With Businesses growing rapidly by leveraging intelligent technologies to make informed decisions. A lot of data is available across the web, but the quality of data plays a crucial role in making sure the decisions taken can help you stand out.  

Business Intelligence (BI) and Data Engineering processes rely heavily on accurate, reliable data to generate meaningful insights. Thus, making sure the data is free from errors, inconsistencies, and biases is not optional. 

But how do I ensure my data doesn’t skew my decision?   

Data Cleaning  

Data cleaning, also known as data cleansing or data scrubbing, is the process of identifying and rectifying inconsistencies, and errors within data sets. Data cleaning is a vital task within the data engineering and BI pipeline, ensuring that the data used for analysis and reporting meets quality standards.  

Data cleaning involves various tasks. These are  

  • Removing duplicate records: Eliminating identical entries that can skew analytical results.  
  • Handling missing values: Imputing or removing missing data points to prevent bias in calculations. 
  • Correcting data errors: Rectifying data entry mistakes, formatting discrepancies, or erroneous data.  
  • Harmonizing data formats: Ensuring consistent data formats across the dataset.  

Data Cleaning not only ensures the availability of filtered data and smart insights but also makes sure you don’t have to spend unnecessary spaces and time crawling through redundant data. Thus, to maintain data integrity and speed removing these specific data types can help  

  1. Invalid Data: Invalid data refers to entries that do not conform to the defined data types or range constraints.
    For instance, If a column expects numeric values, but there are non-numeric characters in the data, such entries should be removed to maintain data integrity.  
  2. Inconsistent Units of Measurement: Data may sometimes contain measurements in different units, leading to confusion during analysis.
    For example, If a dataset contains weights in both kilograms and pounds, it is crucial to convert all entries to a consistent unit of measurement before further processing.  
  3. Unnecessary Whitespace and Special Characters: Leading and trailing whitespace or special characters in data fields can impact the accuracy of the analysis. Removing these extraneous characters ensures uniformity and facilitates meaningful insights.  
  4. Typographical Errors: Data entry errors, such as typos or misspellings, can distort analysis results. Thorough data cleaning involves identifying and correcting such errors to prevent them from influencing decision-making. 
  5. Incomplete Data Records: Incomplete data records with missing values or null entries can cause analytical issues. Depending on the significance of the missing information, data engineers may choose to either impute missing values or remove records with excessive missing data.  
  6. Redacted or Sensitive Information: If data contains sensitive or personally identifiable information (PII) that should not be exposed, data cleaning should redact or anonymize such information to protect privacy and comply with data protection regulations.
  7. Deprecated or Outdated Data: In certain cases, historical data that is no longer relevant may hinder analysis. Keeping data up to date and removing obsolete records ensures that decision-making is based on current and relevant information.  
  8. Misinterpreted Data: Data may sometimes contain entries that have been misinterpreted or misrecorded. Data cleaning involves reviewing and verifying such entries to ensure the accuracy of the dataset.  
  9. Duplicate Observations: Duplicate records may arise due to data integration from multiple sources or data collection errors. Identifying and removing duplicate entries helps prevent skewed analysis results. 
  10. Irrelevant Outliers: While some outliers may be genuine and essential for analysis, others may be irrelevant or due to data errors. Data engineers must distinguish between relevant and irrelevant outliers and handle them accordingly. 

Making sure you do it can boost your decision-making process by ensuring no decision is based is twisted due to errors in data. Failing to conduct data cleaning can lead to severe repercussions, affecting accuracy and credibility.  

Consequences of Skipping Data Cleaning  

Data Scrubbing is a crucial part of the business intelligence processes, as we wouldn’t want our systems to give us skewed results based on redundant or flawed data. This can lead to a lot of crucial discrepancies and the consequences of feeding twisted data can lead to extreme financial losses and disowning by the customers.  

The consequences just don’t end with the losses. The issues created because of skipping data scrub may lead to a number of consequences. Here are some of the notable consequences  

Customer Dissatisfaction  

Neglecting data cleaning can lead to inaccurate customer information within CRM systems. This can result in misguided marketing efforts, irrelevant offers, and poor customer service. Customers may feel frustrated or alienated, leading to a decline in customer satisfaction and loyalty.  

Financial Losses  

Inaccurate or incomplete financial data can lead to erroneous financial reports and projections. Decision-makers may make financial decisions based on flawed data, leading to financial losses or missed revenue opportunities.  

Regulatory Compliance Issues  

In industries with strict regulatory requirements, such as healthcare or finance, unclean data can lead to compliance issues. Non-compliance with data protection and privacy regulations can result in legal penalties that might damage the company’s reputation.  

Inefficient Resource Allocation  

Inaccurate data can misrepresent the performance of various departments or projects. Decision-makers may allocate resources ineffectively, leading to suboptimal results and hindering the achievement of organizational goals.  

Poor Data Analysis and Reporting  

Unclean data can undermine the credibility of data analysis and reporting. Decision-makers may lose trust in BI insights, leading to a lack of confidence in data-driven decision-making and reliance on intuition or gut feelings instead.  

Operational Inefficiencies  

Data errors can propagate through various operational systems, leading to inefficiencies in supply chain management, inventory control, and production processes. This can result in wasted resources, increased operational costs, and delays in product delivery.  

Missed Business Opportunities  

Unclean data may obscure valuable insights and obscure potential business opportunities. Without accurate data, companies may miss chances to innovate, expand into new markets, or address emerging trends effectively.  

Damaged Reputation  

Inaccurate or biased data can lead to incorrect conclusions in reports or public statements. A company’s reputation may suffer if stakeholders, such as investors or customers, discover inconsistencies or inaccuracies in the presented data.  

Decreased Competitiveness  

In today’s data-driven business landscape, organizations that fail to prioritize data cleaning risk falling behind their competitors. Companies that leverage accurate data insights gain a competitive advantage by making more informed and agile decisions.  


Data cleaning is a critical task that underpins the success of data engineering and BI processes. By ensuring data accuracy, consistency, and reliability, data cleaning empowers organizations to make informed decisions based on trustworthy insights.   

Neglecting data cleaning can lead to inaccurate, biased, and unreliable results, undermining the very essence of data-driven decision-making. Specific types of data, such as outliers, inconsistent entries, and redundant information, must be removed during the data cleaning process to maintain integrity.   

At Canopus Infosystems, we understand the importance of data cleaning. Our data engineering and BI services are in compliance with privacy and security protocols, enabling businesses to unlock the true potential of their data and gain a competitive advantage in the dynamic business landscape.  


5 mins read


Gaurav Goyal

He is the Chief Technical Officer and Co-Founder at Canopus Infosystems Pvt Ltd. He completed his graduation in Computer Programming in 2003 and has experience in managing data science teams, quantitative research, and algorithmic trading. He’s a proven track record in specialties like robust statistics, machine learning, large data analytics... with excellence and delivered 500+ projects to 200+ clients with his teams.

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