Online BI Learning Resources and Technologies

Actuarial Expertise: Advanced statistics Analytical concepts leveraging emerging data sources Business Continuity and Crisis Management Capital estimation/capital management Corporate Data Warehouse Data structure, aggregation and reconciliation Decision Trees Demand and elasticity modeling Eagle Eye Emblem Financial and insurance acumen Financial planning Financial statement analysis Generalized Linear Models (GLMs) Geospatial modeling IGLOO/Remetrica/@Risk Insurance pricing Location intelligence[…]

BI Roles and Responsibilities

Here I list some of the BI Roles and Responsibilities I know, and which may help my peers to design their best career plan. Career Level Student Junior Experienced Manager Director / Lead Senior Director – C level   Top profiles: Enterprise Architect, Business Product Manager: • University degree, MBA preferred, in marketing, business intelligence, management or related field[…]

Data Masking

Data masking is the process of changing a copy of the production data into SIT and UAT environments in order to make it anonymous. This serves different purposes, most importantly: Protecting the privacy of the production data while providing realistic data values and formats. There are multiple methods for data masking, and it also depends on the . Randomization[…]

BI Product Owner and Managing Vision

Here is a nice article on managing BI with a scrum approach:$FILE/Whitepaper%20Barry.pdf I recommend starting with vision analysis sessions.. What is the vision of the company, what are the objectives, how they reach them. Then Draw a Mind-Map: Of vision (Big Hairy Audecious Goal BHOG, e.g. become Fortune 500) and objectives: Such as Increase[…]

Azure Machine Learning

Azure Machine learning is IDE where data scientists can develop machine learning experiments, operationalize them and integrate them into solutions using web services. This can be accomplished with the following steps:   – Create a training experiment. – Add your modules to it:   Click Run to run the experiment first from the bottom pane[…]

Fact Table Design Options

Comparison of Deisgn and ETL patterns for Fact Table, depending on its usage, and performance requirements: Criteria Full Re-flush Snapshot with Insert then Archive Differential Facts Periodic Snapshots ETL Structure Simplest and may be required when using any other pattern, once an error happens. 2 Snapshots: one new and one old snapshot data. Requires differential calculations[…]