Embedded Analytics in Modern Applications: 5 Key Features to Consider

Workflows infused with insights help better and faster decision making. For example, the GPS navigation system embedded in your car helps to navigate efficiently, offers the latest views on traffic, and enables switching to alternate routes in real time while driving. Similarly, embedded analytics offers guidance with latest insights within the existing business applications.

What is Embedded Analytics?

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. Users need insights the most while using applications and performing actions within their daily business workflows like tracking sales leads, optimizing inventory levels, reviewing marketing plans, or verifying credit ratings. With embedded analytics infused in such workflows, users can get insights automatically and use them instinctively without interrupting their work or getting distracted by switching to other systems.

Embedded analytics ensures a seamless user experience and removes the complexities of learning a new system. The following are the main characteristics of embedded analytics:

• Rapid integration: It helps integrate multiple data sources and enables rapid analysis of the latest available data.
• On-demand insights: It offers always-available and on-demand insights at the right place and the right time when users need it the most.
• Democratization of analytics: It simplifies analytics by providing insights in a familiar environment and empowers more users to access and understand it better.

Why is Embedded Analytics Important for Businesses?

Understanding the benefits of embedded analytics, organizations across industries are leveraging it to address many essential use cases. The embedded analytics market is expected to grow at a compound annual growth rate (CAGR) of 14.70% by 2030. Organizations are realizing the importance of embedded analytics in the following ways:

• Reduce friction in analytics experience: Embedded analytics improves users’ interaction with data. Users do not have to leave their familiar interface to get insights. This reduces considerable friction and cognitive load that users experience while switching between multiple user interfaces. When analytics is made automatic, intuitive, and seamless, users adopt it without any resistance or hesitation.
• Reduce time-to-insights: Being accustomed to easy information access and intuitive experiences of consumer applications, user expectations from enterprise-grade applications have increased. They now expect insights to be available at their fingertips, so that they are ready to convert opportunities and tackle issues early. Embedded analytics put insights right in the place where users need it, thus reducing dependencies on analysts and eliminating delays.
• Empower employees with democratized insights: Organizations are leveraging embedded analytics to democratize insights and encourage data-driven decision making in their workforce. When employees are able to access insights intuitively, they become data-driven, self-reliant, and proactive in their work. This empowerment of the workforce results in increased productivity and innovation.
• Increase ROI on analytics: By simplifying the insight discovery and consumption process, embedded analytics not only increases user adoption but also establishes an insight-driven decision making culture. This saves huge engineering efforts in creating ad hoc reports, saves support costs and helps organizations improve ROI significantly.
• Differentiate products and make them stickier: In addition to their employees, organizations can extend the benefits of embedded analytics to their customers too. By embedding analytics, organizations can convert their applications into data-enriched products, thereby differentiating themselves from competition. The insight-infused products increase customer engagement, create more value for customers, and improves customer satisfaction.

5 Key Embedded Analytics Features for Modern Applications

Let us look at the analytics features that can be embedded in modern applications.

Intelligent Search Box

Embedded search makes enterprise data easy to access and intuitive to query, just like Google search. An intelligent search box offers natural language search, search suggestions, ambiguity corrections, and context recognition. When this search is embedded in a business application, users can ask ad hoc questions in a simple language. For example, a RevOps company offering revenue intelligence to sales teams empowered its users by embedding MachEye’s intelligent search box to ask ad hoc questions about deals, pipelines, and wins in simple natural language.

Actionable Insights

The value of insights may diminish if not received and used at the right time. Often, actionable insights are available but users don’t access them frequently as it disrupts their natural flow of work. With embedded insights, users receive insights in the context of their workspace itself. This seamless integration of actionable insights makes it easy for users to include them in their daily decisions. For example, if loan officers get insights on applicants within the loan applications portal, they can inspect the applications thoroughly and act on them faster.

Interactive Charts

Users can consume insights better and faster if presented as interesting and engaging data stories. Embedding interactive charts and visualizations not only improves understanding but also encourages users to use analytics more in their day-to-day business. For example, marketers can view campaign performances and important metrics in the form of charts within their digital marketing platforms and take quick actions to modify their strategies.

Refreshable Dashboards

Dashboards provide a good way to compile findings and get a comprehensive view on metrics in a single place. However, creating dashboards, sharing them in various formats and updating information on a periodic basis can be time-consuming. However, dashboards embedded within business applications have a wider reach and can be updated or refreshed within minutes. For example, a digital communications management company reduced time-to-insights by 90% for their customer success managers by embedding MachEye’s analytics capabilities to track product usage and build dynamic reports and dashboard within minutes, instead of hours.

Automated Headlines

Instead of waiting for users to search or ask questions, automated business headlines offer insights as they take place based on user preferences. Embedding automated headlines ensure that users are always aware and informed about the latest happenings in their work. For example, retail managers can receive automated headlines on customer preferences, store inventory, or transactions as they happen in data without searching for them or leaving their sales portal.

Get Actionable Insights with MachEye’s Embedded Analytics

MachEye’s embedded analytics empowers users with true self-service analytics capabilities within their own familiar interfaces. MachEye offers powerful and easy-to-use APIs and SDKs to embed various analytics capabilities such as intelligent search, actionable insights, business headlines, dashboards, and charts within existing applications. With seamless integration of insights in daily business workflows, MachEye helps organizations drive data-driven decision making, increase adoption of analytics, and improve ROI on analytics investments.

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