The crucial role data observability plays in understanding data health

The crucial role data observability plays in understanding data health

Gone are the days of simple data pipelines and a handful of sources; today’s organisations are riding the crest of a data revolution, where complexity is met with opportunity. Today, both cutting-edge data infrastructures and advanced technologies enable businesses to handle multiple use cases, and data products now lean on a range of internal and external sources. 

But managing increased amounts of data has its complexities, such as maintaining reliability and accuracy – both of which are crucial components that impact the quality of insights. This is where data observability can be a game-changer. 

By providing a complete view of data sources and systems, data observability empowers data and analytics teams to proactively monitor the heartbeat of their enterprise data systems, swiftly identify and resolve issues when they arise, and ultimately elevate data quality while minimising disruptions and downtime. 

Why is data observability so important? 

When trust in organisational data breaks down, so too does trust in any insights or analytics. This can lead to lost opportunities, revenue, decreased team morale and even compliance risks. Thankfully, data observability can head the trust challenges off at the pass by demystifying the ‘WHY’ behind these issues. This means that organisations can take a more proactive stance within their data infrastructure. Precisely characterises data observability as an indispensable toolkit within their Data Integrity Suite, granting organisations the capability to maintain vigilance over their data infrastructure.

Data observability is the safe keeper of trust and reliability, delivering a host of vital benefits:

  • Trust in data and analytics: With data observability, accurate reporting and analytics are upheld. Anomaly detection and proactive alerts help people to address potential data issues before they escalate, ensuring business continuity and preventing costly downstream disruptions.
  • Streamlined operations: The beauty of data observability lies in its ability to provide crucial foresight, such as swift identification of root causes for operational issues. This way, organisations save valuable time and resources, minimising the impact of disruptions.
  • Reduce risk and reinforce transformation initiatives: Data observability becomes critical as businesses embark on bigger digital transformations. Empowering data engineers and other stakeholders with a comprehensive understanding of data dynamics bolsters the foundation for successful transformation.  

What are the main components of data observability?

Data volume

Volume plays a pivotal role in data observability, serving as a barometer of data source health and completeness. For example, a sudden plunge from 500 million rows to a mere 5 million should never escape notice. Volume monitoring keeps a keen eye on unexpected surges in new records. So in a world where data flows incessantly, keeping tabs on unexpectedly high volumes of support requests, orders, purchases and so forth, companies can detect issues (or potentially foul play) early and address them proactively.

Data distribution

This refers to the pattern or spread of data values within a dataset and how the data is distributed across different ranges, intervals, or categories. By monitoring the distribution, data teams can detect data drifts, data skews, or unexpected shifts in data patterns that may impact the accuracy and effectiveness of analytics, machine learning models, and decision-making processes. Data distribution is an essential aspect of data observability as it enables organisations to ensure the data’s consistency, detect outliers from the accepter norm of an existing data landscape, and maintain data integrity, thereby supporting meaningful decision-making.

Data schema

Schema is the way your data is defined and organised within a system. It’s essentially the blueprint of data within a database and holds profound implications for data integrity. It helps you understand and monitor who makes changes and when they occur. Take this example – a new attribute column is added to your customer database – this seemingly innocuous alteration can reverberate throughout your data ecosystem. Pre-existing records might have null values or default settings, introducing variables that impact analytics. To maintain a clear understanding of data dynamics, the schema changes should be monitored for better data insights and vigilance. 

Data observability is an essential feature of effective DataOps. Armed with comprehensive insights into data health, dynamics, and integrity, data observability empowers businesses to harness the power of their data, paving the way for a future defined by informed decisions and trust in the data-driven journey.

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