Traditionally, reporting has been managed by data teams. Many growing companies rely on spreadsheets, but as data accumulates and becomes overwhelming, they turn to data teams to automate the process. However, analytics engineers can have an unmatched impact by driving strategic decisions independently, transforming how data shapes business direction.
Every successful company gathers increasing amounts of data, and there are countless ways to meet reporting needs. Operational analytics, in particular, significantly enhances the value of data teams, enabling them to generate actionable insights that propel business strategies forward. These insights directly influence revenue, organizational objectives, and long-term plans.
But it’s not a simple “problem identified, problem solved” process. Here’s how operational analytics works in practice:
The Role of Operational Analytics
Businesses gather data from various sources—such as SaaS applications (Salesforce, HubSpot) and internal systems—and store it in a data warehouse. With a rich data context, the warehouse enables informed decision-making. Analytics engineers, drawing on this data, identify relationships between specific events (e.g., from Google Analytics) and craft conditions to determine the next optimal step.
In simple terms: if poor performance in area X is caused by factor Y, the system automatically takes action Z, solving the issue based on real data. That’s operational analytics at work.
Think of the Data System as a Human Body
- Senses (sight, sound, touch): These are the tools that gather data from various sources.
- The Brain: This is the data warehouse, processing all sensory inputs and making sense of the data.
- Reflexes: Operational analytics are like reflexes—they trigger actions without manual input, ensuring smooth operation, much like breathing happens without conscious effort.
A skilled data analyst, armed with operational data models, reduces complex business challenges to a straightforward equation. These models guide future actions by tying performance metrics directly to business goals. Operational Data Models, coined by Benn Stancil, treat a business as a collection of models that directly impact revenue.
Take Facebook, for example. Its revenue formula might look like this:
A data analyst can create a model that tells the business which of these metrics to focus on to drive the most impact—and why.
Is Operational Analytics Right for You?
The stage of your company will influence how beneficial operational analytics can be. For startups with 20–50 employees, manual reporting tools like Google Analytics may no longer cut it. At this point, investing in a data warehouse becomes critical. Although operational analytics may require upfront investment and could take up to a year to fully realize benefits, it’s especially valuable for established companies looking to scale effectively.
A Data Team That Works Alongside Engineers
At Kormoan, our Data Team collaborates closely with backend engineers and other departments, allowing us to rapidly build, test, and deploy solutions. This close partnership ensures data collection is flawless and efficiently handled throughout all stages—minimizing problems down the road.
Real-Life Applications of Operational Analytics at Kormoan
One of our clients needed a talent audition app similar to TikTok, featuring bite-sized content. Our Data Team was tasked with building both a search engine and a recommendation system. By leveraging operational analytics, we made both systems more effective.
- Search Engine: Instead of relying solely on backend data, we integrated behavioral data (user events) to deliver more relevant search results. This data was processed via OpenSearch, a fast, machine-learning-driven search engine, enabling users to access real-time insights directly from the app.
- Recommendation Engine: Similarly, we combined backend data with behavioral data to determine which new content to recommend. Instead of starting with manual content curation, we built a Minimum Viable Product (MVP) based on data from the start, ensuring a seamless transition to AI-driven recommendations.
Want to Implement Operational Analytics?
Most businesses choose AWS as their cloud platform, and at Kormoan, we’re well-versed in AWS and other data technologies like Snowflake, OpenSearch, and more cost-effective open-source options. While operational analytics requires specialized skills and continuous refinement, executing it properly gives you a powerful competitive edge that’s hard to beat.
