Knowledge of Data Capabilities and Limitations Lead to Big Data Benefits

Big Data

Big Data can make benefits to a person if he or she have the knowledge in materials and technologies used / Photo by: DARPA via Wikimedia Commons

 

Real benefits of big data can only be gained if one has sufficient knowledge of materials and technologies, capacities in statistical analysis, and unwavering commitment to learning, according to IT news site ETCIO.

With the increasing volume, velocity, and variety of data that comes with the deployment of new technologies, determining data value requires specialized knowledge, ETCIO said. It added that a negligent approach to data only leads to poor decision-making, which could result in substandard production, investment loss, and even employee injury.

Implementing new technologies like Industrial Internet of Things (IIoT) would mean expanding the exposure to data science. This particular field integrates several disciplines to explore data, determine new patterns, question and confirm the hypothesis, and use machine learning algorithm. Such responsibilities fall on the shoulders of data scientists, statistical specialists, and machine learning specialists.

ETCIO stated that a data scientist should understand organizational operations, which include technology, material, and processes. Meanwhile, a statistical specialist should be able to abide by stringent statistical approaches in order to challenge assumptions from analysts and management. These people are the best line of guards against making rash decisions.

Machine learning specialists, on the other hand, are those who program smart devices to respond to inputs. Being one requires a deep understanding of manufacturing operations, as well as the outputs and consequences of machine behaviors.

An IIoT venture should also come with sensible and proficient planning of data collection, the IT news site said. It added that intelligent data gathering indicates focusing on recording the important events, information structure to support analysis integrity, and protecting data from unwanted intrusion and noise.

"Not only will managers make decisions from your recorded data, [but] increasingly autonomous machinery will also demand integrity to support operations and prevent employee injuries," ETCIO explained.

One of the greatest challenges to this is countering noise in data capture. Progressing operation requires adjusting the type and frequency of data capture in order to prevent unnecessary information capture. Moreover, the network should also be designed to prevent noise and crosstalk on wired and wireless communications. As the volume of network communication ramps up, the risk to data integrity will also increase—meaning it will be critically important to instill signal integrity.