Do you see differences in maturity of analytics across business units in an organization?

By: Bart Baesens, Seppe vanden Broucke

This QA first appeared in Data Science Briefings, the DataMiningApps newsletter as a “Free Tweet Consulting Experience” — where we answer a data science or analytics question of 140 characters maximum. Also want to submit your question? Just Tweet us @DataMiningApps. Want to remain anonymous? Then send us a direct message and we’ll keep all your details private. Subscribe now for free if you want to be the first to receive our articles and stay up to data on data science news, or follow us @DataMiningApps.

You asked: Do you see differences in maturity of analytics across business units in an organization?

Our answer:

Definitely! Indeed, big data and analytics have matured differently across the various business units of an organization. Triggered by the introduction of regulatory guidelines (e.g. Basel II/III, Solvency II), many firms (especially financial institutions) invested in big data and analytics for risk management for quite some time now. Years of analytical experience and perfectioning contributed to very sophisticated models for insurance risk, credit risk, operational risk, market risk and fraud risk. The most advanced analytical techniques such as survival analysis, random forests, neural networks and (social) network learning have been used in these applications. Furthermore, these analytical models have been complimented with powerful model monitoring frameworks and stress testing procedures to fully leverage their potential.

Marketing analytics is somewhat less mature with many firms starting to deploy their first models for churn prediction, response modeling or customer segmentation. These are typically based on simpler analytical techniques such as logistic regression, decision trees or k-means clustering. Other application areas such HR and supply chain analytics start to gain traction although not many successful case studies have been reported yet.