How to have greater data science project success

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

Translate the business need to problem prediction

The process of deciding what to predict -- or “Prediction Engineering” -- is the most significant factor in data science expectations falling short. Companies frequently build predictive models that cannot be operationalized for the use case they were designed to address. The reality is that obtaining value from data science isn’t just picking what to solve, it’s how you solve it.

Throughout a data science project, organizations must ask, “How will a prediction be used?” When predictive models fail to make it to production, the loss isn’t just months of work without a return on that investment -- it’s also the cost of a missed opportunity.

Experiment with new predictive models

Prediction Engineering frequently uncovers many approaches to address a business’ needs. You should pick one to get underway, but you will find down the road that other ideas have value.

The key to extracting the most from your data is to find untapped opportunities first rather than fine-tuning every last improvement in an existing model. Companies frequently build an initial predictive model and then become convinced that it must be improved before proceeding. However, we’ve realized that optimization is often premature.

Get the right people involved in the process

Placing tools into the hands of the right people makes a significant difference in the return on investment 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 discoveries into production. The second scenario is that a company delivers a product or service effectively, but doesn’t have a data science team to engage.

By using advances in automation technology, those responsible for delivering value can be enabled directly to participate in the data science process. In the two years since it was demonstrated that automation technology can reach human performance levels, we’ve seen first-hand that people who have never built predictive models can solve real business problems using new technology -- and do it both simply and efficiently.