• Sean Cogan

Customization: the key to effective data automation


When we at Switchboard talk about customization, people sometimes think we are talking about creating custom reports and dashboards. While this is certainly true, there is more to customization than just data visualization applications. At Switchboard, we are also talking about smart data pipelines customized to your evolving requirements.


Think about how much data has changed in the past decade alone. Now try to imagine how different things will be in five, seven, or ten years’ time. The data you are using now will likely be different from the data you will leverage in the future: the Metaverse alone is going to create petabytes of data! A truly data-strong organization needs to have the capability to access foundational data (which is explained here) in real time and take action on it. This holds true as you identify new sources, bifurcate existing ones, and scale.


This is what we mean when we say customization: it’s really about navigating changes in your entire data ecosystem, not just dashboards and reports.


To fully understand how this works, it might help to illustrate with a current Switchboard client who is scaling globally. This client, an educational technology solution, collects data on how students, teachers, and schools learn. Historically, they have done a pretty good job capturing insights about their customers in the US.


But, as they expand internationally, they need to add new data sources which are in formats not typically seen in US-based reporting. These sources are relatively new and not easy to identify. But, if this client wants to scale, they need this data to be normalized to fit their existing database structure. And the only way they can effectively scale is if they are set up to create custom connectors to integrate these new data sources automatically. Fortunately, their DataOps infrastructure is prepared to navigate whatever changes arise.


So, how can any organization hope to keep up with these mind-boggling changes while maintaining their current reporting? My colleagues and I have been blogging for a while now that data-forward organizations should be looking into cutting-edge data automation as a starting point.


For starters, your DataOps infrastructure needs to have the capacity to integrate new data sources. This will help to generate those critical, business-driven insights to help you make the right decisions seamlessly as changes arise. Furthermore, your data science and data engineering teams need to constantly stay on top of changes and be mindful of future data requirements. That is what we mean by true (DataOps) customization.


In sum, having customized data goes beyond implementing a DataOps solution that is restricted to setting you up and letting you go. Instead, it requires flexibility and scalability to bring in data that changes, might not currently exist, or grows exponentially. Are you ready for what comes next?


To find out how you can customize your data journey as you grow, connect with us today.