Collecting, cleaning, and analyzing data to extract meaningful insights and support decision-making through statistical thinking and visualization.
Data analysis is discovering patterns in raw data to support business decisions. It spans collection, cleaning, exploratory analysis, statistical testing, visualization, and communication. The core is asking the right questions, finding answers through data, and connecting them to action.
You can open data in a spreadsheet or basic tool and understand its structure. You grasp the meaning of rows, columns, and fields, and can sort and filter data to find what you need. You use basic aggregation functions like averages and sums, and can create simple charts to visually represent data.
What Comes Next
If you have achieved most of this checklist, you are ready to enter the Advanced Beginner stage of the proficiency model — systematically cleaning data and summarizing it with descriptive statistics.
SFIA 9 defines data analytics competency across 7 levels from Level 2 (assist) to Level 6 (lead), providing autonomy and complexity criteria directly used for level boundary setting.
Entry-Level, Mid-Level, Senior 3-tier structure with Analytical/Technical tracks, reflecting stage-specific competency differences in checklist behavior criteria.
Awareness-Comprehension-Application-Influence 4-stage proficiency framework providing governmental authority as an accredited data competency standard.
5-stage cognitive development model defining skill acquisition stages, providing theoretical basis for data analysis proficiency progression from spreadsheet exploration (L1) to paradigm shift (L7).
