Software engineers have known that they need automated monitoring systems to scale their work and keep focused on building new, value added features. These systems use tests that monitor what they’ve already built and send alerts, updates and insights if something is performing differently than expected. This concept, observability, has moved into the data space for data teams and will move fully into the business analytics with business observability.

The time for business observability has arrived.

Business teams have hit a breaking point with traditional methods analyzing data. The data modeling and business intelligence toolkit forces everyone at in a data-driven organization to learn complex coding, data concepts and visualization so that they can use data to make decisions. Through the advances in the modern data stack, such as data warehouses, analytics engineering, and semantic layers, the business teams now have a reliable single source of truth on which observability can be used. Business teams can use tools like Push.ai to subscribe to metrics they care about and receive automated updates and insights that give them real-time observability of their data.

By adopting the principles of observability, business teams can more reliably hit their goals. They can get insights such as critical trends, outliers and forecasts to help them better manage their company’s performance and achieve goals. To understand this further, let’s break the definition of business observability down into the two parts, business and observability. We’ll start with observability because that is a major new concept for most business people.

What is Observability

Observability is a proactive, automated approach to monitoring critical aspects of a system. For software engineers, this has meant building observability into their products, such as the load time on pages or the amount of disk usage for a server. By implementing observability they are able to ensure that predetermined conditions are met, such as performance being above some threshold or a system operating with a steady trend.

Observability helps software engineers continue to build more features and products because they know that they’ll be alerted if anything breaks in the existing systems. Automation allows them to focus on new products for their organizations because they are confident they’ll be alerted if old products break and need attention.

In recent years, we’ve seen observability move into the data space, with a huge wave of companies started after 2019 that are helping data teams manage the data infrastructure. Those services are helping ensure data quality so that teams can access their modeled data with confidence.

To recap, observability helps teams build reliable systems achieve their objectives.

Observability is a proactive, automated approach to monitoring critical aspects of a system.

Observability for Business

Applying observability to business teams, we can start monitoring the metrics that represent the business. A semantic layer of metrics that represents the business model, built in a specific tool or directly in Push.ai, allows teams to understand their business by structuring data for automated monitoring. The important part of observability systems is to identify when expected conditions are not met or will not be met in the near future. For a business stakeholder, this typically occurs when there are outliers, changes in trends or projections with their metrics.

Some of the major focus areas for business observability are:

• KPI Monitoring validates that KPI are as expected, within thresholds or recent values
• Trend Forecasting ensures that trends are steady and goals will be achieved
• Impact Analysis analyzes the changes in a KPI based on important dimensions

To finish with a quick example, we can look at a typical sales team. The team can implement business observability by tracking metrics such as Call Volume and New Revenue. To do this, they need to set up observability for the number of calls in a given day and track their pace to hit quota for a given month. With observability in place, the team will get alerts if they are not pacing to quota and determine a course of action, such as increase call volume to compensate or work with marketing to generate more or better leads.