Investing in ML Is like Investing in Mobile 10 Years Ago — It Can Transform Your Business

ML, however, is the next frontier in data analysis. It is a discipline where computer programs make predictions or draw insights based on patterns they identify in data and are able to improve those insights with experience — without humans explicitly telling them how to do so

Note that with ML you can go well beyond using internal data — the power of ML can often be enhanced by marrying internal with external data to drive new insights that were not previously possible.

The most common use case is mass customization — finding the products that are most likely to be relevant for your customers more quickly and efficiently

Machine learning can save you time and make your resource investment more effective when it comes to business processes and decisions.

ML is not a magic wand for your business. The first challenge in ML is figuring out the business impact the technology aims to drive. ML is a solution — you need to first define the problem

In order to maximize the value of ML to the business you need an ongoing collaboration between product managers and data scientists, where it is the responsibility of the product managers to ensure that the problems being solved are the most impactful ones for the business.

Where do people in my company today apply knowledge to make decisions that could be automated, so their skills could be better leveraged elsewhere?

What is the data that people in my company normally search for, collect or extract manually from certain repositories of information and how can this be automated?

Do I have a clear segmentation of my customers based on their preferences, behaviors and needs? Is my product / experience customized for each segment

Can I customize the experience for each individual customer based on what I know about them or their interaction with my site / app / product? How could I create a better, faster or otherwise more delightful experience for them

How can I better identify good vs. bad customer experiences? Can I detect issues that will negatively impact customer experience or satisfaction before they happen or spread

What are the metrics or trends that if I could correctly predict would have a meaningful impact on my ability to serve my customers or otherwise compete in the industry, e.g. forecast demand for certain categories of products, cost fluctuations etc.?

What are the key entities about which I gather data (people, companies, products etc.)? Can I marry that data with any outside data (from public sources, partners etc.) in a way that tells me something new or useful about those entities? Useful to whom and how? For example: Identify potential customers when they are on the verge of looking for your product, understand how external factors affect demand in your industry and react accordingly, etc.