Data: the new wealth of industrial operations

Updated: Mar 31

Article written by Israel Pineda CTO Wiibiq


Modern organizations are constantly evolving in order to survive the fierce competition in which they are involved. This has led to industrial operations leaving more and more data registered in the different repositories that organizations use. Although it is true that modifications can be made to traditional information systems (Examples: ERP, CRM, MRP, etc.); The best and natural thing to do is to work with a real-time operation performance management system. Starting with the above; we generate the necessary data to couple mathematical models, simulations and artificial intelligence; that is, a situation of exponential growth of the data; this is known as Big Data. The natural question that the leaders of the organizations should ask themselves is: What is the use of the data of my operation? The answer is: "for everything".

While it is true that this is an ideal position, it is also true that the potential of data cuts across our organizations and their operations; however, the problem lies in organizations having to build trust in data analysis projects, something that is not yet fully true in the Latin American region. Confidence must be born from companies and expert consultants on the subject; who must provide concrete deliverables, beyond a simple “for everything”. Data analytics companies and consultants must be able to ground models and algorithms to solve their clients' day-to-day problems. For example, it should be shown how to make predictions of when is the best moment to carry out the maintenance of our machines based on the data of sensors and IoT.


Likewise, in other areas of the company you can take advantage of market segmentation, portfolio collection, or the purchase of new machinery. The data allows us to understand the real problem in our plant, not what the user generally thinks is the problem. The industry needs to see tangible evidence of the benefits of using data for strategic decision making. The current problem is that most Big Data and data analysis projects promise many things, none of them tangible.


Saying that we are going to improve efficiency and effectiveness sounds good, and it is true, but it is an effect of the tangible improvement of operations and of the indicators that companies can take a census of. Therefore, the core is to acquire the data. The more tangible and real problems of the operation of the industry are resolved, the market, progressively, will generate more and more confidence in the data. It will be a virtuous dynamic circle .


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