What makes manually cleaning data challenging? And the best alternatives

Data and insights are critical to any modern, growing business, powering everything from AI models to business intelligence dashboards. Yet the quality of that data directly impacts the quality of the decisions we make. Enter data cleaning: the essential process of identifying and correcting errors, inconsistencies, and inaccuracies in datasets. Without thorough data cleaning, your best analytics tools and sophisticated models will be operating on flawed inputs. This blog post, explores the intricacies of data cleaning—particularly the challenges of doing it manually—and provides insights into tools, techniques, best practices, and answers to frequently asked questions about data cleaning and its significance.

Why does data cleaning matter?

Organizations across industries rely on data to drive strategic decisions, measure performance, and discover insights. However, raw data—especially when gathered from various sources—often contains errors such as typos, missing values, duplicated records, and other irregularities that lead to inefficiencies or false conclusions. This makes data cleaning a critical step in any data-driven project.

Yet, data cleaning is not a trivial task: it involves knowledge, expertise, and a substantial time commitment. While there are several automated tools that streamline and expedite the process, many organizations still rely on manual data cleaning to handle nuances or complexities that algorithms might miss. This raises a simple but important question: What makes manually cleaning data challenging? In this post, we explore the nature of data cleaning, real-world examples, the challenges behind both automated and manual methods, the tools available, and best practices for achieving reliable, high-quality data.

By the end, you’ll have a clear understanding of:

• What data cleaning is and why it is crucial.
• Examples of data cleaning in action.
• Why manual data cleaning can be so complex and time-consuming.
• Tools and techniques that help streamline data cleaning.
• Risks, best practices, and common pitfalls to watch out for when cleaning data.

What is data cleaning?

Data cleaning—also known as data cleansing or data scrubbing—is the process of preparing raw data for analysis or use by detecting and correcting (or removing) inaccurate, incomplete, or irrelevant records. The main purpose of data cleaning is to ensure that datasets are free of errors and inconsistencies, so that any subsequent analysis or modeling yields valid results.

Data cleaning addresses a range of issues that can plague datasets:

• Typographical errors: Misspellings, stray symbols, or inconsistent formatting.
• Missing values: Blank fields that are critical for analysis, requiring imputation or other handling methods.
• Duplicate records: Multiple entries representing the same entity, causing double counting or confusion.
• Inconsistent data: Different naming conventions (e.g., “NY” vs. “New York”) that skew data insights.
• Outliers: Extremely high or low values that might be genuine or the result of input errors.

When done right, data cleaning makes your datasets accurate, consistent, and ready for reliable analysis. The result is improved decision-making, reduced risk, and greater trust in the insights derived from that data.

Examples of data cleaning

Data cleaning can occur in many different contexts. Below are some common examples:

1. Customer Relationship Management (CRM) data - CRMs often contain numerous records for the same customer, or input errors resulting in incomplete addresses. A data cleaning process in this scenario involves merging duplicate entries, fixing or standardizing address formats, and removing obsolete contacts.
2. Healthcare records - Patient data often arrives from multiple sources—labs, clinics, hospitals—and each may have its own format for capturing patient information. Data cleaning helps unify formats, remove duplicates, handle missing data (e.g., test results), and ensure all vital metrics adhere to a consistent standard.
3. Financial Transactions - Banks and financial institutions rely heavily on accurate transaction data for compliance and analytics. Cleaning financial datasets might mean ensuring consistent currency formatting, reconciling transaction time zones, and correcting or flagging any anomalies indicative of fraud or errors.
4. E-commerce Listings - Online retail platforms deal with thousands (or even millions) of product listings. Data cleaning includes standardizing product names and descriptions, removing duplicate listings, ensuring accurate price formats, and handling missing attributes, such as category labels.

What makes data cleaning challenging?

Data cleaning poses challenges on multiple fronts—ranging from the scale and variety of data sources to the complexity of errors that can appear. In broad terms, these challenges can be divided into two categories: Challenges of automated data cleaning and challenges of manually cleaning data.

Challenges of automated data cleaning

1. Limited contextual understanding - Automated tools rely on predefined rules or machine learning models to detect irregularities. They can quickly flag inconsistent values, duplicates, or missing data, but they often lack the context to determine if a certain pattern is genuinely incorrect or just an edge case.
2. Complex data structures - With the rise of big data, unstructured or semi-structured information (e.g., social media content, IoT sensor data) is becoming more common. Automating the cleaning of such data can be extremely difficult, as the rules for validating and parsing the data are not always clear.
3. Scalability vs. customization - Many automated tools are either highly scalable but lacking in flexibility, or highly customizable but struggle when dealing with extremely large datasets. Finding a balance can be challenging.
4. Over-reliance on pre-set rules - Automated systems rely on rules crafted by human experts or machine learning models trained on historical data. These rules may not adapt quickly to new or unexpected input formats, leading to inaccurate “cleaning” actions (e.g., inadvertently removing valid outliers).

Challenges of manually cleaning data

When it comes to manual data cleaning, humans take center stage in curating and correcting datasets. This can be advantageous for nuanced decisions but also introduces major hurdles:

1. Time-intensive process - Manual data cleaning often involves going through records one by one, comparing sources, or verifying entries against external references. This is incredibly time-consuming, especially for large datasets. The amount of manual labor required can bring projects to a standstill.
2. Higher cost - Labor costs for manually cleaning data can add up quickly. A dedicated team or data analyst might spend hours or days combing through thousands of entries. In the short term, this may seem like a reasonable solution, but it can become prohibitively expensive as datasets grow in size and complexity.
3. Human error - Ironically, one of the biggest issues with manual data cleaning is that humans make mistakes—potentially introducing new errors in the process. Typographical mistakes, misclassifications, or overlooked duplicates can creep in during manual correction.
4. Subjective judgment - Some data inaccuracies require subjective judgment to resolve—like deciding whether to keep an outlier or how to fix an unusual entry. While a human is often better equipped than a machine to apply context, this can also introduce inconsistencies if multiple people are cleaning data without a strict set of guidelines.
5. Inconsistent standards - Different team members might use different conventions for naming, formatting, or categorizing data. Without robust documentation and guidelines, the dataset ends up with inconsistent standards—even if each individual “cleaned” the data with the best intentions.
6. Scaling Challenges - If your organization relies solely on manual data cleaning, scaling to handle larger datasets and more frequent data updates becomes a logistical nightmare. It’s not only expensive but also hinders real-time decision-making.

One of the biggest challenges of all is reproducibility and documentation. When manually cleaning data, you may lose the context of why a certain record was cleaned a certain way. When you go back to that data at a later date, you may completely forget the context (or perhaps someone else did the cleaning), and you end up spending a disproportionate amount of time trying to understand the source of the data.

What happens if data is not cleaned?

Failing to clean your data can lead to severe implications for your business or project:

1. Inaccurate insights - Dirty data produces unreliable analyses. Decisions made on the basis of flawed analytics can be detrimental—leading to faulty strategies, misguided resource allocation, or missed opportunities.
2. Wasted time and resources - Time spent analyzing or modeling incorrect data is time wasted. Repeating the process once errors are discovered leads to higher costs and prolongs project timelines.
3. Compliance and legal risks - Certain industries—such as healthcare or finance—must adhere to strict regulations governing data integrity. Using uncleaned or inconsistent data can lead to regulatory non-compliance, fines, and reputational damage.
4. Missed business opportunities - Incomplete or erroneous records (e.g., inaccurate customer information) can hamper marketing efforts, distort customer insights, and lead to missed sales or partnership opportunities.
5. Damaged brand reputation - If stakeholders discover that your analytics or AI systems rely on inaccurate data, it undermines trust in your capabilities and tarnishes your brand.

What is the impact of data cleaning?

On the positive side, properly cleaned data directly translates to:

1. Better decision-making - High-quality data enables teams to identify genuine trends, customer needs, or operational inefficiencies. Data cleaning ensures these insights are derived from accurate sources.
2. Higher ROI on data initiatives - When insights are reliable, the return on investment for analytics projects or AI implementations is higher. Accurate data underpins effective forecasting, budgeting, and strategic planning.
3. Reduced risk - From regulatory compliance to cybersecurity, cleaner data decreases vulnerabilities. You’re less likely to face fines, lawsuits, or data breaches when your processes are consistent and transparent.
4. Efficiency gains - Teams that spend less time “fixing” data errors can focus on higher-value tasks like advanced analytics and model development. Clean data saves time and frustration at all levels of an organization.
5. Stronger stakeholder confidence - Reliable, consistent data bolsters trust among leadership, investors, and partners. When you back your decisions with data that has been properly cleaned and validated, you present a more credible case.

What are the risks of data cleansing?

Despite its benefits, data cleaning can come with certain risks:

1. Over-fitting or over-simplification - In an attempt to remove outliers or invalid records, you might inadvertently discard legitimate but rare cases, leading to biased or incomplete datasets.
2. Misinterpretation - If you’re manually cleaning the data without a clear set of guidelines, subjective judgments can lead to data that reflects biases or flawed assumptions.
3. Loss of historical context - Data cleaning sometimes involves removing outdated records, but these historical entries may still have long-term analytical value. Overzealous cleaning can strip away trends or patterns crucial for deep historical analysis.
4. Tool misconfiguration - Automated tools need to be configured properly. If the parameters for cleaning are set incorrectly (e.g., thresholds for outlier detection), valid data may get removed or inaccurate data may go unnoticed.

Data cleaning tools and techniques

Data cleaning methods and tools can vary based on an organization’s needs, the complexity of the dataset, and the technical expertise of the team. Below are some popular tools and techniques:

Spreadsheet tools (e.g., Microsoft Excel, Google Sheets)

• Pros: Familiar interface, quick for small datasets, basic formulas, and filtering options.
• Cons: Difficult to scale for large or highly complex datasets, prone to human error if formulas and references are not carefully managed.

Database queries (SQL)

• Pros: Powerful for structured data, capable of handling moderately large datasets, straightforward for identifying duplicates, filtering missing values, or applying transformations. AI has made data cleaning with SQL particularly easy, especially when embedded in a data analysis environment.
• Cons: Requires knowledge of SQL; unstructured or semi-structured data not easily managed; can be time-consuming to create complex queries.

Dedicated data cleansing tools (e.g., OpenRefine, Trifacta, Talend, Alteryx)

• Pros: Specialized features for data profiling, cleaning, and transformation. Often come with visual interfaces that make it easy to spot errors and inconsistencies.
• Cons: Can have a learning curve; some are commercial products with licensing fees; may not seamlessly handle extremely large datasets without specialized infrastructure. A lot of these tools have preconfigured data cleaning rules as well, and are less flexible than the combination of Python, SQL and AI.

Programming languages (e.g., Python, R)

• Pros: Highly flexible for automating data cleaning tasks. Libraries such as Pandas (Python) or dplyr/tidyr (R) offer robust capabilities for data wrangling, missing-value handling, and merging datasets.
• Cons: Requires programming expertise; can be time-consuming to write scripts from scratch, especially for large-scale or complex data scenarios.

Machine learning for data cleaning

• Pros: Advanced techniques can detect anomalies or predict missing values based on patterns in the data. ML-based approaches can adapt to new data structures or changing trends.
• Cons: Requires labeled data to train models; results can be opaque; mistakes can be made if models are not monitored and continually updated.

AI data analysis platforms (eg., Fabi.ai)

• Pros: Platforms like Fabi.ai combine SQL, Python, no-code and AI to reduce the time it takes to clean, process and analyze data by up to 94%. These all-in-one platforms represent a massive efficiency boost while providing the necessary customization needed to handle your specific data.
• Cons: AI data analysis platforms are generally designed around structured or semi-structured data. Unstructured or media data is best handled in special tools.

Data cleaning best practices and common pitfalls

Best practices

1. Establish clear data governance policies - Define roles, responsibilities, and standards from the outset. This includes naming conventions, data formats, and acceptable ranges for numerical fields.
2. Document everything - Keep a record of what actions were taken to clean the data, which fields were altered, and why. Proper documentation helps maintain consistency and makes it easier to review or replicate processes.
3. Profile your data first - Before you start cleaning, invest time in data profiling. Understand the scope of missing values, outliers, and duplicates. Tools or queries that generate descriptive statistics can guide a more targeted cleaning strategy.
4. Automate Where Possible
   Even if you rely on manual cleaning for final checks, automate repetitive tasks such as removing duplicates or standardizing formats. This reduces human error and speeds up the process.
5. Validate intermediate steps - Rather than cleaning all at once, break the process into steps and validate at each stage. For example, after removing duplicates, verify that no unique records were lost.
6. Involve domain experts - Data anomalies may require subject matter expertise to interpret. Healthcare data cleaning, for instance, benefits greatly from a medical professional who understands what constitutes a plausible range for certain health metrics.
7. Adopt a continuous cleaning process - Data cleaning is not a one-off event. As new data flows in, it must be checked and cleaned in near-real-time or at scheduled intervals.

Common pitfalls

1. Lack of version control - Without versioning, it’s easy to lose track of what changes were made to the dataset. This complicates auditing and error tracing later on.
2. Ignoring data that doesn’t fit expectations - Outliers might be errors, but they can also reveal critical insights. Blindly removing them can skew your analysis.
3. Overlooking metadata - Metadata (data about your data) can provide clues about format, range, or creation time. Failing to use metadata results in superficial cleaning processes.
4. Assuming automated tools solve everything - Even the best tools require human oversight. Over-reliance on default settings or rules can lead to new data quality issues. This is where the power of AI with SQL and Python  really shines. You can quickly create custom code for data cleaning with full transparency on how its operating.
5. Inconsistent collaboration - If multiple people are cleaning the data without coordination, you might end up with contradictory fixes and formatting styles.

Conclusion

Data cleaning is an indispensable step in the journey of harnessing data for valuable insights—yet it’s often underestimated. Whether you choose to implement automated solutions or rely on manual scrutiny, understanding the challenges and risks helps you design a cleaning process that is both effective and scalable.

Manually cleaning data is challenging because it demands significant time, labor, and expertise, and is still susceptible to human error. Meanwhile, automated data cleaning can be powerful but isn’t always foolproof, especially when complex context and domain knowledge are required. Striking the right balance often entails combining the best of both worlds: letting machines handle repetitive, rules-based tasks and enlisting human expertise for complex or nuanced decisions.

At Fabi.ai, we believe that clean data is the foundation for all successful AI and data analytics projects. Our mission is to empower organizations with the knowledge, tools, and strategies they need to maintain high-quality data. By investing in solid data governance, robust tools, and continuous cleaning practices, you’ll ensure that your data remains a reliable asset rather than a bottleneck.

Key takeaways

1. Data cleaning is critical to ensure high-quality, reliable data for analytics, AI, and decision-making.
2. Automated tools can speed up the process but can’t always capture complex context.
3. Manual cleaning allows for nuanced, context-based decisions but is time-intensive and prone to human error.
4. Consequences of not cleaning data include inaccurate insights, compliance risks, and wasted resources.
5. Balancing automated and manual methods is often the optimal solution.
6. Best practices include establishing data governance policies, documenting changes, validating in stages, and continuously cleaning data.
7. AI is making the power and flexibility of Python and SQL accessible to data practitioners of all levels, changing the way data cleaning is done.

By recognizing these challenges and implementing recommended best practices, organizations can navigate the complexities of data cleaning and use reliable, high-quality data to drive meaningful insights and impactful outcomes. If you have a CSV or Excel file and want to see how Fabi.ai can help you quickly clean your data with AI, you can get started for free in just a few minutes.