What Makes Data Usable?

In the realm of data science, there is a crucial distinction between “information” and “data.” While information can be a broad term encompassing any collected facts or details, data refers to information that is meticulously curated, standardized, and ready for analysis. To be considered data, the information must be perfect—precisely collected, uniformly formatted, and consistent across all sources. Any deviation, no matter how minor, renders the information unusable as data. This precision is what allows data to be reliably used for analysis, trend identification, and decision-making.

Consider the analogy of screws in a coffee can. If you have a can filled with screws of different sizes, types, and materials, you technically have a collection of screws, but without sorting and organizing them, they aren’t usable for a specific task. Similarly, when information is collected without standardization—whether through inconsistent classifying labels (e.g., “V,” “Volts,” and “Voltage” all being used to classify the same type of data point), varied units of measure, or non-standardized data entry practices—it becomes a jumble of unusable data. Just as you need to sort and organize the screws to find the right one for a specific job, information needs to be uniformly collected and standardized to transform it into usable data.

Data can also become unusable when it is stored on personal devices or in non-integrated platforms. When information resides solely on an employee’s personal device, it effectively becomes locked away and vulnerable to loss, especially if that employee leaves the organization. This compromise of access can be detrimental, as the data might not have been backed up or transferred to a central, company-owned platform. Additionally, even if information is documented, if it’s saved in various, non-integrated systems, it becomes fragmented and difficult to aggregate, analyze, or utilize to draw meaningful conclusions. This is akin to having multiple coffee cans of screws scattered across different locations, making it nearly impossible to have a meaningful sense of your current inventory let alone find and use the right screw when you need it.

Usable data, on the other hand, incorporates data science and possesses certain key characteristics. It is uniform and standardized, not only by information type but also across all users and departments within an organization. This uniformity ensures that the data can be easily integrated, compared, and analyzed. A data specialist will also consider how the data will be used later and build the enablement of those needs into an ideal data structure. Moreover, usable data must be easily exportable and should reside in a company-owned, cloud-based platform that integrates seamlessly with other systems. This ensures that the data is always accessible, secure, and ready for analysis. The goal is to create data that can be effortlessly used to infer conclusions, conduct detailed analysis, and identify trends and outliers that drive business decisions.

To generate usable data, organizations must implement Standard Operating Procedures (SOPs) that dictate how information should be collected, documented, and stored. Even simple tools like internal checklists can significantly improve data quality, much like they improve health outcomes in hospitals by ensuring consistency in procedures. Furthermore, leveraging technology is essential—online forms that standardize data entry, photo apps that ensure images are immediately available online, and centralized cloud-based systems all contribute to creating high-quality, usable data. Solutions like EMPEQ’s Fast Site Survey for standardized data collection, managing site photos, organizing / curating data, and enabling accurate workflows exemplify the kind of technology that supports this goal. By planning how data will be used before it is collected and aligning collection methods with database structures, organizations can maximize the effectiveness and usability of their data, ultimately driving better outcomes and more informed decision-making.

Collecting and analyzing the energy-related data is considered the cornerstone for energy auditors. Enormous amount of data may be collected, but when it comes to using such an enormous amount of energy data – which are usually are collected from different sources and may not be well organized – an advanced and smart data management systems will be essential.

Using smart technologies for analyzing the vast amount of data in energy audits has several benefits, such as: better precision for finding inefficiencies, optimizing equipment performance, recognizing abnormalities, and identifying energy savings opportunities. Ultimately, clean data is a key ingredient in our quest to create a clean planet.