5 steps to creating an embedded analytics application

‘Success with embedded analytics requires a focus on the user experience, understanding the value analytics brings to each persona, and matching capabilities to users’ needs’

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Whether a company wants to increase its competitive edge, boost its customer service efforts or improve the efficiency of its marketing activities, analytics plays a key role.

However, when data is not readily accessible, employees often find themselves waiting days, or even weeks, for the IT or analyst teams to generate reports.

Without adequate and timely information about markets and customers and their related needs, everyone from sales and marketing managers, to customer service operators, have to ‘fly blind’ and end up making decisions based on hunches instead of data. This is no longer acceptable, and employees are making it known.

In today’s data-driven driven world, people expect information to be available at their fingertips, whenever they want it, and wherever they are. People need answers quickly, and they choose technologies that provide the most optimised and visually appealing analytic experience.

Embedded analytics incorporates relevant data and analytics into the applications people are already using every day, such as CRM, ERP and financial systems. In a study by Logi Analytics, 84% of application providers said embedded analytics is important to their users, and 43% of application users said they adopt embedded analytics regularly – double that of traditional business intelligence (BI).

>See also: Why predictive analytics is defying one of security’s greatest myths

Success with embedded analytics requires a focus on the user experience, understanding the value analytics brings to each persona, and matching capabilities to users’ needs.

Here are five steps for creating an embedded analytics application that will drive both user satisfaction and adoption.

1. Create user profiles

The first step is the generation of user profiles within the organisation. When creating these profiles it is important for businesses to understand who their target users are, as well as their roles and responsibilities. Keep in mind that these may be new users who do not currently use the application, and may need to be familiarised with the processes.

2. Determine the value of analytics

The next step is to identify the problems that analytics can help address for each user profile, and then qualify and quantify the value the data brings. Usually, value can be expressed as increasing efficiency, effectiveness, revenue and customer satisfaction, as well as reducing costs.

3. Identify the best-fit analytic experience

Matching users and their profiles to one of more personas that best describe how they work with data is essential. For example, information consumers prefer a defined experience where they can view information, interact with dashboards and reports, and personalise individual views of information.

Content creators want a managed experience where they query governed data sources, create dashboards and reports, and share what they have created with colleagues. And data analysts need a self-directed experience where they start with a blank canvas, connect to their own data source, and discover new insights in a more exploratory manner.

4. Match functionality to user needs

When it comes to analytics, there can be a lot of functionality that is overwhelming for some users, especially those who are new to the software. Users should only be given the functionality and specific data they require to work smarter, so take time to consider what types of visualisations, interactivity and data you want to display. You can always release more functionality and data as adoption grows and new questions arise.

5. Choose the depth of integration

Businesses must also consider how deeply integrated their analytics need to be within the user experience. Depending on the depth of analytics, businesses should think about how the applications could be used as a way to improve user experience while creating a differentiated product.

>See also: Business users neglect deep data analytics in favour of limited self-service tools – research

There are a few different ways to embed analytics within an application.

A gateway to analytics or security integration is often the first stage of embedding. In this model, the analytics application has integrated security with the core application. Users only need one set of login credentials, which are passed from the core application to the analytics application via single sign on. There are still two applications, but the access to analytics is embedded in the core application.

This model is most useful when businesses have multiple applications and want to create a single application that can source data from multiple points. This works particularly well if the analytics application is based in the cloud, and serves data from on premise and cloud applications.

Inline analytics is traditionally the most popular form of embedding, where analytics functionality appears within the overall UI of the application. It is often implemented as a reports tab, or a module within the application. Another example is a homepage dashboard within the application that users can see and access once they log in. Application providers often choose this model when users demand easy and frequent access to analytics.

Infused analytics or workflow integration is the final model, where analytics is embedded within user workflows and becomes a core part of the overall user experience. One way to infuse analytics is to provide analytic content “in the moment” or in existing application screens where users are making decisions and taking action. This model is most effective for application providers who want to position analytics as a core capability by bringing together insight and action into the same context.

There is a lot to think through when embedding analytics into an application, and it’s not going to happen overnight. But by considering these five steps before starting to build, businesses will find it less daunting – and their users will experience greater productivity, and more efficient decision making.


Sourced from Tom Cahill, VP EMEA, Logi Analytics

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