Embedded analytics vs business intelligence. The term “embedded analytics” is often used interchangeably with “business intelligence”--but are they really the same thing? Both take data and turn it into insights, so what’s the difference?
Business intelligence (BI) refers to the systems, processes, and other resources that pull data from a variety of sources and organize and analyze it to fuel business decisions. Much like business intelligence, embedded analytics relies on data from many systems and sources. The difference is in the collection process.
Embedded analytics sources data from integrations within existing systems and platforms. It’s the combination of the dashboards, reports, and metrics your business needs to make data-driven decisions--already embedded inside business applications and software.
But if data already exists in our application, then how is embedded analytics different?
While it’s true that the data lives inside the application, without embedded analytics, the data isn’t organized or displayed in an intuitive, actionable way. When software vendors build a product, they tend to focus most of their resources on achieving the end-goal of the product and reaching go-to-market quickly and as inexpensively as possible. As a result, reporting and dashboards are often omitted or added as an afterthought after the product launches.
Here’s the problem. Once consumers start using the application, they realize the potential for all of the data hiding inside it and desperately want access to it. That’s where embedded analytics comes into play.
What are some use cases for embedded analytics?
Software vendors may choose to partner with an embedded analytics provider instead of building their own solution for a variety of reasons. For example, if business intelligence isn’t at the core of their product, it saves them from redirecting valuable development resources to hire a BI partner to manage embedded analytics. Buying an embedded solution versus building one in-house accelerates speed-to-market, and with a product that is ultimately more powerful than it would have been before.
Embedded analytics isn’t just for software vendors, though. The benefits and use cases expand beyond the world of software development to power business processes and teams of all kinds.
One of the biggest benefits of embedded analytics is the ability to nest reports and data inside the applications that teams use the most. Whether an internal portal, a customer service platform or a help desk system, embedded analytics mines the kind of day-to-day user data that drives operational efficiency.
If you manage a team, you may need to share performance data with other groups or your executive team. With embedded analytics, other teams can “guest view” reports without the need to purchase additional user profiles.
Another common use for embedded analytics is to share data with external audiences like customers, partners or website visitors. The ability to share live data not only builds trust with your audience but also allows your organization to shine a credible spotlight on your success.
Why do you need embedded analytics?
In addition to creating a better user experience, embedded analytics drives end-user adoption by encouraging users to harvest data from the applications they use every day. When data becomes an automatic, integrated part of a user’s workflow, they will be more likely to turn to information instead of instinct as a driver for business decisions.
Embedded analytics also reduces friction for users by eliminating the need to log into a separate platform to access data. When the information they need is already nestled in the applications they use on a daily basis, teams become more self-sufficient and empowered and less reliant on IT.
As Internet of Things (IoT) and big data projects become more prominent, businesses will benefit from using an embedded analytics solution to intelligently manipulate massive amounts of data into a simple, digestible format.
How do you implement embedded analytics?
There are three different phases or methods for implementing an embedded analytics strategy. By using a phased approach, you can start small and scale over time to meet the level of analytics maturity you need to meet your business goals.
Loose coupling/branding
The first phase offers a quick way to start leveraging data inside an application without a big development burden. “Loose coupling” is the idea of adding your branding elements and an API integration to your interface inside a simple portal. It’s as simple as building the reports, setting up user profiles and deploying the portal to your team.
Inline charts and reports
The second phase builds on the branded approach and directly embeds dashboards and reports within your application or web pages. The information is visible to customers but blends easily into your application by using your branding and messaging. While this phase requires a bit more developer experience, it’s easy to add stylesheets and branding elements with simple CSS. In this phase, you have the freedom to experiment with new features and automation.
Seamless API Integration
When you reach the third phase, you’ve already built a customized interface with the right controls and data layouts to meet your needs. It’s time to supercharge your ability to build new datasets and maintain security protocols through a seamless API integration. It offers advanced features and easier administration, as well as the ability to customize the application of datasets, based on the information you want to see.
Considerations for software vendors
Before you choose an embedded analytics provider and embark on the first phase of your journey, you should consider several important criteria. First, make sure the partner you choose will protect your business over any competing interests and offer the right level of support and training to your team. Secondly, your partner should offer white labeling so you can lead with your own brand and message. Finally, the right partner will have a simple licensing model and flexible pricing that will be affordable for your customers.
What is the cost of embedded analytics?
It comes as no surprise that the cost of embedded analytics solutions varies widely based on customer requirements and vendor offerings. There are three types of software companies offering embedded analytics today. The first is the multi-billion dollar legacy BI company. Think of the IBMs, Oracles, and Microsofts of the world. Their solutions are designed for enterprise organizations and are often expensive, complex to implement, and require a ton of resources.
The next subset of embedded analytics providers is investment firms like Sisense, Logi Analytics, Domo, Looker, and others. While these products are sleek and much easier to implement, the cost is usually high, and sales tactics are high pressure.
Finally, you have the feature-rich, scrappy bootstrappers of the embedded analytics world, like Yurbi. While these companies are smaller and may not have every feature of a more expensive enterprise solution, they offer concierge support, a focus on customers, and the agility to add features more quickly as you request them.
In general, pricing models can be structured in a variety of ways, from a per-user basis to a data source (or per-database) model. Some vendors also offer an on-premises component to pricing, but this model can become very expensive very quickly.
For more information about vendor-specific pricing, check out some research we’ve gathered over the years. The bottom line? Make sure you fully understand pricing and licensing before you select an embedded analytics vendor.
At some point, every organization struggles with the best way to use and present information. Put your data to work for you by using the right dashboard and reports. Embedded analytics takes your data “problem” and turns it into a solution to help you make the right business decisions.
To discuss your embedded analytics requirements and use case further contact us.