A recent study by Domino Data Lab revealed that businesses need to better support data science initiatives in order to improve operations and productivity.

Indeed, it was stated that 82% of data executives in the US were concerned about a major revenue loss or hit to brand reputation due to bad or failing models as well as low investments that would lead to short-term payoffs. These were reported to be due to the fact that their organizations have not hired enough data scientists and that their companies were too siloed off to be effective and give clear roles.

The study showed that executives are not making investments in the right places to support expectations for revenue growth. It was said that in order to properly scale data science, businesses need to invest in sustainable processes to develop, deploy, monitor, and manage models at scale.

Hence, the report outlined a few things that slowed down the advancement of data science. It was found out that short-term investments were preventing growth expectations, unimproved models were bringing higher risk, and the role of data science was too unclear. It was then suggested that better models would generate more revenue and that teams working together would achieve their goals.

Moreover, 68% of data executives stated that it is rather difficult to get models into production to impact business decisions, while 23% declared that their models never get an update. However, not improving models can lead to less productivity or rework as well as security, compliance risks, and even discrimination and bias in modeling.