5 Key Factors that Define Reliable Data

Data plays a fundamental role in most organizations these days. Companies can’t get value from poor-quality data, whether used for building business strategy or as a base for a whole new product. Data reliability is an integral part of successful data-driven processes.

In this article, we will explore the basics of data reliability: the key factors of data reliability, the different ways this term is used in data science, and how to avoid working with unreliable data.

Growing demand for reliable data

With the increasing significance of data, there’s a corresponding rise in demand for high-quality, dependable data. Data reliability serves as the cornerstone of data integrity, aiming to uphold certain standards to ensure the trustworthiness of the data being utilized. It strives to optimize data management processes, facilitating streamlined operations.

In the broader domain of data science, data reliability denotes the consistency and reliability of data. Experts often emphasize the continuous enhancement of data-related processes within organizations to effectively manage data and deliver value to users.

In statistical contexts, data reliability concerns the consistency of data. A reliable dataset consistently produces the same results across repeated data collection processes.

Key factors that define reliable data

In essence, reliable data can be trusted to consistently capture the intended information accurately and with high availability. Ensuring data reliability demands sophisticated methods of collection, validation, and quality assurance, with the goal of safeguarding data across its entire lifecycle.

To maintain the highest level of data reliability, some companies enlist dedicated data reliability engineers to address quality and availability concerns.

Typically, major data reliability issues come to light during testing or when stakeholders report problems. These issues often stem from incidents related to data quality. However, it’s essential to distinguish between measuring data quality and measuring data reliability.

While data quality focuses on the correctness and usability of the data, reliability assesses whether the data can be consistently reproduced and relied upon. Nonetheless, certain dimensions of data quality are inseparable from data reliability.

Here are five dimensions related to data quality that are crucial for ensuring data reliability:


Consistent data signifies coherence and uniformity across multiple systems. Data that lacks consistency may present conflicting information within datasets, leading to confusion and potential errors.


Although accuracy and reliability are distinct concepts, they often coincide. Accurate data, free from errors, is essential for reliable conclusions. Timeliness is sometimes considered a separate dimension but is integral to data accuracy.


Data validity pertains to whether the data effectively represents what it is intended to measure.


Completeness assesses the comprehensiveness and entirety of data, ensuring that all necessary information is available without any missing values.


Data availability ensures that an organization’s data is accessible to its users and stakeholders whenever required.

Trust in data

However, achieving trust in data extends beyond merely meeting criteria and examining specific data files. Establishing a culture of trust surrounding data necessitates the efforts of dedicated data teams committed to ensuring data quality. This entails fostering a shared understanding of data across the organization and upholding integrity in their endeavors.

Exploring the domain of cultivating trust in organizational data and ensuring its reliability reveals a plethora of interconnected concepts and goals. This is due to the continuous nature of enhancing data reliability.

For instance, consider the concept of data observability. Observability outlines how a company can monitor and manage the health of the data it utilizes.

Additionally, trust in data is intertwined with a more recent concept known as data downtime, which warrants exploration. Data downtime aims to highlight instances of poor data quality or unavailability.

Data reliability vs. data validity

Data reliability and data validity are often conflated, but they represent distinct aspects of data quality. Data validity centers on the accuracy and appropriateness of data in measuring its intended parameters. On the other hand, data reliability emphasizes the consistency with which data yields expected outcomes.

In essence, data validity is a component of data reliability. For data to be considered reliable, it must first be valid. For instance, if a dataset intended for analyzing for-profit companies includes information about non-profits, it becomes invalid. Such invalid data would consequently produce unreliable results, undermining the overall reliability of the dataset.

How to identify unreliable data?

Detecting unreliable data is essential for ensuring accurate analysis and informed decision-making. When key data reliability standards aren’t met, it indicates poor data quality.

Various factors contribute to data quality issues within organizations, including human errors, technical glitches, external influences, and inadequate data management practices.

If suspicions arise regarding data reliability and no automated alerts are in place, paying attention to specific indicators within the dataset can help pinpoint potential issues:

  • Origin: Assess the source of the data to gauge its credibility.
  • Data collection method: Understand the methodology employed for data collection.
  • Outliers: Identify values or elements that deviate significantly from the expected range.
  • Inconsistencies: Scrutinize for conflicting or contradictory information.
  • Missing values: Consider the reasons behind missing data and evaluate if they are random or systematic.
  • Historical data: Compare new data with historical records to detect unexplained discrepancies.
  • Duplicate entries: Eliminate redundant data entries to prevent skewed results.
  • Pattern recognition: Look for repetitive patterns, especially in survey responses, which may indicate unreliable data.

By observing these indicators, organizations can uncover potential data reliability issues and take corrective actions to enhance data quality.

Building products with reliable data

Ensuring reliable data is crucial for building products that meet quality standards. While there’s no one-size-fits-all solution, data-driven organizations can adhere to key principles to continuously enhance data reliability.

Implementing robust data management policies is fundamental. These policies establish clear standards and guidelines for data collection, processing, storage, and security, ensuring better data quality and integrity throughout its lifecycle.

Automation plays a vital role in addressing data reliability issues. By automating various data management tasks, organizations can improve reliability across different stages, from data sourcing to processing, and even in alerting teams about potential data-related issues.

When sourcing data from external providers, evaluating the reliability of the provider and their data is paramount. Opting for experienced and dependable data providers ensures access to high-quality data. Thorough documentation accompanying large-scale datasets is essential. Reliable datasets often come with detailed documentation outlining data collection methods, any applied transformations, known limitations, and other relevant information.

Why is reliable data worth the investment?

Investing in reliable data is essential for achieving meaningful business outcomes. In fact, data that lacks reliability often proves to be a poor investment. While we’ve discussed strategies for ensuring data reliability within organizations, it’s important to recognize that many data-driven products rely on external data sources.

When procuring external data, organizations should aim to obtain high-quality data that minimizes the need for extensive resources to rectify issues arising from poor quality. The data being purchased must be both relevant and reliable. From our experience, we’ve identified five key questions that can assist in selecting the most suitable data provider prior to making a purchase.

Final thoughts

Finally, as data plays an increasingly integral role in decision-making throughout organizations, prioritizing data reliability is paramount.

With growing complexity comes new challenges that must be navigated. Nevertheless, the primary objective remains leveraging the organization’s data as efficiently as possible, making data reliability indispensable in achieving this goal.