Data
ROI for Data Work
Curated Learning Resources
How to think about the ROI of data workData teams need to focus on impact, not just outputs. The closer your work is to impacting key metrics, the easier it is to measure ROI. Systems roles like data engineers multiply impact by improving tools used by many analysts and scientists. They should maximize the number of consumers and impact per consumer. KPI roles should reduce steps between work and metrics, focusing on high ROI opportunities. Data scientists work faster with good data models, and engineers impact more people with quality models. Everyone plays a role in maximizing impact.
Related Skills
- MLOps Platforms
- Prioritization for Data Work
- Structuring Data Teams
- Effective Dashboards
- SQL
- Machine Learning
- Data Science Career Ladders
- Data Engineering
- Neural Networks
- Analytics
- Analysis Documentation
- Data Infrastructure
- Cohort Analysis
- Data Tools
- ETL
- Data Soft Skills
- Data Dictionary
- Data Governance
- Data Roadmaps
- Event Data
- Personalization
- OCR
- Data Warehouse
- Deep Learning
- Sampling Algorithms
- Data Intuition
- Linear Regressions
- Data Cleaning