In this episode, Karim Hamroun, Data Governance Manager at Micropole, a Talan company, sheds light on a crucial subject: data observability. A little-known concept, but one that is fast becoming an indispensable pillar for data-driven organizations.
What is data observability?
Karim compares data observability to the plumbing in a house. The idea is to continuously monitor the quality and flow of data passing through the company's various pipelines, just as one would monitor the flow, pressure or temperature of water in pipes. The aim? Quickly detect any anomalies, prevent incidents and ensure optimum responsiveness.
This industrial, automated approach is essential as companies become increasingly digital and dependent on growing volumes of data. Until now, many had already implemented infrastructure-centric observability (servers, RAM, etc.), but had not yet applied this level of control to the content of the data itself.
Why has data observability become essential?
With digital transformation, data is at the heart of decision-making. Yet poor quality data, or a delay in detecting an incident, can have far-reaching impacts: loss of operational efficiency, errors in strategic reporting, even damage to the company's reputation.
Karim points out that while data observability is often practiced in a traditional way, with business teams manually monitoring certain indicators, this method quickly shows its limits. Modern observability solutions industrialize this monitoring, automate detection and facilitate incident management.
How does Data Governance fit in with observability?
Effective observability also depends on good data governance. To act quickly and effectively when faced with a problem, you need to clearly define responsibilities (who's in charge of what?), levels of criticality, response times (SLAs) and escalation processes.
Karim reminds us that not all data is created equal: "hot" data, whether transactional or strategic, requires priority monitoring to guarantee the continuity of critical services.
Tools for data observability
The market today offers turnkey solutions for automating data observability, such as Sifflet and Soda Data Quality, specialized in data quality. These tools connect easily to existing information systems (IS), enable quality business rules to be defined, automatically open incident tickets, and help to reduce the upstream and downstream human workload.
Where do companies stand in terms of observability maturity?
Not all organizations start at the same level. An essential first step is to have a clear understanding of your data assets, a fundamental element of Data Governance. This involves mapping data, identifying critical business processes and formalizing governance rules.
Without this prior structuring, implementing an effective observability approach is complex. The key questions to ask concern the definition of critical processes, incident management and the allocation of responsibilities.
