In modern times, data has emerged as one of the most valuable assets. With an increasing number of companies relying on data-driven insights to inform critical business decisions, the accuracy, reliability, and quality of this data are paramount. According to a study by Gartner, a mere 20% of analytic insights are projected to translate into tangible business outcomes, with poor data quality identified as the primary hindrance preventing the realization of anticipated business value across all initiatives.
While extracting insights from data is crucial, it is equally important to assess the robustness of the data within your system to prevent instances of missing, erroneously entered, or misapplied data. This is where data observability becomes indispensable. Data observability aids organizations in effectively managing, monitoring, and identifying issues within their data and data systems proactively, thereby mitigating the risk of “data downtimes” – periods marked by incomplete or inaccurate data.
What is Data Observability?
Data observability is the capacity of a company to comprehensively grasp the condition of its data and data systems. It entails obtaining complete visibility into data pipelines, enabling organizations to develop tools and processes that facilitate understanding of data flow within the organization. By pinpointing data bottlenecks, data observability helps prevent data downtimes and inconsistencies, empowering teams to maintain optimal data quality.
The Five Pillars of Data Observability
The five pillars of data observability serve as comprehensive descriptors of an organization’s data status at any given moment. These pillars, when integrated, maximize the observability of a data system. According to Barr Moses, CEO of Monte Carlo Data, they are:
- Freshness: Maintaining up-to-date and synchronized data within complex data systems is a significant challenge for modern organizations. Incorporating data freshness into the data observability framework enables continuous monitoring of data timelines, ensuring the organization’s data remains current.
- Distribution: Data accuracy is paramount for building reliable data systems. The distribution pillar assesses the variance within the system, identifying inaccuracies. By monitoring data values for inconsistencies, this pillar prevents erroneous data from entering the system.
- Volume: Monitoring data volumes is crucial for the health of a data system, ensuring that intake meets estimated thresholds and storage capacity meets demands. Tracking volume helps maintain data requirements within defined limits.
- Schema: With organizational growth and the addition of new features, schema changes are inevitable. However, poorly managed schema changes can disrupt application performance. The schema pillar within the data observability framework ensures accuracy, currency, and regular auditing of database schema elements such as tables, fields, and columns.
- Lineage: A comprehensive understanding of the data ecosystem is vital for effective management and monitoring. Data lineage facilitates tracing the flow of data through systems, answering questions about connections with external sources and internal data structure. Combining the other pillars, data lineage provides a unified view, enabling the creation of a blueprint for the data system.
Why Is Data Observability Important?
Data observability holds significant importance as it extends beyond mere monitoring and alerting. It empowers organizations to achieve comprehensive understanding of their data systems, facilitating proactive resolution of data issues within intricate data scenarios and even preemptively averting them.
Data observability enhances trust in data so businesses can confidently make data-driven decisions
Data observability plays a crucial role in fostering trust in data, enabling businesses to make confident, data-driven decisions. While data insights and machine learning algorithms offer immense value, inaccuracies and mismanagement of data can lead to severe repercussions.
For instance, in October 2020, Public Health England (PHE) encountered a significant error in its daily Covid-19 infection tally due to an Excel spreadsheet limitation. This oversight resulted in the omission of 15,841 new cases between September 25 and October 2. Consequently, the government’s “test and trace” program failed to contact tens of thousands of individuals who tested positive for Covid-19.
By facilitating swift and efficient monitoring and tracking of situations, data observability empowers organizations to make decisions based on data with increased confidence.
Data observability helps timely delivery of quality data for business workloads
Ensuring the availability and accuracy of data in the correct format is paramount for every organization. Various departments within an organization rely on high-quality data to execute their business operations effectively—data engineers, data scientists, and data analysts all depend on data to derive insights and analytics. The absence of accurate and quality data can lead to disruptions in business processes, which can prove costly.
For instance, consider an ecommerce store that aggregates data from multiple sources such as sales transactions, stock quantities, and user analytics into a central data warehouse. The sales department requires transactional data to generate financial reports, while the marketing department relies on user analytics data to optimize marketing campaigns. Data scientists utilize this data to train and deploy machine learning models for the product recommendation engine. Any discrepancy or inaccuracy in one of these data sources could significantly impact various facets of the business.
Data observability serves to ensure the quality, reliability, and consistency of data within the data pipeline. By providing organizations with a comprehensive view of their data ecosystem, data observability enables them to identify and address issues that may disrupt the flow of data within their operations.
Data observability helps you discover and resolve data issues before they affect the business
Data observability is instrumental in identifying and rectifying data issues proactively, averting potential impacts on business operations. Unlike conventional monitoring systems that solely focus on anticipated metrics or known anomalies, data observability extends its scope to unforeseen circumstances.
For instance, in 2014, Amsterdam’s city council suffered a €188 million loss due to a housing benefits error. The council’s software, programmed in cents rather than euros, inadvertently led to erroneous disbursements. This resulted in low-income families receiving significantly more than expected, with some receiving €15,500 instead of €155. Remarkably, the software failed to alert administrators of this error.
Data observability goes beyond detecting anticipated issues, identifying unforeseen situations that may escape notice. By tracking relationships to specific issues and providing contextual information, it facilitates root cause analysis and remediation, preventing potential disruptions to business operations.
A new stage of maturity for data
The emergence of Data Observability products such as Monte Carlo Data signifies a new era of maturity for data management. In this stage, data teams are required to comply with rigorous Service Level Agreements (SLAs) to align with the demands of their business. Data integrity is crucial, necessitating that data be consistently fresh, accurate, traceable, and scalable, with minimal downtime, enabling businesses to efficiently leverage data for operational purposes. However, the question remains: How does the remainder of the data stack rise to meet this challenge?
