September 7, 2016
4 BI projects examples
The discipline of business intelligence (BI) has come a long way from Excel spreadsheets to fully embedded analytical tools. Still, it’s not uncommon for enterprises to struggle with choosing the right level of BI integration out of multiple options.
As seen in our practice, businesses are finding themselves at different stages of embedding analytics into their core operational applications. The reasons for this are not only in budget and time limitations. Sometimes, the depth of integration depends on a company’s specifics that need to be considered before launching into analytics at all.
To explain these dependencies, our BI consultants have put together some of our BI projects examples to illustrate 4 common levels of embedding analytics into enterprise software. Let’s take a look at levels 0 to 3.
At this level, a company would use a completely standalone analytics tool that doesn’t automatically communicate with the core data generating app. An example is exporting data from a certain app for further analysis in Excel, thus creating a new copy of data along the way.
User experience: Users would be working with two apps that look and operate differently, as well as requiring separate credentials to access.
When to use: The right answer is never. The two apps would have no synchronization, and data would need to be manually updated all the time.
The only exception is when you can’t afford embedding for some reason, for example, when you are just at the beginning of your product lifecycle and analytics comes from a third-party app. It works alright at this initial stage, but you should aim at integrating analytics deeper into the core app as soon as it becomes feasible.
In one of our BI projects, an Australian customer was to launch a nationwide online pay-as-you-use recruitment platform that could help HR managers cut recruiting costs by up to 80%. Itransition designed and developed a scalable and secure media content processing system supporting metadata management and media distribution tools that also enabled video streaming.
Since the platform supported BI-based mapping of recruiters and candidates, analytics was initially planned to be embedded in the app. Yet because the customer was a startup, they decided to go for manual data export while at the MVP stage. No need to say, it turned out to be a costly and tedious process down the line.
As soon as the customer scaled up, we embedded the planned analytics features into the app right away, which dramatically increased the app’s efficiency.
At this level, the two apps would be connected through a single user sign-on. Security would be integrated into the core app.
User experience: Despite a slightly more convenient sign-on process, users would still be using two separate apps. So while the overall experience is more seamless, the disadvantages are comparable to level 0.
When to use: Essentially, there can be four use cases:
Tips: Remember about the user roles and rights, and tailor authentication features accordingly to make sure your 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 you maximize the potential of this embedding level for an effective data analytics strategy.
In another of our BI projects examples, one of the leading US brand consulting agencies needed BI software to help their clients monitor, collect, analyze and manage data about their brands online.
The project objectives were to minimize the time required to transfer and merge stats collected by different apps, to cut efforts spent on locating and accessing distributed data, and to boost analytical efficiency with rich visualizations.
To achieve these goals, Itransition developed a brand management BI solution with multiple independent modules:
All these modules were connected through a single user sign-on to create an impression of working within one app.
As a result, the analytical kernel offered a 360-degree view of brands with the ability to configure and customize reports as well as matching the monitored brand specifics.
This BI solution became a core business-critical tool for the customer. It helped to increase operational efficiency, provide clients with value-adding services, identify weak links in business processes, and strengthen those to power data-driven decision making.
At this level, analytics is finally embedded in the core app, although at the UI level. This is the most common analytics embedding model.
Technically, it can be implemented as a report module, a tab, or a dashboard visible to users on the app's home page immediately after they log in.
User experience: The look-and-feel of the analytical components matches that of the main app.
When to use: This is the way to go when analytics has to be accessed frequently and as easily as possible. It’s also the choice for those who need convenient reporting modules.
Tips: You can make user experience even more cohesive by customizing your embedded analytical app to complement other products in your tech ecosystem. If this is your business intelligence implementation plan, it’s best to choose an API that is stress-free not only to implement but also to maintain and update.
A UK-based provider of reporting, budgeting and reconciliation software needed an upgrade to their ERP reporting and query app for business users.
Itransition’s team added vital analytical features, extending the system functionality and introducing new supported platforms. The results exceeded everyone’s expectations, with more than 50,000 corporate end users earned and the app outperforming its competitors.
Part of the success was in developing an advanced commenting engine and creating a framework to define a set of report variables of different types. This reporting feature included:
At this top level, analytics becomes an integral part of the main app. This should be your ultimate goal of analytics embedding.
This way, analytics is built into the app screens and can be 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 analytical potential of an organization and thus reaching strategic goals that would otherwise be unattainable.
User experience: It’s the most frictionless out of all the four levels. Analytics is embedded within major workflows and is at the core of the experience.
When to use: The scenarios include business cases of offering personalized customer experience and support, as well as when analytical features are necessary to align the insight with the immediate action in the same context.
Tips: The embedded analytics and the main app should be put in the same context. When transacting and updating data from the analytics part of the app, it should be implemented by calling the backend API of the core app while enforcing business rules, or through direct data callbacks to the database.
In this project, the customer was one of the leading oil & gas enterprises that required an update to their existing reporting system.
The challenge was to put together performance data on key business dimensions that was scattered across documents and reports created by different employees and departments. With this data shared only via a monthly newsletter, decision making was extremely slowed down. To increase data analysis efficiency, it was necessary to introduce a centralized solution for corporate data storage, processing, visualization, and transfer.
Itransition’s team addressed the challenge and delivered an automated KPI reporting tool that met the following objectives:
As a result, the customer got access to 10 toolbars with KPIs presented as diagrams. They were united in a single user-friendly interface. At the same time, the solution also enabled data filtering by department. For in-depth analytics, users could quickly drill down to more granular KPIs from any point on a diagram.
Illustrated with our own BI projects examples, these four levels of embedded analytics all form a continuous journey, and it’s important to understand where you stand to yield positive results from your analytical efforts.
The first two levels—zero embedding and unified sign-on—are only intermediary and should be upgraded as soon as your organization finds resources to take this step.
The upper two levels—shared UI and full-fledged embedding—are the ones where analytics can be unleashed most efficiently. They vary in efficiency, though. While you can add more user convenience with UI-level embedding, the fourth level is where you can fully rely on your enterprise data to make strategic decisions.
Hopefully, this illustrative guide shed some light on how to achieve the ultimate investment/value ratio when choosing your appropriate level of analytics embedding.
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