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In this special guest feature, Kensu CPO and Founder Andy Petrella explained that as application observability has become a core component of DevOps teams, data observability has followed the same path and data teams will They point out that it helps reduce maintenance costs and extend value. Create from data and maintain trust in data. Andy is the author of his first O’Reilly book on data observability, Fundamentals of Data Observability. Kensu is a data observability solution provider that helps data teams create more value from their data by trusting what they have to offer.
The role of data within organizations has changed significantly. Over the last few years, data has migrated from assets to the core fabric of an organization. Industries rely heavily on the use of data to recommend or create products and improve user experience. Ultimately, data will become a critical factor, and data issues can directly impact an organization’s competitiveness, revenue, and even survival.
In parallel, this new paradigm is also impacting the structure of data teams. To get the most out of the data they own, companies have invested heavily in their data teams and sequenced their value chain with specific roles such as data scientists and data engineers. This structure is intended to improve the overall performance of your data team by clearly defining data producers that build pipelines and data consumers that consume data and create models and reports. The downside to this approach is that it inherently creates silos, creating communication and ownership issues.
As both data incidents and silos within data teams continue to expand, data observability is emerging as a new solution category in the Innovation Trigger phase of the 2022 Gartner Hype Cycle for Emerging Tech. Beyond data quality, data observability monitors data inside and outside the applications that process it. Therefore, it provides data teams with accurate, real-time insights such as:
- metadata (schema)
- Metrics (standard deviation, mean, etc.)
- data lineage
- Application version
- pipeline name
Troubleshoot data incidents
These capabilities enable data teams to troubleshoot data-related incidents faster and prevent their propagation. In a data-critical industry, this is a game-changer in improving data reliability. For example, in the banking sector, detecting missing values before they impact reporting dashboards used by hundreds of employees is critical to the decision-making process and the confidence users have in the insights they receive.
Make your data team more efficient
In addition to better managing data issues, data observability also plays a role in the working dynamics of data teams. Better visibility into data usage and incidents gives data teams the information they need to communicate better and clearly define responsibilities. For example, data engineers and scientists can more easily define SLAs when they have a good understanding of their data. Chief data officers should consider this advantage as important as the ability to handle data problems more effectively. In fact, when building the data value chain at the core of your organization’s value proposition, you should pay attention to communication and trust.
As application observability has become a core component of DevOps teams, data observability has followed the same path, helping data teams reduce maintenance costs, scale up value creation from data, help maintain the trust of
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