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've checked off most of this list, you're ready for the Data Analyst stage, systematically cleaning data and summarizing it with descriptive statistics. Bandura(1977)'s Social Learning theory suggests watching data analysis demonstrations and studying analysis report examples builds the confidence to handle data on your own.
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.
Presents a 5-stage analytics maturity model (Analytically Impaired to Analytical Competitors) with organizational analytics culture case studies, providing practical grounding for L4-L7 checklists on quantifying business impact and establishing organizational analytics systems.
