Companies are collecting and processing more data than they did before and much less than they will tomorrow. After infusing a data culture, it is essential to have complete and continuous visibility of your data. Why? To anticipate any problem and any possible degradation of the data. This is the role of Data Observability.
4.95 billion Internet users. 5.31 billion mobile users. 4.62 billion active social network users. The figures in the Digital Report 2022 Global Overview by HootSuite and We Are Social illustrate just how connected the entire world is. In 2021 alone, 79 zettabytes of data were produced and collected, a figure 40 times greater than the volume of data generated in 2010! And according to figures published by Statista, by the end of 2022, the 97 zettabyte threshold would be reached and could be doubled by 2025. This profusion of information is a challenge for a lot of companies.
Collecting, managing, organizing, and exploiting data can quickly give a headache because, as it is manipulated, and moved around, it can be degraded or even rendered unusable. Data Observability is one way to regain control over the reliability, quality, and accessibility of your data.
What is Data Observability?
Data Observability is the discipline of analyzing, understanding, diagnosing, and managing the health of data by leveraging multiple IT tools throughout its lifecycle.
In order to embark on the path of Data Observability, you will need to build a Data Observability platform. This will not only provide you with an accurate and holistic view of your data but also allow you to identify quality and duplication issues in real time. How can you do this? By relying on continuous telemetry tools.
But don’t think of Data Observability as just a data monitoring mission. It goes beyond that – it also contributes to optimizing the security of your data. Indeed, permanent vigilance on your data flows allows you to guarantee the efficiency of your security devices and acts as a means of early detection of any potential problem.
What are the benefits of data observability?
The first benefit of Data Observability is the ability to anticipate potential degradation in the quality or security of your data. Because the principle of observability is based on continuous, automated monitoring of your data, you will be able to detect any difficulties very early.
From this end-to-end and permanent visibility of your data, you can draw another benefit: that of making your data collection and processing flows more reliable. As data volumes continue to grow and all of your decision-making processes are linked to data, it is essential to ensure the continuity of information processing. Every second of interruption in data management processes can be detrimental to your business.
Data observability not only limits your exposure to the risk of interruption but also allows you to restore flows as quickly as possible in the event of an incident.
The 5 pillars of data observability
Harnessing the full potential of data observability is all about understanding the scope of your platform. This is built around five fundamental pillars.
Pillar #1: Freshness
In particular, a Data Observability platform allows you to verify the freshness of data and thus effectively fight against information obsolescence. The principle: guarantee the relevance of the knowledge derived from the data.
Pillar #2: Distribution
The notion of distribution is essential when it comes to data reliability. The concept is simple: rely on the probable value of data to predict its reliability.
Pillar #3: Volume
To know if your data is complete, you need to anticipate the expected volume. This is what Data Observability offers, which allows you to estimate, for a given sample, the expected nominal volume and compare it with the volume of data available. When the variables match, the data is complete.
Pillar #4: The Schema or Program
Know if your data has been degraded. This is the purpose of the Schema, also called the Program. The principle is to monitor the changes made to any data table and data organization to quickly identify damaged data.
Pillar #5: Lineage
By ensuring metadata collection and rigorous mapping of data sources, it is possible, like a water leak in a faucet, to pinpoint sources and points of interruption in your data handling processes in the shortest time possible and with great accuracy.
Understanding the difference between Data Observability and Data Quality
If data observability is one of the elements that allow you to continuously optimize the quality of your data, it differs, however, from Data Quality which prevails over Data Observability. Indeed, in order for observability to be fully utilized, Data Quality must first be assured.
While Data Quality measures the state of a dataset, and more specifically its suitability for an organization’s needs – while Data Observability detects, troubleshoots, and prevents problems that affect data quality and system reliability.
