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.
See how proactive data observability can help you detect data incidents earlier and resolve them faster.
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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:
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.
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:
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.