With the rise of powerful cloud data platforms, there’s buzz around the term “data apps”. What exactly is a “data app”, what is new here, and what possibilities could this unlock?
First, a clarification for anyone outside the startup bubble. Every application is at the core an UI/UX on top of data. Just think of your experience ordering an Uber or booking a hotel or navigating a payment - these are all data-intensive applications. However, the term “data app” as used recently within the data software ecosystem connotes the use of data warehouses (or broadly cloud data platforms) as the primary source.
With this definition, let’s address the obvious - there is a multi-decade history of building reports and visualizations on top of data (primarily) sitting inside a data warehouse-like architecture. While not the most powerful applications, these reports were the proto-data apps.
But here’s what’s new - 1) the breadth and depth of data being collected is 100x vs the last decade 2) these cloud data platforms are able to link cross-functional data and process them into valuable new pieces of information or knowledge. This point is key - we are transforming and creating new information that is only possible in these cloud platforms 3) the way we work is rapidly shifting into collaborative, information-driven flows. With these three factors put together, there is an organic pull towards building user experiences or applications on top of these cloud data platforms - our emerging “data apps”.
Let’s pick a specific example to elucidate this trend. B2B sales is no longer a top-down, pre-product, value-persuasion function. Users try the software for cheap/free, and there are key product signals that can guide the sales team. Product and sales data can be cross-linked and processed in the cloud data platform, and then feed an app that enriches the sales team’s daily experience. It’s no surprise there are many startups targeting this exact use case, building on top of the data warehouse.
Setting aside applications that target specific domains, here are a few examples of common workflows in organizations that data applications can power:
- Quantifying how granular segments affect metric trends before a monthly business review
- Assessing the impact of a new feature or an intervention across a set of metrics. (in general, being able to see connected metrics for any analysis - after all, no metric is an island)
- Sizing the metrics impact of new ideas or initiatives - if we impacted only new users by X%, what does it mean for total revenue?
- Generating flexible behavioral segments, and then measuring the results of interventions on these segments
The “data apps” movement symbolizes the much needed blending of rich datasets, UI/UX and organizational workflows. It makes data and information come alive, directly relevant and usable in the consumer’s workflows.
Reports were the proto-data apps. But, emerging data apps on modern cloud data platforms that leverage rich cross-functional information can delight users in ways that classic reporting could never enable. This is the exciting future in front of us.