Using Data Science in Global Mobility – Telling the Right Story and Predicting the Future

The year was 2013 and data scientists discovered that there had been more data been collected between 2010-2012 than throughout all of human history. Since then, this has gone up exponentially.

As global mobility professionals, we seek data where we can to improve operations. Service providers will survey the shipment experience, the RMC performance, and how the employee likes the policy. We capture metrics on home sale duration, time to move, and immigration clearance.

With all of the data from our employees, clients, and with the world's data at our fingertips… What do we do with it?

Data Collection

Predictive analytics show us that there are correlations in data trends that we have never before seen. The "robots" are in fact more perceptive than we are. This means that it is our responsibility as the lowly human intelligences to collect as much data as possible and ensure its quality.

In the past, surveys have been a mobility manager's best friend. It allows them to get the feedback directly from the source. The problem with surveys is that they are a one-time feedback source where it is difficult to say what their emotional state is. Did the shipment just get delivered and their heirloom vase was broken? Did they finish the move and their family is having a hard time adjusting to a new culture?

Pulse surveys are quickly trending as a leading form of feedback collection. Keeping things simple with a one-click approach allows for a mapping of ups and downs. Additionally, it significantly increases your capture rate so you no longer just see the extremely happy or bitterly upset. The two best forms of pulse survey that we have seen are pictures (happy, neutral, upset) and five-star rankings.

Your relocation systems and tools are a key source of data. From the moment of authorization, you are collecting information. Who are they, where are they coming from, going to, what is their level in the company, etc. Throughout the process, you should capture services open and closed times, costs, and benefits used/unused. Chatbots are highly effective at capturing the questions from transferees and business partners showing gaps in the policy and training.

After a move, collecting qualitative data in a simple format is highly informative. Most often we see this work well in an open text box on a five-star survey.

Lastly, collect the data from your business partners. Global Mobility as a team is the enablement resource for Talent Acquisition and HR. By understanding their hiring plans and attrition rates, you can capture talent planning and ROI metrics.

Data Validation

Garbage in, garbage out is a common phrase used by data scientists and is important for us to remember. The first defense of this is capturing meaningful data in your relocation management systems and ensuring that your RMC keeps quality data integrity. Even something as simple as ensuring your authorizers have access to the data requested and enter it in as they should create much more solid down-stream data.

Data analysts and auditors can be used to check for anomalies. These may come in the form of incorrect spend capture, distance moved, exception validation and separation, etc. If you have any anomalies, ensure that they are either corrected or removed from the data set.

Data Analysis

Now that you have the data and confirmed it is accurate, it is time to see what that data's story is. When analyzing big data that can be highly matrixed, there are many ways of going about doing this.

Excel is the most common and basic. Putting all of the data points into a master sheet can allow you to graph, filter, and analyze the data is a functional but basic way. It is basic as the story is told by the viewer.

A tool such as Alteryx is an improvement to this. Alteryx can process the data for you while looking for trends. These trends can give you a glimpse into the future by analyzing the past.

If your firm has a data science team or partner with a data science consultant, you access to tools that have machine learning capabilities. This is where the exciting things happen such as having a machine tell you trends that you had never know were correlated through their neural networks. The "learning" part of machine learning references the machine's ability to learn from mistakes and get exponentially better with each validation. At this point, you can predict future trends with a high degree of accuracy.

Where does machine learning help Global Mobility? It analyzes the real estate market to determine a loss or gain before it happens. It sees pricing trends to know the best time of the year to move your talent. It provides the information for your talent management strategists to move the right people, into the right roles. It accesses who is right for that global assignment.

Telling the Story

Storytelling is as ancient as humankind. A proper storyteller can activate the part of the brain of the listener that they would activate if they were doing what is being told. This is where you come in.

Global Mobility as a function of any business is often overlooked as a kind of necessary piece of the greater pie, but it isn't very exciting. This changes when you use data, in the right way to tell the story.

A visual is committed to memory 60,000 times faster than text and is retained 2-10 times better than facts. Our primitive traits draw us to stare at the fire. It is bright, it stands out, and it keeps us warm – an essential value-add. When telling your story, you must identify what the recipient needs to know and use the campfire approach.

The executive team is concerned about budget, employee engagement, and time to get them in the role. For a policy change, you are telling the story of where you came from complete with employee feedback data, exception data, spend, and attrition rates. You show where the budget is impacted but also the projected impact on engagement, retention, ROI, and having the right talent in the right role. You do this on attractive graphics with color in specific areas to draw the eye, the data needed to support the decision, and simplified points so that it can be shared to others that are not in the room.

Stories hold value because they are told, retained, and shared again. By using data science, you are telling the right story that will be retained, shared, and with a little help from the robots – help you predict the future.

Published: March 20, 2019

Andrew Bruzzi


Vendium Global, LLC


©2020 by Vendium Global, LLC.