H ow well do you know your worker program data?  Does the thought of business intelligence, predictive analytics, or data science send you in a panic?  If so, you’re not alone.  In this blog, we’ll examine the analytics journey and evolution for MSP programs.  We break it down into four categories, demonstrating how simple data can transform into organizational intelligence.

To support this journey, we picked a common MSP program metric – Time to Fill – and will examine its data in each of the four categories.

PROGRAM ANALYTICS JOURNEY

Data:

This category is basic data capture, is enabled by your program’s VMS, and is fairly standard within generation one MSPs.  Raw data is the foundation for all subsequent categories.

If you take the journey of the Time to Fill metric as an example, this category encompasses the fields used to build time to fill, such as a request open date and request filled date.

Information:

In the Information category, you can begin using the Data to build operational and ad-hoc reporting.  This allows MSP programs insight into their program such as the number of active contractors, spend details, and additional metrics.

Following along in our Time to Fill journey, we are now able to compile this metric onto tabular reports allowing a program to see what the time to fill of each request was.

Knowledge:

The next category is knowledge. In this category we start to become more descriptive about the metric.  No longer one average score for the program, you will start to visualize the data in charts and trends with the ability to interact by key data points such as labor category, job title, location, or hiring group.

Through this data discovery, you can diagnose why program Time to Fill may be high or low, and pinpoint key factors that support why the overall metric is a certain way.  MSPs that are more consultative versus transactional will perform well here.

Intelligence:

In the final stage, we look to the future and steer the program behaviors to accomplish long-term goals.  We use past outcomes to determine new processes and establish norms.  We combine the knowledge with intelligence to predict and control where we are going.

Based on our Time to Fill knowledge from the previous category, the MSP can now guide the client on hiring cycles.  If one market performed poorly, the program can examine beyond Time to Fill to determine if hiring habits, culture, or market pressures is creating the delay.

 

PONTOON ANALYTICS IN ACTION

At Pontoon, we transform data into intelligence. Our skilled team of Business Intelligence Analysts leverage a wide array of processes and tools to consult and co-develop our customer’s talent strategy. This helps cultivate and place the right talent in the right roles at the right time.

Beeline welcomes this guest post from our partner, Pontoon. This post represents Pontoon’s opinions and not necessarily those of Beeline.

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