Understanding Data Sources Quiz

Study Notes

Accurate: Data should reflect true values and realities.
Complete: Data must include all relevant information and fields.
Cost-effective: Resources used to collect data should justify its value.
Understandable: Data should be presented in a way that is clear and easy to interpret.
Relevant: Data must be pertinent to the specific business context or issues being addressed.
Accessible: Data should be readily available for use when needed.
Timely: Data needs to be current to inform decisions effectively.
Easy-to-use: User-friendly data formats facilitate better analysis.

Internal sources include financial records, HR information, and production data.
External sources like government agencies, newspapers, and customer feedback provide added context.
Structured Data: Organized in fixed formats (e.g., databases with rows and columns).
Unstructured Data: Raw forms lacking predefined formats, such as text from social media.
Metadata provides details about data files, assisting in their organization and retrieval.

Conceptual Modelling: Identifying organizational data needs through stakeholder engagement.
Logical Modelling: Formally documenting data requirements.
Physical Modelling: Managing relationships and structures between various datasets.
Advantages include improved data handling, enforced business rules, enhanced data quality, and better consistency.

Finance leverages data for decision-making, forecasting, and reporting.
Collaboration between finance professionals and data scientists is crucial to effective data modeling.

Methodical Approach: Define analysis objectives, identify data needs accurately, and request necessary data formally.
Data Collection: Distinguished between informal continuous learning and formal targeted data collection.
Types of Information:
- Quantitative: Can be quantified (e.g., financial statements).
- Qualitative: Subjective and cannot be quantified (e.g., brand reputation).
Data Quality: Analyze relevance, quality, and completeness during the analysis process.

Functions like CLEAN, CONCATENATE, LEFT/MID/RIGHT, and TRIM support organizing data effectively.
Ensures data accuracy and relevance for analysis.

Assembling Information: Organizing financial and non-financial data cohesively.
Analysis for Insights: Recognizing patterns and deriving meaningful insights from data.
Advising to Influence: Providing insights that guide strategic decision-making.
Applying for Impact: Supporting initiatives that align with organizational goals.

CLEAN: Removes non-printable characters from text entries.
CONCAT: Joins multiple text strings into one.
EXACT: Compares two text strings for exact match.
FIND: Returns the position of a substring within a string (case-sensitive).
SEARCH: Identifies the position of a substring within a string without case sensitivity.