In the dynamic world of startups, the race to deliver feature-rich products often leads to a common mistake: developing analytics using existing in-house application engineering resources. This approach, while seemingly integrated, is incorrect because it misaligns resources away from their core competencies, uses tools that don’t scale well, and wastes effort reinventing the wheel. The consequences? A user experience that doesn’t quite hit the mark, escalating costs, and delayed product enhancements.
Startups ambitiously add analytics to their roadmap, using available resources to craft these capabilities as though they were mere features. Initially harmless, this strategy soon unfolds into stretched engineering teams, sluggish reporting, degraded application performance, spiraling expenses, and a bottlenecked development process.
A Shift in Perspective
The essence of building effective analytics lies in the approach. The key? Early integration of off-the-shelf analytics products supported by a dedicated data platform. This strategy not only eases the burden on engineering resources but also unlocks a realm of enhanced functionalities, efficiency, and scalability.
Why Off-the-Shelf Embedded Analytics?
Leveraging ready-made analytics products, powered by a purpose-built Data Lakehouse solution, allows startups to:
Implementing Embedded Analytics The Right Way
When creating this new capability, it is critical to put in place an architecture that leverages best practices and the newest developments to ensure you create a solid platform for growth. A few key points to consider are:
In addition, product users are increasingly demanding the ability to interact with the data and application via Conversational Interfaces to get the answers they need, so support for these new interfaces is very important.
Addressing the Skills Gap
One reason why startups hesitate in pursuing this path is that it requires them to step back from doing incremental changes and thinking about incorporating a different set of skills and processes: their Application Team is not a Data Team. The tools, processes and skills are different (i.e., Go vs. Python, DevOps vs. DataOps, Postgres vs. Snowflake, etc.)
So how to get started? Hiring a junior level person to “start developing reports” is a recipe for disaster. Same thing with “someone with 10 years of experience at a large company” that will bring in a set of now-obsolete technologies just because they are familiar with them.
Especially at the outset, it is critical to consider the skills gap and look for a person or partner that can design and deliver the right strategy – someone who has done this before at a similar company, recently and multiple times if possible.
The journey towards integrating top-tier analytics into your product relies on wisely choosing tools and expertise rather than starting from scratch.
Opting for off-the-shelf solutions backed by real data platforms, architected and implemented by experienced professionals, allows startups to bypass common in-house development setbacks, setting the stage for product success.
For startups embarking on this transformative path, Fractal River stands ready with insights and assistance to help you incorporate differentiated analytics capabilities into your product. We provide you with an instant, fractional “Data Team as a Service” to design and implement your embedded analytics offering, while developing your internal capabilities at the same time.