Three Use Cases for ML
Our Top Three Use Cases
In any enterprise, there are likely to be use cases that can achieve early stage economies. Whether the job is to spot patterns, monitor, analyze, report, filter, predict, plan or regulate there are ways you could be using today that you’re probably missing out on. Find out how you can take your machine learning project forward.
There are many ways machine learning can be applied in your enterprise to bring customers more value, improve customer experience, and reduce operating costs by automating data processing and speeding up time-to-value. That said, it’s not always obvious what SORT of tasks can be automated, so here are three great examples.
1. Marking and checking
Machine learning is good at following a set of rules and learning from them. This has helped the technology to find a role in auditing documents in the legal profession, marking exam papers in education, qualifying topics and themes in requests for proposals (RFPs) so that previously used responses can be re-applied.
2. Spotting patterns in large volumes of data
Image you want to understand patterns of behavior in the way customers interact with your business. This requires lots of information to be captured and examined. It may be the sheer volume of data that needs processing makes it uneconomic to have a human do it. Another example of this type of use is found in data security provisioning where login behaviors of users can be vetted against learned behaviors that suggest a threat.
3. Appreciating cause-and-effect consequences
Machine learning can be used to capture information on problems and issues, to then learn from those experiences to predict likely consequences. This sort of use case can exist in case file systems and in cases when the health and performance of assets needs to be closely monitored for potential downtime; or to predict capacity planning issues.
What We Think
It’s easy to get overwhelmed by machine learning. Executive teams can find the subject so mind-blowing, and have so much fear of the consequential impacts on their businesses (particularly the ‘fear of change and transformative costs’), that they put the whole subject on ice. We’d argue that’s not the way to approach machine learning. We’re a long way from the Armageddon myth of Terminator films. Don’t feel that you have to "eat the whole pizza". Every individual, and business, can start small and pick off the simpler use cases that yield the biggest business value for the smallest net change in operational behaviors.
Be it a big step or a small step, it’s true to say that every business has an opportunity to apply machine learning in their business ‘somehow’ to bring additional value to customers and reduce cost. That said, it does take leadership and courage sometimes to discover it.