Chapter 3

How to be Successful with Embedded Analytics

Explore the five steps to create a great user experience.

Focus on the user experience

Success with embedded analytics requires a laser focus on two pieces: the user experience and close integration. It is critical to understand the value it brings to each persona, and match capabilities to needs.

A user experience is more than just pretty visualizations, ease of use, and the reduction of click counts. Yes, these things are important, but you can accomplish even more. Here are five steps to creating a great embedded analytics app that will drive both user satisfaction and adoption.

5 Steps to Creating a Great User Experience and Tight Integration

1. Create user profiles

Understand your target users, including their roles and responsibilities. Keep in mind that these include prospective users.

2. Determine the value of analytics

For each profile, think about how analytics help the business. Quantify the return and assess its true value. Usually, value can be expressed as increasing efficiency and effectiveness, increasing revenue, reducing costs, or improving customer satisfaction.

3. Identify the best-fit analytics experience

Match users to one or more personas that best describe how they need to work with data:

  • Information consumers prefer a defined experience. They opt to view analyses that have been prepared for them. These users interact with dashboards and reports as well as personalized views of the information.
  • Content creators want a managed experience where they can query governed data sources, create dashboards and reports, and share what they’ve created with colleagues.
  • Data analysts need a self-directed experience. They start with a blank canvas and connect to their own data sources. These analysts discover new insights in a more exploratory way.

4. Match functionality to user needs

To avoid overwhelming users with features, give them access to only the functionality and data they need to work smarter. Release more functionality and data as adoption grows and new questions arise.

Tips

  • Use a capabilities map to match users to the functionality they need (see an example in the next chapter).
  • Produce mock-ups of the functionality you want to implement.

5. Choose the depth of integration

Think about how the role of analytics in the application impacts the user experience. We have seen how consumer web applications embed analytics deep into the context of the application workflow. This could very well be a way to improve the user experience and create a differentiated product.

Taking these steps will help you to develop project requirements and to prioritize phases.

User Experience Example

Let’s say you have a set of healthcare applications. These might be the user stories you construct.

  • When treating a patient, a doctor may wish to study the patient’s vital metrics in comparison to those of their peer group. Having access to this information makes the doctor much more efficient and improves patient care.
  • Every time a hospital administrator wants to check inventory levels, they consult a dashboard. If the admin sees that items need to be replenished, they can take action. They may choose to purchase new equipment directly from the dashboard. The dashboard provides efficient delivery of patient care with full control of costs. This effort has led to a 5 percent decrease in yearly wasteful spending.
  • A hospital research analyst wants to know two stats: where readmissions are highest and which phase of the patient care cycle needs to be monitored. This analysis must be made accessible for others to create reports. This information sharing can lead to improved compliance with readmissions standards. The result is better care for the patient and a 10 percent reduction in costs.

Common Features in Embedded Analytics

Common features in embedded analytics look a lot like those in BI, but there is one difference. The functionality is integrated into the application’s UX.

The capabilities embedded in each app vary. We have specified the frequency in which we see each feature implemented in the information below:

1. Information Delivery

The main reason software providers take on an embedded analytics project is to improve how data is presented. In addition to satisfying users’ informational needs, the look and feel of these capabilities should align with the style of the embedding application.

  • Dashboards and Data Visualizations     
    Included are a range of visualizations, such as charts, gauges, heat maps, and geographic maps. These tools enable users to quickly draw conclusions and monitor key performance indicators. They can be presented in the context of a single chart or in a collection of visualizations in a dashboard.
  • Reports     
    A tabular display of data, often with numerical figures grouped in categories. Interactivity can include dropdowns and filters for users to slice and dice data.
  • Mobile     
    Capabilities are made available to users through their mobile devices. These ensure accurate visual displays as well as compatibility with mobile device features such as “touch input.”
  • Scheduling and Exports     
    Dashboards and reports can be scheduled for delivery and used in conjunction with thresholds/alerts. They may also be exported to other formats for printing and offline access.

2. Interactivity

Embedding analytics inside the app presents interesting ways for users to interact with their data. These capabilities lead to a more informed and productive UX.

  • Linking     
    This enables the user to click on a visualization or report and navigate to a different analysis or application page — and vice versa. Alternatively, the interaction can be made to change part of the screen rather than the entire screen.
  • Personalization     
    Users may bookmark important visualizations and reports or pin them to the top of a dashboard for easy access.
  • Dashboard and Report Authoring     
    Users can create their own data visualizations, construct dashboards and reports, and share what they’ve created with their colleagues.
  • Workflow     
    Embedded analytics functionality keeps users in their workflow. Charts embedded on an existing app page help guide user behavior.
  • Write-backs     
    An example is a report with editable data cells where users can update the displayed data. The database will be updated to match the data in the report.
  • Processes     
    Analytics are sometimes very tightly integrated with app functionality. A visualization with selectable regions (on a map or the area of a scatter plot) allows users to perform an action on the selected record(s). They can click on a point of interest in a chart or report and drill down or up.

3. Analysis

Application providers can enhance the value of the product by giving sophisticated users ways to perform their own analyses, create benchmarks, apply proprietary analytics to their data, and find innovative ways of incorporating external data sets.

  • Visual Analytics    
    Users are given data from which they can uncover new insights. The data set for each end user can be restricted depending on the user’s role. They can also create custom calculations and metrics, and build new data visualizations.
  • Benchmarking    
    Users can compare their stats against industry benchmarks and identify areas for improvement. Some cloud applications can even provide new benchmarks based on customer data.
  • Advanced Analytics     
    Some apps provide a unique value proposition through the development of advanced (and often proprietary) statistical models. These advanced analytics become easy for users to apply in their own analyses.
  • External Data     
    Some apps gather data from external sources and then deliver these through a single view or dashboard. The application thus becomes a vital information hub. Third-party data might include industry benchmarks, data feeds (such as weather and social media), and/or anonymized customer data.

Four Approaches to Data Analytics

The world of data analytics is constantly and quickly changing. BI tools emerged in response to users seeking “one version of the truth” due to the aggregation of data from multiple applications.

Following, users reverted to the adoption of non-traditional BI tools to access their data. This put tremendous pressure on IT teams to get up to date. It also led to the development of new and different approaches to delivering analytics.

Traditional BI platforms have improved but remain essentially the same. New data discovery solutions now offer business analysts something better than Microsoft Excel—with minimal dependency on IT resources.

Choosing the best solution for your dashboards and reports starts with understanding the 4 types of analytics solutions on the market.

The Four Approaches:

  • Building with UI Component Libraries
    App developers will often first think to build visualizations on their own using UI Component libraries. In some cases, such as when building a proof-of-concept for a newly envisioned application, this approach can be a good starting point. If the app has simple requirements, basic security, and no plans to modernize its capabilities at a future date, this can be a good 1.0.
  • Traditional BI Platforms
    Traditional BI platforms are centrally-managed enterprise-class platforms. These sit on top of data warehouses that are strictly governed by IT departments. The role of traditional BI platforms is to collect data from various business systems. It is organized to create a top-down model that is used for analysis and reporting. Tradition BI has been a popular way for large businesses to launch their data analytics.
  • Data Discovery Applications
    Data discovery is the capability to uncover insights from information. With data discovery applications, the focus moves away from IT-driven systems towards a self-service approach to delivering data to business users and empowering everyone to be an analyst. These applications typically offer a graphical front end for data manipulation, in-memory processing, and support for direct connectivity to a variety of data sources. Data discovery applications use a range of methods such as heat maps, pivot tables, pie charts, bar graphs, and geographical maps to help users accomplish their goals.
  • Embedded Analytics Development Environments
    Analytics development environments are designed from the ground up for software teams to embed analytics into existing business or commercial applications. They offer out-of-the-box functionality to create and customize dashboards and reports, provide a flexible self-service experience to end users, connect to and leverage the power of modern data sources, and offer simple implementation options, including cloud deployments.

Benefits of Each Approach

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Benefits
Advanced visualizations
Robust customization
Personalized self-service
Whitelabeling
Specialized skills NOT required for users
Embedded within the application/user workflow
Quick go-to-market
Lower upfront costs
Low-code/no-code development
Lower development costs
Horizontal and vertical scale options
Reduces adhoc reporting
Pixel-perfect & operational reporting
Extensibility
RESTful APIs
Full stack solution
Cloud-ready, DevOps-friendly architecture
Source-specific Data Connectors
Connects to any data source
Optimized query processing
Single Sign-On Integration
Documentation and support
Flexible Pricing Options
Embedded Analytics
UI Components
Traditional BI
Data Discovery
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Tips for a Successful Embedded Analytics Project

Here are three tips for managing a successful project.

Have a Vision, But Build in Phases

Building analytics into your application can be overwhelming as you foresee how far you must go to reach your vision. And inevitably new ideas surface along the way. That’s okay. Before you try to take on the whole ocean, remember to start small and build on your successes. Start with one user, one persona, one problem, and one report. Get feedback and move forward. Requirements shift and evolve over time as users start to see what’s possible. The key is to stay agile and approach embedded analytics in an iterative way.

Involve Internal and External Stakeholders

There’s nothing more frustrating than building out a really cool feature that no one uses. To avoid this, be sure to get regular feedback from internal and external stakeholders. Build enhancements into your appl as demand dictates. Utilize screen mockups early in the process and review these with customers to validate your strategic plan. Ask what they like, what they don’t like, how they would use it, and what suggestions they have to make the product better. This feedback will help you stay focused on solving real user problems. Furthermore, it will enable participants to become advocates when analytics becomes available all over.

Perform a Usability Study to Identify Gaps

You should conduct on-site usability studies with select customers to see how they actually use the app. The point of such a study is to find out in advance what problems will bother your users. Be prepared for users to complain when they are lost or frustrated – that’s exactly the kind of feedback you need. Avoid helping users to get to the right answer. Instead, ask them to complete specific tasks. Learn how they expect to navigate your application to accomplish their tasks. Ask them to rate certain aspects of the app and prioritize enhancement requests. Ask open-ended questions to get the most feedback and avoid questions that can be answered with a simple yes or no.

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