Module 2

Nature of Data

Data is a collection of facts obtained through experience, observation, or experiments.

Lowest Level of Abstraction

Data is the base level from which information and knowledge are derived.

Data Quality and Integrity

Crucial aspects that ensure accurate and reliable analytics results.

Metrics for Analytics Ready Data

Reliability, accuracy, accessibility, security, privacy, richness, consistency, timeliness, granularity, validity, relevancy.

High Granularity

Highly detailed data (e.g., transaction-level data) that enables fine-grained analysis.

Low Granularity

Aggregated or summarized data (e.g., monthly totals) for trend and overview analysis.

Implications of Data Granularity

Higher granularity increases flexibility but also data volume and storage needs.

Structured Data

Data organized in fixed fields, easily processed by computers (e.g., numeric or nominal).

Unstructured Data

Data not organized in a predefined manner, targeted for human processing (e.g., text, images).

Semi-Structured Data

Partially organized data like XML, HTML, or log files.

Data Taxonomy

Classification of data based on structure and processing method (structured, unstructured, semi-structured).

Descriptive Statistics

Summarize and describe data characteristics (e.g., mean, median, mode).

Inferential Statistics

Use sample data to make inferences or predictions about a population.

Business Reporting

Transforming data into meaningful information to support decision-making.

Visualization Importance

Helps communicate insights, trends, and patterns effectively.

Visualization Techniques

Methods such as charts, graphs, dashboards, and heatmaps.

Value of Visual Analytics

Combines data visualization with analytical reasoning to enhance business insights.

Dashboards

Tools that display real-time data summaries; useful but have limitations in depth of analysis.

Granularity Example

Item-level sales data is high granularity; monthly total sales is low granularity.

Data for Business Intelligence

Used to transform raw data into actionable insights for decision-making.