In Industry 4.0, data plays a central role. With the development of faster networks, wider storage space and new sensor technologies, companies have access to ever higher volumes of information. Sometimes even too much.
An increase of this magnitude can be too big of a challenge to manage. Hence, the growing need to filter this massive amount of information available in order to obtain valuable, smart and useful data. This is the main difference between Big Data and Smart Data.
Definition of Big Data and Smart Data
The term Big Data describes huge volumes of data, both structured and unstructured, collected every day by factories. They are defined by various properties, particularly by four key elements:
On the other hand, Smart Data “are data that convey and provide valid, well-defined and significant information”. Therefore, they are the Big Data that has been filtered, processed and prepared for the production context, in order to lead, once analyzed, to a more efficient decision-making process.
Data analytics, which is the set of techniques aimed at acquiring useful information for making “smart” decisions, is responsible for collecting and transforming data into usable information by eliminating the noise.
Big Data vs Smart Data: main differences
1. Smart Data is more targeted.
Big Data does not always targeted to respond to the specific needs of the company. Often, they are difficult to align with the business context and this generates confusion, risking to overwhelm and distract those who work there. Otherwise, Smart Data is more precise, allowing companies to use the information collected in a useful and functional way.
2. Smart Data is of superior quality
Smart Data is data that has already been filtered and screened. Which makes them superior in quality as they are error-free and more accurate. This allows companies to make smarter decisions and achieve better results in terms of productivity, safety and efficiency.
3. Smart Data allows for a higher level of customization
Unlike Big Data, Smart Data is more contextualized and calibrated to specific needs. Companies can thus obtain precise information on their business in order to implement customized and ad hoc solutions.
4. Smart Data is more useful for Machine Learning
More accurate and usable data promote and optimize the machine learning process, already partially supported by Big Data. If machines receive filtered information, they can identify patterns and perform specific tasks on their own more effectively and efficiently.