Use case enablement

Learn what use cases can be built and deployed using Joule

What will we learn on this article?

This article highlights what use cases mean and which can be supported through Joule.

Let's first get on one page together what a use case is:

A use case is a concept in software development and product design that outlines how users interact with a system to achieve specific goals. It details success and failure scenarios, as well as important variations or exceptions. Use cases can be documented in writing or illustrated in a diagram or graph using a use case modeling tool.

We will showcase some example business applications that can be accomplished through the use of Joule such as:

• Customer consent management

• Geospatial intelligence

• Proactive inventory management

• and analytics-driven use cases:

  • Real-time metrics

  • Predictive analytics

We will also examine technical use cases such as event stream deduplication, the time-consuming task of data wrangling and data orchestration.

What are use cases in Joule?

In short

Joule provides out-of-the-box, ready-to-use code that enables quick deployment and immediate business impact.

In detail

Joule empowers business developers to create both business and technical use cases through pre-built and custom data integrations, event processors and analytics.

Its flexibility supports a variety of use cases, including data encryption, real-time metrics, custom analytics, and machine learning predictions.

Now, let's explore some potential use cases that can be build with Joule.

Use case examples

We will look at 3 different spaces of use cases:

1. Business use cases

2. Analytical use cases

3. Data orchestration use cases

Business use cases

This category of use cases focuses on applying customer-facing insights, generating alerts and determining the next best actions.

1. Customer consent management Apply static or dynamic opt-In / opt-out lists to drive event filtering.

2. Geospatial intelligence Develop geospatial applications that track entities in specific locations, like shopping malls or stadiums, over time.

3. Proactive inventory management Capture real-time inventory levels to manage and forecast stock by location, helping to optimise inventory and boost sales revenue.

4. Sensitive customer data management Remove or mask sensitive customer information (i.e. credit card number) before distributing to consuming systems.

Analytics driven use cases

The extensive use of analytics in use cases is increasingly essential for differentiating customer-facing offerings.

1. Analytic driven alerting Combine real-time metrics and live events to proactively alert when a system is nearing failure in a manufacturing environment.

2. Stream based predictive analytics Leverage machine learning models within a stream to predict in real-time. i.e. Likelihood to convert to being a valued customer.

3. Real-time metrics Define and monitor custom metrics to drive proactive customer support and drive higher brand satisfaction and stickiness. i.e. Improve customer experience for mobile phone usage

4. Contextual analytics Join events with slow moving customer profile data to determine next best action. i.e. Customer website dynamic promotions.

Data orchestration use cases

This category of use cases play primarily a supportive role to the above mentioned use cases and therefore considered more technical in nature.

1. Once-only processing (stream deduplication) Process events just once through event deduplication. i.e. Improve customer event processing by focusing only on critical events and reducing the risk of errors.

2. Data wrangling Prepare data for subsequent pipeline processing by transforming event attributes into a desired format. i.e. Machine learning predictions may require input data to be engineered into suitable target variables using techniques like normalisation.