Part one of three.
Data observability has evolved in concert with advances in technology and data-centric practices. During the pre-computing era, data observability was implicit and dependent on the accuracy of manual data entry. With the advent of computers in the mid-20th century, the focus shifted to ensuring accurate input and output in mainframe systems. The emergence of relational databases in the 1980s introduced structured data storage, prompting a drive for data integrity and consistency – then in the 2000s, the creation of terabyte databases introduced new challenges, including a heightened need for observability as organizations grappled with massive datasets.
The ascent of AI and machine learning in the 2010s underscored the critical role of data quality in training models and mitigating biases. Today, in the 2020s, data observability is a universal system requirement, supported by advanced tools and platforms to monitor and enhance data quality, reliability, and consistency throughout its lifecycle.
Currently, the progress of data observability is closely tied to the needs of enterprises and institutions. In response to the mandate for near-real-time decision-making and competitive advantage, database administrators (DBAs) and system admins are seeking better ways to view the data lifecycle, and understand the quality, reliability, and consistency of their data and IT systems. With these goals in mind, organizations are turning to Generative AI and Predictive AI – hoping that advanced algorithms and massive processing power will take data observability to the next level.
Is Common Practice the Best Practice?
In contemporary business environments, the importance of data observability platforms in monitoring, managing, and upholding data quality cannot be overstated: these indispensable tools furnish real-time insights into data pipelines, and contribute significantly to the preservation of data accuracy, integrity, and reliability. The practical benefits? Enhanced data quality, superior decision making, and greater operational efficiency. Key functionalities embedded in these platforms – e.g., anomaly detection, data lineage tracking, and automated alerting – play pivotal roles in promptly identifying and resolving issues.
Nevertheless, there’s a problem: the implementation and maintenance of data observability systems is complex, demanding substantial budget allocation and a pool of skilled personnel. Further, the evolving nature of data itself may be the most formidable challenge faced by organizations. Data formats are characterized by constant change and increasing complexity, and many existing data observability platforms offer reactive solutions, addressing events after their occurrence, thus risking significant downtime. For example, some processes rely on log data for anomaly detection, providing near real-time analyses but falling short of real-time insights. This lag in responsiveness can lead to costly repercussions.
While certain analytical approaches can assist in identifying issues reactively, the risk of prolonged delays remains. The central tenet of data observability is to proactively identify trends and head off problems. Visualization of data transactions and system resources empowers DBAs to make informed decisions and generate reports, aiding organizations in navigating challenges associated with expanding data pipelines.
However, the pivotal challenge lies in determining necessary actions before an event occurs. And this requires the integration of the original data observability process with AI. From an organizational standpoint, we need to ask: What aspects require monitoring to safeguard against potential issues, while fostering a proactive data observability framework?
In our next blog, we will explore how AI is solving these data observability challenges in dynamic business environments.
