This article was written by Sara Roberts. As Co-Founder and Principal Consultant at Category One Consulting (C1C), Sara is committed to helping organizations maximize their people and program effectiveness through the application of research, analytics, and evidence-based practice.
Organizations have understood the importance of using data to inform financial, sales, and marketing decisions for quite some time; however, this data-driven focus has only recently extended itself to talent-related decisions. Organizations are now searching for ways to use data to help them better hire, develop, and retain their employees. Improving these practices in a data-driven way can have large effects on organizational effectiveness and profitability.
This data-driven approach has become quite popular with many organizations either developing their own internal talent analytics teams or turning to one of the many consulting firms that now deliver talent analytic solutions. Many of these organizations are also placing a large focus on climbing to the top of the analytic maturity model, which has led me to ask…Does analytic maturity really matter in talent analytics?
Analytic maturity models illustrate the various stages of analytics, typically in a manner that suggests that certain types of analytics (usually those that are the most complicated) are superior to others. They often start with some form of descriptive analytics or reporting as the most basic level, then move through a variety of other types of analytics before topping out at predictive or prescriptive analytics. Although there are hundreds of analytic maturity models that vary in terms of the number of stages, labels and definitions given to each, and the overall design of the model, they all seem to share one common message – you haven’t attained analytic greatness until you reach the top! I disagree with this premise.
Although I am excited about the increasing popularity of making data-driven talent decisions and do believe there are different types of analytics, I also believe there is entirely too much focus on analytic maturity. As a result, we at Category One Consulting utilize a model that has the same four stages, labels, and definitions as many other models; however, the design of our model places more focus on the type of question being asked than the analysis being conducted. Furthermore, it shows that each type of analytics can be considered optimal and relevant when applied to the appropriate type of question.
An overview of each type of analytics is provided below, with a focus on the type of question first and the type of analysis second:
1. Descriptive analytics
2. Diagnostic analytics
3. Predictive analytics
4. Prescriptive analytics
To read the original article, click here.
- Services: Hire a Data Scientist | Search DSC | Classifieds | Find a Job
- Contributors: Post a Blog | Ask a Question
- Follow us: @DataScienceCtrl | @AnalyticBridge