Data
Data is the root of information systems.
- Data: raw facts
- Information: data with context
- Knowledge: information with understanding
- Wisdom: knowledge with experience
Personal information
You expose a lot of personal information everytime you browse the web, including what you did, who you are, and where you are.
Websites use cookies to store information about you on your device, so that later when you visit their website again, they know it's you.
Even if you go incognito, your activity is still visible to your ISP and websites you visit.
Business analytics
A term that describes the massive volume of data that companies collect. This data can be structured or unstructured, and are stored in distributed databases. These data are usually generated by users. The five Vs of big data are:
- Volume - Amounts of data
- Velocity - Speed of data generation
- Variety - Different types of data
- Veracity - Quality of data
- Value - Data is not useful unless it is used The value of big data is how organizations uses them to generate insights from data analyses for decision making.
Steps for conducting business analytics:
- Identify problems/objectives
- Prepare clean, correct, consistent data, which should reflect trends
- Use technologies, business knowledge and analysis techniques to generate insights for better decision making
- Collection
- Storage - Databases
- Visualization - Excel
- Processing - Excel
- Reporting - OLAP / Dashboards Or other general purpose tools like Python
Analysis techniques can include the usage of data to:
- Describe history - Explore patterns and structures in data
- Explain history - Find relationships, correlation and causality
- Predict future - Trend / predictive analytics, ML
Visual display of various key performance indicators and metrics, in a user friendly way. Might provide real-time data.
Online Analytical Processing, a technology that allows users to extract and view business data from different points of view.
There are some challenges in business analytics:
- When a firm makes decisions based on wrong prediction, it is overexposed to risk
- Using bad data can give wrong estimates
- When the market does not behave as it did in the past, data-driven models are not effective
- Historical inconsistency