If data is the new oil, how are you tapping into your reserve of information?

By Curtis Thomson, simPRO Executive Director

Capturing data is not always fun. Often, the boring part of day-to-day work is recording information about what was done on a job, when the job was done, what parts were used, what the outcome was, and the list goes on and on.

Then, in addition to job details, there is capturing of the back-of-house data such as your invoices, orders, inventory levels, equipment tracking, human resources information and other details that can be mind-numbing to track.

What is the purpose of capturing all of this data?

The reasons range from needing to justify what was done so it can be invoiced, reporting on statutory requirements, preparing financial reports for stakeholders, and providing job reports for your customers. However, once this data is captured, the resolution of the data is often then lost in an ocean of information that can be difficult to decipher or relegated to graphing trends and EOY results. The reality is that often the value in the data lies in the detail, not in the macro level, and its potential is lying completely untapped.

More companies are waking up to the real power of their data. Recently, two giants of the tech industry acquired data analytics and reporting companies (Looker and Tableau) for billions of dollars. This is a testament to the way in which businesses are awakening to the power of harnessing data to help customers.

“Occasionally an existing market is up-ended and an entirely new approach causes a complete transformation of how humans can solve a problem,” Lookers CEO Frank Bien says.

So what does this mean for typical field service companies?

For field service businesses, data has always played a critical role in terms of working out how much to charge, deciding which areas of the business are most profitable, forecasting sales and making other financial decisions. Also, we often talk about different views on the same data and the difference between financial reporting and management reporting.

While those scenarios are important, data usage doesn’t stop there. Good visibility of your data can help a business anticipate what is going to happen. With a solid data-driven approach as opposed to running on a gut feeling, a business can look through the front windshield and see where they are going rather than looking through the rear view mirror and seeing what has already happened. The sheer amount of data field service companies capture creates many opportunities for improvement including serving customers better and improving the bottom line.

While there are challenges associated with data capture, there are many ways to ease the burden. For example, there are systems that can automatically receive and process information from third parties.

Challenges can also come from trying to interpret the tidal wave of information you are receiving. For example, using the wrong tools or untrained users can easily lead to misinterpreted data. However, in many ways, data interpretation and analytics are becoming more accessible through the use of cloud computing, storage, processing power and analytical tools.

There are also tools, such as the Internet of Things (IoT) which can augment existing datasets and provide actionable insights at incredible rates by generating and transmitting information on the status of monitored equipment.

The shifting sands of the maintenance world have moved the industry from a reactive maintenance model to more of a preventative model. Now, many businesses are looking to make the leap to predictive or condition-based maintenance models using live data they receive from equipment being monitored by IoT sensors to determine the current condition of their equipment.

This is helping maintenance service companies transform, giving their customers a better quality of service, and providing more actionable information at a more competitive price. This is also providing service technicians with better information so they can perform better on jobs, and focus on the maintenance and jobs that actually need doing. At the same time, this new model is allowing companies to scale and become more profitable.

Like oil, data is more useful and valuable when it is processed.

Advanced reporting engines and machine learning can be used to gain valuable and often unexpected insights into vast amounts of data. Using visualizations is a proven and effective way of making sense out of large datasets. At the most basic level of understanding datasets, it is key to have the flexibility to generate dashboards and graphs to highlight directions and trends, and then drill down into the details when required.

Analyzing data and drawing useful correlations between events or predictions of success is where machine learning comes into its own. It's not just about what will happen and when, but also why it will happen. A simple example of this in practical terms is being able to predict whether a job will be resolved on the first visit, and sending a technician that will most likely make that outcome happen. An interesting and perhaps unexpected correlation here may be the likelihood of a first visit resolution based on the time of day the technician is dispatched.

Data has the power to drive powerful change in business.

Field service companies are generating huge amounts of information which, if used right, can help them predict anything from issues a customer is likely to encounter to how likely it is that a customer will become a long-term, loyal patron.

Gut feelings can only get you so far on the journey of growing a successful field service business. Transitioning to a data-driven business where decisions are made based on concrete information and accurate predictions helps grow a much more sustainable, successful venture. Data can be the most valuable, untapped resource in your business, and it is up to you to take advantage of those insights.