The rise of operational analytics will create new roles and opportunities for data teams.
Operational analytics is a way to deliver data directly to teams, individuals and other stakeholders throughout a company, so it can be used to improve the efficiency and effectiveness of an organization’s operations. Instead of using dashboards or reports to understand data, operational analytics drives action by automatically delivering real-time analytics, insights and data models to the exact place it’ll be most useful.
In simple terms, operational analytics pulls data from your data warehouse and feeds it into operational tools like Salesforce, Zendesk or Intercom. With the right data stack, operational analytics makes it possible for a company’s data warehouse to act as a new kind of Customer Data Platform – one that enables high-scale, product-led companies to deliver personalized experiences that customers historically only get from, say, a small neighborhood boutique.
Traditional analytics forces data stakeholders to chase data. Operational analytics brings data to your data stakeholders. This shift happens because of the process by which data is delivered.
Traditional analytics includes business intelligence dashboards, spreadsheets, and monthly reports. In order to create any of these things, you or your data stakeholders need to seek out data—to query and then visualize it.
Operational analytics delivers data automatically from a data warehouse into an individual tool. Instead of seeking data out, you’re building a pipeline to deliver real-time data right to your stakeholder.
This change from seeking to delivering causes a fundamental shift in the function of data. Data is no longer shelved away in your warehouse or delivered in the form of static snapshots. It’s active and comes to your stakeholders.
When teams start to understand that this kind of functionality is available and see what it can do for them, data suddenly becomes an actionable tool, rather than something for reactive analysis.
Data will follow in the footsteps of DevOps. What I mean is that there’s an emerging stack of data technology that opens the door for new roles and ways for the data to contribute to the success of the organization.
DevOps arose to close the gaps between developers and IT by creating a loop of communication using the principles of continuous integration and continuous delivery. The role of a DevOps engineer is to use these principles to facilitate the work of developers.
DataOps arose for similar reasons—to close the gap between data people and IT. Following similar principles as the DevOps engineer, the role of a DataOps engineer is to facilitate the work of data people.
The next step for the data world is the emergence of a new data role whose responsibility is to facilitate the work of knowledge workers using operational analytics. This new opportunity is the role of the analytics engineer.
The analytics engineer will work with the modern data stack to build pipelines that deliver real-time data automatically. That modern data stack looks something like this:
Fivetran to collect data
Snowflake to store structured and semi-structured data
dbt to standardize data models
Census (heyo 👋) to send data back to individual tools
This modern data stack is lightweight, easy to set up, and easy to use. Together, these tools free up the data team to develop a new area of expertise (exemplified in the analytics engineer) solely dedicated to managing the flow of data.
With operational analytics, data teams become active participants in the business’s success, because they’ll help drive operations. Teams across the organization will realize data is an active asset they can do things with, while the data team starts to structure itself around the opportunities that arise.
A simple way of understanding how operational analytics makes data teams more vital is with support tickets. Operational analytics can place payment-plan data within each ticket and then automatically prioritize all of them based on that data.
This means CS reps don’t have to use their gut or look for data to decide which ticket is most important. The automatically delivered data will tell them, so they can resolve the most important tickets first.
Zoom out and apply this example to the data needs of every department. With operational analytics, data becomes not just vital but also embedded in everything that gets done. It becomes an undercurrent of insights facilitating the success of each individual contributor. And your data team is at the center of it all.
Implementing operational analytics doesn’t require you to lobotomize your current data infrastructure. Start small with your data warehouse, dbt, Census, and one use case (like the CS rep example).
Once you start, operational analytics will snowball, becoming central to how your business operates. If you’d like us to show you how, schedule a free demo of Census, and we’ll help you get things rolling.