When everything you need to make decisions or take actions is available in a single interface, you have clear visibility, better awareness of all options, and quicker access to insights. For example, e-commerce apps offer you the convenience of shopping for a wide range of products, paying utility bills, recharging subscriptions, and transferring to third-party wallets from a single app. Similarly, with travel bookings apps you can not only book tickets for multiple transport modes, but also plan your entire itinerary, book accommodation, rent cars, and get sightseeing recommendations.
Embedding crucial capabilities in a workflow makes the entire interaction experience seamless, frictionless, and effortless. Embedded Business Intelligence (BI) does the same for business analytics by offering insight-infused workflows for better and faster decision making.
What is Embedded Business Intelligence
Embedded Business Intelligence (BI) refers to the analytics capability of providing actionable data-driven insights within the natural workflow of core business applications in a seamless manner. Embedded BI ensures that you can take all decisions and actions within the same interface and with a familiar user experience, without switching between applications and losing your context.
Everyday business workflows such as tracking sales leads, optimizing inventory levels, reviewing marketing plans, or verifying credit ratings can be enhanced by embedding insights at the point of decision making. For example, by receiving useful insights on credit history, defaulted payments, purchase habits, and risk scores within a loan application workflow, lending executives can get comprehensive reading about the applicant and process loan applications faster, without logging in to different portals to gather different data points.
How Embedded Business Intelligence Works
Embedded BI is a way of making contextual business insights available to users in various formats and at relevant touchpoints. For example, embedded BI may appear as:
- A native search box in support portals for customer support representatives
- A business headline in an investment management website for investment managers
- An in-app insight in a network monitoring system for system administrators
- A chart in a sales management portal for regional sales heads
- A dashboard for employee evaluation in a human resources management solution
Advanced data analytics platforms usually offer the same robust analytics capabilities in embedded mode as available in their applications. With the help of powerful and easy-to-use APIs and SDKs, such platforms can embed their analytics offerings seamlessly in existing business applications, without requiring any significant overhaul of existing infrastructure.
Embedded Business Intelligence vs. Traditional Business Intelligence
Traditional BI is restrictive in terms of access to data and ability to perform analysis in a self-service way. Traditional BI was mainly developed for advanced users like data engineers and analysts, so it requires a high level of technical proficiency and skills. Extracting insights is a time-consuming process full of iterative requests and manual reporting, resulting in delays, dependencies, and outdated insights.
Embedded BI helps counter the limitations of traditional BI by democratizing data, simplifying analytics, and providing faster access to insights at places where users need them the most. McKinsey’s report on Data Driven Enterprises of 2025 predicts that “By 2025, data will be embedded in every decision, interaction, and process.” Embedded BI enables organizations to become data-driven by helping users naturally and regularly leveraging data in their work. Embedded analytics also increases the value of business applications, transforms them into data products, and ensures better returns on analytics investments.
Which AI technologies are used in Embedded Business Intelligence
Embedded BI employs a range of technologies that come under the umbrella technology of Artificial Intelligence (AI).
Natural Language Processing (NLP) and Natural Language Generation (NLG): Natural Language Processing (NLP) and Natural Language Generation (NLG) are integral components of AI analytics. With NLP, users can type their questions in simple language, eliminating the need to learn SQL or rely on experts for guidance. AI-powered embedded BI understands natural language and automatically generates the SQL to fetch the answer. NLG complements AI analytics by providing generative content capabilities, presenting answers in the form of text summaries, audio narratives, and visualizations that are easily understandable by users.
Machine Learning (ML): Various machine learning models and AI algorithms enhance the enterprise search by identifying, calculating, and predicting outcomes correctly. These models and algorithms can extract actionable insights such as anomalies, outliers, analogies, clusters, trends, predictions, root cause analysis, and influential business drivers from enterprise data. They can be customized to address the specific business objectives of an organization.
Large Language Models (LLMs): With their recent popularity and advancements, LLMs have gained useful applications in data analytics and business intelligence. LLMs are used to understand metadata, identify the right context of data, and make data consistent and refined for analysis. LLMs are also useful in understanding unwanted terms and jargon in user entered search queries to extract the right insight. When it comes to presenting insights, LLMs contribute to text generation by cleaning up and contextualizing content for its users.
Benefits of Embedded Business Intelligence
The embedded analytics market is expected to grow at a compound annual growth rate (CAGR) of 14.70% by 2030. More and more organizations are realizing the benefits of embedded BI and are leveraging it for various use cases.
- Gain a frictionless analytics experience: Embedded BI provides insights in an interface with which users are familiar and hence improves users’ interaction with data. Users don’t have to switch between applications every time they need insights. This reduces significant cognitive load. Embedded BI makes analytics intuitive and seamless, thus helping users to adopt it without any resistance.
- Access insights faster: Embedded BI makes insights available exactly where users need it, thus reducing dependencies on analysts and eliminating delays. With real-time access to actionable insights, they can convert opportunities faster and tackle problems early.
- Increase value of products: By embedding BI in their business application, organizations can increase the value customers derive from their applications. Organizations can also differentiate themselves from competition by transforming their applications into data-enriched products. Such insight-infused products increase customer engagement and improve customer satisfaction.
- Improve returns on analytics investments: Embedded BI simplifies the insight discovery and consumption process, increases user adoption, and improves operational efficiency. This saves huge engineering efforts in creating ad hoc reports, reduces support costs, and improves ROI on analytics investments.
- Stimulate a data-driven culture: By leveraging embedded analytics to democratize insights, organizations can promote data-driven decision making within their workforce. When employees are able to access insights intuitively, they become data-driven, self-reliant, and proactive in their work. An empowered workforce results in increased productivity and innovation.
How MachEye Shapes Decision Making with Embedded BI
MachEye’s Embedded BI Copilot 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.
- Intelligent Search Box: MachEye’s SearchAI is an intelligent search box that offers natural language search, search suggestions, ambiguity corrections, and context recognition. When this search is embedded in a business application, it empowers users to ask ad hoc questions in a simple language and get instant answers.
- Actionable Insights: With MachEye’s 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.
- Interactive Charts: Users can consume insights better and faster if presented as interesting and engaging data stories. MachEye’s embedded interactive charts and visualizations not only improves understanding but also encourages users to use analytics more in their day-to-day business.
- Refreshable Dashboards: Dashboards provide a good way to compile findings and get a comprehensive view on metrics in a single place. MachEye’s embedded dashboards can be updated or refreshed in no time, thus saving the efforts to update and distribute latest insights to a wider audience.
- Automated Business Headlines: Instead of waiting for users to search or ask questions, MachEye’s 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.
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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