3 Tips to Ensure Machine learning Use Cases Provide Real Value

In the world of big data and machine learning, it’s easy to get caught up in the excitement of the shiny, new use cases and applications. There is so much potential for these technologies to solve problems and provide valuable insights, but it’s crucial to assess the viability of a new machine learning use case and ensure that it provides real value and solves real problems for customers. Here are three considerations when deciding what use cases to work on first:

Value Over Interest

One common mistake is to choose problems that are interesting to solve, rather than those that are valuable to solve. While it can be tempting to tackle a problem just because it seems novel or cutting-edge, the best use cases are those that provide significant value to stakeholders. To determine the value of a potential use case, ask yourself questions like:

• What business problems does this use case solve?

• What benefits does this use case provide?

• What kind of impact will this use case have on the bottom line?

By focusing on value rather than interest, you can ensure that your efforts will be well-spent and that your use cases will make a real impact.

We take the time to dig deep into the issues at hand when we engage with our clients. It is important for us to ensure that the use case has the potential to make a significant contribution to the bottom line.

Evaluate the Potential Value

Once you have a potential use case in mind, it’s important to do a thorough cost/benefit analysis to show what the potential ROI is. This analysis should take into account both the costs of developing and implementing the use case, as well as the potential benefits that it could provide. Although it can often be complicated to determine what the potential ROI could be, there is still value in assessing it at a high level and using this to compare against other use cases. The key is to be as realistic as possible when estimating costs and benefits, taking into account factors like:

• The cost of data acquisition, cleaning, and preprocessing

• The cost of model development and training

• The cost of infrastructure and software

• The potential revenue or cost savings that the use case could generate.

By conducting a rigorous cost/benefit analysis, you can ensure that your use case is financially viable and that it will deliver real value to your customers or stakeholders. Beyond this, it will give you a chance to assess the opportunity cost of multiple use cases. If the cost is high, but the solution could provide a good return, it is often still worth pursuing.

Robustness at every step

To ensure the success of machine learning systems, it is crucial to have a strong foundation in place. This involves establishing good data infrastructure, architectural design, and efficient workflows. A good data infrastructure ensures that data is stored, processed, and accessed efficiently, while good architectural design makes the system scalable, reliable, and efficient. Finally, efficient workflows ensure that the development process is well-organised and effective, making it easier to complete projects on time and within budget.

By focusing on these key areas, machine learning systems can be built on a strong foundation that enables accurate results, valuable insights, and flexibility for future growth. Once a solid foundation is in place, future use cases can be tested and rolled out faster and more efficiently. As a result of building robust foundations for our clients, we have been able to deliver a lot more use cases over time. This has also helped us deliver up to seven times faster than before. A further benefit they received is the ability to continuously update existing models and build new machine learning models, resulting in continuous improvements in incremental revenue.

Adoption

Finally, it’s important to consider whether the end users will actually use the solution. Even the most valuable use case won’t be successful if it doesn’t get adopted by the people who are supposed to use it.

To ensure adoption, it’s important to involve end users in the development process, getting their feedback and incorporating their input as much as possible. Additionally, it’s important to ensure that the solution is user-friendly, easy to use, and integrates seamlessly into existing workflows.

Conclusion

Choosing appropriate use cases is critical in ensuring value can be returned on every project. Use these three points above to see if your use cases will really have the impact you expect. By focusing on value, conducting a rigorous cost/benefit analysis, and ensuring adoption, you can ensure that your use cases are successful and provide real value to your customers or stakeholders.

If you’re looking to assess the viability of a new machine learning use case in more detail, our team of experts can help. Contact us to learn more about our services and how we can guide you through the process of selecting and developing successful use cases that provide real business value.