The Future of Embedded Analytics in a Data-Driven World

Today enterprises continue to search for ways to provide more and deeper data-driven insights.  Infragistics’ Casey McGuigan explores emerging opportunities for in-app or embedded analytics and shares insights from a recent Infragistics survey.

Tags: BI, data, embedded analytics, Forrester, Infragistics, insights, time series,

Casey McGuigan, Infragistics
Casey McGuigan
product manager
Infragistics


"Developers are turning to embedded data visualizations as a means of differentiating their app and reducing churn."

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July 23, 2020
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We know that collecting data is relatively easy, yet interpreting that data is difficult. Embedded analytics, which converts raw data into business intelligence, presents developers with tremendous opportunities. 

 

Many of us have begun integrating these sophisticated capabilities into our software products as standard. In this climate, here at Infragistics, we conducted a survey to explore the opportunities and appetite for embedded analytics. 

 

As a result, we found less than one-quarter (22%) of the developers we surveyed have no plans to adopt embedded analytics solutions. So, the question remains – why not? In this post, I’ll look at embedded analytics in historical content, and shares some insights from our survey and other research about its prospects and future trends.  

 

Business intelligence is becoming a valuable tool in driving business decisions. Increasingly, end users, such as sales, logistics, HR and marketing managers want timely, visual and interactive access to their business unit data, and they expect analytics will be seamlessly integrated into the applications they’re already familiar with.

 

Unlike the historical business intelligence market that required data specialists to upload data into a separate BI program to perform analytics, embedded analytics are integrated into a developers’ application. This means that developers can make their applications more valuable if they enable their customers to see the app’s data in reports or visualizations right away. 

 

Forrester reports that between 60 percent and 73 percent of all data within an enterprise goes unused for analytics. One reason why enterprises are not taking full advantage of their data is that existing programs interrupt user flow, requiring employees to use a separate tool. If you embed analytics in existing apps, analytics become intuitive and spur faster and better decision-making.

 

Developer Motivations for Embedded Analytics 

Developers embracing embedded analytics have done so for a variety of reasons, according to our research here at Infragistics. 

 

We found that their top motivator is to increase customer satisfaction (36%), followed by the ability to make their app more visually appealing (23%), and gaining a competitive advantage (22%). While these are all valid reasons, if you are considering embedding analytics, you should also take into consideration the benefits you can provide outside your company – and think about these investments that can bring value to your end users as well.  

To illustrate this point, we also found 25% of our survey respondents reported that embedded analytics’ greatest usefulness for their end customers was to make better business decisions, followed by improving productivity (22%), and increasing sales/revenue (18%). These are just some of the ways in which embedded analytics are transforming business operations for end users.

 

Users are hungry for apps that provide real, lasting value. Imagine the opportunities that analytics can provide throughout organizations when they unlock valuable data from huge volumes of collected information. 

 

For example, a task management app can show the end user how they spend their time at the office. By providing embedded visualizations, the app could offer insights into how the end user could use their time better. For instance, a simple chart can show that they are spending over a third of their time in unproductive meetings. 

 

It’s a Data-Driven World

Data is everywhere and its potential just keeps growing. According to McKinsey, “The volume of data continues to double every three years as information pours in from digital platforms, wireless sensors, virtual-reality applications, and billions of mobile phones.”

 

Successful organizations put data at the heart of their operations, as it is a powerful frontier for innovation, competition, and productivity. Big data can be used to derive insights and obtain value in many ways – especially in 

(a) cost reduction (gathering data insights can help organizations gain an understanding of their overhead and pinpoint the practices which don’t justify their costs), 

 

(b) efficient processes (quickly accessing relevant data can reduce cycle time for complex and large-scale analytics from hours to seconds), and 

 

(c) business decision-making (analyzing transactions to better understand market trends).

Today’s app developers are competing in a crowded market. Embedded analytics is one way to differentiate yourself and offer added value. If an app provides only short-term worth, users are likely to rarely use it or even uninstall it.

 

Increasing numbers of developers are turning to embedded data visualizations as a means of providing this kind of value, differentiating their app and reducing churn. 

 

Our survey found that the technology is beginning to gain more widespread acceptance and adoption by software developers, 25% of whom have been using embedded analytics for 5+ years. An additional 30% have been using embedded analytics for less than five years and 23% plan to embed in the future. 

 

What Does the Future Hold for Embedded Analytics?

Today, the embedded analytics sector is enjoying a wave of innovation. The newest embedded analytics features address the latest trends in data analytics: predictive and advanced analytics, machine learning, R and Python scripting, and Big Data connectors. 

 

These enhancements allow businesses to quickly analyze and gain insights from internal and external data to sharpen decision-making.

 

Some of the advanced functions enterprises should keep their eyes on include:

  • Outliers Detection—Easily detect points in your data that are anomalies and differ from much of the data set.
  • Time Series Forecasting—Visual predictions are made based on historical data and trends, useful in applications such as sales and revenue forecasting, inventory management, and others.
  • Linear Regression—Find the relationship between two variables and create a line that approximates the data, letting you easily see historical or future trends.

These are just a few of the exciting developments in this still-maturing technology. We are on the cusp of the embedded analytics revolution. 

 

As its capabilities evolve and its benefits become more well-known, we expect to see more organizations using embedded analytics to make business decisions, take action on insights, reach new markets, and drive revenue. Whether you work in an enterprise or develop apps for end users, embedded analytics are here to stay – and poised to power an exciting future.

 


Casey McGuigan holds a BA in mathematics and an MBA, bringing a data analytics and business perspective to Infragistics. Casey is the product manager for the Reveal embedded analytics product and was instrumental in product development, market analysis, and the product's go-to-market strategy.

 




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