Organizing field data collection operations from scratch can be stressful and overwhelming. Typically, they involve a lot of people and occur in dynamic environments, where control is limited. Here are nine common mistakes you should avoid when preparing field data collection operations:
1 – Not planning ahead of time
Field data collection operations are complex and require careful preparation. Skipping the planning part is one of the most common pitfalls! Too often does this lead to having your teams realize they are unable to collect data properly once in the field. This will most likely delay their work and waste precious time. In the worst cases, your teams may even need to stop their operation and return to the office.
Make sure to build a list of all factors that could prevent your teams from conducting their field operation, and find ways to attenuate them. These factors may be:
Learn more on data collection operation planning with the article: 10 questions to help plan your field data collection
Think ahead of anything that might go wrong because it probably will!
All your team members must know of these risks and be aware of the actions to take in their occurrence. Let’s look at the second most common mistake: Lack of training.
2 – Not training your teams enough
In many things, training is the key to success. This is especially true when speaking of field data collection operations! Properly trained personnel usually perform faster, better, and with more confidence. Not only should training help your teams collect data, but it should also instruct them to find the right data!
Having the right data increases confidence in data sets, which enables optimal quality analysis and reporting. In addition to data quality, proper training helps ensure your teams are all working on the same level.
Everyone sees the world differently. Consequently, when different people are collecting data, their perceptions likely vary. For example, a person might report a piece of equipment as dangerously damaged. Another person could find it to be functional. Calibrating your team’s processes reduces variations in data sets, which reinforces their validity.
Properly training your teams increases the quality of data they collect. Contrarily, sending them in the field without the right tools and equipment could compromise their work.
3 – Not using the right tools
Providing the appropriate equipment to your teams will improve their efficiency in the field and the accuracy of the data they collect. These tools come in various forms:
- GPS receivers
- Tablets with a data collection app
- Measurement equipment
The main expense in the field most likely will be your personnel, so investing in tools to improve their efficiency makes sense. Trying to save a few dollars on equipment may end up inflating the costs of your operation!
The right tools should boost your team’s potential in the field, as long as they know how to use them.
4 – Not practicing with your equipment
So, think you’ve found the perfect tool, one that promises to solve all of your problems? Great! Now please spare yourself from another costly mistake. Sending your teams in the field without a thorough knowledge of their equipment is a recipe for disaster! Any equipment or software requires a learning period. Even the simplest of tools take time to master.
How this new tool can integrate your workflows also deserves some of your consideration. Should you completely change the way you work to use this tool? Wouldn’t it be better if this tool was flexible enough to integrate your workflows easily?
Let’s move on to another common mistake in data collection operations regarding workload.
5 – Underestimating the data processing workload
Field data collection is hard work! Not only do your teams need to stay intellectually focused throughout the entire day, but they are also at the mercy of the environment: Rain, wind, sun, etc.
By the time your teams return to the office after a long day collecting data, they may be tired, even exhausted at times. How can you ensure that they process data properly and promptly? Things may be tougher if you’re still using paper forms or inappropriate tools. Sometimes, you may even need to copy complete datasets manually. The best way to avoid bottleneck situations in data processing is to assign enough people to the task.
Here is something I’ve learned from working for more than 10 years as a field data manager in emergency response: You should assign an average of one person to process the data manually* collected by three field teams. Using a data management platform means analyzing the same given amount of data will require fewer people.
Now that you’re taking into consideration data processing, have you thought of how much data your operation requires?
6 – Collecting too much information
Have you ever heard of big data and its promise to revolutionize the world? For the benefit of your data collection operation, please steer clear from this concept! Instead, try to focus on collecting just the right amount of data. Try not to collect unnecessary data just in case ”it might eventually become of some importance in a vague and distant future”! Collect only what you need! Not only will over-collecting data decrease your team’s efficiency in the field, but it will also make data processing more complex.
7 – Collecting too few information
Collecting too little information will jeopardize your ability to meet your goals. You might think this is self-evident, believe me, I wish it was. The harsh truth is that you cannot create knowledge out of missing data. Let’s not get into details about this, but try to make sure to include all required fields in your form, to enable your data analysis team to work properly. How can you be certain it does? By confirming they have all the required data when you get your field team to practice with their equipment.
This covers most aspects of the data your field team will actively collect, now if only there would be some information to verify it! This brings us to another common data collection operation mistake…
8 – Not collecting metadata
Metadata represents data collected by your teams indirectly, which pursues what they’ve collected. At the bare minimum, your metadata should help you answer these three important questions:
- Who collected each data
- When was the data collected
- Where was the data collected
Metadata is invaluable for quality assurance, quality control (QAQC), and to get information from data many years after its collection.
So, you think you’re all set? Wait, here comes the most common mistake that could ruin your field operation.
9 – Not focusing on your objectives
So far, we’ve touched base on how complex field data collection operations can be, and how many factors may threaten their outcome. Sending people in the field to collect data, bring it back and process it, is a challenging endeavor.
It’s easy to stray from the objectives that justify your field data collection in the first place, with all these steps necessary to ensure your operation’s success. When in doubt, step back and take some time to reflect on your objectives. Focusing on your goals will enable you to make the right decisions and better articulate what you expect from your teams in the field.
Now that you know of all these mistakes that may threaten your data collection, we hope you’ll be able to avoid them! Don’t forget that your field operation’s success begins with thorough preparation. If you’re looking for a way to make your field operation simpler, we invite you to try Coral Collect for free.
*Writer’s note: Collecting data with paper forms, a GPS receiver and photographic camera.