How to Add Context to Your Data Analysis

Posted by Lauren Decker on Mar 10, 2016 9:59:45 AM

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The rise of big data is more than a passing trend. It’s a new business practice that’s here to stay, which has been made evident by businesses continuing to invest in business systems and data scientists. Research firm International Data Corp predicts annual spending in big data technology and services will reach $48.6 billion in 2019.

Companies investing in big data see the potential to leverage this information to make informed business decisions, predict trends, and ultimately, gain an edge of their competitors. However, the companies who will see the most return on big data will have to invest more than just dollars.

The companies who will be the most successful in using big data will invest the time to learn how, when, and where to leverage the data they are collecting. If big data isn’t used contextually, businesses could be at risk of spending lots of time and money on purchasing and building a data warehouse that isn’t reaching its full potential.

For example, if you’re evaluating your relocation program, you may be looking for statistics like number of relocations administered, total cost, and number of exceptions. From your research, you may find that your company administered 30 relocations, for a total cost of $325,000, and 5 exception requests.

Based on this information, you may conclude that your relocation program is healthy. You are spending an average of $10,833 per transferee and only 17% of your relocations have requested exceptions.

To truly understand what this data for your relocation program, you need to add more context. Take your analysis a step further by applying these two tips for adding context to your data.

1. Connect the Dots Between Other Important Data Sources

When analyzing data, you may need to have various information sources at your disposal. While companies are continually trying to bring data together into one single place, the reality is that the data we need may still be collected and stored in a few different locations. Failing to bring these data points together may result in only seeing a small piece of the pie.

Start by brainstorming the different data points you will need and identifying where they live. As you go throughout the project, you may find you need to add more data points, but this will give you a good starting point.

In this relocation example, you may need to pull in information such as different policy tiers, roles/positions, origin cities, destination cities, and family status (single person vs. family). Ideally, you’ll be able to collect this information all from one place, such as your relocation management company or relocation management software, but it’s possibly you may have to gather this from different sources.

2. Break Data Into Segments

Once you have collected all the applicable data, you may have what seems like an overwhelming amount of information. Breaking this data down into logical categories or segments will make it easier to digest the data.

Similar to bringing in additional data points, breaking information into segments or categories also helps you add context. You can start to look at different populations and groups to understand how they compare to the overall average. You may also be able to draw conclusions about the data based on categories.

For your relocation program, you may choose to create segments based on policy, city, role/position, or family status. Once you have created segments based on the data you’ve pulled together, you will gain a much deeper understanding of your data.

Let’s take a look at the relocation program analysis now that we have added contextual data and broken it into segments.

Previously, we learned that the spend is an average of $10,833 per transferee and only 17% of your relocations have requested exceptions. With this additional data, we now know the following:

  • 15 individuals have relocated with an average spend of $5,000; there have been 0 exceptions requested

  • 10 singles have relocated with an average spend of $10,000; there has been 1 exception requested

  • 5 executives have relocated with an average spend of $30,000; there have been 4 exceptions requested

By adding context, we can see that the average spend varies greatly depending on the role and family status. We also learn that the exceptions are predominantly associated with executives. Earlier, your analysis showed that the exceptions were infrequent, but now you can see that exceptions are actually fairly common with the executive group. Knowing this, you could dig further into this segment to learn what cities these executives are relocating to, what benefits they are receiving, etc. to understand what is causing the exceptions. 

As you can see, adding context to your data doesn’t always require a data scientist or advanced business intelligence system. You can add context to your data every by taking the time to pull together all of the relevant data points as possible and segmenting based on logical groups and categories. Adding this layer of analysis to your relocation program can help ensure that your investment in data collection doesn’t go to waste and you can make informed decisions that help your relocation move forward!

using data for relocations

Topics: Relocation

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