How to have more successful data science projects

When starting a data science project, focus your effort on these three things

Translating the business need to prediction problem

The process of deciding what to predict, or “Prediction Engineering”, is the single biggest reason data science endeavors fall short of expectations. Too often, companies build predictive models that cannot be operationalized for the use case they were meant to address. The reality is that getting value from data science isn’t just picking what to solve, it’s how you solve it.

Throughout a data science project, organizations need to ask “how will a prediction be used?”. When predictive models don’t make it to production, the loss isn’t just months of work without a return on investment, but the cost of a missed opportunity elsewhere.

Experimenting with new predictive models

Prediction Engineering frequently uncovers many approaches to address a business need. You have to pick one to get started, but the other ideas have value down the road.

The key to making the most of your data is finding untapped opportunities rather than tweaking the last improvements out of an existing model. Too frequently, after companies build an initial predictive model, they get convinced they need to improve it before moving on. However, we’ve seen optimization is often premature.

Getting the right people involved in the process

Putting tools into the hands of the right people makes a big difference in ROI from data science investments. The struggle to get the right people involved manifests in two ways.

The first scenario arises when a company has brilliant data scientists, but fails to transfer the discoveries made by data scientists into production. The second scenario is a company that effectively delivers a product or service, but doesn’t have a data science team to engage.

By using advances in automation technology, it is now possible to directly enable those who are responsible for delivering value to participate in the data science process. In the two years since we demonstrated that automation technology can reach human performance, we’ve seen first-hand people who have never built predictive models solve real business problems using the new technology.