Which Data Science Project?

We  dealt with exactly this, with AI projects and with and any new tech projects.    Below article very nicely put piece worth reading.    Even more straight forwardly put:  Start simply, where you have data, know your goals, can define current process and measures.   Increase credibility to leverage new projects.

How to Decide Which Data Science Projects to Pursue   By Hilary Mason in the HBR

In 2018, every organization has a data strategy. But what makes a great one?

We all know what failure looks like. Resources are invested, teams are formed, time goes by — but nothing comes of it. No one can necessarily say why; it’s always Someone Else’s Fault.

It’s harder to tell the difference between a modest success and excellence. Indeed, in data science they can they look very similar for perhaps a year.  After several years, though, an excellent strategy will yield orders of magnitude more valuable results.

Both mediocre and excellent strategies begin with a series of experiments and investments leading to data projects. After a few years, some of these projects work out and are on their way to production.

In the mediocre strategy, one or two of these projects may even have a clear ROI for the business. Typically, these projects will be some kind of automation for cost savings, or applying machine learning to an existing process to improve its efficiency or performance. This looks a lot like success, and it may suffice, but it’s missing out on the unique advantages of an excellent data strategy.

In an excellent strategy, more data projects have worked out, and they were surprisingly cost-effective to develop. Further, the process of building the first few projects inspires new project ideas. In an excellent strategy, the projects will include automation and efficiency and performance improvements, but they will also include projects and ideas for new revenue generation and entirely new businesses driven by your unique data assets. The data teams work well together, build on each other’s work, and collaborate smoothly with their business partners. There’s a clear vision of what the machine-learning driven future of the business can look like, and everyone is working together to achieve it.  .... "