In today's data-driven world, businesses have a vast amount of information at their fingertips, which can be used to make better decisions, optimize processes, and drive growth.

However, this large amount of data generated by various businesses is challenging to be accessed and analyzed.

Traditional BI tools require users to switch between applications and platforms, disrupting their workflows and making it difficult to draw actionable insights from these data.
According to a report by Verified Market Research, embedded analytics market size was valued at USD 36.91 Billion in 2019 and is projected to reach USD 102.24 Billion by 2027, growing at a CAGR of 14.65% from 2020 to 2027. Data bridge reports a higher number of around USD 168 Billion by the year 2030.

What does this mean?

Embedded analytics is the future of business intelligence, as it empowers organizations to leverage data as a strategic asset and make informed decisions at the speed of business. By embedding analytics directly into business applications, companies can:

• Stay ahead of the curve
• Anticipate customer needs
• Drive innovation in their respective industries

But what is embedded analytics? Let’s find out!

What is Embedded Analytics?

Embedded analytics is the integration of data analysis tools and visualizations directly into business applications, such as CRM systems, marketing automation software, or financial management tools. This enables employees to access valuable insights and make data-driven decisions without having to switch between multiple applications or rely on IT teams.

With embedded analytics, businesses can analyze large amounts of data in real-time, gain actionable insights, and make informed decisions that can improve efficiency, productivity, and profitability.

For example, sales teams can quickly identify which products are selling well and adjust their strategies accordingly. Whereas, marketing teams can analyze customer behavior to optimize campaigns.

What to Prepare Before Integrating Embedded Analytics?

Before integrating embedded analytics into the required software applications, several factors must be considered. By defining the business requirements and goals, ensuring data quality and security, and providing a seamless user experience, the full potential of the data can be unlocked to drive meaningful business outcomes.‍

Defining the business requirements and goals

• What is the goal set to be achieved with embedded analytics?
• Is it to improve decision-making, optimize processes, or gain a competitive edge?

Understanding the goals help in choosing the right embedded analytics solution and tailor it to the required needs.

Data sources and quality of the data

• Embedded analytics solutions integrated with data sources that are clean, organized and ready for analysis ensures better results.
• Incorporating embedded analytics on an application with poor-quality data can lead to inaccurate insights and decisions.  

User experience

• The process of the access and interaction of the end-users with the embedded analytics needs to be established.
• With self-service capabilities and a customizable dashboard, the user interface can be made intuitive and feasible. Users require sufficient choice of data and visualizations.

Data security

Data security is also critical when embedding analytics.

• Robust data security features, including access controls, data encryption, and compliance with relevant regulations have to be offered in the solutions.
• Compromising on the privacy or security of the data can lead to legal and reputational risks.

Scalability of the embedded analytics

The final important factor is to consider the scalability of the embedded analytics solution.

• Ensuring the solution’s ability to handle the load is crucial as the  data volume increases with constant growth in the business.
• To achieve this, the solution can be scaled horizontally and vertically with intake of multiple users and data sources.

Preparing for embedded analytics, though may seem daunting, ensuring a successful integration is worth the effort which helps in obtaining the most of the investment and providing a seamless experience for end-users.

Common Capabilities of Embedded Analytics Platforms

Embedded analytics tools offer a range of capabilities that enable businesses to integrate data analytics directly into their software applications. These capabilities can vary depending on the forum, but there are some common features that most embedded analytics platforms offer. This section will explore some of these common capabilities and their benefits.

1. Data Visualization

One of the primary capabilities of embedded analytics platforms is data visualization. By providing visualizations directly within software applications, end-users can quickly identify patterns, trends, and anomalies in their data without switching to a separate BI tool.

Embedded analytics platforms also offer customizable dashboards that allow end-users to create their own personalized data views. This can include selecting the metrics and visualizations most relevant to their workflows and arranging them in a way that makes sense for their specific use-case. This can improve user engagement and adoption, as end-users can tailor the analytics to their needs.

2. Self-Service Analytics

Often, self-service analytics capabilities are offered by embedded analytics softwares, allowing end-users to create and run queries and reports without IT involvement. This reduces the burden on IT teams and enables end-users to get the insights they need in real-time, improving decision-making and speeding up processes.

3. Predictive Analytics

Certain embedded analytics platforms also extend its application to predictive analytics capabilities.This enables businesses to forecast future outcomes based on historical data, which include identifying patterns, trends, and anomalies that can inform decision-making and improve business outcomes. Predictive analytics can be particularly useful for businesses that want to optimize operations and anticipate future market trends.

4. Data Security and Compliance

Robust data security and compliance features, including access controls, data encryption, and compliance with relevant regulations are also provided by embedded analytics. This ensures that sensitive data is protected and that businesses comply with legal and regulatory requirements.

Overall, embedded analytics can improve decision-making, optimize processes, and gain a competitive edge in today's data-driven world by providing data visualization with customisable dashboards, self-service analytics, predictive analytics, and data security and compliance.

What Are the Use Cases of Embedded Analytics?

Embedded analytics involves integrating analytical capabilities directly into a business application or platform, providing users with real-time insights and data-driven decision-making capabilities. Some of its use-cases include:

• Customer Analytics: This involves analyzing customer data to gain insights into customer behavior, preferences, and needs. This information can be used to develop better products and services, improve customer experiences, and increase customer loyalty.

• Operational Analytics: Analyzing operational data to optimize business processes, identify inefficiencies, and reduce costs is extremely beneficial. This can include monitoring key performance indicators (KPIs) such as inventory levels, production rates, and service levels.

• Financial Analytics: Financial data is beneficial to gain insights into revenue, expenses, cash flow, and profitability. This information can be used to make informed financial decisions and develop strategies for growth and profitability.

• Marketing Analytics: Analyzing marketing data to gain insights into the effectiveness of marketing campaigns, customer acquisition, and retention. This information can be used to optimize marketing strategies and improve ROI.

• Supply Chain Analytics: Supply chain analytics can be used to optimize inventory levels, reduce lead times, and improve logistics. This can include monitoring supplier performance, shipping times, and inventory levels.

• Human Resource Analytics: Analyzing human resource data to optimize workforce management, identify skill gaps, and improve employee engagement. This can include monitoring employee performance, turnover rates, and training effectiveness.

Examples and Uses of Embedded Analytics

One popular example of the application of embedded analytics is DataBrain. DataBrain uses embedded analytics in its platform which ensures a high-quality experience to the users. The platform promises a host of features such as:

1. User-friendly interface: This feature allows users to easily navigate and use the platform.
2. Customizable dashboards: Customized dashboards with the metrics can be created using the platform that tracks the necessary data.
3. Data visualization: Data visualization tools that allow users to create charts, graphs, and other visual representations of their data are also available in the platform.
4. Data integration: The platform integrates with various data sources, allowing users to import their data from different sources.
5. Automated insights: Users can quickly identify trends, patterns, and anomalies in their data.

Explore more about DataBrain here

Embedded analytics can be used in various industries and applications, providing businesses with valuable insights into their operations, customers, and markets. In this section, we'll explore some common use-cases of embedded analytics and how they can benefit businesses.

Business Intelligence

Embedded analytics can be used for business intelligence, providing end-users with real-time insights into their data. This can include metrics such as sales, revenue, and customer engagement, allowing businesses to identify patterns and trends that can inform decision-making and drive growth.

Healthcare

Healthcare applications can utilize embedded analytics to provide doctors and healthcare professionals real-time insights into patient data. Various fields of healthcare professionals can make informed decisions and improve patient outcomes using these vital signs, medical history, and treatment plans.

Manufacturing

In the field of manufacturing, embedded analytics can be used to provide insights into production processes, inventory levels, and supply chain management. This can help businesses optimize operations, reduce costs, and improve efficiency.

Financial Services

Embedded analytics can be used in financial services applications, providing insights into customer data, fraud detection, and risk management. It helps businesses to make informed decisions and mitigate risks, improving customer satisfaction and profitability.

Retail

Retail applications including customer behavior, inventory management, and marketing campaigns can be analyzed and assessed with the help of embedded analytics. In businesses, customer engagement and loyalty, optimization of the pricing strategies, and drive sales can be improved.

Government

Implementing embedded analytics extends even to the government applications, providing insights into public services, citizen data, and policy decisions. Government makes informed decisions and improves public services, driving better outcomes for citizens.

Weather Forecasting

In weather forecasting, vast amounts of meteorological data, such as temperature, humidity, wind speed, and precipitation, are analyzed with embedded analytics to generate accurate weather predictions. Embedded analytics can also enable visualization of weather data through maps, charts, and graphs, making it easier for meteorologists and other stakeholders to understand and interpret weather information.

Agriculture

Sensor data from smart agriculture devices, such as soil moisture sensors, weather stations, and drones, to monitor crop health, irrigation scheduling, and pest control, can be embedded into farm management systems, crop monitoring applications, or precision agriculture platforms, allowing farmers to make data-driven decisions on planting, fertilization, irrigation, and harvesting, leading to improved crop productivity and resource utilization.

It is quite evident that utilizing embedded analytics, several companies stay ahead of the competition and drive meaningful business outcomes as it provides real-time insights, improves decision-making, and optimizes the processes. It has the potential to revolutionize various domains with its vast variety of features.

How to Choose the Best Embedded Analytics Solution to Modernize Your Application?

With the existence of so many available options, choosing the best-embedded analytics solution for any business takes time and effort. However, a few key considerations help in making an informed decision and selecting a solution that best meets the required needs. Let’s explore some key steps in how to choose the best embedded analytics solution.

Defining the Requirements

Defining the requirements and understanding the needs of the analytics platform is very crucial in choosing an embedded analytics solution. Hence, identifying the types of data that needs to be analyzed, the level of analytics sophistication required, and the specific features and functionality required are part of the process. Once the requirements are clearly understood, the diverse solutions that meet the needs of the application can be evaluated.

Evaluating the Capabilities

When evaluating different embedded analytics solutions, it's important to consider the capabilities and the way it is aligned with the requirements. Solutions offering various analytics capabilities, including data visualization, predictive analytics, and real-time reporting have to be identified. Additionally, the ease of integration with the existing systems and the scaling of the solution to the growth of the business can be considered.

Consideration of Security and Compliance

Data security and compliance are critical considerations when choosing an embedded analytics solution. The data can be protected by looking for solutions that provide robust security features, such as encryption and access controls. Additionally, if applicable to the industry, the compliance of the solution with the relevant regulations like the GDPR and HIPAA can also be considered.

Evaluation of User Experience

The user experience is an important consideration when choosing an embedded analytics solution. Provision of a user-friendly interface and easy-to-use features that allow users to interact with the data and gain insights quickly along with the confirmation of the options of customizations such as creating custom dashboards and reports are important.

Identification of Support and Training

Choosing an embedded analytics solution that provides adequate support and training is essential to ensure the best utilization of the platform. Looking for solutions that offer a range of support options, such as online documentation, email support, and phone support are important. Ensuring that the solution provides training resources, such as online tutorials and user guides can help in speeding up the process.

Consider the Total Cost of Ownership

Finally, when choosing an embedded analytics solution, it's important to consider the total cost of ownership. Solutions offering transparent pricing models, additional costs, such as implementation and maintenance fees and the ability of the solution being able to provide value for money and alignment with the business objectives are to be considered.

To sum it up, careful consideration of the requirements, capabilities, security, compliance, user experience, support and training, and total cost of ownership are required for choosing the best-embedded analytics solution for the business . By evaluating different solutions and weighing these factors, the most appropriate solution that meets the needs and helps in achieving the business goals can be selected.

Conclusion

Embedded analytics is becoming increasingly popular across various industries, and it offers numerous benefits to organizations of all backgrounds and sizes. By providing end-users with immediate access to data insights, embedded analytics can help organizations make informed decisions quickly and improve business outcomes.

Based on the it’s vast host of features and ease of setup, DataBrain is an ideal platform to choose for embedded analytics, as it provides a comprehensive suite of data analytics tools that can be seamlessly integrated into existing applications and systems.

If you're looking to implement embedded analytics in your organization, consider partnering with a reliable and experienced provider like DataBrain. With DataBrain, you can unlock the full potential of your data and gain a competitive advantage in your industry.

Contact us today to learn more about our embedded analytics solutions and how we can help your organization succeed.