Energy data collection is essential for managing and analyzing energy data. This includes utility bill data, interval meter data for the whole building, and submeter data in real time. The process of manual collection of data and manually analyzing it for possible errors is now an outdated procedure. Manual processing of a single invoice involves several procedures, which include awaiting receipt, preparing or sorting, scanning and extracting, email invoice images, and manual data entry and analyzing data. When updating large datasets using this old technique, the cost of collection and analysis will be high and it also consumes more time. It is unfortunate that some companies are still spending money on manual processes in this era of digital transformation and automation. Document scanning companies can capture very large datasets or invoices faster, using scanning devices and convert them into digital format without sacrificing data quality.
There are four types of data sources and data collection methods: administrative data, statistical surveys, modeling, and in-situ measurements. Administrative data refers to the set of data derived from an administrative source. Statistical surveys refer to both sample surveys and censuses. A data model is an abstract model that organizes the elements of a database at the conceptual, physical and logical levels. In-situ measurements require a sample environment that is compatible with the spectroscopic method. The objective of energy data entry is to compile accurate data in the most efficient way, ensuring accuracy and quality. The data should be able to provide excellent insights that would facilitate good decision making. Energy data is present in different forms and qualities, which often comes from sensors and is to be collected automatically as far as possible. The data thus obtained is reliable and current. When preparing data, the type and the extent of data collected are not the only main factors, but also the selection of suitable EDMS (Electronic Document Management System) software. EDMS software should be in a position to request information from sensors as well as data from other systems or third sources seamlessly. EDMS software thus analyses all available input data to communicate and may use this data for automated actions. Energy data management outcome is purely based on how good the data is. The four objectives of effective energy data analysis are: understanding energy use and costs, understanding variability, calculate targets and track progress, and model energy demands.
Modernizing energy data collection by automation can boost operational efficiency and drive cost savings. It also empowers the energy management personnel to focus on what they do best. A misconception about modernization is that you will be wasting a lot of money on it, but actually it is saving by making fundamental changes after careful examination and calculation. Such decisions are implemented with reference to the available budget. In addition to the direct cost to the organization and across departments, energy analysts can identify and find solutions as well as set targets for measuring utility performance. As any experienced data entry company would endorse, the goal of modernizing energy data collection and analysis is to transform accurate and validated data to its clients faster without sacrificing data quality. Discovering relatively new techniques and inexpensive changes for modernization can result in significant business process improvement.