Everyone wants to build the next great software product, the one that customers can’t live without and the one that grows seemingly by magic through word of mouth alone. But achieving this goal takes more than just a great product and a dedicated team behind it, it takes an ecosystem.
The term “ecosystem” has come to mean many different things in the software world over the years, but at their core, ecosystems are platforms that grow to be more than the sum of their parts by encouraging users to build, contribute and share, which in turn extends their reach far beyond what the company originally intended.
In years past, building an ecosystem meant having an API or an app store that would, theoretically, attract developers to extend your platform’s capabilities in new and exciting ways. But this approach created a catch-22 situation. Developers wanted a large user base before committing to write apps for a platform, but users would never come to a platform that didn’t have a large selection of apps. Ultimately, this paradox caused many ecosystems to fail and countless others never to begin in the first place.
Building an ecosystem for your data-driven application doesn’t have to be difficult or complicated however. That’s because the best way to expand your platform is by adding self-service analytic capabilities, allowing users to create their own charts, reports, metrics and dashboards. When users can explore their data and discover their own insights, and even combine data from your platform with data from other sources, you platform will become a lot more useful and engaging.
That’s where no-code applications, like Qrvey, can help. No-code apps don’t require developers, they can be built and maintained by everyday business users. This important distinction can drastically increase the adoption, usage and excitement surrounding your platform and can help you generate the viral growth you’ve been looking for. No-code apps are also great for your users, because it allows them to build and share exactly what they need, when they need it. They can get up and running faster and easier than ever before, which encourages them to create, explore and try things that previously were out of reach. What once took developers weeks or months to build can now be built by users in just hours or days.
Business intelligence and analytic products have struggled with the concept of ecosystems for far too long. These products have always seen low user adoption, limited engagement and for many users, sharing their reports, dashboards and insights was anything but enjoyable. Qrvey has changed the business analytics equation by embracing no-code analytic applications, making analytics completely self service.
Analytic apps also come with additional benefits as well, like the ability to be easily be embedded or shared practically anywhere. They can also include data and user security, ensuring that data privacy is always maintained.
All of these benefits add up to a full-featured, incredibly flexible platform that is both easy and enjoyable to use. Both of which are key elements to building a successful ecosystem.
David is the Chief Technology Officer at Qrvey, the leading provider of embedded analytics software for B2B SaaS companies. With extensive experience in software development and a passion for innovation, David plays a pivotal role in helping companies successfully transition from traditional reporting features to highly customizable analytics experiences that delight SaaS end-users.
Drawing from his deep technical expertise and industry insights, David leads Qrvey’s engineering team in developing cutting-edge analytics solutions that empower product teams to seamlessly integrate robust data visualizations and interactive dashboards into their applications. His commitment to staying ahead of the curve ensures that Qrvey’s platform continuously evolves to meet the ever-changing needs of the SaaS industry.
David shares his wealth of knowledge and best practices on topics related to embedded analytics, data visualization, and the technical considerations involved in building data-driven SaaS products.
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