Cognitive Load Measurement (CLM), like any scientific method, employs tools and techniques to collect data for further examination and analysis. It stems from Cognitive Load Theory (CLT), which strives to interpret how cognitive load impacts an individual's ability to perform tasks and assimilate information. Methods of cognitive load measurement include subjective rating scales, secondary tasks, and physiological measures.

Crucial characteristics of Cognitive Load Measurement include:

• Data Accuracy: The tools used to measure cognitive load provide a high degree of accuracy, offering precise, reliable data.
• Range of Applications: With many human tasks involving cognition, CLM has a wide array of applications, covering fields from communication, eLearning to advanced computer interfaces, and more.
• Multiple Measures: Different methods can capture different dimensions of cognitive load, including intrinsic load, germane load, and extraneous load.
• Research-driven: CLM is an extensively research-based practice, underpinned by years of cognitive and educational psychology studies.
• User-based metrics: Rather than solely relying on traditional metrics like success rates or task completion time, CLM can provide insights on user cognitive processes, which often prove critical in system design and evaluation.
• Cost Implication: While some tools and methods for cognitive load measurement can be expensive due to complex technology, many cost-effective methods like self-ratings are also widely used in the field.

A successful implementation of cognitive load measurement in a particular scenario or environment requires thoughtful planning and design. This includes considering factors like the type of cognitive load to be measured, the chosen methodology's potential pitfalls, and the target population's characteristics. Crucially, conducting a thorough cost-benefit analysis and assuring ethical considerations are in place is pivotal to a successful cognitive load measurement implementation.

Cognitive Load Measurement is a valuable resource, offering several particular advantages:

• Research and Development: CLM is invaluable in assessing the cognitive load of various tasks or systems, informing prototyping and designs, thus fostering more effective and efficient solutions.
• Effective Training: By assessing cognitive load during learning activities, trainers and educators can extend more adaptively paced training, ensuring learner comprehension and reducing the risk of overload.
• Improved user experience: In the increasingly digital economy, using CLM in user experience assessment for software, websites, and other technological interfaces can lead to improved design, functionality, and, ultimately, user satisfaction.
• Customer Insight: CLM reveals cognitive processes that a user experiences while interacting with a product or service, empowering businesses to adapt their offerings to enhance user experience better.
• Science-Based Decision Making: Cognitive load data enables rational, fact-based deliberations and decisions, particularly critical in instructional design and cognitive ergonomics.

Despite its advantages, there are certain challenges and limitations associated with CLM:

• Resource Intensity: Some methods such as physiological measures may be resource-intensive, requiring sophisticated equipment and expert analysis.
• Intrusiveness: Tools such as eye tracking may be viewed as intrusive by some users, which could potentially impact the measurement's reliability and accuracy.
• Inter-individual differences: Cognitive load can vary significantly among individuals based on their cognitive abilities, prior knowledge, and other factors. Thus, it might be challenging to establish a standard measurement that applies to all users.
• Subjectivity: Self-rating methods, although cost-effective and widely used, can be affected by a participant's subjectivity, limiting the reliability of the data.
• Time-sensitive: The cognitive load can change rapidly throughout a task, making it hard to measure accurately over more extended periods.

In conclusion, while cognitive load measurement is a promising tool with considerable potential, its effective application requires careful thought and design. By balancing the associated challenges and capitalizing on the potential benefits, organizations across sectors can use cognitive load data as an invaluable tool for decision-making, process improvement, and overall success.