Close the talent gap
The data science talent gap is ever-increasing. Domino empowers “low code” analysts and data scientists to work in R and Python alongside more advanced data scientists. That unlocks more capacity for your team, while upskilling your workforce.
- Analysts who learn code can advance their careers into data science, improving recruitment and retention.
- Data scientists can easily mentor and collaborate with analysts since everyone is speaking the same language(s).
- Novice R and Python coders will rapidly gain skills using Domino Code Assist and a searchable repository.
Unify on a common platform
Maximize the productivity and impact of your analysts and data scientists by enabling them to collaborate and work together on Domino. By using tools like Domino Code Assist, analysts can easily generate code in Python or R. They all get access to professional coding languages, packages, tools, and a unified compute infrastructure. All of the necessary data is easy to find, access, and manage. This unification eliminates silos across your team and avoids costly duplication of effort by easily standardizing and reusing code. Governance and security processes are consistently applied.
- Novice coders can build on the work of experts with access to code libraries and examples.
- Rapid code iteration and team collaboration lead to innovation and delivery of high-performing models and analyses.
- Models are fully reproducible regardless of who built them and the data, languages, or tools used.
Deliver higher business value
Data and analytics leaders are under greater pressure to deliver value than ever before. No-code options just haven't lived up to their promise, leading to siloed projects, limited talent pools, one-off insights, and POCs that never make it to production. Leading organizations are shifting their focus to building a broad talent base of analysts with Python, R, and other coding skills.
- Code is fast, flexible, transparent, and efficient to write, edit and reuse.
- Python and R are the gold standards for analysis and data science.
- Data science innovations are created with code.