Enterprise data engineers confront multiple hardships in maintaining companies’ internal data actionable every day. Data stacks tend to sprawl extensively, and more external data sources and end users get connected to cloud warehouses. As a result, lots of erroneous and anomalous data emerges and goes unnoticed until it directly impacts the downstream processes.

Gather’s study revealed that bad enterprise data’s average cost was $12.9 million. Conversely, timely detection, isolation, and troubleshooting of common data anomalies can safeguard business reputation, prevent privacy policy infringements, and eliminate financially devastating data downtime.

What Is Data Anomaly?

A data anomaly appears as an inconsistent data point and gives itself away by its abnormal behavior compared to the rest of the data assets. Anomalous data is classified by origin into intentional and unintentional ones.

The intentional anomaly type emerges from event-based, fast-paced systems where multiple agents write the same shared data table field. Unintentional anomalies are mostly the product of input errors and distortions during data sourcing.

Data anomalies affect calculations and skew analytical insights that underlie essential business decisions. Therefore, they can undermine the overall trust in enterprise data or even halt data operations. 

Common Data Anomaly Types

Data deviations manifest in three common forms:

• Outliers (point anomalies). Outliers are data points whose values are far beyond the predefined baseline. Common reasons for this include security breaches, incorrectly set input thresholds or malfunctioning processing.
• Contextual anomalies. These are inconsistent and redundant data points generated due to imperfect data sourcing or processing techniques. They are not outright outliers, as they might be expected to occur in a specific context. For example, a spike in seasonal goods orders might be a contextual anomaly.
• Collective anomalies. In contrast to contextual anomalies and outliers, these are arrays of data sets that fall outside the baseline value.

Data Health Maintenance and Anomaly Detection

At a basic level, enterprises must develop a strict data quality assessment and maintenance framework. Only afterward should they operationalize anomaly detection practices. The long-term data health maintenance will require:

• Establishing a uniform data quality profile and company-wide data governance principles.
• Mapping definite data lifecycle patterns as a “norm”.
• Adjusting thresholds for data health monitoring and automating reporting on anomalies.
• Comprehensive analysis of data anomaly cases. Data quality teams must devise anomaly scoring systems, set uniform troubleshooting procedures, and prioritize resolving malfunctions that are detrimental to business objectives.
• Establish a predictive analytics layer atop the data warehouse so data teams can leverage anomaly forecasting. ML-powered forecasting features are already available in Snowflake and AWS clouds.

The root cause analysis of anomaly-related data issues requires an in-depth examination of data lineage and a comprehensive review of system logs. Today’s root cause analysis automation relies on machine learning algorithms. This allows data engineers to pinpoint the exact moment when the deviation occurs in the data evolution process and identify its core cause.

Automated Data Observability Layer to Challenge Anomaly-Driven Data Outages

The deployment of automated observability systems is a step toward cost-efficient and proactive prevention of data anomalies that impact crucial business operations.

Why Is Data Observability Solution a New Normal?

What is data observability in terms of enterprise data-driven operations? It can be defined as a combination of consistent supervision of the data lifecycle and proactive correction of flawed datasets and workloads. However, proactive data health monitoring isn’t the purpose in itself.

Observability solutions empower data specialists to manage cloud data warehouses effectively. They reveal how to optimize data schema for more agile and fail-proof business operations, minimizing the risks of data anomaly incidents. Consequently, this results in improved data growth and decreased costs of data stack maintenance.

Reasons for Businesses to Install Data Observability System

• Seamless integration with CDWs. To map data schema and put all data transformations under the 24/7 monitoring observability tool doesn’t require custom integrations or any changes in the current cloud data infrastructure. You can access detailed real-time dashboards once it will consume your meta-data.
• No-code approach. Benefit from zero-touch installation. Modern observability software automatically deploys monitors and recognizes normal data behavior patterns to flag deviations and inconsistencies automatically.
• Cost-effective troubleshooting. With observability solutions like Revefi, you can investigate anomalous data occurrences and resolve data issues faster. Automated root cause analysis provides a clear and comprehensive view of data lineage in minutes instead of hours.

Stable performance without data outages. Enterprise workloads scale up and down all the time. The observability system lets the data team respond promptly to troublesome and anomalous data and prevent potentially adverse scenarios for key business data consumers. This eliminates unwanted headaches for the entire staff and ensures business runs as usual.

Published By: Aize Perez