Written by Harry Dix |
Looking to learn about the basics of embedded analytics and what it entails? You've come to the right place. Let’s get started with the first essential consideration.
Differences Between “Embedded Analytics” and “Analytics”
Embedded analytics does what any analytics does: It transforms data stored at different locations into comprehensive charts and dashboards. It goes from data integration to data transformation, data modeling, and data visualization.
The main difference is that embedded analytics as an individual software solution does not look and act as a standalone solution but, instead, is integrated within another software product (application) or web portal. That integration might be of purely an aesthetic nature — just on the surface — or, alternatively, far deeper into the foundations of the two tools.
Typically, end users of embedded analytics solutions (business managers and specialists) may not necessarily even recognize that they are working with embedded analytics in the business process, whereby dashboards and visualizations are embedded into another application or web portal — they simply see it all as one tool.
As highlighted in the definition below, bringing embedded analytics to a business process gives the end user quick and easy access to data visualizations within their daily workflow, all without the need to switch between multiple applications (i.e., from their current workflow to a separate analytics tool).
Gartner defines embedded analytics as:
“ ... a digital workplace capability where data analysis occurs within a user's natural workflow, without the need to toggle to another application.” (Gartner Glossary, Information Technology Glossary, E, Embedded Analytics)
Very importantly, embedded analytics also enables software companies to acquire and fully integrate an analytics platform with their own SaaS product without the need to heavily invest in the development of their own in-house solution (built from scratch).
Key Embedded Analytics Features:
- SSO: Single sign-on ensures that end users access data visualizations embedded into another application just with one login, not two, for two tools.
- UI customization toolkits: The essential part of advanced embedding is the ability to customize the look of the whole user interface (branding, use-case-specific needs, and so on), or individual charts.
- Lifecycle development environment: This one is especially important for software companies as they need to further develop, version, test, release, and align the changes in the embedded analytics platform with their SaaS product as one body.
Who Is Interested in Embedded Analytics
While there are numerous use cases for embedded analytics, there are essentially two main reasons for companies to pursue embedded analytics:
- Data-driven organization: This type of organization wishes to instigate a data culture within its business, and steer their internal teams, or external partners, into being data-driven, giving them easy and centrally managed access to data (and the benefit that it brings) on a daily basis.
- Software product companies (SaaS): These companies are developing their own product (e.g., application, web portal, or similar) in order to enhance its capabilities by integrating it with an analytics platform, essentially turning the two products into one.
What to Look for in an Embedded Analytics Solution
As part of the process of acquiring an embedded analytics solution, you will no doubt have a whole list of different criteria in mind when considering different vendors:
- Build or buy: It's the age-old question. Particularly for software product vendors looking to embed analytics into their SaaS product, one classic inquiry they’ll face is: “Should we develop an in-house analytics solution embedded into our product, or purchase an embedded analytics solution from a specialized data analytics provider?” Read this guide for further insight.
- Scalability: Will the solution scale with your future growth plans?
- Self-service with drag-and-drop visualization: Can my employees, or customers’ end users, create their own analytics visualizations or dashboards without prior technical knowledge?
- Security and privacy: Who has access to what data, and how is the data of different teams or customers separated?
- Predictable pricing: Is the pricing model transparent, and how does it affect current and future profitability (in line with growing user numbers and/or data volume)?
Types of Analytics Embedding
As highlighted above, one of the key considerations when pursuing embedded analytics is how to go about actually embedding the analytics into the application or web portal it is destined for. There are two main ways by which to integrate your analytics. These include:
- Basic embedding via iFrame: This allows for the simple embedding of customized, preset visualizations, dashboards, or even a user-friendly, self-service analytics interface with single sign-on incorporated. You can even think of it (in a simplified sense) as a copy-paste function.
Note: While white-labeling (self-branding) is, strictly speaking, not embedded analytics, it also may be an option for companies that require their app and analytics solution to be kept separate. It enables customization of the “look and feel” of the analytics interface, in line with company branding, logos, and other signature features.
A Quick Recap
To recap briefly, embedded analytics is:
- The point at which two pieces of software become one, with analytics integrated into a user’s workflow
- Used for two main reasons: by data-driven organizations to enhance decision making and, in turn, growth, and by software vendors (SaaS) to complement and enhance their software product
- Offered in many shapes and sizes, and having a list of key criteria and functionality to consider is paramount in finding the right solution for your organization and use case
- Able to be embedded and integrated to varying degrees, depending on the use case
Ready to Learn More?
Are you ready to learn more about embedded analytics and the benefits it can bring to your business? Dig into the details with our embedded analytic starter guide; it explores the key aspects of embedding analytics, what to look out for, and where to start. Alternatively, continue to the embedded analytics page to discover the GoodData platform and its full feature set.
Written by Harry Dix |