CT109-3-1 Digital Thinking and Innovation: Data vs. Information

Data vs. Information

• Data is raw, unorganized facts that need to be processed.
• Data can be simple and seemingly random until it is organized.
• Processed, organized, and structured data is called information.

Data Analytics

• Data analytics is the science of analyzing raw data to make conclusions and enhance productivity and business gain.
• Extracted data is cleaned and categorized to analyze different behavioral patterns.
• Techniques and tools used in data analytics vary according to the organization or individual.

Needs for Data Analytics

• Four main factors that signify the need for data analytics:
  • Gather hidden insights from data with respect to business requirements.
  • Generate reports from data and pass them to respective teams and individuals.
  • Perform market analysis to understand competitors' strengths and weaknesses.
  • Improve business requirements and customer experience through data analysis.

Tools in Data Analytics

• Various tools have emerged with different functionalities, including:
  • R programming: leading analytics tool for statistics and data modeling.
  • Python: provides machine learning and visualization libraries like Scikit-learn, TensorFlow, Matplotlib, Pandas, and Keras.
  • Tableau Public: free software that connects to any data source, creates visualizations, maps, and dashboards with real-time updates.
  • QlikView: offers in-memory data processing, data association, and visualization with data compression.
  • SAS: programming language and environment for data manipulation and analytics, accessible and can analyze data from different sources.
  • OpenRefine/GoogleRefine: data cleaning software for cleaning messy data, transformation, and parsing data from websites.
  • Microsoft Excel: widely used tool for data analytics, mostly used for clients' internal data, summarizes data with a preview of pivot tables.
  • RapidMiner: integrated platform that can integrate with various data sources, used for predictive analytics, data mining, text analytics, and machine learning.
  • Konstanz Information Miner (KNIME): open-source data analytics platform for analysis, modeling, reporting, and integration.
  • Apache Spark: large-scale data processing engine that executes applications in Hadoop clusters, popular for data pipelines and machine learning model development.

Data Analyst

• A data analyst is a professional who analyzes data by applying various tools and techniques, gathering required insights, and translating numbers into plain English.
• Skills required for a data analyst include:
  • Statistics
  • Data collection
  • Data cleaning
  • Data analysis
  • Hidden insights discovery
  • Data visualization
  • Reports generation
  • Machine learning

Big Data Analytics

• Big Data: a concept in Software Engineering referring to large sets of machine-generated data that are unstructured and not easy to use with traditional RDBMS concepts.
• Big Data Analytics: complex process of examining large and varied data sets to uncover information that can help organizations make informed business decisions.
• Four types of Big Data Analytics:
  • Descriptive: learning from past behaviors to understand how they might influence future outcomes.
  • Diagnostic: root cause analysis to answer the question "Why did this happen?"

Uses of Big Data Analytics

• Using Big Data Analytics to boost customer acquisition and retention, e.g., Coca-Cola's digital-led loyalty program.
• Offering marketing insights and solving advertisers' problems, e.g., Netflix's targeted advertising based on past search and watch data.
• Risk management, e.g., UOB bank's risk management system that reduces calculation time of value at risk.
• Driving innovations and product development, e.g., Amazon Fresh and Whole Foods.
• Supply chain management, e.g., PepsiCo's use of huge volumes of data for efficient supply chain management.

Learn the difference between data and information, and how data analytics can help turn raw facts into useful insights. This quiz is part of the CT109-3-1 Digital Thinking and Innovation course, focusing on innovation through analytics.