We’ve all been told that the best way to succeed in today’s marketplace is to enable your company to make quick, data-driven decision. But as software vendors embrace this “data driven” mantra, it becomes clear that we need to begin looking at our data in a whole new way. The need quickly arises for new types of data solutions to collect, organize, analyze and automate our data and then distribute insights and analysis in a quick-and-easy fashion to those who need them. Self-service data platforms like Qrvey were specifically created to meet these new challenges, and in this article, I’ll review my top five criteria for choosing a self-service data solution for your platform.
1) Capabilities
The first thing you’ll obviously want to consider when choosing your data solution are its capabilities. There are plenty of individual solutions in the market to help your product collect data, analyze that data and make it actionable using workflows and automations, but only Qrvey offers an all-in-one solution that is both self-service ready and easily embeddable. This allows data, analysis and insights to reach more people, in more places, than ever before.
2) Architecture
The second thing to consider is the solution’s architecture. This topic has many facets. As a data platform, it’s important that ALL of your existing data can be easily ingested. That’s why Qrvey has adopted an “all data accepted” philosophy that can handle structured, semi- and unstructured datasets, whether they live on-premise or in the cloud. Speaking of the cloud, Qrvey’s serverless, AWS-based architecture means you save big on hosting costs compared to server-based solutions.
3) Embedded Analytics Capabilities
Being able to ingest and analyze your data is great, but without the ability to output the results where your users can easily access them, you’re missing half of the value. The entire Qrvey platform was built from the ground up for embedded analytics use cases. We offer both a robust API and prebuilt widgets to make the embedding process seamless and all inclusive. We also feature a mobile-first approach that ensures embeddability applies to any size device.
4) Performance and Scale
The next item on the list of things to consider is performance. As I’ve already mentioned, Qrvey is an all-in-one platform that lives on modern, cloud architecture. This provides us with the utmost in performance, even with large, diverse data structures and in complex multi-tenant environments. When offering analytics and automation for SaaS application end users, the value of performance cannot be understated, especially as your product comes to depend on data services Qrvey provides.
5) Business-y Considerations
Finally, there’s the business side of your data solution to consider. Topics like pricing and licensing model are certainly a factor, especially in embedded and OEM deployments. But there are other factors to consider as well, including your vendor’s business model. Is OEM a large part of their business? If not, upcoming features and enhancements might not come to you in a timely manner or might not be embeddable at all. At Qrvey, OEM clients are always a priority, making your business, our business.
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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