Gartner defines embedded analytics as a digital workplace capability that allows users with data analysis capabilities within their natural workflow instead of having to toggle to another application. Typically, the areas where embedded analytics is used include:
● Inventory demand planning
● Marketing campaign optimization
● Sales lead conversions
● Financial budgeting.
In the last few years, data generation and technological advancements have accelerated tremendously. For instance, bytes of data generated increased from 2.5 quintillion bytes every day in 2018 to nearly 1.7 MB every second by 2020. There has been rapid adoption of technologies such as IoT, cloud services, AI/ML, and data generation which has provided people with access and the ability to harness analytics across business applications. Users can view data in context and garner valuable insight to make informed decisions, which lead to better outcomes.
As a result, the embedded analytics market is also growing, from USD 36.08 billion in 2020 to USD 77.52 billion by the end of 2026, at a compounded annual growth rate of 13.6%.
Why Embed Analytics in Your App?
Data and analytics solutions are acknowledged as being critical components of digital transformation initiatives. Integrating them with the process workflows as embedded analytics
helps businesses experience significant benefits such as marketplace expansion, revenue growth, and competitive advantage.
With embedded analytics, customers are given access to the data they need in a timely manner, empowering them to analyze and make informed decisions. Users are also given the freedom to choose from a variety of dashboards, charts, graphs, and KPI widgets to visualize data in the most appropriate manner and draw their own conclusions. This helps them with improve customer experience by responding immediately and in the best possible way to their requirements. It helps identify strengths and weaknesses and increase operational efficiency. It also helps different teams to collaborate and work together on increasing efficiency and effectiveness of their improvement efforts.
By embedding analytics into their products, app developers can:
Enhance Application Value: Measure usage by number, depth, and session length to assess the value of your product. By embedding analytics into the app, users can access key metrics while using it, reducing exits and increasing session lengths. The insights also help with identifying strengths and areas for improvement. It can also be a key differentiating factor, enhancing the value of the application.
Facilitate Data-driven Decisions: Access to data visualizations within the app enables users to make informed decisions based on real-time data analytics. It helps uncover insights otherwise not easily available. It will also help to draw correlations and discover interrelationships between data.
Improve Pricing Strategy: Plugging in pre-built data visualizations when building new products can enhance the value of the app and increase its usefulness for the customers. This can help with pricing the product at a premium and improve profitability.
Benefits of Embedded Analytics
Data is the new oil that is helping businesses become more efficient and profitable. With embedded analytics, companies can increase their competitive advantage. Embedding analytics is proliferating across industries and functions. For instance, finance tools embed analytics tools to help customers analyze their income and outgoes. Utilities related tools help customers identify usage patterns and optimize consumption to lower energy bills. It can help discover new markets or build new features that customers seek. It can help serve customers better by anticipating their needs and providing timely service.
According to one Frost & Sullivan guidance, with embedded analytics, organizations can improve customer experiences, increase operational efficiencies, and reduce the time to market new products and services.
5 Kinds of Embedding
There are five levels at which analytics may be embedded. These are:
Secure Custom Portals: The visualizations and reports are aggregated and published to a portal that could be meant for internal purposes or external, for customers and partners. Such portals are secure, with controls, and enable personalization, scheduling, and custom styling and branding.
SaaS/COTS Embedding: In this kind of embedding, two-way interactivity is possible with authentication and row-level controls for secure access to data. Typically, these are commercial off-the-shelf software (COTS), and so, it is essential to ensure that it does not need a separate analytical interface for running analytics.
Real-time Interactive: Also called context analytics, it can be accessed from specific areas or functions within enterprise software or a bespoke solution. This needs rich software development kits (SDKs) that can provide both interactive and predictive capabilities. Such a solution is cloud-friendly, is flexible and agile, and can be upgraded and customized..
Action-oriented Analytics: This is a very high level intelligent data application with low-code or no-code development capabilities that can learn and adapt. It can facilitate event triggers, automation, and workflows, triggering action and supporting scenarios even if analysis is not possible.
Which of these 5 levels of embedded analytics will go into an app will depend on the application needs and the development environment.
Indium for Embedded Analytics Solution
Indium Software is a digital engineering solution provider with capabilities in app engineering and data and analytics. The cross-domain expertise helps the team develop innovative solutions to meet the unique needs of its customers. The team works closely with the customers to understand their needs and offer solutions that can help them improve their competitive advantage and app value.