Business Analytics Resources Posted to the SIG Linkedin Group

We post educational articles on the topic of businss analytics and the expanding BA role on the IIBA Business Analytics Linkedin Group (join here). Here is an archive:

Are you an Analytics BA or aspiring to be one?
Here's a well researched job description. What is your experience? 
The Evolving Job Description for an Analytics Business Analyst by Sterline Seals, CBAP

Capturing requirements for analytics projects using DMN
Here is an article on using decision modeling and the Decision Model Notation (DMN) standard to capture a clear set of business objectives and subject matter expertise for analytics and big data projects. Study after study shows that the lack of clear business objectives is the key reason for the high failure rate (in terms of business value realized) of big data and analytics projects. 
Decision Fundamentals: Making Analytics More Accessible

"If data scientists focus on exploration and discovery and executive sponsors create the vision around big data, business analysts do the critical work in between."
Interesting observation on this Teradata post 
Big Data Business Analysts

A good topic for a future webinar - The problem with P-Values

How not to analyze noisy data: A case study. - Stats Blog

Engage and Educate - good advice from Ugly Research.

For those new to predictive analytics, a useful primer -

Business analytics for Australian government
Developed back in 2013, the Big Data Strategy is a good read on the approaches by Australian Government to develop infrastructure for business analytics.
This document was developed for Australian Government agency senior executives with responsibility for delivering services and developing policy. It outlines future work by the Government that will assist agencies to make better use of their data assets whilst ensuring that the Government continues to protect the privacy rights of individuals.

Machine Learning vs Statistics - useful KDnuggets article
Machine learning is all about predictions, supervised learning, and unsupervised learning, while statistics is about sample, population, and hypotheses. But are they actually that different? 

The Business Analyst’s Next Frontier: Advanced Analytics
Good discussion by Blue Hill research on advanced analytics capabilities now being built into BI offerings and the implications for business decision makers evaluating software investments and for business analysts.

Data Science is a team sport
Great article by Bob Roger, Intel's Chief Data Scientist for Big Data - data science is a team sport, requiring technical understanding (IT, analytics) and business domain knowledge and "an appreciation for how real business value can be created for the organization."

The Business Analyst’s Guide to Hadoop
Alteryx has a nifty guide to Hadoop for BA's, highlighting the importance of the BA role in articulating and delivering business value from Big Data: "While IT professionals who utilize Hadoop are well-versed in the defining attributes of Big Data, many are unable to identify and articulate potential business value and use cases for Big Data. This is where you, the business analyst, come in. Why? Because no matter the technology underpinnings, you understand the answers your business needs—and the relevant questions to ask in order to uncover them." 

Decision modelling - a tasty analogy
Andy Gray of Deloitte Australia makes the case for using decision modeling (Technique 10.17 in the BABOK® v3) for analytics projects and gives a shout-out to our recent IIA Leading Practices webinar. Andy makes the key point that the "analytics function cannot be effective in helping to meet business objectives without a firm understanding of what decision(s) must be improved." Decision modeling (or "modelling" in the UK spelling) is an effective technique for BA's to take the lead in clarifying business objectives for analytics projects.

What's the difference between BI reporting and actionable information from Data Science?
See how BI reporting can be augmented with actionable information from data science thanks to these great examples from Vijay Kotu and RapidMiner.