Sun Oct 03 2021

How to identify a good data science use case?

By Tim Reus

Companies have a lot of data, but it isn’t always clear how to turn it into added business value. In the webinar below, Xomnia's Analytics Translator Tim Reus explains how to identify a good data science use case. Scroll down to download Xomnia’s use case canvas and watch the webinar to see Tim demonstrate how we use it at Xomnia in our data science projects.

How to find the right data science use case for your company?

We believe that AI and data should be tailored to solve challenges in your company, and not the other way around. Therefore, the journey to create and execute your data-driven strategy and deliver useful data products should start by clearly answering fundamental questions:

1) WHAT are the data opportunities for our company & WHY should our company chase these data opportunities? → Define your value proposition.

2) HOW might we achieve the selected use cases? → Conduct a capability assessment.

3) WHEN to develop which data products and organizational enablers? → Set a data and AI

Download the data science use case canvas

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Why are use cases important?

A use case is a question that can be answered using data. Finding the right use case is essential to the success of your data science project because it enables you to:

  • Understand your problem from an end-user perspective
  • Find the right data-driven solution
  • Define how to measure the project’s success which ensures that your solution adds business value
  • Go beyond data science in a technical sense and view your use case from a business perspective

Xomnia can help you all the way - from setting data strategies to executing them. Get in touch for a consultation.

Where to find a good use case?

There are four common areas for businesses to discover good data science use cases:

1) Corporate strategies

Examples of these use cases are recommender engines, data-driven marketing, and predictive maintenance. However, these strategies are often at a too high a level for a viable use case.

2) Operational problems

Those are the most common sources of data science use cases. Some possibilities are stock inventory predictions, picture recognition for data entry, decision support tooling, and pre-validations in processes.

3) Data insights

Often, a data analyst or data scientist will notice an anomaly or something that stands out within the data. This can be translated into a use case. At LinkedIn, for example, a data analyst noticed that the data on users’ connections could be used for the platform to suggest relevant new connections.

4) External factors

Those can also facilitate a good use case. For instance, the COVID-19 pandemic has certainly been a change catalyst worldwide. We’ve seen data science implemented in South Korea’s drive through virus testing sites where image recognition and heat sensors are used to determine if someone has a fever. A business or industry can also be pressured to change their strategy by innovative disruptors so that they avoid losing market position. We saw this develop when Deliveroo disrupted the casual dining industry.

How to measure a use case?

Tim recommends measuring the use case against both business and technical key performance indicators (KPIs). This will ensure that your data science project adds business value. You should also assess the use case at the beginning, middle, and end of the project.

Examples of business KPIs of a use case:

  • Average order size
  • Customer satisfaction
  • Time on page

Examples of technical KPIs of a use case:

  • Accuracy
  • Mean Squared Error
  • Response time
  • Recall

Download the use case canvas

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How to fill a use case canvas?

When working to map out a good data science use case with Xomnia’s clients, Tim implements a use case canvas. He goes step-by-step through this process in the video above. As you implement the use case canvas in your business, try to answer the following questions:

  1. What goal is supported with this use-case?
  2. What is the objective of the use case?
  3. How do we measure success?
  4. Who is the use case owner?
  5. What type of models can be possibly used?
  6. Potential impediments (e.g., legal, privacy, third parties)?
  7. Who will use the product in the end?
  8. What data is required?
  9. Where would the product run (which infrastructure or environment)?
  10. Who are other important stakeholders to involve in the project?
  11. Any other things worth noting?

Tim emphasizes that engagement is fundamental to every use case. So, make sure you put the people in the center of the implementation. Data science is always a tech problem, but never just a tech problem!

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