Business Intelligence Data Warehousing BIDW Basics 101

BIDW may employ more stable, heavy duty and less flexible architecture, schema and data store than startups in the Silicon Valley. Such may be a sacrifice for security, stability which many fortune companies rely on.

Structured Query Language (SQL)

Despite the popularity of many new data stores and technologies such as Hadoop, Spark, Pandas etc, many companies still require Business Analysts to be fluent in sql. Never forget SQL.

Graphical User Interface (GUI)

GUI interface helps business users query and drill data without the help of the development department. The schema and database are still designed and implemented by dev.

Online Analytical Processing (OLAP)

Provides a GUI to query platform for business users to do data explorations with minimum help from dev department. 

Analysts and decision makers can quickly and efficiently do data analysis and ad hoc reporting without too much help from a data scientist or database administrator. 

The schema, reports, and drilling depth may need to be pre-planned, designed and tested before being released to business users.

This is also a large scale system, suitable for companies such as Macy's, Gap, Walmart which have millions of new sales record per hour. 

OLAP is for data exploration by large businesses.

Data Warehousing 

Data Warehousing is a serious challenge for large companies with many transactional records, product offerings across many departments. 

Many DW providers can also provide integrated data mining, business intelligence services build on top of proprietary DW hardware (including server stack) and software.

Best Practice

• Sales teams on-the-road often needs faster, better data information on mobile devices to seal a deal. Don't be surprised if they get mad when numbers are off! They bring home the dough. 

Questions that Business Analysts and Decision Makers are Interested In

• Who are the best customers? aka Who are the customers with the best Customer Life Value
• Causal relationship: 
• Results of recent experiments (More prevalent in Startup Culture)
• Hypothesis if one segmentation is actually different from another
• Is the result significant or is it random chance
• Please note that causal relationship determination requires controlled studies to control for extraneous variables. In many industries, such as biotech, statistical significance is a must, a prerequisite for next step analysis or more business investments. 
• Demo graphics of customers. Summary statistics, customer segmentation and more. 
• How to measure profitability and other Key Performance Indicators (KPI)