Data normalization is a strategy used to increase the comparability of your data by taking raw data and adjusting it based on other values onsite. Normalizing your water and energy data based on per unit of production can help manufacturers get a clearer overall picture of what’s happening onsite.
Imagine you’re an operations manager.
You’re recording the monthly costs and usage for energy and water but the data-entry is manual and frustratingly slow.
Your supervisor asks you to explain a 5% increase in the electricity usage last month.
You double check the data and consider the production schedule and weather, but you can’t say definitively what caused the increase.
Your recording the data, but you can’t connect it your operations in a way that is meaningful. That’s because you’re using absolute data.
Absolute vs Normalized Data
Absolute data expresses operational performance in terms of what overall levels of performance are in specific areas of interest (e.g., water use) for an organization as a whole (i.e. total kWh). Normalized data expresses how performance in one area (e.g., energy) correlates to performance in another area (e.g., units of production) – as in energy consumed per unit of production during a specific period of time (i.e., kWh/unit of output).
By normalizing your water and energy usage based on per unit of production you’ll have greater insight into what’s happening at your facility.
Converting Absolute to Normalized Data – Per Unit of Production
Data loggers are often used to automate the tracking and reporting of data onsite giving you daily visibility into your energy and water usage- helping you to increase efficiencies and save money. However, while data loggers can solve the data entry challenge and help with visibility, it can’t convert the data from absolute to normalized.
However, by connecting your data logger into Provision’s KPI Dashboard, manufactures can not only automate their tracking and reporting but they can automatically convert absolute data into normalized data – providing you greater insight into your data and more data transparency.