In this special guest feature, Andy Petrella, CPO and founder of Kensu, points out that as application observability became a central element for DevOps teams, data observability is set to follow the same path and help data teams to lower maintenance costs, scale up value creation from data, and maintain trust in it. Andy is the author of the first O’Reilly book about Data Observability: “Fundamentals of Data Observability,.” Kensu is a data observability solution provider that helps data teams trust what they deliver and create more value from data.
The role of data has profoundly changed within organizations. Over the last few years, data moved from being an asset to being the core fabric of organizations. Industries heavily rely on data usage to recommend or create products and improve the user experience. Ultimately, data became a critical factor, and data issues can directly impact an organization’s competitiveness, revenue, and, thus, survival.
In parallel, this new paradigm is also affecting the structure of the data teams. In order to leverage as much as possible the data they have, companies have heavily invested in data teams and sequenced the value chain with specific roles, such as data scientists and data engineers. This structure aims to improve the data team’s overall performance by clearly defining who, on the one hand, are the data producers building pipelines and who, on the other hand, are the data consumers creating models and reports consuming the data. The drawback of this approach is that silos inherently appear, creating communication and ownership issues.
As both data incidents and silos within data teams have kept growing, data observability, which is in the Innovation Trigger phase on the 2022 Gartner Hype Cycle for Emerging Tech, has emerged as a new solution category. Going beyond data quality, data observability monitors data in and out of the applications that process it. Therefore, it provides data teams with accurate and real-time insights, including:
- Metadata (Schema)
- Metrics (e.g., standard deviation, mean)
- Data lineage
- Application version
- Pipeline name
Troubleshoot data incidents
These capabilities allow data teams to troubleshoot data-related incidents faster and prevent them from propagating. In any industry where data is critical, this is a game-changer in improving data reliability. For instance, in the banking sector, detecting missing values before it impacts the reporting dashboard used by hundreds of employees is critical to the decision-making process and the trust users have in the insights they receive.
Enhance data team efficiency
In addition to better managing data issues, data observability also has a role to play in the working dynamics of the data teams. As it offers more visibility about data usage and incidents, it puts into the hands of the data teams the information required to communicate better and clearly define responsibilities. For instance, data engineers and scientists can more easily define SLAs once they have a good understanding of the data. Chief Data Officers should consider this benefit as significant as the capability to handle data issues more effectively. Indeed, they should pay attention to communication and trust when building the data value chain, which is at the core of the organization’s value proposition.
As application observability became a central element for DevOps teams, data observability is set to follow the same path and help data teams to lower maintenance costs, scale up value creation from data, and maintain trust in it.
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