Data Analytics Concepts

Effective communication in data analysis

• Presenting useful information to stakeholders is key to being an effective communicator.

• Using complex jargon to impress the audience.

• Focusing solely on data accuracy without considering the audience.

• Delivering information in a lengthy report format.

Understanding the audience in data presentation

• Effective communicators tailor messages to enlighten audiences, even if the content is complex.

• Data presentation should always be simple and straightforward.

• The audience does not need to understand complex content.

• Presentations should focus solely on the data without considering the audience.

Commonly used data visualisation tool: Infogram

• Infogram is a commonly used data visualisation tool.

• Excel is the most popular spreadsheet software.

• Tableau is primarily used for database management.

• PowerPoint is a presentation software.

The objective of data analytics is to identify trends and draw conclusions from large data sets.

• Identifying trends and drawing conclusions.

• Collecting data without analysis.

• Storing data for future use.

• Visualizing data without interpretation.

Pattern recognition involves identifying trends in customer purchasing behavior.

• Identifying trends in purchasing behavior.

• Analyzing customer demographics.

• Forecasting future sales based on past data.

• Segmenting customers into different categories.

Data procurement includes collecting data from enterprise resource planning systems.

• Collecting data from ERP systems.

• Collecting data from customer relationship management systems.

• Collecting data from supply chain management systems.

• Collecting data from data warehousing systems.

Tools for creating charts or graphs

• Microsoft Excel

• Adobe Photoshop

• Notepad

• Microsoft Word

Organizing data into a usable form is part of data provisioning.

• Organizing data into usable form.

• Collecting data from various sources.

• Storing data in a database.

• Analyzing data for insights.

Role of Excel in data analytics

• Excel spreadsheets are a simple technology used for data analytics.

• Excel is primarily used for graphic design and multimedia projects.

• Excel is a programming language used for data analysis.

• Excel is only useful for financial modeling and not for data analytics.

Considerations for data analytics

• Nature, volume, and required sophistication of answers are key considerations in data analytics.

• Only the volume of data is important in data analytics.

• The speed of data processing is the most critical factor in data analytics.

• Data analytics is solely concerned with the accuracy of the data.