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An embedded analytics platform is a service for companies creating production-grade analytics visualizations from their raw warehouse data. Typically, this involves several steps including:
To summarize, a company just needs to connect their data warehouse, create graphics from that data warehouse, then embed those results into their application via an embedded analytics provider.
Embedded analytics platform providers are used to the typical workflow when it comes to initially creating and iterating on analytics experiences for customers. Here are some of the many reasons to pick an embedded platform as opposed to building in-house:
These platforms optimize for best-practices out-of-the-box to avoid mistakes with embedded analytics. There are a lot of commonalities amongst features in enterprise-grade reporting products, which embedded analytics platforms aggregate into a cohesive product. A great example of this is the Explo Report Builder, which is inspired by enterprise-grade self-service reporting products available in the market today.
Surprisingly, companies with analytics in their application that use embedded analytics platforms oftentimes are tweaking the end user experience 2+ times per week based on client demand; with an embedded analytics platform, this tweaking is extremely easy. By constantly improving an analytics experience, companies gain credibility and trust with their customers and are able to keep up with the fast-paced changes in the market.
Oftentimes, there are different individuals who do any of the following in a company:
With embedded analytics platforms, the unique roles of each of these users can be taken into account. You can configure different permissions by user, or even completely separate each persona’s workflow within an embedded analytics platform application to maximize productivity of the team. Without an embedded analytics application, most of these workflows can be unclear and disjointed.
Engineering time is extremely expensive. With developing an analytics solution in-house, oftentimes it is the engineer that must implement the SQL logic, dashboard styling, and more. With embedded analytics platforms, this business function can be offloaded to non-technical individuals on the team to solve, without the need to work with engineering to create or deploy changes.
Building scalable systems for real-time analytics can be difficult to do in-house as an early or even mid-stage startup. For example, Explo has built a complex infrastructure for efficiently querying data from a series of data warehouses and managing those connections. To build this in-house could take an engineering team months or years without a noticeable improvement to the core product.
Your users (both internally and externally) may want to query data without the need to use SQL or other database-specific logic. Instead, they would like to ask for what they’re looking for in natural language. With the rise of artificial intelligence (AI), machine learning, and Large Language Models (LLMs), it’s becoming increasingly easy to offer just that. The major embedded analytics platforms have already incorporated AI into their native solutions, enabling use cases for analytics that were only possible in science fiction to be possible for every user.
Your users expect things like self-service reporting, customization, and more from your analytics offering on day 1. Instead of building this from scratch, you can lean into an embedded analytics platform to get this functionality immediately, while leaning into the edge case solutions that these tools have already thought of.
Explo - A great solution for building internal or external BI tooling. Explo is made to scale with your team, whether a small team of founders or a larger enterprise team.
Metabase - A great open-source solution, primarily focused on an internal use case. Metabase is great for eventually being able to self-host if that’s the goal.
Tableau - While potentially more expensive, Tableau has been around a long time and is very capable of scaling with a company’s needs.
Looker - While app speed is a concern, Looker is still one of the leading solutions in the market for internal BI, and more recently embedded analytics as well.
Thoughtspot - A great option if your company is looking to lean into the AI revolution.
Mode - An option if the persona within your company is primarily data teams.
Sisense - A great option, particularly for the business analyst persona.
It doesn’t matter! The best embedded analytics platforms optimize their experience for each persona, growing with a team as it goes from founder-led analytics to developer-led analytics to product-led analytics. Through each step of the way, the best embedded analytics platforms keep the experience simple enough for ease-of-use, but configurable enough to allow for many cooks in the kitchen to create a wonderful end experience.
Hopefully, this has provided a helpful overview of what an embedded analytics platform is, how they operate in the market, and, most importantly, how they collectively push the bounds of what’s possible in customer-facing applications.
