Companies need to adopt a new way of approaching data, one that pushes insights to team members instead of having to monitor hundreds of dashboard and manually analyzing thousands of possible dimensions. As the number of metrics that businesses need to track increases, they must learn to use Observability to scale their work effectively.
Software engineers have known that to scale their work and keep focused on building new, value added systems, they have to build in tests and monitoring so that the systems they’ve already built can send automatic alerts if something is performing in ways it shouldn’t. This concept, observability, has been moving into the data space where data teams have started to benefit from the peace of mind that comes from having robust tools that send a notification when something is wrong. The time to add observability to business needs has arrived.
Business teams today have hit a breaking point with traditional methods of getting value from data. The data modeling and business intelligence toolkit forces them to learn complex coding and data concepts so that they can use data to make decisions. Because of advances in the modern data stack, such as data warehouses, analytics engineering, and data observability, they now have a reliable single source of truth. Combining this with an open semantic layer in tools such as dbt and Metrics Flow makes observability possible.
By adopting the principles of observability, business teams can more reliably hit their goals. They can get insights such as critical trends, unexpected values or forecasted values to help them better manage their company’s performance and achieve goals.
In short:
Business observability allows any team to more reliably achieve objectives.
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.
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 to achieve their objectives, but it has been largely reserved for software engineers.
Observability is a proactive, automated approach to monitoring critical aspects of a system.
Applying this to business teams, we need to understand how they can structure data to build automated monitoring and achieve the promise of observability. The important part of observability systems is to identify when expected conditions are NOT met. 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:
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.
Britton is the CTO of Push.ai and oversees Product, Design, and Engineering. He's been a passionate builder, analyst and designer who loves all things data products and growth. You can find him reading books at a coffee shop or finding winning strategies in board games and board rooms.
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