Data reliability refers to the completeness and accuracy of data as a measure of how well it can be counted on to be consistent and free from errors across time and sources.
The more reliable data is, the more trustworthy it becomes. Trust in data provides a solid foundation for drawing meaningful insights and well-informed decision-making, whether in academic research, business analytics or public policy.
Inaccurate or unreliable data can lead to incorrect conclusions, flawed models and poor decision-making. It’s why more and more companies are introducing Chief Data Officers—a number that has doubled among the top publicly traded companies between 2019 and 2021.1
The risks of bad data combined with the competitive advantages of accurate data mean that data reliability initiatives should be the priority of every business. To be successful, it’s important to understand what’s involved in assessing and improving reliability—which comes down in large part to data observability—and then to set clear responsibilities and goals for improvement.
Implementing end-to-end data observability helps data engineering teams ensure data reliability across their data stack by identifying, troubleshooting and resolving problems before bad data issues have a chance to spread.
Learn how to leverage the right databases for applications, analytics and generative AI.
Register for the IDC report
Measuring the reliability of your data requires looking at three core factors:
1. Is it valid?
Validity of data is determined by whether it’s stored and formatted in the right way and that it’s measuring what it is intended to measure. For instance, if you're collecting new data on a particular real-world phenomenon, the data is only valid if it accurately reflects that phenomenon and isn’t being influenced by extraneous factors.
2. Is it complete?
Completeness of data identifies if anything is missing from the information. While data can be valid, it might still be incomplete if critical fields are not present that could change someone’s understanding of the information. Incomplete data can lead to biased or incorrect analyses.
3. Is it unique?
The uniqueness of data checks for any duplicates in the dataset. This uniqueness is important to avoid over-representation, which would be inaccurate.
To take it one step further, some data teams also look at various other factors, including:
- If and when the data source was modified
- What changes were made to the data
- How often the data has been updated
- Where the data originally came from
- How many times the data has been used
Measuring the reliability of data is essential to helping teams build trust in their datasets and identifying potential issues early on. Regular and effective data testing can help data teams quickly pinpoint issues to determine the source of the problem and take action to fix it.
A modern data platform is supported not only by technology, but also by the DevOps, DataOps and agile philosophies. Although DevOps and DataOps have entirely different purposes, each is similar to the agile philosophy, which is designed to accelerate project work cycles.
DevOps is focused on product development, while DataOps focuses on creating and maintaining a distributed data architecture system that delivers business value from data.
Agile is a philosophy for software development that promotes speed and efficiency, but without eliminating the “human” factor. It places an emphasis on face-to-face conversations as a way to maximize communications, while also emphasizing automation as a means of minimizing errors.
Data reliability and data validity address two distinct aspects of data quality.
In the context of data management, both qualities play a crucial role in ensuring the integrity and utility of the data at hand.
- Data reliability focuses on the consistency and repeatability of data across different observations or measurements. Essentially, reliable data should yield the same or very similar results each time a particular measurement or observation is repeated. It’s about ensuring that the data is stable and consistent over time and across different contexts.
- Data validity, in the sense of data validation, concerns the accuracy, structure and integrity of the data. It ensures that any new data is formatted correctly, complies with the necessary rules and that it’s accurate and free from corruption. For instance, a date column should have dates and not alphanumeric characters. Invalid data can lead to a variety of issues, such as application errors, incorrect data analysis results and overall poor data quality.
Although data reliability and data validity are related, they are not interchangeable. For example, you might have a highly reliable data collection process (providing consistent and repeatable results), but if the data being collected is not validated (it doesn’t conform to the required rules or formats), the end result will still be low-quality data.
Conversely, you could have perfectly valid data (meeting all format and integrity rules), but if the process of collecting that data is not reliable (it gives different results with each measurement or observation), the utility and trustworthiness of that data becomes questionable.
To maintain data reliability, a consistent method for collecting and processing all types of data must be established and closely followed. For data validity, rigorous data validation protocols must be in place. This might include things like data type checks, range checks, referential integrity checks and others. These protocols will help ensure that the data is in the right format and adheres to all the necessary rules.
All data reliability initiatives pose considerable issues and challenges in many areas of research and data analysis, including:
The way data is collected can greatly affect its reliability. If the method used to collect data is flawed or biased, the data will not be reliable. Additionally, measurement errors can occur at the point of data collection, during data entry or when data is being processed or analyzed.
Data must be consistent over time and across different contexts to be reliable. Inconsistent data can arise due to changes in measurement techniques, definitions or the systems used to collect data.
Human error is always a potential source of unreliability. This can occur in many ways, such as incorrect data entry, inconsistent data coding and misinterpretation of data.
In some cases, what is being measured can change over time, causing reliability issues. For instance, a machine learning model predicting consumer behavior might be reliable when it’s first created, but could become inaccurate as the underlying consumer behavior shifts.
Inconsistent data governance practices and a lack of data stewardship can result in a lack of accountability for data quality and reliability.
When data sources change or undergo updates, it can disrupt data reliability, particularly if data formats or structures change. Integration of data from different data sources can also lead to data reliability issues in your modern data platform.
Duplicate records or entries can lead to inaccuracies and skew results. Identifying and handling duplicates is a challenge in maintaining data reliability.
Addressing these issues and challenges requires a combination of data quality processes, data governance, data validation and data management practices.
Ensuring the reliability of your data is a fundamental aspect of sound data management. Here are some best practices for maintaining and improving data reliability across your entire data stack:
- Standardize data collection: Establish clear, standardized procedures for data collection. This can help reduce variation and ensure consistency over time.
- Train data collectors: Individuals collecting data should be properly trained to understand the methods, tools and protocols to minimize human errors. They should be aware of the importance of reliable data and the consequences of unreliable data.
- Regular audits: Regular data audits are crucial to catch inconsistencies or errors that could affect reliability. These audits should be about finding errors, but also about identifying root causes of errors and implementing corrective actions.
- Use reliable instruments: Use tools and instruments that have been tested for reliability. For example, if you’re using stream processing, test and monitor event streams to ensure data is not missed or duplicated.
- Data cleaning: Employ a rigorous data cleaning process. This should include identifying and addressing outliers, missing values and inconsistencies. Use systematic methods for handling missing or problematic data.
- Maintain a data dictionary: A data dictionary is a centralized repository of information about data, like types of data, meanings, relationships to other data, origin, usage and format. It helps maintain data consistency and ensures everyone uses and interprets data in the same way.
- Ensure data reproducibility: Documenting all the steps in data collection and processing ensures others can reproduce your results, which is an important aspect of reliability. This includes providing clear explanations of methodologies used and maintaining version control for data and code.
- Implement data governance: Good data governance policies can help improve the reliability of data. This involves having clear policies and procedures about who can access and modify data and maintaining clear records of all changes made to datasets.
- Data backup and recovery: Regularly back up data to avoid loss of data. Also, ensure that there’s a reliable system for data recovery in case of data loss.
Data observability is about understanding the health and state of data in your system. It includes a variety of activities that go beyond just describing a problem. Data observability can help identify, troubleshoot and resolve data issues in near real-time.
Importantly, data observability is essential to getting ahead of bad data issues, which sit at the heart of data reliability. Looking deeper, data observability encompasses activities like monitoring, alerting, tracking, comparisons, analyses, logging, SLA tracking and data lineage, all of which work together to understand end-to-end data quality, including data reliability.
When done well, data observability can help improve data reliability by making it possible to identify issues early on, so the entire data team can more quickly respond, understand the extent of the impact and restore reliability.
By implementing data observability practices and tools, organizations can enhance data reliability, ensuring that it is accurate, consistent and trustworthy throughout the entire data lifecycle. This is especially crucial in data-driven environments where high-quality data can directly impact business intelligence, data-driven decisions and business outcomes.
IBM® Databand® is observability software for data pipelines and warehouses that automatically collects metadata to build historical baselines, detect anomalies and triage alerts to remediate data quality issues.
Supporting ETL and ELT patterns, IBM® DataStage® delivers flexible and near-real-time data integration both on premises and in the cloud.
An intelligent data catalog for the AI era, IBM® Knowledge Catalog lets you access, curate, categorize and share data, knowledge assets and their relationships—no matter where they reside.
Now you can scale analytics and AI with a fit-for-purpose data store, built on an open lakehouse architecture, supported by querying, governance and open data formats to access and share data.
Take a deep dive to understand what data observability is, why it matters, how it has evolved along with modern data systems and best practices for implementing a data observability framework.
Ensuring high-quality data is the responsibility of data engineers and the entire organization. This post describes the importance of data quality, how to audit and monitor your data and how to get buy-in from key stakeholders.
When it comes to data quality, there are quite a few important metrics, including completeness, consistency, conformity, accuracy, integrity, timeliness, availability and continuity, just to name a few.
Footnotes
1 In data we trust (link resides outside ibm.com), PwC, 28 April 2022