What is Data Reliability?

The delivery processes for analytics data have now reached mission-critical status, necessitating the highest levels of data reliability.

With the evolution of analytics from traditional data warehousing to contemporary cloud-based methods, the spectrum of captured data types and the data stack facilitating their delivery have also progressed.

In modern analytics, various forms of data are managed, including data-at-rest, data-in-motion, and data-for-consumption. The data stack operates in near real-time, demanding data reliability that matches its pace and for this most turn to https://www.acceldata.io/.

It’s crucial to delve into what data reliability entails in modern analytics and acknowledge the necessity for a novel approach to maintain agility and operational efficiency in data and analytics processes.

Data Reliability Explained

In today’s digital age, data stands out as businesses’ most valuable asset. With the surge in digitalization, organizations are amassing vast quantities of data, which holds the potential to reveal trends, decipher customer behavior, and inform strategic decision-making. Yet, the cornerstone of realizing these benefits lies in possessing reliable data.

Reliable data is both accurate and complete, instilling confidence in its ability to guide business decisions effectively. Achieving data reliability encompasses ensuring data is highly available, of high quality, and delivered in a timely manner. This reliability hinges on data being free from errors, inconsistencies, and bias.

The integrity of data reliability forms the bedrock of data quality management, playing a pivotal role in maintaining customer trust and ensuring business success. In essence, high data reliability assures that your data is:

Accurate

Data accuracy denotes that information is precise, error-free, and reflects real-world objects or events faithfully. It should be up-to-date and inclusive of all pertinent data sources, ensuring reliability and trustworthiness in decision-making processes.

Complete

Completeness refers to the inclusivity and comprehensiveness of available data. A dataset must encompass all necessary information to fulfill its purpose effectively. Incomplete or convoluted data renders it either unusable or prone to erroneous decision-making.

Consistent

Consistency in data is crucial to ensuring accurate analysis and outcomes. It pertains to the uniformity of data across related databases, applications, and systems, adhering to specific standards for reliable interpretation and decision-making.

Uniform

Data uniformity entails adherence to consistent structural standards, fostering clarity and coherence in its interpretation. Lack of uniformity can lead to misunderstandings and errors, potentially impacting business operations adversely.

Relevant

Relevancy underscores the importance of data alignment with its intended use or purpose. Only data that contributes meaningfully to decision-making processes holds value, highlighting the necessity for data relevance in maximizing utility and efficacy.

Timely

Timeliness signifies the currency and relevance of data, enabling agile decision-making processes. Fresh and up-to-date data is imperative, as outdated information can result in delays and financial implications, emphasizing the importance of timely data delivery.

Why is Reliable Data Important?

Reliability plays a pivotal role in upholding the quality of data, with poor data quality reportedly costing organizations an average of $15 million annually, according to a Gartner survey. The repercussions of inadequate data reliability can be detrimental to business value.

Data reliability encompasses monitoring and offering critical insights into all four key components of the data supply chain: data assets, data pipelines, data infrastructure, and data users. A robust data reliability solution correlates information across these components, providing multi-layered data insights to pinpoint and address reliability issues at their root, thus averting future outages or incidents.

For effective, timely decision-making, high data reliability is indispensable. When data reliability is compromised, business teams are deprived of a comprehensive and accurate understanding of their operations, increasing the risk of poor investments, missed revenue opportunities, and flawed operational decisions.

Persistently low data reliability erodes trust in data among business teams, leading to a greater reliance on instinctive decisions rather than data-driven ones.

Legacy Data Quality

In the past, data processes for delivering data for analytics were primarily batch-oriented and geared towards handling highly structured data. Data teams had minimal visibility into these processes and focused their data quality efforts primarily on the output intended for consumption.

Historically, data quality processes:

Operated in batch mode, typically conducting periodic “data checks” on a weekly or monthly basis.
Primarily encompassed basic quality checks.
Were designed to work solely on structured data within the data warehouse.
Sometimes involved manual queries or visually inspecting the data.

The tools and processes for data quality in legacy systems were constrained by the limitations of data processing and warehousing platforms at the time. Performance constraints dictated the frequency of data quality checks and imposed restrictions on the number of checks that could be performed on each dataset.

Data Reliability Issues

In modern analytics and data systems, the challenges with data and data processes have grown:

The increasing volume and variety of data make datasets more complex and raise the potential for problems within the data.
Real-time data flow could lead to incidents that go unnoticed.
Complex data pipelines have many steps, each of which could fail and disrupt the data flow.
Tools in the data stack can only show what happened within their processing and lack information about the surrounding tools or infrastructure.

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

Data Reliability vs. Data Quality

Data reliability represents a significant advancement beyond traditional data quality. While encompassing data quality, it extends to include additional functionalities essential for supporting modern, near-real-time data processes.

Incorporating the characteristics of modern analytics, data reliability offers:

Enhanced data monitoring checks on datasets, encompassing aspects such as data cadence, data drift, schema drift, and data reconciliation to accommodate the increased volume and variety of data.
Continuous monitoring of data assets and data pipelines, along with real-time alerts to facilitate near-real-time data flow.
End-to-end monitoring of data pipeline execution and the status of data assets across the entirety of the data pipeline, enabling early detection of issues.
Comprehensive insights into data processes, captured throughout the data stack, allowing for in-depth analysis to pinpoint problems and identify root causes.

Data Reliability – Key Elements

Data reliability is centered on four core pillars:

Data Pipeline Execution

When the data flow within a pipeline encounters disruptions, it can hinder users from accessing timely and accurate information, leading to decisions based on incomplete or erroneous data. To preemptively address such challenges and safeguard business operations, organizations require data reliability tools capable of offering a holistic view of the pipeline. Monitoring data movement across diverse cloud environments, technologies, and applications poses a considerable challenge for organizations. A unified view of the pipeline allows them to pinpoint issues, assess their impact, and trace their origins.

To ensure data reliability, data architects and engineers must systematically gather and analyze thousands of pipeline events, detecting anomalies, predicting potential issues, and swiftly resolving them.

Data pipeline execution facilitates the following benefits for organizations:

Incident Prediction and Prevention: Provides analytics on pipeline performance trends, identifying early indicators of operational issues. This enables organizations to anticipate anomalies, automate preventative measures, and expedite root cause analysis by correlating related events.

Enhanced Data Consumption: Monitoring the throughput of streaming data streamlines data delivery to end-users, optimizing query and algorithm performance. It helps identify bottlenecks, streamline resource allocation, and provides tailored guidance for refining deployment configurations, data distribution, and query execution.

Operational Optimization and Capacity Planning: Facilitates capacity planning by predicting resource requirements to meet service level agreements (SLAs). It aligns deployment configurations with business needs, forecasts shared resource costs, and offers deep insights into data usage patterns to manage pipeline flow effectively.

Seamless Integration with Critical Data Systems: With comprehensive observability tools, data pipeline execution offers visibility across Databricks, Spark, Kafka, Hadoop, and other widely-used open-source distributions, data warehouses, query engines, and cloud platforms.

Data Reconciliation

As data traverses through the pipeline, there’s a risk of incomplete or corrupted arrival at the destination. For instance, while 100 records might depart from Point A, only 75 may reach Point B. Alternatively, all 100 records could arrive, but some may be corrupted during the transition across platforms. Ensuring data reliability entails the ability to swiftly compare and reconcile the actual values of these records from source to destination.

Data reconciliation involves automatically assessing data transfers for accuracy, completeness, and consistency. Data reliability tools facilitate reconciliation through rules that compare source and target tables, identifying discrepancies like duplicate records, null values, or schema alterations for alerting, review, and reconciliation. These tools also integrate with data and target BI tools to track data lineage end-to-end and simplify error resolution during data movement.

Drift Monitoring

Changes in data can significantly affect data quality and subsequent business decisions, necessitating continuous monitoring for two primary types of drift: schema drift and data drift.

Schema drift pertains to structural changes introduced by various sources as data usage expands across the organization. Without monitoring, these changes can compromise downstream systems, disrupting the pipeline.

Data drift encompasses any change in a machine learning model’s performance due to changes in input data. These changes may stem from data quality issues, alterations in upstream processes, or natural fluctuations. Monitoring for schema and data drift is crucial for ensuring data reliability and alerting users before these changes impact the pipeline.

Data Quality

Traditionally, companies have grappled with data quality challenges, relying on manual creation of data quality policies and rules, often enforced using master data management (MDM) or data governance software from established vendors. However, these solutions are outdated and ill-suited for managing the vast data volumes and dynamic structures of today.

To effectively manage the complexities of modern data environments, data teams require a modern platform harnessing machine learning to automate data reliability at scale.

Key Characteristics of Data Reliability

When seeking the best data reliability platform, consider these key characteristics:

Comprehensive Data Checks: Ensure the platform conducts checks at every stage of the data pipeline, covering all data types—data-at-rest, data-in-motion, and data-for-consumption.

End-to-End Monitoring: Look for capabilities enabling full monitoring of data throughout the pipeline. This facilitates a “shift-left” strategy, allowing early issue detection before reaching the data warehouse or lakehouse.

Early Issue Detection: Prioritize platforms offering early detection mechanisms, alerting teams to potential issues before they impact downstream processes. Swift remediation helps prevent corrupt data and maintains accurate analytics results.

Scalability: Choose a platform capable of scaling to handle increasing data volumes and pipeline complexity without sacrificing performance or reliability.

Integration and Compatibility: Seek platforms that seamlessly integrate with existing data infrastructure, tools, and workflows. Compatibility across various data sources, analytics platforms, and cloud environments ensures smooth adoption and deployment across the organization.

What Can You Do with Data Reliability?

With data reliability, you can:

Establish data quality and monitoring checks across critical data assets and pipelines efficiently, leveraging built-in automation to enhance coverage of data policies.
Continuously monitor data assets and pipelines, receiving alerts in the event of data incidents.
Identify data incidents, analyze associated data to pinpoint root causes, and devise resolutions to mitigate issues.
Track the overall reliability of data and data processes, ensuring alignment with service level agreements (SLAs) for business and analytics teams relying on the data.

Shift-Left Data Reliability

Enterprise data originates from a diverse array of sources, including internal applications and repositories, as well as external providers and independent data producers. For companies specializing in data products, a substantial portion of their data often comes from external sources. Given that the end product is the data itself, ensuring reliable integration of data with high quality is paramount.

The initial step towards achieving this is to shift-left the approach to data reliability, ensuring that incoming data meets stringent quality standards from the outset. However, shifting left requires careful planning and cannot be implemented haphazardly. Data Observability plays a pivotal role in shaping data reliability, but it’s imperative to select the right platform to ensure the ingestion of only high-quality, trustworthy data into the system.

High-quality data serves as a catalyst for organizations to gain competitive advantages and consistently deliver innovative, market-leading products. Conversely, poor-quality data yields unfavorable outcomes, leading to subpar products and potential business setbacks.

Data pipelines responsible for feeding and transforming data for consumption are becoming increasingly intricate. These pipelines are susceptible to breakdowns caused by data errors, flawed logic, or insufficient resources for data processing. Thus, the primary challenge for data teams is to establish data reliability early in the data journey, thereby optimizing data pipelines to meet the business and technical requirements of the enterprise.

As data supply chains grow more complex, challenges arise in several areas:

The expanding number of data sources being integrated.
The sophistication of the logic employed for data transformation.
The increasing demand for resources to process data.
Traditionally, data pipelines underwent checks only at the consumption stage. However, contemporary best practices advocate for data teams to “shift-left” their data reliability checks to the data landing zone, enhancing data quality and integrity from the onset of the data journey.

Conclusion

The imperative for robust data reliability within enterprises is underscored by the critical role data plays in driving competitive advantages and innovation. As organizations contend with a burgeoning influx of data from diverse sources and increasingly complex data pipelines, the need to establish high-quality, trustworthy data at the outset of the data journey becomes paramount. Shifting left in data reliability practices, bolstered by the adoption of suitable Data Observability platforms, empowers data teams to preemptively identify and rectify data issues, ensuring the integrity and efficacy of data-driven decision-making processes. By prioritizing data reliability and embracing modern approaches to data quality assurance, enterprises can navigate the complexities of contemporary data ecosystems and leverage data as a strategic asset to fuel business growth and success.