by Angela Guess
A recent press release reports, “Domino Data Lab, provider of the most advanced data science platform, today announced the findings of a survey of more than 250 data science leaders and practitioners. The survey revealed factors that enable data science teams to achieve return on investment (ROI), top capabilities contributing to their success, and priorities they must address this year. Ultimately, model-driven organizations — those who’ve mastered the technical and management practices necessary to embed algorithmic-driven decision-making at the core of the business — are reaping the most returns. Download the full report here. ‘The survey findings highlight a divide between organizations that view data science as a technical practice as opposed to an organizational capability woven throughout the business fabric,’ said Nick Elprin, co-founder and CEO at Domino. ‘Those who treat data science as a core business capability are better equipped to build and deploy models quickly, manage and monitor them in production, and calculate models’ impact on the business’.”
The release goes on, “The survey, which ran from November 2017 through January 2018, surfaced four fundamental findings: (1) Collaboration is a foundational factor driving success. Collaboration was cited as the primary factor contributing to success with data science; 72 percent of organizations considered “model-driven” (based on number of models in production, ability to control them and quantify their impact) named collaboration as the main attribute of success, as opposed to 63 percent of organizations not considered model-driven. (2) Key barriers prevent organizations from achieving success. Most organizations (90 percent of respondents) see data science driving innovation in their business, but only nine percent can quantify the business impact of all their data science projects. Furthermore, only 30 percent of companies can claim to have more than five models in production. The challenges preventing organizations from becoming model-driven can be categorized into four barriers: silos of knowledge, iteration friction, static infrastructure, and model liability.”
Read more at Domino Data Lab.
Photo credit: Domino Data Lab