By Jonathan Fowler
Senior BI Developer, Kopis
“Machine Learning” has eclipsed “Big Data” as the Business Intelligence buzzword. Many available tools make it easier than ever to train, test and deploy machine learning models for business tasks. These platforms offer an exciting step forward for businesses not yet at the machine learning level of BI maturity.
My experience both teaching analytics and working with companies at varying levels of BI maturity allows me to offer a word of caution: Don’t fall into the trap of believing a technology package will be a cure-all. These tools are amazing, but they won’t work unless you’ve first defined a purpose. Don’t invest in an enterprise-grade machine learning solution without knowing ahead of time what you expect to feed into it — and what you expect to get out of it.
If you’re wondering what to expect, here are two practical examples:
Predicting Critical System Errors
We at Kopis have amassed a lot of data at with Vigilix, our proprietary systems monitoring platform. The Vigilix agent running on client machines logs critical system events and errors, and over time that generates a lot of data points. What good are these terabytes worth of data? By applying machine learning algorithms to the available data, we can model what particular system configurations or string of events may precipitate a critical system error. That model can be applied to future events and generate predictive alerts for critical errors before they happen. More data means better training and testing data for model optimization.
Retaining Your Employees
Imagine: A multinational manufacturing firm with thousands of employees worldwide faces an unusually high employee attrition rate every year, but its HR department can’t make sense of it. They have terabytes of data from different internal systems at their disposal. Wouldn’t it be great if they could leverage all that internal data to figure out what factors run common among employees who leave the company? I was part of a team earlier in my career that did just that. The result was a regular weekly report that listed the employees most likely to leave the company in the next six months based on the models we developed, trained and tested.
Ultimately, the role of machine learning in your organization depends on your business goals and company strategy. It’s not something you do just because other companies are doing it. Of course, with any BI initiative, plenty of “unknown unknowns” are uncovered as projects progress. But you should have clear initial goals for what you want machine learning to improve in your business.
So, where to start? There are two recommended routes: Amazon AWS and Microsoft Azure.
It’s important to know there are multiple options in both these cases, and which one you choose largely depends on what you have in place in your enterprise environment.
Whichever platform you choose, Kopis can help you navigate the decision and get the most out of your machine learning project. If you have any questions, please contact me at email@example.com, and we can help you decide what is right for your company.
Editor’s Note: Kopis is a sponsorship partner of the South Carolina Association of CPAs.