31 January 2022

Embedded vs. Standalone Analytics – Which is the Right Fit for You?

embedded vs. standalone analytics

Given the times, many businesses across the globe realize that traditional methods to enable analytics do not work or are not relevant anymore. Waiting for professionals to run analytics to give us results and put the data together before turning them into actionable insights is a tedious and lengthy process, a story of yesterday. At present, we need real-time data-driven insights and self-service analytics to keep pace with trends and market fluctuations that tend to occur in the blink of an eye.

But, how do we achieve them? There are many analytics solutions to choose from in the market, and making the right choice maybe difficult. Our focus today will be on two common ones, which are: embedded and standalone analytics solutions. The primary difference between the two is that embedded analytics is integrated within an application or another piece of software. In contrast, standalone analytics is delivered separately and often require third-party tools/processes to access.

At To-Increase, we offer business analytics solutions that can be embedded in Microsoft Dynamics 365 F&SCM, Power Apps, MS Teams, and Microsoft CE. These solutions cater to various industries, covering different business functionalities to drive optimized business outcomes.

In this blog, we will compare the two analytical models by understanding in detail what embedded and standalone are, along with the pros and cons that drive them, so that you are better equipped with the knowledge to decide what is best suited for you.

What is embedded analytics?

As the name suggests, embedded analytics software enables data analysis within a single application and becomes a part of a business's natural workflow, eliminating the need to switch to a separate application. It is widely used for gaining department-specific insights. As an example, let's consider the context of embedded analytics within the sales function. The sales teams need to understand the win rate, total revenue, pipeline splits, etc., to track numbers concerning their opportunities—with embedded analytics; they can get all these insights in one single place.

Embedded analytics was developed considering the lack of accessibility and integration between users and data in the first place. It allows easy access and execution of data management, visualization, and reporting tasks inside the solution, offering better usability for business users. If we rely on the numbers, 83% of users prefer to stay with one single application, as compared to switching to standalone.

The future for embedded analytics, as per Looker will focus on data-centric awareness and training to help users better understand what’s happening in their business, interact with data directly, and create an effective data strategy. You can expect to see a rise in the data application marketplace, which will act as a repository for sharing knowledge and making it easily accessible. The no-code or low code citizen developer approach with easy packaging for self-service use is making its place. For instance, today’s SaaS products, such as Salesforce, Microsoft Dynamics, and others, come with easy-to-report on structures.

What is standalone analytics?

Standalone analytics tools combine data from multiple sources to solve complex problems. Let's take the return on investment on decisions as an instance. In this case, there is a need to carefully consider varied aspects on multiple levels, such as internal costs, external costs, revenue splits as per the different categories, among others. The data points reside in different systems, and therefore a standard analytics and BI tool is essential to get the insights. Beyond this, it also allows you to view data from other relevant sources.

Similar to the traditional business intelligence model, standalone analytics is accessible on a separate platform or an application. The point of integration happens among the main data-generating application and the analytics application. A typical example of this is Google Analytics, which requires a special login to access the data and insights, while the data generating product itself does not have a business user interface.

With standalone analytics, different systems, varied data models, and complex logic make standard enterprise business intelligence and analytics tools crucial to tying business value across domains together. Moreover, growth in composable data and analytics is estimated with an aim to provide a flexible, user-friendly, and easy-to-use experience, which can help connect data insights to actions/business goals by using elements from various solutions. This enhances collaboration and performance in the organization.

Comparing the two analytical models

Now that we understand what each analytics model is, let’s look into the advantages and disadvantages that set embedded vs standalone analytics apart.

Benefits of Embedded analytics solution

One of the main strengths of embedded analytics tools is the smooth integration with applications, processes, and software for everyday workflows. They offer rapid access to analysis and visualization of data, providing up-to-date insights in a simple manner, which accelerates the entire analytics flow in the organization.

Some additional benefits of embedded analytics are:

  • Provides dashboards based on your application’s features
  • Enables easy customization of actions based on users’ needs
  • Integrates easily with other business properties, such as webpages, commercial software, portals, etc.
  • Offers more control over data security, especially when dealing with sensitive data

Limitations of Embedded analytics solution

Embedded analytics is not without its share of concerns. The analytics model is not very easy to use, might require technical knowledge or resources to an extent, and typically involves a larger budgeting commitment when it comes to implementation. Some other constraints of embedded analytics:

  • Restricted to a single application, with no possibility to manage any supporting functions outside of it
  • Allows limited customization due to configurations being predefined, which may not work for everyone
  • SaaS or PaaS software-dependent from a capability perspective

Benefits of Standalone analytics solution

Standalone analytics can be effective if an organization is equipped with time and resources to convert its big and wide data into actionable insights. This can allow companies to improve their performance by enabling better decisions. A few aspects that incline in favor of standalone analytics are:

  • Integrates data from various sources in a compatible format
  • Extensive features to help with overall data management
  • Helps simplify complex data analysis
  • Allows extensive customization given the flexible underlying frameworks

Limitations of Standalone analytics solution

From a user point of view, standalone analytics offers a fragmented experience with the users having to work with separate applications, each with a different look, feel, and mode of operation. Here are some of the limitations standalone analytics solutions pose:

  • Requires effort to transform data into relevant insights
  • Application and analytics are in different environments
  • More time-consuming when it comes to realizing analytics
  • Difficult to maintain and includes extra overhead costs
  • Requires technical know-how to navigate

Embedded or standalone analytics - What’s better?

Now that you recognize the strengths and limitations each analytics model offers, you can have a better sense of what would work for you. When selecting a data analytics solution, there is no right or wrong, and it's entirely a business decision, given your current situation. Identifying factors such as business objectives, scalability, budget, integrations to existing systems, resources at hand, and data sources can be a good place to start.

However, understanding the need of the hour can help you stay ahead of your game and prepare. No matter the industry, users are looking for easy access to high-quality, real-time insights to help them make confident, informed decisions. Because of this, there is an increase in embedded applications and self-service analytics, placing the power in users’ own hands. At the same time, organizations are increasingly adopting best-of-breed applications, which just means a lot more applications to deal with. Extracting insights from these applications needs intelligent analytics solutions to consolidate and create data models, craft repeatable logic, and construct powerful visualizations.

To keep up with the times, the first step would be to have your data-driven journey planned right from the start, and with the appropriate analytics & BI foundation to succeed. We have put together an ebook that helps you understand the need to be data-driven and how new-age analytics & BI tools can help.

Want to dive into the world of data-driven decision-making? Grab a copy of our ebook and kickstart your analytics journey. 

Get insights on the right steps to successfully implement analytics in your organization.

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Sakshi Dhakad,
Sakshi Dhakad,
Senior Product Manager, Manufacturing

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