How to Identify and Fix Unreliable Data

Data is the cornerstone of just about everything businesses do, from assessing customer behavior patterns and driving employee productivity to creating sophisticated financial analysis and forecasts. But as business operations and data needs grow, the risks of data becoming unreliable do as well. And if it does, the result is often flawed analyses and decision-making, creating consequences that can directly impact business performance and growth.

What Is Data Reliability?

Data reliability generally refers to the process of ensuring data consistency, timeliness, accuracy, and completion. To achieve data reliability, businesses should focus on both the technical and practical aspects of their data.

On the technical side, that means adhering to strong data management protocols such as proper formatting and storage. On the practical side, businesses can optimize strategic elements of data quality, including timeliness, relevance, and accessibility.

Reliable data forms the foundation of informed business decisions and operational efficiency. Without trustworthy data, leaders are at risk of making misguided conclusions about performance, which can lead to missed revenue opportunities, wasted resources, and decreased profitability.

Key Takeaways

• Data must be complete, valid, and consistent across all systems and departments to reliably support business operations.
• Common reliability issues, such as errors from manual entry and poor data governance, can compromise data and, in turn, the decisions made based on it.
• Biased, manipulated, or corrupted data can have the same result.
• Long-term data reliability is built on proactive strategies and ongoing practices that include standardized collection processes, regular audits, and proper security measures.

How Is Data Reliability Evaluated?

Less than half (44%) of data and analytics leaders say their teams are “effective in providing value to their organization,” according to one survey. How can organizations turn this around to ensure that data is producing reliable and effective intelligence? The most useful evaluation points are often industry- and business- specific, though organizations can universally start by examining the following three key dimensions in their data:

• Data completeness refers to the presence of all essential information needed to make a decision. Gaps in data easily skew analyses and consequently misinform an organization’s next move. For example, if a company is missing data on customer demographics or purchasing behavior, it might mistakenly invest in a marketing channel that does not effectively reach its target audience.
• Data validity ensures that data meets specific criteria, such as proper formatting and standardized categories, to accurately represent designated and relevant metrics. For example, a birth date that falls in the future or a phone number with a missing digit would cause invalid data requiring a follow-up, ultimately undermining operational efficiency.
• Data consistency means data is uniform across different systems, departments, and time periods, to present the same results no matter how or where it’s accessed. For example, customer data in a customer relationship management system should match the information that appears in enterprise resource planning and accounting systems.

Examples of Unreliable Data

Even data issues that seem minor can have significant, even catastrophic, real-world consequences. In one dramatic example from 1999, NASA’s Mars Climate Orbiter burned up in the Red Planet’s atmosphere due to a data consistency error involving English and metric units. Back on Earth, understanding common types of unreliable data helps business leaders identify and fix potential issues before they spread widely enough to impact revenue. The following demonstrate telltale signs of unreliable data:

• Manual errors: Even sophisticated systems require a degree of human input, which increases the likelihood of unintended mistakes. Whether due to typos, misunderstood entry protocols, an incorrect translation or conversion, or simple oversight, these errors can mushroom as they pass through different systems and processes, creating significant reliability issues.
• Incomplete or outdated values: Missing fields, partial records, and outdated information can occur when systems lack proper maintenance, data collection processes change, or data quality processes aren’t followed. For example, as customers switch jobs, relocate, or change relationship status, their contact information can easily become outdated, manifesting in issues such as emails that bounce back or phone calls that reach wrong numbers.
• Inconsistent sources: When companies combine data from multiple sources without standardized processes and rules in place, the result can be inconsistencies in formatting, measurement units, or naming conventions. Similarly, consistency problems can accumulate when compiling unstructured and structured data. This is common among businesses with disconnected software platforms or dealing with the IT fallout from an acquisition or merger.
• Bias: Bias can enter datasets in various ways, including selective sampling, leading survey questions, or flawed assumptions when information is collected. This form of data unreliability hinders analyses and challenges decision-making, though the information is seemingly valid. Biased data is also dangerous for businesses that rely on predictive analytics.
• Instrumentation issues: Hardware malfunctions, software bugs, or system misconfigurations can generate unreliable data without analysts realizing it. As with biased data, data that appears clean and organized might contain inaccuracies. For example, a faulty sensor might incorrectly record temperatures and an improperly calibrated system could consistently miscount inventory.
• Non-representative samples: Data collected from a subset of information or a cohort that doesn’t accurately reflect the right business context can create misleading conclusions. This occurs when sample sizes are too small, certain groups are overrepresented, or data is collected during outlier periods. For instance, year-round customer analysis that only surveys customer satisfaction during busy sales seasons with long waits and large lines can skew scores negatively.
• Manipulated data: Individuals or teams may deliberately tamper with data to shape specific outcomes or hide problems. Alterations can be simple (number rounding, cherry-picking data points) or complex (statistical manipulations), making it especially difficult to detect without strong data governance policies and objective audits. For example, inventory counts might be deliberately increased to conceal shrinkage.
• Corrupted data: Information can deteriorate or become otherwise damaged during file transfers, system migrations, or maintenance and backup processes. Missing records, scrambled values, or incomplete transfers may make it easier to recognize corrupt data, but defects are not always detectable to the eye. Without robust and redundant backup and verification protocols in place, this so-called “silent data corruption” can have a widespread impact.
• Bugs or malware: Software bugs often stem from programming errors made during development, compromising data integrity and leaving systems and programs vulnerable to cyberattack. Without strong cybersecurity measures in place, these vulnerabilities can be exploited by criminals (external or internal) deploying malware to steal or alter data.
• Poor data governance: Clear data management policies and procedures help safeguard against inconsistent, outdated, inaccurate, or otherwise unreliable data. A strong data governance framework should define data quality standards, assign clear ownership responsibilities, and set up ways to validate, monitor, and improve data accuracy throughout its life cycle.

How to Fix Unreliable Data

To best prepare for detected cases of unreliable information, organizations should prepare plans for response and repair. Specific actions will vary among businesses but typically include the following essential steps:

1. Identify the source of the unreliable data: Reviewing data-entry procedures, examining system logs, and analyzing data transfer processes are all ways for companies to trace the origin of data. By understanding where and how the source of unreliable data, businesses can address root causes, be they manual data-entry processes, inadequate validation procedures, or other potential issues.
2. Establish the type of issue and scope of the impact: Is unreliable data affecting isolated pieces of information or larger datasets? How is the shortcoming impacting operations and strategic decisions? This evaluation should inform the way a business prioritizes data challenges and allocates resources. For example, customer contact information errors might need immediate attention if they are interfering with delivery schedules.
3. Implement corrective measures to cleanse the data: Potential corrective strategies for unreliable data span a range of actions: updating outdated records, correcting formatting inconsistencies, implementing new validation steps during data collection, or removing duplicated entries. New cleansing processes may inadvertently introduce new problems, so strategies should be tested and monitored for consistently accurate results.
4. Communicate the data issue and document the resolution: Throughout the entire process, stakeholders should remain apprised of data reliability issues and the appropriate corrective actions. This creates opportunities for stakeholder input about unforeseen consequences in mitigation efforts and leaves a clear paper trail to signal and prevent similar issues or recurring problems.
5. Establish data control practices to prevent future issues: Preventive measures include implementing data validation rules, automated quality checks, oversight protocols, and enhanced security features. Ongoing training sessions about the proper way to handle data can also help staff adhere to these measures.
6. Review the implemented resolution to ensure fixes remain effective: Continue to monitor data to confirm that unreliability issues have been resolved without further incident. This often involves additional validation steps to verify accuracy, such as comparing data across systems, matching real results with forecasts based on this data, or conducting sample audits of potentially affected datasets.

How to Strengthen Data Reliability

To maintain high-quality data over time, business leaders need to develop proactive strategies that address both technical and procedural aspects of data management. The following describe best practices businesses can use to strengthen data reliability:

• Establish a standardized data collection process: Develop protocols for how data should be gathered, formatted, and entered into systems. This includes defining required fields, acceptable value ranges, and data-entry procedures. Automated validation tools use these rules to flag any fields that violate predetermined criteria, such as a date in the wrong format. For example, even something as simple as using drop-down menus instead of free-text fields can prevent manual errors and formatting inconsistencies across departments.
• Conduct regular data audits: Schedule data-quality reviews across all systems to check for completeness, accuracy, and consistency while identifying any potential or growing concerns. Many data platforms have built-in audit tools that automatically flag anomalies and outliers. Additionally, industries bound by specific regulations, such as HIPAA for healthcare providers, must audit in compliance with mandated guidelines to maintain data integrity.
• Maintain thorough data documentation: Keep detailed records of all data sources, definitions of data types, relationships among data across systems, and usage guidelines. This information should be continually updated and easily accessible to authorized users; it’s also helpful for training new hires. These records also allow analysts to assess the thoroughness of current practices and make informed recommendations for improvement.
• Understand your data sources and instrumentation: Be aware of all data systems and sources, including any limitations or potential failure points. This knowledge should include sensor calibration requirements, data-update schedules, and known concerns about specific data collection methods. Ongoing performance monitoring helps businesses identify potential issues companywide before they affect data reliability.
• Set validation standards for new data sources: Criteria about data quality and compatibility should be standardized before new data sources are integrated into existing systems. Comprehensive onboarding procedures should include testing data migration processes for formatting consistency, completeness, and ability to handle errors.
• Develop data security and access control: Integrate security measures that protect data and help prevent unauthorized modifications include user authentication and access privileges, access logs, and data modification protocols. Ongoing user training and system updates can go far toward protecting business data from misuse, software bugs, and cyberattacks.
• Review data backup and recovery procedures: Establish comprehensive backup systems and regularly test recovery processes to ensure data retrieval should problems arise. Some systems may offer features for multiple backup copies, verified backup integrity, automated scheduled backups, and fully documented recovery procedures.
• Ensure that stakeholders understand data with periodic training: Develop and maintain training programs that help staff understand data-handling procedures, quality standards, and the importance of reliability. With data standards and threats always evolving, periodic training sessions keep staff updated on current best practices to spot and prevent common data reliability issues. Training should include practical advice and role-specific examples that illustrate the impact of unreliable data on specific jobs and workflows.

Centralize Data Reporting With NetSuite Analytics

Managing data reliability across multiple systems, departments, and processes can be challenging, especially when trying to maintain consistency while scaling operations. NetSuite Analytics and Reporting standardizes data collection, automates data validation, and ensures consistent data across the entire organization. With NetSuite’s customizable reporting capabilities, data teams have the necessary visibility into both operational and financial performance throughout the company.

With built-in features such as role-based dashboards, automated alerts for data anomalies, and preconfigured key performance indicators, NetSuite allows businesses to integrate real-time data from multiple sources into a single, cloud-based platform. That means stakeholders have secure access, no matter where they are. Powerful analytics tools enhance data reliability and create data-driven decisions through customizable workflows, drag-and-drop report builders, and visualization capabilities.

Data can be the starting point for effective business decisions and operations—but only if every piece of data is reliable. Implementing comprehensive data management practices, such as standardized collection, regular audits, detailed documentation, and proper security measures, helps companies overcome common data reliability hurdles and empower high-quality processes. Armed with reliable data, business leaders can make well-informed decisions and confidently adapt to new market forces and customer demands.

Unreliable Data FAQs

What makes a data source unreliable?

A data source becomes unreliable when it fails to consistently provide accurate, complete, and timely information. This could stem from manual-entry errors, inconsistent collection, poor governance, system bugs, corruption during transfer, inadequate validation controls, or data aging out of relevance. Biased sampling methods or intentional manipulation also can render a data source unreliable.

Is data reliability the same as data validity?

Reliability and validity are related but distinct concepts. Reliability refers to the consistency and stability of data over time and across different systems. Validity focuses on whether the data accurately measures what it says it does. For example, a system might reliably collect customer feedback scores but fail to capture satisfaction rates due to poorly designed survey questions.

Is data reliability the same as data quality?

Data reliability is an important aspect of data quality. Data quality is a broader term that includes reliability and other factors such as relevance. Data can be reliable and accurate but still be considered low-quality if lacks relevance or necessary context.