Business intelligence (BI) has come a long way from Excel spreadsheets to fully embedded analytical software tools. Still, it is not uncommon for enterprises to struggle with choosing the right level of BI integration out of multiple options.
Businesses find themselves at different stages of embedding analytics into their core operational applications. The reasons for this are not only budget and time limitations. Sometimes, the depth of integration depends on a company’s specifics that needs careful consideration before launching analytics at all.
To explain these dependencies, we have described four levels of embedding analytics into enterprise software. With this information, especially coupled with BI consulting, a company can define the best approach for implementing BI capabilities.
Level 0: No embedding
At this level, an enterprise uses a fully standalone analytics tool that does not automatically communicate with the core data-generating app. An example is exporting data from a particular app for further analysis in Excel, thus creating a new copy of data along the way.
Users work with two apps that look and operate differently and require separate access credentials. It may negatively affect productivity since employees have to switch between different tools throughout the work process. Moreover, such an approach to business analytics typically implies manual data entry and export, which makes the work even less productive.
When to use:
Preferably never. The two apps have no synchronization, so you will have to update the data manually every time.
The only exception is when a company can’t afford embedding for some reason, for example, when it is just at the beginning of a product lifecycle and analytics comes from a third-party app.
It works fine at this initial stage, but companies should aim at integrating analytics deeper into the core app as soon as it becomes feasible.
Level 1: Embedding for security
At this level, the two apps connect through a single-user sign-on. In such a case, security is integrated into the core app.
Despite a slightly more convenient sign-on process, users still use two separate apps. Therefore, while the overall experience is more seamless, the disadvantages might be comparable to level 0.
When to use:
Essentially, there can be four use cases:
- When a company’s solution consists of multiple apps, and it uses a standalone analytics app to access data from one or more core apps.
- When the analytics app is located in the cloud and extracts data from on-premises or cloud apps.
- When a company can only purchase an analytics app separately from the core app.
- When a company uses this embedding method as an intermediate step and plans to integrate analytics later.
Enterprises should remember the user roles and rights and tailor authentication features accordingly to ensure users only see the information they are allowed to see.
Synchronization between user profiles and the core app, as well as enhanced security in multi-tenancy environments, will help companies maximize the potential of this embedding level for an effective data analytics strategy.
Level 2: UI sharing
At this level, analytics is finally embedded in the core app, although at the UI level. It is probably the most common analytics embedding model.
Technically, analytics can be implemented as a reporting module, a tab, or a dashboard visible to users on the app’s home page immediately after they log in.
Here, the look-and-feel of the analytical components matches that of the main app.
When to use:
It is the way to go if you need to access analytics frequently and as easily as possible. It is also the choice for those enterprises that require convenient reporting modules.
Developers can make the user experience even more cohesive by customizing an embedded analytical app to complement other products in an enterprise’s tech ecosystem. For instance, developers can customize dashboards and interfaces, enabling enterprises to provide employees with a high-quality user experience tailored to the specifics of their corporate workflows.
If a company goes with UI sharing, it could also consider using an API, which is stress-free to implement, maintain and update continuously.
Level 3: Fully embedded analytics
At this top level, analytics becomes an integral part of the main app. It should be the ultimate goal of analytics embedding for any enterprise.
This way, analytics is built into the app screens and provided on demand wherever the user needs to tap into it to make data-based decisions or trigger analysis-based transactions.
The advantage of this approach is in making the most out of the analytical potential of an organization and thus reaching strategic goals that would otherwise be unattainable.
It is the most frictionless out of all the four levels. Analytics is embedded within major workflows and is at the core of the experience.
In addition, since employees can easily share relevant data and insights via one digital solution in a couple of clicks, this approach may also be considered the most effective regarding employee productivity and collaboration.
When to use:
The scenarios include business cases of offering personalized customer experience and support, and situations when analytical features are necessary to align the insight with the immediate action in the same context.
You should put the embedded analytics and the main app in the same context. When transacting and updating data from the analytics part of the app, implement it by calling the backend API of the core app while enforcing business rules, or through direct data callbacks to the database.
Know your analytical goals
These four levels of embedded analytics form a continuous journey. It is worth understanding where a company stands to yield positive results from its analytical efforts.
The first two levels, zero embedding and unified sign-on, are only intermediaries and should be upgraded as soon as an organization finds the resources to take this step.
The upper two levels, shared UI and full-fledged embedding, unleash the analytics in the most productive way. They both vary in efficiency, though. Therefore, while companies can add more user convenience with UI-level embedding, the fourth level is where they can fully rely on their corporate data to make strategic decisions.
How to achieve the ultimate investment/value ratio when choosing an appropriate level of analytics embedding? Build your own successful case, considering the available resources and industry-specific business needs.