What is Data Reliability?

The processes for delivering the data for analytics have become mission-critical. Nowadays, data demands mission-critical treatment with the utmost data reliability.

As analytics evolved from traditional data warehouse approaches to modern, cloud-based analytics. So did the types of data captured and used. Additionally, the data stack delivering the data also evolved.

Modern analytics deal with different forms of data: data-at-rest, data-in-motion, and data-for-consumption. And the data stack moves and transforms data in near real-time, requiring data reliability that keeps pace.

Let’s explore what data reliability is and means in modern analytics. A fresh new approach to data reliability is necessary to maintain agile and operational data and analytics processes.

Data Reliability Explained

Today, data has become the most valuable asset for businesses. With the rise of digitalization, organizations are generating and storing a massive amount of data. This data helps identify trends, understand customer behavior, and make strategic decisions. However, the starting point to achieving these outcomes is having reliable data. Reliable data is accurate and complete. It's data that businesses can trust to inform their decisions.

Data reliability consists of the insights and process by which your data is highly available, of high quality, and timely. For data to be considered reliable, it must be free from errors, inconsistencies, and bias. Reliable data is the foundation of data integrity, which is essential for data quality management and to maintain customer trust and business success. of high data reliability mean that your data is:

Accurate

As the name implies, this characteristic of data means that the information is accurate and the data contains no errors and conveys true information that is both up-to-date and inclusive of all relevant data sources.

Data accuracy is the extent to which data correctly reflects the real-world object or event it represents. Accuracy is a highly essential characteristic as inaccurate data can lead to remarkable issues with severe consequences.

Complete

“Completeness” refers to how inclusive and comprehensive the available data is. The data set must include all of the information needed to serve its purpose. If data is incomplete or difficult to comprehend, it either is unusable or inadvertently gets used in ways that will lead to erroneous decisions.

Consistent

Inconsistent data can cause incorrect analysis and outcomes. Data consistency refers to the degree to which data is uniform and conforms to particular standards. When the data present across all the related databases, applications, and systems is the same, it is considered consistent data.

Uniform

Data must adhere to a certain consistent structure, which gives it a sense of uniformity. If the data is not uniform, it can lead to misunderstandings and errors, which can impact business operations.

Relevant

Relevancy is an essential trait, when it comes to data characteristics, as there has to be a good reason when you are collecting all the data and information in the first place. The data must be compatible with its intended use or purpose. If the data is irrelevant, it won’t be valuable.

Timely

Timeliness refers to how current and relevant the data is.  Data must be fresh so that business teams can execute in an agile manner. The timeliness of data is an important trait as out-of-date information can cost time as well as money.

Why Reliable Data is Important?

Reliability plays a crucial role in maintaining high-quality data. According to a survey by Gartner, poor quality data causes organizations an average of $15 million in losses per year. Poor data reliability can destroy business value.

Data reliability monitors and provides critical insights about all four key components within your data supply chain: data assets, data pipelines, data infrastructure, and data users.  A data reliability solution will also correlate the information about these four components to provide multi-layer data that allows the data team to determine the root cause of reliability problems to prevent future outages or incidents.

Highly reliable data is essential for making good, just-in-time business decisions. If data reliability is low, business teams don’t have a complete and accurate picture of their operations, and they risk making poor investments, missing revenue opportunities, or impairing their operational decisions.

Consistently low data reliability will cause business teams to lose their trust in the data and make more gut-feel decisions rather than data-driven ones.

Legacy Data Quality

In the past, data processes for delivering analytics focused on batch-oriented and highly structured data. Data teams had very limited visibility into the data processes and processing and focused their data quality efforts on the data output from the processes: data-for-consumption.

Legacy data quality processes:

• Were run in batch, performing semi-regular “data checks” weekly or monthly,
• Only performed your basic quality checks,
• Only ran on structured data in the data warehouse.
• Were sometimes performed by manual queries or “eyeballing” the data

Legacy data quality tools and processes had limitations of data processing and warehousing platforms of the time. Performance limitations restricted the frequency of data quality checks and capped the number of checks per dataset.

Data Reliability Issues

With modern analytics and modern data stacks, the potential issues with data and data processes have grown:

• The volume and variety of data make datasets much more complex and increase the potential for problems within the data,
• The near real-time data flow could introduce incidents at any time that could go undetected,
• Complex data pipelines have many steps, each of which could break and disrupt the flow of data,
• Data stack tools can tell you what happened within their processing but have no data on the surrounding tools or infrastructure.

To support modern analytics, data processes require a new approach that goes far beyond data quality: data reliability.

Data Reliability vs. Data Quality

Data reliability is a major step forward from traditional data quality. While it includes data quality, it encompasses much more functionality that data teams need to support for modern, near-real-time data processes.

By considering the new characteristics of modern analytics, data reliability ensures a more comprehensive approach to handling data. Data reliability provides:

• More substantial data monitoring checks on datasets such as data cadence, data drift, schema drift, and data reconciliation to support the greater volume and variety of data,
• Continuous data asset and data pipeline monitoring and real-time alerts to support the near real-time data flow,
• End-to-end monitoring of data pipeline execution and the state of data assets across the entire data pipeline to detect issues earlier,
• 360-degree insights into data processes capture information across the data stack to drill down into problems and identify root causes.

Data Reliability - Key Elements

Four pillars underpin data reliability:

Data Pipeline Execution

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 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 organizations. 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.

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.

Benefits of Data Pipeline Execution

Data pipeline execution enables organizations to:

• Predict and prevent incidents: 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. It 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: 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, data pipeline execution can provide comprehensive visibility over Databricks, Spark, Kafka, Hadoop, and other popular open-source distributions, data warehouses, query engines, and cloud platforms

Data Reconciliation

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.

Drift Monitoring

Changes in data can skew outcomes, so it’s essential to monitor for changes in data that can impact data quality and, ultimately, business decisions. Data is vulnerable to two primary types of changes, or drift: schema drift and data drift.

Schema drift refers to structural changes introduced by different sources. As data usage spreads across an organization, different users will often add, remove, or change structural elements (fields, columns, etc.) to better suit their particular use case. Without monitoring for schema drift, these changes can compromise downstream systems and “break” the pipeline.

Data drift describes any change in a machine learning model with input data that degrades that model’s performance. Data quality issues, an upstream process change (like replacing a sensor with a new one using a different unit of measurement), or natural drift (such as seasonal temperature changes) could cause the change. Regardless of what causes the change, data drift reduces the accuracy of predictive models. These models are trained using historical data; as long as the production data has similar characteristics to the training data, it should perform well. But the further the production data deviates from the training data, the more predictive power the model loses.

For data to be reliable, the organization must establish a discipline that monitors for schema and data drift and alerts users before they impact the pipeline.

Data Quality

For years, companies have grappled with the challenge of data quality, typically resorting to manual creation of data quality policies and rules. These efforts were often managed and enforced using master data management (MDM) or data governance software provided by long-established vendors such as Informatica, Oracle, SAP, SAS, and others. However, these solutions were developed and refined long before the advent of the cloud and big data.

Predictably, these outdated software and strategies are ill-equipped to handle the immense data volumes and ever-evolving data structures of today. Human data engineers are burdened with the task of individually creating and updating scripts and rules. Furthermore, when anomalies arise, data engineers must manually investigate, troubleshoot errors, and cleanse datasets. This approach is both time-consuming and resource-intensive.

To effectively navigate the fast-paced, dynamic data environments of today, data teams require a modern platform that harnesses the power of machine learning to automate data reliability at any scale necessary.

Key Characteristics of Data Reliability

Many data observability platforms with data reliability capabilities claim to offer much of the functionality of modern data reliability mentioned above. So, when looking for the best possible data reliability platform, what should you look for?

Traditional data quality processes were applied at the end of data pipelines on the data-for-consumption. One key aspect of data reliability is that it performs data checks at all stages of a data pipeline across any form of data: data-at-rest, data-in-motion, and data-for-consumption.

End-to-end monitoring of data through your pipelines allows you to adopt a “shift-left” approach to data reliability. Shift-left monitoring lets you detect and isolate issues early in the data pipeline before it hits the data warehouse or lakehouse.

This prevents bad data from hitting the downstream data-for-consumption zone and does not corrupt the analytics results. Early detection also allows teams to be alerted to data incidents and remediate problems quickly and efficiently.

Here are five additional key characteristics that a data reliability platform should support to help your team deliver the highest degrees of data reliability:

Automation

Data reliability platforms should automate much of the process of setting up data reliability checks.  This is typically done via machine learning-guided assistance to automate many of the data reliability policies.

Data Team Efficiency

the platform needs to supply data policy recommendations and easy-to-use no- and low-code tools to improve the productivity of data teams and help them scale out their data reliability efforts.

Scale

Capabilities such as bulk policy management, user-defined functions, and a highly scalable processing engine allow teams to run deep and diverse policies across large volumes of data.

Operational Control

Data reliability platforms need to provide alerts, composable dashboards, recommended actions, and support multi-layer data to identify incidents and drill down to find the root cause.

Advanced Data Policies

The platform must offer advanced data policies that go far beyond basic quality checks such as data cadence, data drift, schema drift, and data reconciliation to support the greater variety and complexity of data.

What Can You Do with Data Reliability?

Data reliability is a process by which data and data pipelines are monitored, problems are troubleshot, and incidents are resolved. A high degree of data reliability is the desired outcome of this process.

Data reliability is a data operations (dataOps) process for maintaining the reliability of your data. Just like network operations teams would use a Network Operations Center (NOC) to gain visibility up and down their network, data teams can use a data reliability operations center in a data observability platform to get visibility up ad down their data stack.

With data reliability you:

• Establish data quality and monitoring checks for critical data assets and pipelines. Use built-in automation to ensure efficiency and expand coverage of data policies.
• Monitor your data assets and pipelines continuously, getting alerts when data incidents occur.
• Identify data incidents, review and drill into data related to these incidents to identify the root cause and determine a resolution to the problem.
• Track the overall reliability of your data and data processes and determine if the data teams are meeting their service level agreements (SLAs) to the business and analytics teams who consume the data

Shift-Left Data Reliability

Enterprise data comes from a variety of sources. Internally, it comes from applications and repositories, while external sources include service providers and independent data producers. For companies that produce data products, it’s typical that they get a significant percentage of their data from external sources. And since the end product is the data itself, reliably bringing together the data with high degrees of quality is critical.

The starting point for doing that is to shift-left the entire approach to data reliability to ensure that data entering your environment is of the highest quality and can be trusted. Shifting left is essential, but it’s not something that can simply be turned on. Data Observability plays a key role in shaping data reliability, and only with the right platform can you ensure you’re getting only good, healthy data into your system.

High-quality data can help an organization achieve competitive advantages and continuously deliver innovative, market-leading products. Poor quality data will deliver bad outcomes and create bad products, and that can break the business.

The data pipelines that feed and transform data for consumption are increasingly complex. The pipelines can break at any point due to data errors, poor logic, or the necessary resources not being available to process the data. The challenge for every data team is to get their data reliability established as early in the data journey as possible and thus, create data pipelines that are optimized to perform and scale to meet an enterprise's business and technical needs.

Addressing Complexity in Data Supply Chains

We mentioned earlier how data supply chains have gotten increasingly complex. This complexity is manifested through things like:

• The increasing number of sources that are being fed.
• The sophistication of the logic used to transform the data.
• The number of resources required to process the data.

Consider that data pipelines flow data from left to right, from sources into the data landing zone, transformation zone, and consumption zone. Where data was once only checked in the consumption zone, today’s best practices call for data teams to “shift-left” their data reliability checks into the data landing zone.

Requirements to Shift-Left the Data Reliability

The ability for your data reliability solution to shift left requires a unique set of capabilities to be effective. This includes the ability to:

Perform Data Reliability Checks

It includes checking data reliability before data enters the data warehouse and data lakehouse. Executing data reliability tests early in pipelines prevents bad data from entering transformation and consumption zones. This prevents disruptions in downstream data processing and analysis.

Support for Data-in-Motion Platforms

Supporting data platforms like Kafka facilitate effective monitoring of data pipelines. Monitoring data pipelines in Spark jobs or Airflow orchestrations ensures comprehensive oversight and analysis. This ensures continuous oversight and efficiency in data operations.

Support for Files

Files are often delivering new data for data pipelines. Performing checks on the various file types and capturing file events to know when to perform incremental checks is important.

Circuit-Breakers

These APIs integrate data reliability test results into pipelines. They enable pipelines to halt data flow upon detecting bad data. This prevents it from infecting other data downstream.

Data Isolation

When bad data rows are identified, they should be halted from further processing. They need isolation and subsequent checks to diagnose underlying issues effectively.

Data Reconciliation

With the same data often in multiple places, data reconciliation ensures data remains synchronized across various locations.

Reliable data is the backbone of modern businesses. It helps organizations make informed decisions, optimize processes, and improve customer experiences. Understanding your business's needs and managing data effectively, while ensuring data integrity, harnesses reliable data. This empowers informed decisions that drive business success.

Data Reliability in the Acceldata Data Observability Platform

The Acceldata Data Observability Cloud platform provides end-to-end visibility into business-critical data assets and pipelines. It helps achieve the highest degrees of data reliability for data teams.

You can continuously monitor all your data assets and pipelines. This occurs as data flows from source to final destination, with checks at every intermediate stop for quality and reliability.

Acceldata helps data teams better align their data strategy and data pipelines to business needs. Data teams can investigate how data issues impact business objectives. They isolate errors affecting business functions, prioritize work, and resolve inefficiencies based on business urgency and impact.

The Data Observability platform supports a shift-left approach to data reliability. It monitors data assets across the entire pipeline and isolates problems early, preventing poor-quality data from reaching the consumption zone.

The Data Observability Cloud works with data-at-rest, data-in-motion, and data-for-consumption to work across your entire pipeline.

Enhancing Efficiency with Data Observability Cloud

Data teams can dramatically increase their efficiency and productivity with the Data Observability Cloud. It achieves this through a deep set of ML- and AI-guided automation and recommendations. It also offers easy-to-use no- and low-code tools, along with templatized policies and bulk policy management. Advanced data policies include data cadence, data drift, schema drift, and data reconciliation.

With the Data Observability Cloud platform, you can create a data operational control center. It treats your data as mission-critical, ensuring reliable delivery to the business.

To learn more about data reliability within the Data Observability Platform, please visit our data reliability solutions page. There, you can review some of our data reliability assets and explore how they can enhance your data operations.