ServicePower’s recently published look at trends for field service organizations calls out the role of data and the importance of data quality.
Of course, data is important to running organizations. But today, the bigger issue is about data integrity, data access, and how it is sorted and applied to day-to-day activities. Applications like machine learning and AI depend on data reliability.
Without quality data as the foundation, AI applications will be flawed. Data is a critical part of the feedback loop, validating accuracy and helping the solution to “learn” how to interpret data fields and what data is relevant to a particular query. Data cleansing roots out duplications, decimals that may be overextended, or obvious transposing of fields. Some data points may be so far out of prescribed parameters (like a temperature that is entered as 100,600 degrees F instead of 100.6) that they should be considered obvious errors and eliminated or corrected. This type of cleansing of non-compliant data will keep the algorithms from making odd assumptions. A data cleanse exercise may be a necessary step 0 to undertake before any AI initiatives are executed.
For field service organizations, many critical tasks, like schedule optimization, rely on quality data. If the information about routes, technician availability, or typical repair time is faulty, then there is a good chance the suggested schedule will have flaws as well.
When assigning field workers to jobs, factors such as the drive time for the worker and traffic patterns need to be considered to project accurate arrival windows and the most efficient schedule. By the end of the day, the small errors can add up to large problems, such as field workers needlessly driving across town, missed arrival windows, and growing frustrations from customers, technicians, and dispatchers.
Modern field service solutions take care of the most common data issues. Field service management solutions, like ServicePower, collect data, organize it, and store it in a manner that protects data integrity, context, and accuracy.
Data accuracy. Accuracy is protected by controlling data sources, ensuring only verified users can enter data, and setting rules for how raw data is imported from other platforms. Inconsistencies between fields are flagged for further inspection. Data that is not in compliance with established standards (such as centigrade temperatures instead of Fahrenheit) is caught before it co-mingles with other data, distorting averages.
Data access. Modern solutions also support role-based access so users can easily obtain the information they need while minimizing security risks. Layers of encryption, identity authorization, and fraud detection also help keep systems secure and data accurate. Alerts help managers remain vigilant to possible breaches or attempted misuse of data.
Parameters. Safeguards are built into the system. For example, the solution forces users to comply with proper formatting when entering data, such as constraints on how dates are entered, decimal places, units of measurement, or minimum/maximums. This keeps data consistent so it can be merged and sorted correctly.
Eliminating disparate systems. Because there is one unified system, the entire company operates from the same set of real-time data, eliminating doubts about data accuracy.
Easy-to-use reporting. Modern field service solutions have built-in reporting tools that are easy to use. Users can create personalized reports, track performance indicators, and monitor team results. AI-driven analytics help users make well-informed decisions.
Data quality is key to the operation of many processes within the organization, every hour, every day. Data quality is the essential backbone for AI applications. Data that contains transposed numbers, non-compliant conditions, or mislabeled fields will cause queries to yield skewed results or error reports. With quality data, AI insights can be generated quickly, easily.
Analytics are no longer a once-a-year planning activity for the C-suite. Users throughout the organization rely on data integrity so they can do their jobs effectively. Using a modern field management solution helps ensure data accuracy, essential for effective field service operations.
Read the complete Top Field Service Management Technology Trends for 2025 report here.