The worlds of software, data and business observability share a common goal: to help teams set up proactive monitoring so that they can confidently focus on their tasks while knowing they’ll receive notifications if anything needs their attention. This is allowing all types of teams to scale their impact exponentially, always addressing new problems with new solutions. However, the three worlds have some very clear differences.

Business metrics …

• Operate over much longer time frames, taking days or weeks to change
• Serve a much different purpose, helping achieve business outcomes
• Require additional methods, using forecasting and coordination across teams

To see how this impacts the design of a business observability tool, let’s look at the different parts of Push.ai.

Business Observability Metrics

The metrics that businesses use are often the result of a large amount of data integrations from across many SaaS and application systems. They go through data modeling, typically owned by the data team, so that they can be synthesized and standardized into a reliable semantic layer. The business teams then connect tools, such as Push.ai.

Contrasting this with software observability, the metrics in software are often a simple calculation directly within a single application. Something like the load time of the page is generated automatically and sent to the software observability tool like Datadog to process. There are no intermediary steps that could impact it, such as breakdowns in the ingestion or modeling layers that are required to get to business observability. Software observability typically does not touch any business related metrics, only the applications and software that form many business’ products. To get to business observability, software data has to be extracted and integrated with other data sources to be transformed into relevant metrics.

Data observability sits closer to business observability but focuses on metrics associated WITHIN the data infrastructure, such as the number of rows in a table or the data type of a given column. Data observability tools parse metadata from the warehouse and modeling tools so that data teams can reliably deliver high quality data to all their consumers. Business observability cares about the output of the data team, the metrics, that allow business teams to achieve their goals. Data observability does not focus on analyzing the metric values, only that the systems producing them are accurate.

Metrics Summary

Business Observability vs Software Observability

To get to business observability, software data has to be extracted and integrated with other data sources to be transformed into relevant metrics.

Business Observability vs Data Observability

Data observability does not focus on analyzing the metric values, only that the systems producing them are accurate.

Business Observability Reports

Software and data observability tools typically do not report metrics when systems are operating as expected. However, in businesses there is a need to have teams stay aware of changes in metrics so that they can spot shifts that not even the most complicated AI could detect. This means that teams need to set up the right metrics, reporting to the right people with the right cadence so that relevant stakeholders stay informed.

• Daily/Weekly/Monthly Reports - Sharing results company-wide can develop a data-driven culture where teams understand the impact they’re having and the state of the business. A single channel with the right metrics can also keep everyone aligned on the metrics that matter most and drive focused execution.
• Top Movers - By seeing the largest shifts business metrics on an ongoing basis, teams can spot unexpected and valuable insights. These can then be used to fix any issues that may have happened in a given day such as unexpectedly low revenue.
• Metric Subscriptions - As data teams produce more metrics for more departments, there hits a point where each department or even individual needs to customize the metrics they look at every day. By incorporating subscriptions, the metric definitions can be maintained by the data team while any person can subscribe to and receive reporting and insights on it.

<aside>🔎 For more details on the value reports can bring to business teams, see our article on Implementing Business Observability

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Reports Summary

Reports serve a key function of keeping teams informed of changes in metrics. They don’t have a clear comparable within software and data observability and are exclusive to business observability.

Business Observability Insights

If you’ve used software or data observability tools in the past, you’ll be familiar with some of the ways that they test systems. Although there is some overlap, the types of tests and observations that businesses need to generate insights are much different. You can find more details on the Push.ai Insight Engine™️  in our detailed product page here.

• Outliers - Most observability tools focus on anomaly detection because in software and data the systems are supposed to remain steady. However, in businesses outliers can be expected or unexpected, such as a significant increase in revenue when a promotion is run. These data points may not be anomalies. It is better for the business be aware they occurred and learn from them.
• Trends - Business metrics change over a much longer time horizon. In software and data observability, changes happen very quickly, sometimes within seconds. Businesses on the other hand need to be aware of long-term trends and how those trends are changing. Receiving insights that revenue has been decreasing over the previous month or that it is accelerating can be critical to know about as soon as possible to address changes.
• Projections - Often businesses need to know where their efforts will land them by a given point in time, such as what end of month revenue will be, so that they can communicate with stakeholders. Software and data observability do some of this but the breaking changes in systems can often happen instantaneously from bad code pushes. In businesses the projection is critical to know early so that efforts can be made, such as new projects or additional resources spent, to make sure that goals are attained.

Insights Summary

Business Observability vs Software Observability

Business observability operates over significantly longer time horizons to see the impact from complex business changes.

Business Observability vs Data Observability

Data observability focuses on system changes that could lead to inaccurate data, like a table doubling in size. Business observability generates insights from accurate metric to help teams get early awareness of outliers, trends and projections so they can produce reliable business results.

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