According to a 2021 report by Market Research Future, the global embedded analytics market is expected to grow at a compound annual growth rate (CAGR) of 14.5% from 2020 to 2027, reaching a market size of USD 52.4 billion by 2027.

With the global embedded analytics market projected to reach great heights, it's clear that businesses are recognizing the value of having a tool to make sense of their data. Embedded analytics not only helps businesses make better decisions by providing insights from their data, but it also helps improve operational efficiency and customer satisfaction.

In this guide, we will explore what embedded analytics is, how it works, and why it's important for modern businesses.

What Is Embedded Analytics?

Embedded analytics refers to the integration of analytical capabilities within existing business applications, allowing users to obtain insights and data-driven recommendations without switching to a separate analytics tool. This leads to streamlined decision-making processes, increased adoption and usage of analytics, and improved business outcomes.

With just a few clicks, you can create custom dashboards, reports, and visualizations that are tailored to your specific needs and workflows. This enables you to quickly and easily track key performance indicators (KPIs), monitor progress, and identify areas for improvement.

Recommended Read: How to create & Build Reporting Dashboard in Angular

Difference Between Embedded Analytics And Traditional BI

While both traditional BI and embedded analytics are used to analyze and visualize data, there are some key differences between the two approaches.

• One major difference is the level of integration between the analytics tools and the rest of the organization's systems and processes. In traditional BI, data is typically extracted from various sources and loaded into a separate system for analysis. It can be time-consuming and requires specialized skills. This can create a "silos" effect, where the insights generated by BI tools are not easily shared or acted upon by other parts of the organization.
• Embedded analytics, on the other hand, is integrated directly into existing applications and systems, allowing users to access and analyze data within the context of their work. This makes it easier for users to access and act on data insights, and can help to break down silos by making data more widely available across the organization.
• Another key difference between traditional BI and embedded analytics is the level of expertise required to use the tools effectively. Traditional BI tools often require specialized skills and training, and may only be used by a small number of data analysts or IT professionals within an organization.
• Embedded analytics, however, is designed to be more accessible to a wider range of users, including non-technical decision-makers. By integrating analytics directly into existing applications and systems, embedded analytics makes it easier for users to access and analyze data without the need for specialized skills or training.
• Finally, embedded analytics can provide real-time data insights that are tailored to specific workflows or business processes. This can help to improve decision-making by providing users with timely and relevant data insights that they can act on immediately, without having to wait for data analysts or IT professionals to extract and analyze the data for them.

Overall, while traditional BI and embedded analytics both have their strengths and weaknesses, Embedded BI represents a significant shift in the way businesses approach data analysis and visualization. By integrating analytics directly into existing applications and systems, Embedded BI makes data more widely available and accessible across the organization.

Related Read:

• ‍How Customer-Facing Analytics is Different From Traditional BI
• What is Embedded BI? Benefits, Requirements & Tools

Types of Embedded Analytics

There are two main types of embedded analytics: Operational and Strategic

While operational and strategic embedded analytics may seem distinct, they actually work together to create a complete data ecosystem that empowers users at all levels of an organization.

1. Operational:

Operational embedded analytics allows frontline workers to access and act on real-time data insights directly within their workflows, which can lead to increased productivity, better decision-making, and more successful outcomes. For example, a healthcare provider could use operational embedded analytics to view patient data within their electronic health record (EHR) system, enabling them to make more informed treatment decisions.

2. Strategic:

Strategic embedded analytics, on the other hand, provides a top-down view of an organization's performance, allowing executives and decision-makers to identify trends, opportunities, and risks. For example, a marketing executive could use strategic embedded analytics to track the success of different marketing campaigns across various channels, enabling them to make informed decisions about where to allocate resources.

Why Are Companies Embracing Embedded Analytics?

In today's fast-paced business environment, companies need to be able to make decisions quickly and with confidence. This is where embedded analytics comes in as a game-changer for organizations of all sizes.

By integrating analytics directly into their existing systems, companies can provide their users with real-time access to data insights that are tailored to their specific workflows. This not only saves time and improves efficiency, but it also empowers users to make more informed decisions based on data-driven insights.

Moreover, with embedded analytics, decision-makers no longer need to rely on IT or data analysts to provide them with the data they need. Instead, they can access and analyze data themselves, enabling them to make decisions faster and more accurately.

Finally, with the rise of big data and the Internet of Things (IOT), companies are collecting more data than ever before. Embedded analytics provides a powerful tool for turning this data into actionable insights that can inform strategic decision-making and drive business growth.

Companies also embrace embedded analytics for several other reasons, including:

• Accessibility: By embedding analytics directly into existing applications or systems, users can easily access and analyze data without having to switch between multiple applications.

• Actionable insights: Embedded analytics allows users to view data within the context of their work, making it easier to identify patterns and trends that can inform decision-making.

• Better decision-making: By providing real-time data and insights, embedded analytics can help decision-makers make informed decisions quickly.

• Increased efficiency: Embedded analytics allows users to perform analysis and take action without leaving their workflow, leading to increased efficiency and productivity.

How Does Embedded Analytics Work?

At its core, embedded analytics enables users to access and analyze data within the context of their work, without the need for specialized skills or training. Embedded analytics also works by integrating data analysis and visualization capabilities directly into the software or applications that people use every day. This can lead to improved decision-making, increased efficiency, and better outcomes. This integration can take many forms, including:

Dashboards and reports: Embedded Dashboards and reports can be seamlessly integrated into existing applications or systems, enabling users to view and analyze data directly within the context of their work.

‍Data exploration and analysis: Embedded analytics tools can allow users to explore and analyze data directly within their workflow, without having to switch between multiple applications.

Predictive analytics: Predictive analytics can be embedded into existing applications or systems, allowing users to make data-driven decisions based on predictive models.

Finally, embedded analytics can also support collaboration and knowledge-sharing within organizations. By providing a common platform for data analysis and visualization, embedded analytics tools can help teams work together more effectively and make better decisions based on shared insights.

What is a Modern Embedded Analytics Platform?

Modern embedded analytics platform is a software solution that allows companies to integrate data analysis and visualization capabilities into their existing applications or systems. These platforms provide a range of tools and features designed to make it easy for developers to embed analytics functionality directly into their applications, without requiring extensive coding knowledge.

Modern embedded analytics platforms typically include a variety of data connectors, allowing developers to easily connect to various data sources, including databases, cloud-based storage, and big data platforms. They also offer customizable dashboards and reporting tools that can be tailored to fit the specific needs of each application or system. Some interesting features include:

Data integration: Modern embedded analytics platforms can easily connect to various data sources, allowing businesses to quickly integrate data into their analytics applications.

Data modeling: These platforms allow businesses to create data models and relationships between different data sets, making it easier to perform analysis and generate insights.

Visualization: Embedded analytics platforms provide a range of visualization options, from basic charts and graphs to more complex dashboards and reports.

Security: These platforms typically provide robust security features, ensuring that data is protected and only accessible to authorized users.

Key Embedded Analytics Features

There are several key features that businesses should look for when choosing an embedded analytics platform, including:

1. Real-time data processing: Embedded analytics platforms are capable of processing real-time data, providing up-to-date insights that can be used for real-time decision-making.

2. Machine learning integration: Modern embedded analytics platforms offer machine learning integration, allowing users to apply predictive analytics models to their data for more accurate and insightful predictions.

3. Natural language processing: Some embedded analytics platforms offer natural language processing (NLP) capabilities, allowing users to ask questions in plain language and receive answers in the form of data visualizations.

4. Scalability: Embedded analytics platforms are designed to be scalable, capable of handling large and complex data sets, as well as a high volume of users and queries.

5. Mobile-friendly: Embedded analytics platforms are designed to be mobile-friendly, allowing users to access and analyze data from anywhere, at any time, using their mobile devices.

Read More: Top 7 Embedded Analytics Features You Must Look For

7 Key Elements of Embedded Analytics

Embedded analytics consists of several key elements that work together to provide users with valuable insights and data-driven decision-making capabilities. These elements include:

1. Data integration: It involves collecting data from various sources and systems and transforming it into a common format for analysis.

2. Data modeling: It is the process of creating a model that describes the relationships between different data elements and how they relate to business operations.

3. Data visualization: The process of creating visual representations of data that make it easy to understand and interpret.

4. Data analysis: It involves using statistical and analytical techniques to extract insights and identify patterns in data.

5. Data security: An extremely essential feature for protecting sensitive data and ensuring compliance with regulations and industry standards.

6. User interface design: This focuses on creating intuitive and user-friendly interfaces that make it easy for users to access and analyze data.

7. Integrations: Integrations with other systems and applications allows users to access embedded analytics capabilities within the context of their existing workflows and applications.

When Does Your Organisation Need an Embedded Analytics Software?

Your organization may need an Embedded Analytics Software when you find yourself struggling to make sense of the data you have. Here are some scenarios where implementing an embedded analytics makes sense:

• You need to make data-driven decisions quickly and efficiently
• You want to improve user experience by integrating data analysis and visualization directly into existing applications or systems
• You want to increase productivity by eliminating the need for users to switch between multiple applications or systems for data analysis
• When you need to provide real-time data and insights to decision-makers
• You want to support operational or strategic decision-making processes with actionable insights
• When you need to improve accessibility to data and analytics for users across the organization
• You want to leverage predictive analytics to inform decision-making
• When you need to scale your analytics capabilities to accommodate growing data volumes and user demands
• When you want to stay competitive by leveraging advanced analytics technologies to gain deeper insights and drive innovation.

Some common use cases include:

Sales and marketing: Embedded analytics can help sales and marketing teams track performance, identify trends, and optimize campaigns for maximum ROI.

Operations and logistics: The platform can help operations and logistics teams monitor supply chain performance, identify bottlenecks, and optimize processes for maximum efficiency.

Finance and accounting: Embedded analytics can help finance and accounting teams monitor financial performance, track expenses, and identify areas for cost savings.

What to Consider While Choosing an Embedded Analytics Software?

Embedded analytics software can be a game-changer for businesses that rely on data-driven insights to make informed decisions. Choosing the right embedded analytics software is crucial because it can determine the success or failure of your business. Here are some key factors to consider when choosing embedded analytics software:

• Integration: Ensure that the software can be easily integrated into your existing systems and platforms. The last thing you want is to spend valuable time and resources trying to make the software work with your current systems.
• Customization: Your business has unique needs, so choose a software that can be customized to meet those needs. Look for a solution that allows you to modify the user interface, add new features, and integrate with third-party tools.
• Data Security: Data is one of the most valuable assets of any business. Ensure that the software has robust security measures in place to protect your data from unauthorized access or theft.
• User Experience: The software should be user-friendly and intuitive, allowing users to quickly and easily access the insights they need. It should also have a modern interface that is visually appealing.
• Scalability: As your business grows, so will your data needs. Choose a software that can scale with your business and handle large amounts of data without compromising performance.
• Support and Training: Look for a vendor that provides comprehensive support and training to ensure that your team can use the software effectively. This includes documentation, training resources, and responsive customer support.

By considering these factors, you can choose an embedded analytics software that meets your unique business needs and helps you make data-driven decisions with ease.

How DataBrain Helps With Embedded Analytics?

DataBrain is a modern embedded analytics platform that helps businesses integrate data analysis and visualization capabilities directly into their existing applications or systems. With DataBrain, businesses can easily connect to various data sources, create data models, and help them gain valuable insights from their data.

They have a range of features that can benefit businesses, including easy integration with existing systems and customizable dashboards that can be tailored to meet specific business needs.

One of the key benefits of DataBrain is its natural language query capability, which uses NLP to enable users to ask questions and get answers in plain English. This feature makes it easy for anyone, regardless of technical expertise, to access and understand the insights provided by the software.

In addition, DataBrain provides predictive analytics capabilities using machine learning algorithms, helping businesses anticipate future trends and make informed decisions. Data security is also a top priority, with robust security measures in place to protect sensitive data from unauthorized access or theft.

With its user-friendly interface and simplified setup, DataBrain can help organizations quickly and easily harness the power of embedded analytics to make data-driven decisions and gain a competitive edge in today's fast-paced business environment!

Summing Up

Embedded analytics has emerged as a game-changer in the world of business intelligence and analytics. By seamlessly integrating analytics capabilities into existing applications and workflows, organizations can unlock valuable insights from their data and use them to inform critical decisions and drive business growth.

Whether you're looking to optimize business processes, enhance customer experiences, or gain a competitive edge, embedded analytics can help you achieve your goals.

By following the best practices outlined in this guide and leveraging cutting-edge technologies such as DataBrain, you can start harnessing the power of embedded analytics today and set your organization on the path to success!