An Introduction to Embedded Analytics

Introduction to Embedded Analytics

Embedded analytics enables users to better analyze and use data by incorporating it into their existing business applications, workflows and software such as the product, website or apps.

This integration may be done on any application that the internal team may regularly engage with, such as CRM tools, financial systems, ERP solutions, etc., providing a more data-driven approach to core business functions.

Benefits of Embedded Analytics

One of the major benefits of embedded analytics is the seamless access to valuable business insights for every user within their natural workflow. It brings the following benefits to the business:

Improves data usability through contextualization

When looked at in isolation, data can often be misunderstood or misinterpreted. It can be difficult for a decision-maker to extract insights from this data through charts or trends without the context of the actual setting. Embedded analytics solves this problem by providing robust data visualization and reporting capabilities as part of the system used by a business leader for decision-making.

For example, consider an advertising platform that allows marketers to visualize the impact of their ad in terms of ad spends, ROI and conversions through active forecasting. It can help them decide their budgeting better.

Enhances productivity and improves business performance

Embedded analytics removes the need for users to bounce between multiple applications to find or draw insights into the data they need. This allows them more time to accomplish the tasks that require their skillset and time rather than working on extracting data from main systems, uploading them to analytical tools, extracting insight and reusing the insights in the main systems.

Provides competitive advantage

With the help of embedded analytics, business owners and end users have a deeper understanding of their target market, can discover opportunities for improvements quicker and identify performance issues that require action faster. The result is cost reduction as well as increased profits through a competitive advantage.

Empowers the end user

One of the key problems with traditional analytics is the need for decision-makers to be experts in analytical tools or have technical know-how to run various queries and programs to derive data and then analyze said data to bring out actionable insights. Through embedded analytics, the end user, be it a decision-maker or a retail customer, can do all this without having to rely on a technical expert or learning complex workflows. The capability empowers users to work faster and create impact through data with more comfort and confidence.

Key Features to Expect from an Embedded Analytics Platform

When evaluating an embedded analytics platform, it is important to consider several features to ensure they meet the needs of the organization. Here, they are:

White Labeling

When the users view analytics on an application that doesn’t look and feel as if they are part of the application, their experience dramatically drops. White labeling provides the ability to embed reports, dashboards and visuals to match the business’s unique brand language such as fonts, colors and brand theme.

Provision for Self Service

Embedded analytics platforms should liberate the end user rather than restrict them. This means it must be possible for the user to build custom dashboards, analyze large amounts of data and create custom reports without relying on IT professionals or data analysts. The ideal platform should be able to customize its offerings based on the use case and users.

This is possible with the help of data connectors that let businesses tap into numerous data sources in one place for analysis. The system should also provide an intuitive interface that allows users to simply drag and drop data or select data fields and build visualizations instantly. Ready-to-use visualizations and data templates can be tweaked to suit their varied purposes.

Flexibility and Scalability

The world has become increasingly mobile and business needs are constantly changing. The embedded analytics platform should also reflect and adapt to these needs. It must provide users accessibility from multiple devices or platforms of their choice, especially mobile-based devices. The right system also allows for any increase in existing resources to cater to the increasing needs of the business.

Use Cases of Embedded Analytics

By its nature, embedded analytics is designed to fit the context of the host application. Today, it is used in applications that drive the healthcare sector, retail industry, SaaS, financial services and the insurance industry. It provides both technical and non-technical users with additional capabilities based on the business-specific use cases.

For example, in financial services, the platforms allow the creation of automated processes for reconciliation, balance sheet and bookkeeping tasks. It offers real-time analysis and customizable reports to enable users to make quick informed decisions in a dynamic market while also focusing on KPIs, such as cash flow or inventory turnover.

In e-commerce, embedded analytics can optimize product operations by monitoring inventory, identifying trends and analyzing returns. The platform can be used to understand customer’s purchase journey or get a deeper insight into sales by assessing the performance metrics like growth and margins.

Embedded Analytics Best Practices

The many advantages and use cases of embedded analytics make a great case for its adoption, but, as with any tool or solution, there are some best practices to follow.

The platform should ensure security and compliance with user-level filters that restrict data access to unauthorized users. Single sign on (SSO) allows users to access the application across multiple devices with a single password, lowering the risk of password leakage or breach of security due to multiple passwords and touchpoints. The platform should also provide content filters and run-time parameters to ensure the data shown is contextually relevant based on user-specific content specifications and real-time parameters.

It is also important to consider the flexibility of data architecture within the existing systems. Traditional data analytics rely on rigid data architectures which limits flexibility and scalability. On the other hand, a flexible data architecture allows integration with new data sources, making room for growing data volumes and implementing new analytical methods on the fly.

Lastly, the scalability and performance of the solution under higher data volumes or user loads also matter. It should support scalable data volumes and enable the adoption of new analytical methods quickly. Keeping these factors in mind while choosing an embedded analytics platform will guarantee the business meets its growth and optimization goals easily.

How Kyvos Viz Helps Deliver Embedded Analytics Seamlessly

Kyvos Viz promises to deliver transformational reporting and data analysis experience using AI and machine learning. Users get access to proven, best-in-class analytics, and ETL right inside the business’s application.

The platform provides businesses with advanced features, such as a seamless integration with their app via screen embedding as well as data integration via APIs. It also offers single sign-on (SSO) to help users navigate between the application and analytics pieces without switching multiple windows.

White labeling improves end-user experience and retention, and the platform also comes with a mobile-embedded app. It can release a customized version of the app on the App Store in just a few hours using the embeddable mobile SDK. Users also get an option to deliver reports in 30 international languages.

Conclusion

Embedded analytics is paving the way for the next wave of data and analytics revolution. Through contextual analytics, seamless integration and the ability to cater to multiple data sources and use cases, it has empowered even the non-technical-business users to make informed decisions through actionable insights in real time and in the right context. Embedded analytics is becoming increasingly popular with an estimated CAGR of over 12% till the year 2029. It is no longer an option for a business but an imperative choice when implementing a comprehensive analytics solution.

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