It seems that Business Intelligence software has become a nearly consensual part of any data-driven organization. Today companies large and small are realizing, more than ever, that “data is power”, and that harnessing this power requires the right tools for the job.

But in the near future we might see BI take another direction: Rather than companies merely purchasing dashboard reporting software for the purposes of internal usage, we’ll be seeing a surge in companies looking to integrate advanced analytics and reporting into their own products. Welcome to the world of embedded business intelligence.

What is Embedded BI?

The Real Cost of Embedded Analytics

Simply put, embedded analytics (or embedded BI) means adding features normally associated with BI software – such as dashboard reporting, data visualization, and analytics tools – to existing applications. This can generally be achieved in two ways:

  • In-house development — i.e., the app manufacturer builds its own analytics platform and includes it in its existing product
  • Purchasing and embedding out-of-the-box software — i.e., turning to an external developer and integrating its analytics solution in the application

To Build or not to Build, That is the Question

While both of these solutions are viable ways of adding a BI platform to existing software, it’s widely accepted nowadays that most companies simply don’t have the required technical expertise and resources to develop a truly robust analytics tool.

Business Intelligence is a complicated field that requires specialized knowledge, and if the software developer doesn’t already possess the required manpower and background, acquiring them could be extremely time and resource intensive.

Hence, any developer that hopes to provide an analytics feature that can provide actual unique value to its end customers, and who doesn’t have endless time and endlessly deep pockets, should probably be looking at embedded BI solutions.

Why Embedded BI Solutions Are Getting Big

So what advantages does embedded BI offer to software developers? Why is it poised to be the next big thing in business analytics? Because companies are beginning to realize the power of data and the added value that can be derived from it – not just for themselves, but for their own customers as well.

Collecting data has become easier than ever. Any software application that processes large amounts of data (marketing automation applications might be seen as a typical example, but other examples can be found in a wide range of industries) can also record this data and store it in a fairly structured form — in an automated manner and without the need for any kind of human intervention.

And if you’re already collecting all this data – why let it go to waste? Giving your customers access to it can make your own product that much more valuable. However, raw data is usually less than entirely useful without the means to “crunch” (i.e., clean, analyze and visualize) it. This is usually done with the use of BI software.

The Real Cost of Embedded Analytics

Still… Why Embedded?

But since plenty of dashboard reporting tools exist, including quite a few that can integrate with existing platforms or CSV exports, the question remains – what is the unique value of embedding the analytics platform within the application that’s collecting the data?

The answer is simple: to keep things simple. Users don’t want to alternate between platforms and to become accustomed to a whole new user interface and framework. If they’re dealing with data that is generated or exists within a certain application, it’s much easier for them to proceed to handle that data within the same application rather than being forced to purchase, install and become familiar with an additional tool. This also shortens the time periods that pass between the data being generated and its analysis, which makes for more effective analytics.

For these reasons, embedded BI provides a much cleaner and friendlier user experience for customers, and therein lies their major advantage over solutions that require two separate platforms.

Things to Consider When Evaluating Embedded BI

When looking for an embedded reporting solution it’s important to look into a few key areas to ensure that the solution you choose will meet your needs. For example:

  • Standalone – Does the solution require additional tools from third-party vendors in order to manage, visualize, and integrate data?
  • Scalability – How much data can a single server process? How many users can access and query the database at the same time?
  • Time to Market – How long until the embedded solution is actually up an running for customers? Days/weeks or months/years? Embedding BI should save you time and resources, not make your timeline longer.
  • Customization – What kind of API sets, custom visualizations, plugins, CSS customizations, and white labeling options are available?
  • Security – What kind of options are available to make sure the right people see the right data? Does the software you’re looking at have multi-tenant security and role level BI security?

Why It Will Get Even Bigger

We will venture to guess that the increased adoption of embedded BI solutions in recent years will not be a passing trend; in fact, it would appear that we will be seeing much more integrated BI solutions in the near future.

This prediction is based on the fact that data, and data analytics, are becoming much more of a commodity — and are no longer seen as a luxury item for the larger and richer corporations, but as a must-have for almost any kind of data-driven business, and even for consumers and individuals who would like to be making better-informed decisions in their day to day lives. And with the Internet of Things and the growing propensity of mobile and wearable device usage, the amounts of data anyone can be exposed to at any time grow considerably.

For example, we could easily imagine a reality in which shoppers have a mobile app that analyzes recent changes in product prices — allowing the consumer to decide whether to purchase a certain product on the spot (e.g. when standing in front of the counter at the supermarket), or wait for a better time. Navigation apps are already analyzing traffic data to find the shortest routes, but giving the customer direct access to this data might let him also learn what would be the most gas-efficient or safest one as well, or come up with his own insights. Wearable devices can track a person’s heart rate, speed, and other variables during his or her work out — data analytics could later be used by this person to determine which workouts or exercises were the most effective.

Truly, the possibilities are close to endless. Once consumers realize how much they can better their own lives using data (whether Big or small), they will begin to demand manufacturers to provide them with tools they can use in order to analyze this data themselves. Accordingly, we expect to see an increasing amount of products coming with an embedded analytics feature, presenting a new opportunity for software and application developers and BI vendors alike.

Case Study: Profit Tools

A typical embedded analytics example of how embedded BI is used to transform existing businesses is the story of Sisense customer Profit Tools. This developer Trucking Management Software package was looking to offer added value to their customers by introducing new analytical functionality.

After the vendor selection and implementation processes, Profit Tools was able to offer embedded BI features which gave them their customers in the trucking industry the ability to analyze the full scope of data collected during the trucking life cycle, while also allowing them to identify new ways to improve their profitability – which is always a major issue in the shipping industry, which typically works on tight margins.

The new analytical capabilities revealed a world of new insights to Profit Tools’ clients: if before they might have had trouble understanding whether a certain route was profitable, and to what extent, due to the complex nature of calculating cost attribution – now they were able to single out specific work examples and filter through data from multiple data sources to get to the exact data relevant for a specific segment of travel.

The ability to offer simple, actionable insight, while close to a dozen data sources are being mashed up “under the hood” – demonstrates the new types of revenue and value that application developers are able to provide using the rapidly-developing world of embedded business intelligence technology.

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