The processes for delivering the data for analytics have become mission-critical. Data must now be treated as mission-critical and have the highest degrees of data reliability.
As analytics have evolved from the traditional data warehouse approaches to modern, cloud-based analytics, so have the types of data captured and used and the data stack that delivers the data.
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 and how a fresh new approach to data reliability is required to keep your data and analytics processes agile and operational.
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 can be used to identify trends, understand customers' behavior, and make strategic decisions. However, the starting point to achieving these outcomes is having reliable data. Reliable data is accurate and complete. It is data that can be trusted to inform business 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:
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.
“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.
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.
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.
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.
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.
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.
Historically, data processes to deliver data for analytics were batch-oriented and focused on 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:
Legacy data quality tools and processes had limitations of data processing and warehousing platforms of the time. Performance limitations constrained how often data quality checks could be performed and limited the number of checks that could run on each dataset.
With modern analytics and modern data stacks, the potential issues with data and data processes have grown:
To support modern analytics, data processes require a new approach that goes far beyond data quality: data reliability.
Data reliability is a major step forward from traditional data quality. Data reliability includes data quality but covers much more functionality that data teams need to support for modern, near-real-time data processes.
Data reliability takes into account the new characteristics of modern analytics. It provides:
Data reliability revolves around four pillars:
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.
Data pipeline execution enables organizations to:
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.
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. The change could be caused by data quality issues, an upstream process change such as replacing a sensor with a new one that uses a different unit of measurement, or natural drift such as when temperatures change with the seasons. 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.
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.
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:
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.
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.
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.
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.
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.
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:
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.
We mentioned earlier how data supply chains have gotten increasingly complex. This complexity is manifested through things like:
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.
The ability for your data reliability solution to shift left requires a unique set of capabilities to be effective. This includes the ability to:
It includes checking data reliability before data enters the data warehouse and data lakehouse. The ability to execute data reliability tests on data earlier in the data pipelines keeps bad data out of the transformation and consumption zones.
Supporting data platforms such as Kafka and monitoring data pipelines in Spark jobs or Airflow orchestrations allows data pipelines to be monitored and metered.
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.
These are APIs that integrate data reliability test results into your data pipelines to allow the pipelines to make decisions to halt data flow when bad data is detected. This prevents it from infecting other data downstream.
When bad data rows are identified they should be prevented from continued processing, then they need to be isolated, and ultimately, need to have the ability to have further checks run to dig deeper into the problem.
With the same data often in multiple places, the ability to perform data reconciliation allows data to remain in sync in various locations.
Reliable data is the backbone of modern businesses. It helps organizations make informed decisions, optimize processes, and improve customer experiences. By understanding your business's needs, managing your data effectively, analyzing your data accurately, and maintaining data integrity, you can harness the power of reliable data and make informed decisions that drive business success.
The Acceldata Data Observability Cloud platform provides data teams with end-to-end visibility into your business-critical data assets and pipelines to help you obtain the highest degrees of data reliability.
All your data assets and pipelines are continuously monitored as the data flows from source to final destination and checks are performed at every intermediate stop along the way for quality and reliability.
Acceldata helps data teams better align their data strategy and data pipelines to business needs. Data teams can investigate how a data issue impacts business objectives, isolate errors impacting business functions, prioritize work, and resolve inefficiencies based on business urgency and impact.
The Data Observability platform supports the end-to-end, shift-left approach to data reliability by monitoring data assets across the entire pipeline and isolating problems early in the pipeline before poor-quality data hits 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.
Data teams can dramatically increase their efficiency and productivity with the Data Observability Cloud. It does this via a deep set of ML- and AI-guided automation and recommendations, easy-to-use no- and low-code tools, templatized policies and bulk policy management, and advanced data policies such as data cadence, data-drift, schema-drift, and data reconciliation.
With the Data Observability Cloud platform, you can create a complete data operational control center that treats your data like the mission-critical asset that it is and helps your team deliver data to the business with the highest level of data reliability.