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How to Ensure Data Reliability

Learn how ensure that the data in your organization is accurate, valid, and reliable.

In this article:
What is a cloud data platform?

In today's data-driven world, reliable and valid data are essential for making informed decisions for every enterprise. Data reliability refers to the consistency of data over time, while data validity describes the accuracy of data in measuring what it's intended to measure. Both data reliability and validity are critical to ensuring that the insights and conclusions drawn from enterprise data are accurate and usable.

To ensure data reliability and validity, it's important to understand how to collect reliable data. This involves careful planning and execution of the data collection process to make sure that the data collected is accurate and consistent. There are various methods for collecting data from internal apps, APIs, legacy systems, data repositories, data warehouses, and other sources. The data from these sources need to be reliable in order to be usable for enterprises that want to develop data products and apply that data for optimal business decision-making. 

Data reliability is an evolutionary step forward from traditional data quality. While data reliability is closely related to data quality, it differs in that it supports modern, near-real-time data processes. It helps ensure that data teams maximize overall data quality and can identify and eliminate data outages.

More specifically, data reliability provides:

  • Monitoring and checks on specific datasets. This includes common issues like schema and data drift, data cadence, and data reconciliation.
  • Data pipeline monitoring and corresponding real-time alerts that support data flow in near real-time.
  • Comprehensive, 360-degree insights about what is happening with data processes from information that is captured up and down the data stack to drill down into problems and identify the root cause.

It’s important to differentiate data reliability from data quality. Classic data quality is a measurement of how fit a particular data set is at meeting the needs of its users. Data is considered of high quality when it satisfies a range of requirements, some of which include:

  • Accuracy: The data contains no errors and conveys true information
  • Completeness: The data set includes all of the information needed to serve its purpose
  • Consistency: Data values from different sources are the same
  • Uniformity: All measurements in the data are uniform, i.e. all in kilograms or all in pounds
  • Relevance: The data is compatible with its intended use or purpose

High-quality data is essential for making good business decisions. If data quality management is low or suspect, organizations don’t have a complete and accurate picture of their organization, and they risk making poor investments, missing revenue opportunities, or impairing their operations.

How to Ensure Data Accuracy

Accurate data ensures that enterprises draw meaningful conclusions from their data, and that those conclusions correspond to making informed decisions that lead to successful business outcomes. At issue, however, is that data must be accurate, timely, and fresh in order for it to be usable and impactful. Outdated data prevents real-time decision-making, inaccurate data leads to erroneous conclusions, and if data isn’t available when it’s needed, then it’s essentially useless. How to ensure data accuracy is a critical question that every data organization should ask themselves. Their response should include a purposeful data governance plan to not just create a data reliability framework, but to ensure that it is always-on and continuously improving.

So how do you ensure accuracy in your data entry? First, it's important to double-check the data entered and verify the data source. One data accuracy example is to ensure that all data that enters your data environment is accurate at the time of entry. This is very much of a shift-left approach to data reliability, as prevention of bad data from accessing a data environment is less expensive than correction. 

This idea of shifting left with data aligns with other data issues, like cost optimization. Every data leader knows (and often has learned the hard way, after overspending to correct data issues), that correction is less expensive than failure. This is known as the 1x10x100 rule, whereby, for every dollar it takes to detect and fix a data issue at the source or beginning of the supply chain, it costs $10 to fix in QA once the data has been processed, and $100 to fix the data after it has gone live/production.

Learning how to ensure accuracy in your work largely comes down to paying attention to detail and maintaining a high level of accuracy in all processes. This includes verifying the accuracy of your sources, carefully entering data, and regularly reviewing your work to catch errors or inconsistencies.

How to Ensure Data is Reliable and Valid

As data moves from one point to another through the pipeline, there’s a risk it can arrive incomplete or corrupted. Consider an example scenario where 100 records may have left Point A but only 75 arrived at Point B. Or perhaps all 100 records made it to their destination but some of them were corrupted as they moved from one platform to another. To ensure data reliability, organizations must be able to quickly compare and reconcile the actual values of all these records as they move from the source to the target destination.

Data reconciliation relies on the ability to automatically evaluate data transfers for accuracy, completeness, and consistency. Data reliability tools enable data reconciliation through rules that compare sources to target tables and identify mismatches—such as duplicate records, null values, or altered schemas—for alerting, review, and reconciliation. These tools also integrate with both data and target BI tools to track data lineage end to end and when data is in motion to simplify error resolution.

Data Reliability in Data Pipelines

Why is it important to have data reliability in data pipelines? Data reliability in data pipelines is essential because it ensures that the data being processed and analyzed is accurate and trustworthy. If the data is unreliable, it can lead to incorrect conclusions, poor decision-making, and even business failures.

Learning how to optimize data pipelines with data reliability and how to improve data operations with data reliability is key to ensuring the success of your pipeline. Here are some of the steps you can take to do so:

When the flow of data through the pipeline is compromised, it can prevent users from getting the information they need when they need it, resulting in decisions being made based on incomplete, or incorrect, information. To identify and resolve performance issues before they negatively impact the business, organizations need data reliability tools that can provide a macro view of the pipeline. Monitoring the flow of data as it moves among a diversity of clouds, technologies, and apps is a significant challenge for data teams. The ability to see the pipeline end-to-end through a single pane of glass enables them to see where an issue is occurring, what it’s impacting, and from where it is originating.

Data reliability in data pipelines and adequate data engineering is critical for managing and optimizing pipeline performance. To ensure data reliability, data architects and data engineers must automatically collect and correlate thousands of pipeline events, identify and investigate anomalies, and use their learnings to predict, prevent, troubleshoot, and fix a host of issues. 

Effective data pipeline reliability efforts enable organizations to:

  • Predict and prevent incidents—Compute performance monitoring provides analytics around pipeline performance trends and other activities that are early warning signs of operational incidents. This allows organizations to detect and predict anomalies, automate preventative maintenance, and correlate contributing events to accelerate root cause analysis.
  • Accelerate data consumption—Monitoring the throughput of streaming data is important for reducing the delivery time of data to end users. Compute performance monitoring allows organizations to optimize query and algorithm performance, identify bottlenecks and excess overhead, and take advantage of customized guidance to improve deployment configurations, data distribution, and code and query execution.
  • Optimize data operations, capacity, and data engineering—Compute performance monitoring helps optimize capacity planning by enabling DevOps, platform, and site reliability engineers to predict the resources required to meet SLAs. They can align deployment configurations and resources with business requirements, monitor and predict the costs of shared resources, and manage pipeline data flow with deep visibility into data usage and hotspots.
  • Integrate with critical data systems—With the right observability tools, compute performance monitoring can provide comprehensive visibility over Databricks, Spark, Kafka, Hadoop, and other popular open-source distributions, data warehouses, query engines, and cloud platforms.

Data Reliability for the Modern Data Stack

In the modern data stack, data reliability is vital to ensuring that the data is accurate, consistent, and dependable. In this context, a data stack refers to the collection of technologies and tools that are used to store, process, and analyze data, and it needs to emphasize data reliability to ensure that the data is trustworthy and can be used effectively.

So why does a data stack need to emphasize data reliability? By prioritizing data reliability, organizations can ensure that their data is accurate and dependable. This allows them to gain valuable insights that can be used to make more informed business decisions.

In terms of how to achieve data operational intelligence with data reliability, it’s important to note that data operational intelligence refers to the ability to use data to monitor and optimize business operations in real time. Achieving data operational intelligence requires having reliable and trustworthy data that can be used to make informed decisions quickly.

It’s also important to consider how to align data stack investment with data reliability goals. To do this, organizations must invest in technologies and tools that are designed to promote data reliability. This includes investing in data quality management tools, data validation checks, and data monitoring and alerting systems.

How to Improve Data Reliability

  • Start observing: The first step to increasing data reliability is to reduce the complexity of your data systems. A data observability platform will provide comprehensive visibility of your pipelines, regardless of architecture, and improve your control of all the elements that handle AI and analytics workloads. This high-level view of end-to-end processes enables you to identify and drill down into data and processing issues that cause latency, failures, and other impediments to data reliability.
  • Eliminate data downtime: It’s important to monitor data across hybrid data lakes and warehouses to ensure high data quality and reliability. Compute performance monitoring improves the reliability of your data processing by correlating events across the environment for rapid root cause analysis, analyzing performance trends to predict potential failures, and automating fixes to prevent incidents before they impact operations.
  • Automate data validation: Data drift can affect AI and machine learning accuracy. It’s important to detect drift before impacting operations through continuous automated validation that addresses data quality, schema drift, and data drift to eliminate disruption and improve the accuracy of analytics and AI.
  • Monitor data in motion: Data is never static. Organizations need to classify, catalog, and manage business rules for data in motion through the entire data pipeline with an enterprise architecture that is data source, infrastructure, and cloud provider-agnostic

With all that’s required to ensure data reliability, having end-to-end visibility into your data pipeline is absolutely crucial. Acceldata’s data observability platform enables users to increase data trust, meet SLAs/SLOs, and promote innovation by providing key insights into pipelines. With Acceldata Data Observability Platform, you can quickly identify and resolve issues to prevent trouble down the road, resulting in more accurate, relevant data.

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