Too often, when we look at data, we only see numbers - columns and rows that may seem significant at first glance, but lack depth without proper context. Data Observability changes this perspective. This approach is about understanding what the numbers really tell us about the health, accuracy, and viability of our data systems.
What is Data Observability?
Data observability is not just one thing — it's a skill, but rather a way to monitor the health, accuracy, and overall usefulness of data. It's something that equips data teams with the necessary tools they need to make sure that the data that drives business decisions is not only accessible, but high-quality, well-structured, and up to date.
Some may think it's just about monitoring — no; it's about understanding the data from the inside, as well as its impact on the entire organization. This allows teams to detect inconsistencies, errors or anomalies before they escalate into more serious problems.
“Abilità di dati è l'abilità di un organizzazione di una visibilità di dati più livelli (come pipeline di dati, infrastruttura dati, applicazioni dati)...”
- Gartner
Importance of data observability
Companies grow (or at least some of them), and as they grow, so do their data systems. The complexity of modern data environments means that traditional monitoring tools may not be sufficient because they are unable to capture the complexity and scope of problems that may arise.
By implementing data observability practices, organizations can address these threats before they become a major problem. This includes tracking the status of your data, but above all understanding how it flows through your pipelines — all to quickly detect and fix errors. Beyond the obvious benefits, this improves the ability to meet service level agreements (SLAs) and maintain compliance with regulatory standards.
Benefits of implementing data observability
- Accuracy of decision making: High quality data means better decisions, less risk and more valuable strategic initiatives.
- Increased operational efficiency: By providing accurate insight into data systems, tools with “data observability” on the label help organizations quickly identify inefficiencies and bottlenecks. Fewer bottlenecks mean smoother operations and the ability to solve problems before they escalate into more serious problems.
- More reliable data: The right approach to data observability ensures that all data in the enterprise is accurate, up to date and reliable. This, in turn, helps maintain the integrity of data-driven processes and supports the trust that business units place in reporting and data analysis.
- Cost saving: Who doesn't like to save money sometimes? - The costs associated with low data quality can be quite scary. However, by identifying and correcting data problems at an early stage, organizations can avoid the costly consequences of faulty data that lead to bad business decisions.
- Data Integrity: With continuous monitoring and validation of data, their reliability is unmatched. This supports critical business processes that depend on accurate data.
- Safety: By detecting vulnerabilities and breaches early, data observability improves the overall security of data environments
- Regulatory Compliance: JAs mentioned earlier, improved data tracking and reporting support compliance with data governance and regulatory standards. Because penalties are not something that companies like to deal with.
The Five Pillars of Data Observability
- Freshness: Monitor the timeliness of data updates to avoid the impact of outdated data on business decisions.
- Distribution: Ensuring that data values are within expected ranges to maintain integrity.
- Volume: Verification of completeness of datasets to detect interference or anomalies in source systems.
- Schema: Tracking changes in the structure of the data that may indicate underlying problems.
- Lineage: Documenting data flows to simplify the identification of error sources and support management.
Adapting DBPLUS to data observability
DBPLUS is deeply committed to providing state-of-the-art data management solutions that reflect the latest developments in data observability. By weaving observability into our tools, we enable organizations to effectively monitor, understand, and improve their data systems on the fly.
Our products have been developed in such a way as to embody the basic principles of data observability. This includes real-time monitoring, detailed anomaly detection and accurate data analysis. This allows companies to rely on their data as a trustworthy basis for making informed decisions and strategic plans.
Utilizing all five pillars of data observability
- Freshness: DBPLUS tools constantly monitor data updates, ensuring that the information is always up to date and usable. This is critical to preventing out-of-date data from impacting critical business decisions.
- Distribution: Our software checks that the data values remain within the expected limits, maintaining the accuracy and reliability of the data. This is particularly important in sectors where precision is critical, such as financial services or the insurance sector.
- Volume: We ensure that the datasets are complete and accurately reflect their sources. This is necessary to catch disturbances or anomalies early, before they develop into more serious problems.
- Schema: Our systems monitor changes in the data structure, warning of corrections that may signal problems.
- Lineage: By mapping and visualizing data paths, DBPLUS helps users quickly identify sources of error and understand the entire lifecycle of their data. This not only helps with problem solving, but also supports strong management practices.
In practice, these principles mean that our customers can trust DBPLUS not only in day-to-day operations, but also in strategic data management. For example, in financial services, our tools can quickly detect and alert you to unusual transaction patterns that are critical to fraud detection. In retail, ensuring that customer data is up to date helps tailor offers, increasing both customer satisfaction and engagement.
