Grow your People Analytics Maturity with Exploratory Data Analysis

During my daily discussions with One Model prospects and customers, two consistent themes emerge:  A general lack of understanding predictive modeling and a delay in considering its use until basic reporting and analytical challenges are resolved.

These are understandable, and I can offer a suggestion to overcome both.  My suggestion is based upon seeing successful One Model customers gain immediate insights from their data by leveraging the technology found in our One AI component. These insights include data relationships that can surface even before customers run their first predictive model.

Deeper insights before predictive modeling? How?

To begin, let’s rethink what you may consider to be a natural progression for your company and your People Analytics team.  For years we’ve been told a traditional People Analytics Maturity Continuum has a building block approach that is something like this: 

The general concept of the traditional People Analytics maturity model is based upon the need to master a specific step before progressing forward.  Supposedly, increased value can be derived when each step is mastered, and the accompanying complexity is mastered.  While this may seem logical, it is largely inaccurate in the real world.  The sad result is many organizations languish in the early stages and never truly advance.  The result is diminished ROI and frustrated stakeholders.

What should we be doing instead?  The short answer is to drive greater value immediately when your people analytics project launches.

Properly built data models will immediately allow for basic reporting and advanced analytics, as well as predictive modeling.  I’ll share a brief explanation of two One Model deliverables to help you understand where I’m going with this.

People Data Cloud™️

Core Workforce data is the first data source ingested by One Model into a customer's People Data Cloud.  Although additional data sources will follow, our initial effort is focused on cleaning, validating, and modeling this Core Workforce data. This analytics-ready data is leveraged in their People Data Cloud instance. Once that has occurred storyboards are then created, reflecting a customer’s unique metrics for reporting and analytics. It is now that customers can and should begin leveraging One AI (Read more about People Data Cloud).

Exploratory Data Analysis

One AI provides pre-built predictive models for customers. The capability also exists for customers to build their own bespoke models, but most begin with a pre-built model like Attrition Risk. These pre-built models explore a customer's People Data Cloud to identify and select relevant data elements from which to understand relationships and build a forecast.  The results of this selection and ranking process are presented in an Exploratory Data Analysis (EDA) report. What is exploratory data analysis, you ask?  It is a report that provides immediate insights and understanding of data relationships even before a model is ever deployed.

Consider the partial EDA report below reflecting an Attrition Risk model.  We see that 85 different variables were considered.

One AI EDA will suggest an initial list of variables relevant to this specific model, and we see it includes expected categories such as Performance, Role, and Age.

This first collection of variables does not include Commute Time. But is Commute Time a factor in your ideal Attrition Risk model?  If so, what should the acceptable time threshold be? Is that threshold valid across all roles and locations?  One AI allows each customer to monitor and select relevant data variables to understand how they impact insights into your predictive model. 

Changing the People Analytics Maturity Model into a Continuum

Now that we realize that the initial Core Workforce People Data Cloud can generate results not only for Reporting and Analytics but also for Predictive Modeling, we can consider a People Analytics Maturity Continuum like this:

This model recognizes the fact that basic reporting and analytics can occur simultaneously after a proper data lake is presented. It also introduces the concept of Monitoring your data and Understanding how it relates to your business needs. These are the first steps in Predictive Modeling and can occur without a forecast being generated.

The truth underlining my point is: Analytics professionals should first understand their data before building forecasts.

Ignoring One AI Exploratory Data Analysis insights from this initial data set is a lost opportunity.  This initial model can and should be enhanced with additional data sources as they become available, but there is significant value even without a predictive output. The same modeled data that drives basic reports can drive Machine Learning. 

The greater value of One AI is providing a statistical layer, not simply a Machine Learning output layer.  The EDA report is a rich trove of statistical data correlations and insights that can be used to build data understanding, a monitoring culture, and the facilitation of qualitative questions.  But the value doesn’t stop there.  Integrated services that accompany One AI also provide value for all data consumers. 

These integrated services are reflected in storyboards and include:

• Forecasting
• Correlations
• Line of BestFit
• Significance Testing
• Anomaly Detection

These integrated services are used to ask questions about your data that are more valid than what can be derived solely from traditional metrics and dimensions.  

For example, storyboards can reflect data relationships so even casual users can gain early insights. The scatterplot below is created with Core Workforce data and illustrates the relationship between Tenure and Salary.  One AI integrated services not only renders this view but cautions that based upon the data used this result is unlikely to be statistically significant (refer to the comment under the chart title below).

More detailed information is contained in the EDA report, but this summary provides the first step in Monitoring and Understanding this data relationship. Perhaps one of the questions that may arise from this monitoring involves understanding existing gender differences.  This is easily answered with a few mouse clicks:

This view begins to provide potential insight into gender differences involving Tenure and Salary, though the results are still not statistically significant. Analysts are thus guided toward discovering their collection of insights contained within their data.

List reports can be used to reflect feature importance and directionality. 

In the above table report, both low and high Date of Birth values increase Attrition Risk.  Does this mean younger and older workers are more likely to leave than middle-aged workers?  Interesting relationships begin to appear, and One AI automatically reports on the strength of those relationships and correlations.  Iterations will increase the strength of the forecast, especially when additional data sources can be added. 

Leveraging One AI's capability at project launch provides a higher initial ROI, an accelerated value curve, and better-informed data consumers.  

At One Model, you don’t need to be a data scientist to get started with predictive modeling.

Contact One Model to learn more and see One AI in action.

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