telecommunications companies manage huge amounts of data on mobile phone usage, network performance and customer billing. 

A telecommunications provider checks and cleanses billing data to ensure that customers receive correct bills and that incorrect charges or data usage details are corrected.

Conclusion - Data cleaning as an important tool for future data processing

The future prospects for data cleaning are exciting: with the advent of machine learning and artificial intelligence, automated data cleaning processes are becoming increasingly advanced and efficient. 

This enables companies to cleanse data faster and more thoroughly, which increases business efficiency. 

Data protection and compliance will continue to play an important role as ever stricter regulations require the correct data processing.

The increasing importance of big data and the integration of data from different sources means that data cleaning will continue to play a key role in companies' data strategy in the future. 

Awareness of data quality and data cleaning will grow as companies increasingly recognize that high-quality data plays a crucial role for success in a data-driven world. 

Therefore, you should continue to engage in data cleaning to ensure that your data is reliable, accurate and meaningful and gives you a competitive advantage.

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