Data teams are teams, or teams of teams, that combine data engineering, data science, data analysis, business intelligence, and other data-related disciplines to improve decision making. Teams may form around a discipline to provide a shared service, or may form cross-functionally to serve a specific product or service end-to-end.
Differences and interactions between data discipline
Disciplines related to data have significant overlap in roles and skills, but each has a distinct focus. The needs of the business are driven and validated by business intelligence. Data engineering provide the automation and infrastructure to collect, transform, and pipe data to data analysts and data scientists. Data analysts query data to identify trends, create visualizations, and build reports and dashboards while data scientists build machine learning and statistical models.
Whether a data team is formed around a specific discipline or as a cross-functional team, each team needs to meet a set of minimal skills to work effectively. The table below matches each discipline and skill to a minimum level of proficiency suitable for most contexts where a data team is required.
- Novice: Little or no knowledge required
- Beginner: Knows the basics but requires help to apply successfully
- Competent: Able to perform most tasks adequately without significant help
- Proficient: Able to design, plan, build and coach others on advanced applications
|Skill ⬇ / Role ➡||Business Intelligence||Data Engineering||Data Science||Data Analysis|
Nontechnical needs of data teams
- Deep understanding of the business or domain they're modeling
- Ability to communicate complex analyses in simple terms
Signs of effective data teams
- "It's all just data" - The organization takes the hard work of the data engineering teams for granted. As a consumer of data, it just seems so easy.
- Data is generally trusted and correctly interpreted, thanks to effective communication
Data team resources
Broader Topics Related to Data Teams
Methods to bridge the gap between data and business
Engineering approaches to data management
The scientific method applied to data analysis
Facts, statistics, and references to information
The transformation of data to information
Information Technology (IT)
The principles, practices, and technologies used to build information systems