Learn what bad data is, its types, causes, and how to prevent it to ensure data quality and reliability.
Simply put, bad data refers to incomplete, inaccurate, inconsistent, irrelevant, or duplicate data that infiltrates your data infrastructure through various pathways.
By the end of this article, you will understand:
- What bad data is
- Various types of bad data
- What causes bad data
- Its consequences and preventive measures
Let’s explore these aspects in detail:
Different Types of Bad Data
Data quality and reliability are fundamental requirements across numerous domains, from business intelligence to machine learning applications. Poor quality data manifests in several distinct forms, each presenting specific challenges to data usability and integrity.
Diagram of bad data types
Incomplete Data
Incomplete data occurs when a dataset lacks essential attributes, fields, or entries required for meaningful analysis. This absence of critical information compromises the reliability of the entire dataset and can render it unusable for its intended purpose.
Common causes for incomplete data include intentional omission of specific information, unrecorded transactions, partial data collection processes, data entry errors, and technical failures during data transfer operations.
For example, consider a customer survey missing contact information records. This absence makes it impossible to follow up with respondents later on, as shown below.
Example of missing contact data
Another example involves a hospital database containing patient medical records that lack critical information such as allergies and previous medical history, which can potentially create life-threatening situations.
Duplicate Data
Duplicate data emerges when identical or nearly identical data entries are recorded multiple times within a database. This redundancy creates misleading analytics, incorrect conclusions, and can complicate merge operations while causing system glitches. Statistics derived from datasets containing duplicates become unreliable and ineffective for decision-making processes.
Examples:
- A customer relationship management (CRM) database containing multiple records for the same customer can distort analytical insights, such as the count of unique customers or sales per customer metrics.
- An inventory management system storing identical products under different SKU numbers makes stock estimations inaccurate.
Inaccurate Data
Inaccurate data consists of incorrect or erroneous information within dataset entries.
Even a simple mistake in a code or number due to typographical errors or unintentional oversights can cause severe complications and losses, particularly when data drives decision-making in high-stakes environments. The presence of inaccurate data undermines the trustworthiness and reliability of the entire dataset.
Examples:
- A shipping company database containing incorrect delivery addresses might result in packages being sent to wrong locations, even incorrect countries, causing substantial losses and delays for both the company and customers.
- Situations where a human resource management system (HRMS) contains incorrect employee salary information can create payroll discrepancies and potential legal complications.
Inconsistent Data
Inconsistent data occurs when different individuals or teams use varying units or formats for the same data type within an organization, creating confusion and operational inefficiency. It disrupts uniformity and continuous data flow, resulting in faulty data processing.
Examples:
- Inconsistent date formats across multiple data entries (MM/DD/YYYY vs DD/MM/YYYY), for instance, in a banking system, can cause conflicts and issues during data aggregation and analysis.
Example of inconsistent date formats
- Two stores within the same retail chain entering inventory data using different units of measurement (number of cases vs number of individual items) can create confusion during restocking and distribution processes.
Outdated Data
Outdated data consists of records that are no longer current, relevant, or applicable. In rapidly evolving domains, outdated information is particularly common due to continuous changes. Data from a decade, year, or even a month ago can become not only useless but potentially misleading, depending on the context.
Examples:
- Individuals can develop new allergies over time. A hospital prescribing medications to patients based on outdated allergy information can compromise patient safety.
- A real estate agency listing properties from outdated data sources may waste time and resources on already sold or unavailable properties, reducing productivity and potentially damaging the company’s reputation.
Furthermore, non-compliant, irrelevant, unstructured, and biased data also represent types of bad data that can compromise quality in your data ecosystem. Understanding each of these various bad data types is essential for identifying their root causes, recognizing the threats they pose to your business, and developing strategies to mitigate their impact.
What Causes Bad Data
Now that you have a comprehensive understanding of bad data types, it’s crucial to understand their underlying causes so that you can implement proactive measures to prevent such occurrences in your datasets.
Several factors can cause bad data, including:
Human errors during data entry: This represents the most common cause of bad data, particularly concerning incomplete, inaccurate, and duplicate data. Insufficient training, lack of attention to detail, misunderstandings about data entry procedures, and mostly unintentional mistakes such as typographical errors can ultimately lead to unreliable datasets and significant complications during analysis.
Poor data entry practices and standards: A comprehensive set of standards forms the foundation for building solid and well-structured practices. For instance, allowing free-text inputs for fields such as country enables users to enter different names for the same location (example: USA, United States, U.S.A.), resulting in an inefficiently broad variety of responses for identical values. Such inconsistencies and confusion arise from inadequately established standards.
Migration issues: Bad data doesn’t always result from manual inputs. It can also occur during data migration from one database to another. Such issues cause misalignment of records and fields, data loss, and even data corruption that may require extensive reviewing and correction efforts.
Data degradation: Every small change, from evolving customer preferences to shifting market trends, can impact company data. If the database isn’t consistently updated to reflect these changes, it becomes outdated, causing data decay or degradation. Outdated data provides no real value for decision-making and analysis and contributes to misleading information when utilized.
Merging data from multiple sources: Inefficient combination of data from multiple sources or faulty data integration can result in inaccurate and inconsistent information. This happens when different data sources being combined follow varying standards, formats, and quality levels.
Impact of Bad Data
Processing datasets containing bad data puts your analysis results at significant risk. In fact, bad data can have long-lasting and devastating impacts, especially on data-driven businesses and domains, such as:
- Poor data quality can damage your business by increasing the risk of making poor decisions and investments based on misleading information.
- Bad data causes substantial financial costs, including wasted resources and lost revenue. Recovering from the effects bad data leaves behind can require significant funds and time.
- The accumulation of bad data can even cause business failure, as it increases the need for rework, leads to missed opportunities, and negatively impacts overall productivity.
- As a result, the trustworthiness and reliability of the business decline, significantly affecting customer satisfaction and retention. Inaccurate and incomplete data from the company leads to poor customer service and inconsistent communication.
- In addition, bad data can lead to critical errors that escalate into legal or life-threatening complications, especially in the financial and healthcare sectors.
For instance, in 2020, during the COVID-19 pandemic, Public Health England (PHE) experienced a significant data management error that resulted in 15,841 COVID-19 cases going unreported due to bad data. The issue was traced back to the outdated version of Excel spreadsheets PHE was using, which could only hold up to 65,000 rows, rather than the million plus rows it could actually hold. Some of the records provided by the third-party firms analyzing swab tests were lost, causing incomplete data. The number of missed close contacts with infection risk due to this technical error was about 50,000.
Additionally, Samsung’s fat-finger error that occurred in 2018 ended up dropping the stock prices by around 11% within a single day, eliminating nearly $300 million of market value. It was caused by a Samsung Securities employee due to a data entry mistake when he entered 2.8 billion “shares” (worth $105 billion) instead of 2.8 billion “South Korean Won” to be distributed among employees who participated in the company stock ownership plan.
Therefore, the consequences of bad data should not be taken lightly, and proper preventive measures must be implemented to eliminate the risk.
Preventing Bad Data
No dataset is perfect. Your data is bound to contain errors. The first step to preventing bad data is acknowledging this reality so that you can implement necessary preventive strategies to ensure data quality.
Some steps to prevent bad data include:
- Implementing robust data governance represents a crucial step in establishing accountability and standards across the organization. It can help you establish clear policies and procedures on how to manage, access, and maintain data so that bad data risk is minimized.
- Conduct regular data audits to identify inconsistencies and outdated data before complications arise.
- Regulate the data entry processes by establishing standards, data validation rules, and standard formats and templates across the organization to minimize human errors.
- Well-informed employees tend to make fewer mistakes during data handling and management. Therefore, regular training and update sessions are necessary to keep employees aware of standard processes.
- Regularly back up data to prevent data losses during unforeseen events.
- Use advanced tools designed specifically for data validation to ensure the consistency and integrity of your data. They can provide confirmation on the accuracy and completeness of your data, detecting and correcting potential errors.
Wrapping Up
This article explored what bad data is, the different types of bad data you may encounter, and their underlying causes. In addition, it highlighted the significant negative impact of bad data on data-driven organizations, from financial losses to business failures. Understanding these factors represents the first step in preventing bad data.
Even though there are multiple preventive strategies to ensure data quality, employing a reliable tool specifically designed for this purpose is bound to reduce your workload significantly.
Consider using data scraping tools that enable you to automatically build reliable and clean datasets. This removes the effort from your end and provides you with clean, directly usable data. One such tool that accomplishes this is the Web Scraper API by Webparsers. Not interested in dealing with scraping at all? Register now and download our free dataset samples!