SCTR7: Data Science and Analytics

Both fraudsters and cyber attackers take advantage of an increasingly interconnected world to infiltrate and assault financial and virtual infrastructures. Especially sophisticated electronic interactions bind people together in complex, global networks. While such networks have created new opportunities for interaction and commerce, they have also increased the scope and scale of risks. To stay a […]

Analytics professionals should keep a persistent eye open for opportunities to create cross-functional insights from data. Insights can frequently be leveraged across multiple business domains. However, business silos can restrict ‘forest for the trees’ visibility. To the degree analytics has cross-functional exposure in the organization, for instance via a C-level champion and an Analytics Center […]

As a companion to my recent post “Correlation versus Causation: The Science, Art, and Magic of Experimental Design”, I wanted to offer a more technical exposition concerning data science approaches to focused causal model development. A fundamental question faced by business analytics professionals and data scientists is whether they have a working correlative and causal […]

  This is a review committee working group for practitioners and academics to establish a formal definition and set of classification criteria regarding business analytics model risk. This group has been established based upon interest and feedback concerning a recent set of posts regarding business analytics model risk: http://tinyurl.com/ktabt3q For those interested, you can join the […]

PART 6 OF 7 IN A SERIES ON ADOPTING ANALYTICS CULTURE LINK TO HEADER ARTICLE LINK TO PREVIOUS ARTICLE (5 of 7) 6.    What particular information is gained from social network analysis and how is it interpreted? To recap, we have established that adopting analytics culture requires organizational change management.  Beyond analytics technology and expertise, […]

Business analytics model risk (part 0 of 5): framing model risk – the complexity genie and the challenge of deciding on decision models Introduction to a series of five articles on model risk Here we introduce a series of five articles seeking to frame, define, and categorize business analytics model risk.  The intention is to […]

PART 5 OF 7 IN A SERIES ON ADOPTING ANALYTICS CULTURE LINK TO HEADER ARTICLE LINK TO PREVIOUS ARTICLE (4 of 7) 5.    How can change management be improved via analytics? We have built-up the case for using organizational change management to adopt analytics culture.  By analytics culture, we mean the set of both formal […]

Business analytics model risk (part 4 of 5):  categorizing model risk Following from article 3 of 5 on Business Analytics Model Risk Link to introductory header article (0 of 5) Model risk cannot easily be spoken of as a singular entity.  The topic is complicated in that there are multiple ways of categorizing model risk.  As […]

Business analytics model risk (part 3 of 5):  model scoping and complexity Following from article 2 of 5 on Business Analytics Model Risk Link to introductory header article (0 of 5) In a the first article in this set on business analytics model validation (https://sctr7.com/2013/03/14/model-risk-when-is-a-business-analytics-model-validated/), it was proposed, in summary, that: 1) business decision models are […]

PART 3 OF 7 IN A SERIES ON ADOPTING ANALYTICS CULTURE LINK TO HEADER ARTICLE LINK TO PREVIOUS ARTICLE IN SERIES (2 of 7) 3.  How does change management work? At the core, change management sees the organization as a network of interconnected interests, both shared and conflicting. An organization can be viewed as being […]