Business observability is an incredibly powerful tool that any data team can implement as part of activating data within their organization. It will help frame the metrics so that they are designed to be effective for the teams that need them. That being said, there are some recommended steps in implementing upstream tools and processes to help get the most out of metrics and business observability.

If you feel you may be too early in your data journey to think about business observability, feel free to skip to our post here: When should teams think about Business Observability

Suggested Prerequisites for Business Observability

We believe the business observability is best when metrics centralized and reliable. We also believe that this has become and will be the core responsibility of the data team. To achieve this, we recommend implementing three key tools and processes

• Data Quality: Use a data observability tool like Metaplane or Monte Carlo to ensure the data infrastructure and modeling produce reliable metrics. The amount data integration, transformation and analysis can create error prone steps. Data teams must maintain trust in business metrics for business observability to be useful.
• Data Modeling: Implementing a centralized, open semantic layer for metrics allows all tools, including business observability, can be built on common data and definitions. Data teams can shift their focus to building and maintaining the centralized logic if the data analysis and insight generation are automated through the use of business observability.
• Team Bandwidth: Data teams can to focus their resources to model out each department’s team’s metrics at a detailed level. Often data is privileged to the top, high-level metrics for a company because the data team shares the data modeling and data analysis responsibilities. By working with teams to build out metrics for detailed areas of a business, such as support or operations teams, they can scale their impact to every team member and realize the vision of a data-driven organization at all levels.

Diving deeper into these points, there’s a lot that’s enabled business observability to finally be possible.

Data Quality (Data Observability)

One of the biggest struggles for data teams has been to meet the demands of businesses for new metrics while maintaining their existing data ecosystem. Fortunately observability has become an established part of the data toolkit and teams can select from a wide variety of vendors to deliver high quality, reliable data to their teams. For a view into the trends in data observability, here’s the progress from 2020 to 2021:

Metric Definitions (Data Modeling)

Because of advances in the modern data stack, such as data warehouses, analytics engineering, and data observability, most companies now have a reliable single source of truth. Combining this with an open semantic layer in tools such as dbt or Metrics Flow dramatically lowers the barrier to entry for companies to implement business observability. dbt, the main language data teams use to model their business metrics in the modern data stack, announced in 2021 the creation of the “Metric Layer” that allows teams to define the business critical metrics in the same source of truth they are defining all of the data modeling. This has been expanded in 2022 when they announced their “Semantic Layer” which will expand to allowing teams to dynamically access their source of truth data. Push.ai integrates with the metrics layer, allowing teams to import their metric definitions and receive reports and alerting in minutes. We’ve also seen many teams adopt the metrics layer through our Metrics Builder where they set up metrics and export their metrics back to their source of truth.

You can find more details on how to think about implementing the metrics layer in the metrics category of our blog.

Team Bandwidth (Data Team Responsibilities)

Finally, team bandwidth is through automation and applying the technique of Business Observability. Data teams get out of the fruitless cycle of creating monitoring systems that consume a huge amount of time watching charts and building new ones to hope for answer. Instead, by implementing the modern data stack and inputting their metrics, they can use Push.ai to automatically create observability systems that will give them hours back into their and their business stakeholders day.

The data team can then spend much more time with each team, either performing ad-hoc investigations for strategic decisions or helping new team members create reliable data models and metrics that empower more of the company to be data-driven. In a perfect world every team member will have an observability system that is tailored to the metrics and dimensions they care most about, such as a business executive seeing their department-level OKRs or the customer success person seeing their customer-level health metrics.

Wrapping up: What this starts to do at scale

The data team can systematically go department by department, prioritizing the highest points of leverage, to deliver value. They can do so confidently using data quality and data modeling tools to automate the monitoring for and easily maintain a single source of truth. By adjusting the expectation of the data team within an organization, they can focus all their efforts on analytics engineering knowing that the key insights for their business users will be automatically generated for all their metrics using business observability. This will all result in organization getting more from their investments in data tools and in their data team and creating a true data driven organization.

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