Time is money. And in serving clients, distance is also money. Until now, distance has primarily meant miles between the jobsite and office. But it’s no longer just physical separation we need to worry about. Gaps or poor efficiency in data management can be costly.
Data flow issues can be found in nearly any enterprise where design and management are remote from a project’s assets and operations. But by breaking down barriers—physical or otherwise—between field and office, we discover significant opportunities to improve performance and reduce costs.
Fast Track Conversion: Data to Information
High-speed scanners can collect data at up to a million points per second, and imaging systems can collect hundreds of photos in a very short order. However, the data needs to be turned into information that meets the requirements of the end users.
To get data into information useable by the client we need a strong knowledge of the downstream users’ needs. The field components and office software can optimize the dataflow over the entire process. Producing such an optimal system is not easy, but the results are well worth the effort. Let’s look at two examples.
Mobile mapping systems combine positioning with image capture to collect data while moving at highway speeds. The information from these systems is an ideal tool for the transportation departments in locating and cataloging features along highways. In the U.S., federal highway funding is tied to a state’s ability to track and manage commercial signboards. Therefore, a smooth, accurate data flow is essential. Errors or delays in signage management can translate directly into loss of funding.
The transportation teams can process data from mobile mapping to extract information on the signage location, type and ownership. The data can also be merged with data from handheld GIS units to provide deeper information and quality control. This information can be feed into GIS and sign inventories. All of the systems are configured to optimize efficiency and throughput in both field and office.
A second example can be found in waste management and disposal. Landfill operators need to optimize a site’s permitted airspace utilization. To do so, they must measure and manage incoming material and control the placement in the landfill while complying with a myriad of environmental regulations. It’s a complex process, and it illustrates the risks and how quickly costs of inefficient information flow can add up. Inefficiencies can add to costs for fuel, maintenance and regulatory compliance.
By combining truck counts with images from an unmanned aerial system (UAS) and data from field surveys, operators can compute the volume of material deposited in the landfill. This data lets site managers determine where incoming material should be placed. Instructions can then be loaded onto machine control systems for placement and compaction of incoming material. The approach of blended technologies and an enterprise view of the landfill operation enables the system to optimize individual tasks in a way that delivers the best overall benefits.
Speeding Up Your Information Flow
The goal of modern data management is to provide the user in the field or office with the information and tools needed to complete their tasks quickly—as well as to efficiently deliver accurate results to other stakeholders. Three new methods have produced major improvements in dataflow.
The first, and perhaps most important, advance comes from merging information from multiple sensors or instruments. Some examples include:
- Sophisticated software merges data from GNSS, inertial sensors, photographs and 3D scanning from mobile mapping system to develop detailed datasets over large areas.
- For high-speed railways, track measurement systems combine precise measurements from positioning, tilt and gauge sensors to reduce construction costs and improve and safety and quality control.
- Data from GNSS and barcode or RFID readers combined with office software improves security and asset management for industrial and infrastructure operations.
The second improvement in dataflow comes from understanding the downstream processes. For example, an industrial designer may understand the value of 3D laser scanning. But the designer may not be equipped to manage the millions of points in a 3D point cloud. But when office software models the points into 3D objects and surfaces, the data becomes useful to the designer and other stakeholders. Likewise, results from cadastral, utility or wetlands surveys can be integrated into publicly available GIS and image data produced by governmental agencies. This approach is important in managing time and cost needed to produce and update information over a large area.
The third advance comes from creating systems of hardware and software that dovetail to workflows for specific tasks and objectives. The workflows can be independent from the measurement devices, while keeping the familiar work processes intact. This allows users to select the best measuring solution for each job. For instance, combining GNSS positioning with water well level measuring systems has streamlined monitoring work for environmental remediation sites. This approach often requires interaction with external, third-party sensors, which may include customizing software to support specialized devices or techniques.
These three advances are even stronger thanks to the rapid growth in communications technologies. By using cloud-based approaches and integrating multiple communications capabilities into field systems, we can provide nearly instantaneous exchange of information between field and office. This is often the final link in breaking down the barriers in the process of collecting, managing and delivering information where and when it is needed.
Look at the management of data in your organization. It’s possible you’ll find more than a few gaps or delays. Then think about how you can improve your information flow to reduce the distance between field and office, or between you and your clients. It’s money in the bank.