Class 7: Tableau

Tableau file types

  • Tableau workbook (twb) - data visualizations, no data

  • Tableau data extract (tde) - data only, not presented via visuals

  • Tableau packaged workbook (twbx) - data and visuals in one file

Review: decision types

  • Operational decisions 

    • Supported by transaction processing systems 

  • Managerial decisions 

    • Allocation and utilisation of resources 

    • Supported by MIS 

  • Strategic decisions 

    • Organizational issues, broader-scope 

    • Supported by executive information systems - calls made by execs 

Unstructured vs structured decisions

  • Structured: methodical 

  • Unstructured: ex. Who you should marry

  • Refers to the decision method, not about the underlying problems

OLTP vs OLAP

  • Online Transaction Processing (OLTP) systems collect data electronically and process the transactions 

  • Online OLTP systems support decision making by providing the raw information about transactions and status for an organization

    • Payroll, inventory

  • Online Analytic Processing (OLAP) systems focus on making OLTP-collected data useful for decision making

  • Provides the ability to sum, count, average, and perform other simple arithmetic operations on groups of data

    • Stats, reports, data mining 

 

Business intelligence (BI)

  • Transforms data into actionable intelligence 

  • Bi tools present analytical findings in reports, summaries, graphs, etc

  • Provide them to the right decision maker at the right time with the right information (data relevancy) 

Data warehouse

  • Used to extract and clean data from operational systems 

  • Prepares data for BI processing 

Data mart

  • Data collection, subset of data warehouse

  • Addresses a specific function of the business

  • Ex. Web sales data mart, store sales data mart, inventory data mart 

 

Data mining

  • Discovering trends in datasets to extract relevant insights for decision making 

  • Combines stats, maths, AI, machine learning 

    • Descriptive: explaining what is in the data 

    • Prescriptive: forecasting future outcomes 

  • Data mining examples: Customer segmentation, recommendation engines, fraud detection, credit scoring, medical research

  • Techniques 

    • Supervised: Start with hypothesis and gather data to prove it, use of labeled data to predict outcome 

    • Unsupervised: No model prior to running analysis, uses unlabeled data, hypothesis is created after as an explanation for the results 

    • Clustering - ex. Political affiliation demographics

    • Market basket analysis ex. Amazon ‘others also purchased’ 

    • Anomaly detection ex. Credit card fraud detection 

Midterm material Review 

  • Iclicker practice test 

  • Practice exam 

  • Short answer questions: ERD, data normalization, tableau data visualisation