Mobile data collection – how can it become more than removing the clipboard from the field? Using data collected out of convenience to step up your analytics game.
Today, a utility inspector or field technician is just as likely to be seen carrying a mobile device while carrying out his daily task as a clipboard or crumpled stack of papers piled on the floor of their vehicle. While it is easy to recognize the many benefits of electronic data collection, it can be more challenging to understand and leverage that data that is generated through these activities. The ease and efficiency allowed by checking a few boxes on an electronic form that ties directly to your asset management/ERP systems can be a double-edge sword, and you can easily end up overwhelmed with information. It’s kind of like turning on your windshield wipers to find they only clear half of the windshield – you can see the road ahead but have no idea what might jump out from the shoulder!
So, what are some techniques that you can use to see the whole picture? One solution is so simple that it’s often overlooked: start by dividing your data into two buckets: information that you NEED, and information that you HAVE.
- "Need" bucket: You designed and implemented your data collection systems to achieve a specific result. Construction information, testing results, and asset information are typical attributes that fit here. For gas facilities, think “traceable, verifiable, and complete”.
- "Have" bucket: This is where you put the data that was collected out of convenience “because you’re already there”. The information may be of value in the future but is not an attribute in the defined minimum data-set. It also can be data that is collected indirectly. Think GPS location, time on location, weather information, names of parties on site, etc.
Don’t get ahead of yourself by filling the “need” bucket to the brim. You’re going to need some room to add to it, gently blending in more data from the “have” bucket. Many organizations look at this as a sandbox. Play around a little to see if new information generate that both has value and can be maintained. Taking advantage of this space and being open to new ideas that may come from a bit of experimentation will help define and understand new trends, in turn providing focus on areas that need improvement or can optimize performance.
Let’s look at a real-world example: a team of construction QC inspectors are tasked with verifying that work has been completed in accordance with specified requirements and documenting the results of the inspection. The inspectors are dispatched by type of project, not by project location. Review of the data that they are tasked to collect shows that some inspectors are not able to visit all their assigned projects in the required time. An initial response to this is likely going to assume that more inspectors are required. When taking a step back and analyzing all the available information, however, reveals three attributes that may be of value: GPS tracks, mileage logs, and time on site logs. By analyzing the number of visits an inspector performs while incorporating this supplementary data, it becomes clear that changing the process to dispatch by location instead of project type will minimize travel time and maximize both the number of visits and the time on site. Instead of the cost of adding more staff, a small investment in qualifying inspectors to cover all project types provides a quick, low-cost solution to the problem!
Conclusion: Check With an Expert
Don’t be afraid of collecting too much data – instead, focus your efforts on managing how it is used and validating the value that it is bringing to your program. Let G2 Integrated Solutions build you a sandbox that will let you play but keep the sand out of your house. If you want to ask an expert in leveraging field collected data, please contact us.