Data Analysis (24%) - Domain #3

Mock-up (communication)

A visual prototype or draft of a report or dashboard used to plan layout and get stakeholder approval before building

Accessibility (data communication)

Designing reports and visualizations so they are usable by people with auditory or visual impairments

Sensitive data (communication)

Data that requires restricted distribution due to privacy, legal, or confidential business concerns

Non-sensitive data (communication)

Data that can be freely shared without legal or privacy concerns — such as publicly available information

Technical audience

Stakeholders with data or technical expertise who can interpret detailed metrics, raw data, and complex analysis

Non-technical audience

Stakeholders without deep technical knowledge who need simplified, high-level summaries and plain-language insights

C-suite (user persona)

Executive-level stakeholders — such as CEOs and CFOs — who need high-level KPI summaries, not granular data

Individual contributor (user persona)

Operational-level employees who often need detailed, granular data to complete specific day-to-day tasks

KPI (Key Performance Indicator)

A measurable value that tracks how effectively an organization or individual is achieving key business objectives

Internal audience

Report recipients who are employees or members within the organization producing the data

External audience

Report recipients who are outside the organization — such as clients, partners, regulators, or the public

Descriptive analytics

Analyzes historical data to answer WHAT HAPPENED — uses reports, dashboards, and summary statistics

Diagnostic analytics

Analyzes historical data to explain WHY something happened — uses drill-downs and root cause analysis

Predictive analytics

Uses statistical models and machine learning to forecast WHAT MIGHT HAPPEN in the future

Prescriptive analytics

Recommends specific actions to take to achieve a desired outcome — tells you WHAT TO DO next

Inferential statistics

Uses a data sample to draw conclusions and make generalizations about a larger population

Measures of central tendency

Statistical measures that identify the center or typical value of a dataset — mean, median, and mode

Mean

The arithmetic average of all values in a dataset — sum divided by count

Median

The middle value in a sorted dataset — not affected by outliers like the mean is

Mode

The value that appears most frequently in a dataset

Measures of dispersion

Statistical measures that describe how spread out values are in a dataset — range, variance, standard deviation

Range

The difference between the maximum and minimum values in a dataset

Variance

A measure of how far each data point is from the mean — the average of squared differences from the mean

Standard deviation

The square root of variance — measures how spread out values are around the mean in the same units as the data

Mathematical function

A function that performs numeric calculations on data — such as SUM, AVERAGE, ROUND, or ABS

Logical function

A function that evaluates a condition and returns a result based on true/false logic — such as IF, AND, OR, NOT

Date function

A function that performs operations on date or time values — such as DATEDIFF, DATEPART, or NOW

String function

A function that manipulates text data — such as TRIM, CONCAT, UPPER, LOWER, LEFT, or SUBSTRING

Connectivity issue (troubleshooting)

A problem that prevents access to a data source, database server, or network resource

User-reported issue (troubleshooting)

A data or report problem identified by an end user — not automatically detected by the system

Basic SQL code issue (troubleshooting)

An error in a SQL query causing incorrect results or query failure — such as a syntax error or wrong join condition

Corrupted data (troubleshooting)

Data that has been damaged or made unusable due to hardware failure, transmission errors, or software bugs

Enable logging (troubleshooting)

Activating a system's log recording to capture errors, events, and actions to help diagnose issues

Validate data source (troubleshooting)

Verifying that the data source is accessible, returning expected results, and is the correct source

Consult vendor communities (troubleshooting)

Using official documentation, forums, or vendor support resources to find solutions for tool-specific data issues