1How Platforms and Collaboration Help ‘Model-Driven’ Data Teams Excel
Few organizations are taking full advantage of data science-generated models, even though the models could significantly support business needs, according to a recent survey from Domino Data Lab. The resulting report, titled “Key Factors on Journey to Become Model-Driven,” distinguishes what it defines as “model-driven” companies—those that embed algorithmic-driven decision-making (or data science-created models) at the core of their business. In contrast, “laggard” organizations struggle to manage the few data science models they have, as well as quantify their business impact. More than 250 data science professionals took part in the research. This slide show features highlights of the survey, with charts provided courtesy of Domino Data Lab.
2Few Organizations Excel at Data Science
3Number of Data Science Models Are Limited
4Top Performers Reduce Deployment Time
5Platforms Emerge as Success Driver
6Companies Prepared to Invest in Data Science Departments
7Companies Lack Precise Awareness of Data Model’s Impact Upon Business
8Data/Business Alignment Expected to Increase
9Collaboration Proves Essential
When asked about the top capabilities that contribute to data science success, 64 percent of survey respondents cited collaboration within the team and with business stakeholders. The ability to quantify and communicate the value of data science projects ranked second, as cited by 37 percent of respondents.
10Static Infrastructure Creates Barriers
More than three of five survey respondents said static infrastructure presents a top challenge in becoming a model-driven data science organization. The presence of “iteration friction”—i.e., the lack of a continuous and repeatable data science project lifecycle—was the second biggest challenge, as cited by 55 percent of respondents.