From a growing number of sources, organisations are gathering endless data streams, amassing an ecosystem of data storage, pipelines, and would-be end users.
An IDG survey of data professionals reveals that data volumes are growing at an average rate of 63 per cent per month. As data proliferates, the technologies used to move data have become more complex, resulting in organisations losing visibility on how it is processed.
Also, with every additional layer of complexity, data downtime multiplies. Errors accumulate as data is moved around, and organisations often end up with unreliable data. To keep pace with data, organisations need to invest in technologies to increase data accuracy and prevent broken pipelines. The solution? Data observability. It reduces data downtime by predicting, identifying, prioritising, and helping resolve data quality issues before impacting the business.
In the past couple of years, data observability tools, which maintain the health of data systems by tracking and troubleshooting incidents, have helped data-driven organisations regain control over data processing.
But Observability, as a concept, is not new. DevOps teams keep a tab on the health of their systems so that applications and infrastructure are up and running. Of late, the evolution of data pipelines in charge of moving data from one system to another, extracting data from all of the various systems from which it originates and making it ready for analysis has necessitated the fast development of data observability.
Until a few years ago, data pipelines served the basic requirements for business analytics for organisations — inventory levels, sales pipelines, and other operational metrics. Data engineers used ETL (Extract, Load, Transform) tools to transform the data for specific use-cases and load it in the data warehouse, and data analysts created dashboards and reports using BI software.
Now, data pipelines run with a combination of complex tools such as Spark and Kubernetes that allow data teams to choose the best platforms at each layer of their data stack, but the combination of all these engines makes it difficult to gain visibility into the different parts of the pipelines. Also, if part of the data pipeline happens in a black box, you know what goes in, you know what comes out, but you don’t know what happens in between.
DataOps engineers rely on standard tools to gain insights into data systems, but they often fail to get the business context of data. This missing context does not provide sufficient information about the data quality issues and the potential causes.
This has business implications. Forrester estimates that data teams spend upwards of 40 per cent of their time on data quality issues instead of working on value-generating activities for the business. The purpose of collecting and analysing data is to create business value, but without pipeline visibility, errors accumulate, and business value is destroyed. Data observability comes to the rescue here.
Now, more and more organisations are prioritising data observability. At its core, Observability can be evaluated on the five pillars of data: volume offers insights into the health of the data system; freshness tells you if data is up-to-date, or there are any gaps which are critical for data-driven decisions; distribution tells if your data field values within the accepted range which is essential to build trust in data; schema shows if the formal structure of the data management system changed, and if changed, who made what changes and when; and lineage gives the complete picture of the data landscape — how are your upstream and downstream data sources related and offers insights into governance.
All of these together help to understand the health of the system based on its outputs. When things are wrong, why? Is the system healthy? If not, what happened? When did it happen? Are there other correlated events that help understand what is happening?
It is crucial for any data-driven organisation to have systems and software to help address the need for Observability and monitoring. Observability is often mixed up with data monitoring, but they are not the same. Monitoring is to understand the state of an organisation’s data system using a set of system metrics and logs to get alerts about incidents. Monitoring can detect a known set of failure modes but can’t remedy the black box issue or understand the root cause of a pipeline outage.
Data observability offers a deeper view of a system’s operations. It uses the data and insights that monitoring produces to create a complete understanding of the data system — its health and performance. It sheds light on the workflow occurring in data pipelines, allowing the data engineering team to navigate from an effect to a cause. Detecting unexpected issues with automated rules, data observability tools can proactively prevent errors, reduce data downtime, and improve data quality.
According to Gartner, “Observability is the evolution of monitoring into a process that offers insight into digital business applications, speeds innovation and enhances customer experience. I&O leaders should use Observability to extend current monitoring capabilities, processes and culture.”
Also Read: Are You Keeping A Tab On The Cloud?
Here are a few examples of the benefits that Gartner lists in the Innovation Insight for Observability report.
Improved end-user satisfaction: By reducing the time to identify issues, improved application uptime and performance will reduce customer churn, enhance return rates, and increase client spend.
Lower infrastructure costs: By looking at the data generated, it is possible to optimise infrastructure, for example, to reduce overprovisioning and/or improve efficiency and throughput by identifying bottlenecks.
Tighter integration with the development process: Following “observability-driven development” means that the development team and operations team are working with a single concept of understanding the application’s performance — no matter the application.
Also Read: Why Your Business Needs Anomaly Detection
There are other benefits as well. With data observability, an organisation can:
- Make sense of the complexity
- Detect hard to catch problems and speed up troubleshooting: Identify patterns of when the errors or bottlenecks occur and use the insights to prevent such scenarios in the future.
- Increase automation: Gain greater operating efficiency and produce high-quality software at scale
- Increase productivity of developers
- Understand the real-time fluctuations of your digital business performance
- Optimise investments
- Build a culture of innovation
Achieving data observability isn’t easy. Like DevOps in the early 2010s, it will become increasingly critical within the next five years — all data engineering teams will list Observability into their strategy and tech stack. To deliver the digital experience necessary to remain competitive, organisations must make their digital business observable, as healthy data is trusted, and when put to good use, it powers technology and decision-making alike.
According to Gartner, the adoption rate of observability by enterprises implementing distributed system architectures was less than 10 per cent in 2020. But it predicts this will rise to 30 per cent by 2024. In other words, the future of data observability is looking bright.
