Every data analytics strategy contains the 4Cs: Collect, Cleanse, Compute, and Consume. Collect and Cleanse are the focus of a data warehouse - aligning data and addressing disparate data silos. Consume pertains to the user interface whether it be Oracle BI, Power BI, WebFOCUS, Tableau, etc. Yet it can be argued that the Compute layer is the most important and is frequently misunderstood from three perspectives.
#1: The Value of Calculated Variables or Data Elements.
As an example, it’s less important to know that an RDI occurred (return deposit item or charge back check), it’s more important to know that this was a first RDI on an account following a recent mobile deposit. Such a scenario may trigger immediate action to place a hold and investigate potential fraud on the account. However, without an algorithm or model in place, this scenario often goes unseen.
Another example: consider the number of debit card transactions or ACH transactions on an account. It's more critical, for analytic purposes, to know that, by mid-month, an account has a 50% decline in their transaction volume compared to prior months. This is a potential indication of an account about to close.
#2: What about Machine Learning and Artificial Intelligence?
The second perspective has been recently elevated by many credit unions wanting to define a strategy for Machine Learning (“ML”) and Artificial Intelligence (“AI”). In this frequent conversation, I ask what is the dependent variable you’re trying to:
- Take action on
- Improve or increase
If we dump large quantities of data into ML and AI models, be warned, you’re likely to get some statistically meaningful results. But, you’ll waste significant resources running around and trying to build a model that does what?
ML and AI are best applied to data already aligned correctly that includes numerous calculated fields, and, ideally, used to improve upon an already proven model or equation. In other words, data for the sake of data is meaningless. What is your desired outcome? Focus on that first and the data second.
#3: The Right Messaging
The third perspective revolves around where we all really want to be! We want to KNOW what message works best, to which member, using which channel, at what time, to help drive [insert your CUs goal here!]. That is, we want to move from predictive models to prescriptive action. Doing this properly yields outcomes like: do this and our desired out come will occur 40% of the time. Or, immediately do [action here] because 60% of the time this series of events is related to fraud. These are the action lists the line-of-business leaders, branch and risk managers need to succeed.
Almost always, the calculated fields are the most predictive, that have the highest correlation coefficient and are the most valuable for predictive and prescriptive models. It is the results of calculated fields, algorithms, and models, within specific segments, that direct the actions to generate the data analytics journey return on investment.
Interested in discussing these ideas further? Drop us a note!