Cristina Barros: “We have to democratize access to information and knowledge within organizations”

Did you know that every minute, a company can generate thousands of pieces of data on the factory floor? This huge volume of data is usually identified as Big Data. Despite the growing investment of companies in the implementation of data networks and information systems, their teams still have many difficulties in tracking and aggregating all this data. We asked Cristina Barros, CEO of SINMETRO, how data can be transformed into information and knowledge within organizations. In this article, you will learn that “democratizing access to information and knowledge within organizations” is just the first step to succeed in 4.0 Industry. 

1. What role does Big Data play today in 4.0 Industry? 

Thousands and, in some realities, millions of data are generated per minute on the factory floor of organizations. These come from actions performed in information systems, manual records, machines, equipment, sensors and many other data sources. 

Despite the existence of this universe of data, teams find it very difficult to track and aggregate data, to obtain updated dashboards in real time, which allow evaluating the performance of organizations, processes, teams, customers’ or consumers’ preferences, among others. 

We can see how the 4.0 Industry paradigm is now an established reality, at least in developed countries. The needs arising from the automation and computerization of the factory floor, to increase the competitiveness of products and services and to boost the internationalization processes of companies, have been met through the promotion of various European initiatives and different government incentives, which guarantee interconnectivity, information transparency, decentralized decisions and technical assistance. 

But this path is only possible when we try to understand what data tells us and shows us, to generate knowledge within organizations. Adding attributes to a set of information allows us to give it context or identity. This is the secret of 4.0 Industry. 

2. How important is a data analysis graduate to these companies? 

I would say that a degree in data analysis is, for a company, the guarantee that the data acquired on the factory floor, on the operations floor, is being transformed into information and knowledge and used by employees and teams to support the decision and the definition of new business directions. At the same time, it is a guarantee that processes are optimized. 

This business energy around digitization that I was talking about has promoted and democratized the transformation of factory floors into technological hubs for data collection, through various sensors and the intelligent connection of machines, and it is necessary that there are professionals dedicated to its correct and assertive statistical analysis, but also understood by those who have no training. 

The issue of inclusion is absolutely fundamental, because we have to completely democratize access to information and knowledge within organizations. For example, a graduate in data analysis can carry out an extremely complex analysis, having behind it a complex algorithm for processing this information, but then other employees, such as shop floor operators, supervisors or production managers, who they also use a lot of this information to make decisions, have no training in mathematics or statistics and need to consult the information in a simple way in order to make decisions, in real time, about the processes. 

In Portugal, companies are still identifying ways to respond to questions concerning the coexistence of efficiency, sustainability and social responsibility, where it is necessary to simultaneously guarantee the efficiency of production processes, analyse data from different sources of information, transform data into knowledge and value and reduce GHG (Green House Gas) emissions. 

If we want to go further, soon, the Industry will seek to establish itself as an industry of and for people around the humanization of the use of Artificial Intelligence.  

3. Decisions made without taking the data into account are... 

(…) Compromising the economic profitability of companies, industrial quality, operational efficiency, research capacity, as well as the development and delivery of products and services of excellent quality. 

Generally, companies that make decisions without taking data into account are the same ones that have not yet digitized the key operations of their value chain, in terms of commercial management, budgeting, project management, engineering, purchasing, production, testing, metrology and quality control. They are the ones that still manage the budget operations, projects and production from dispersed excel sheets and the quality control on paper, because they don’t have an industrial data network on the factory floor, the ones that have countless occurrences and constraints with the management of CAD/CAM files, those that do not have CRM tools to support commercial management and, therefore, the records are not 100% reliable. 

This foresees the development of integrated information management solutions, which combine various software, cyber-physical systems (machines and equipment) and algorithms that integrate implicit business logic, allowing tracking and mapping of continuous flows of information. 

The Covid-19 Pandemic context is one of the most pertinent examples, where many changes were implemented as a way of adapting to market opportunities, but many other redefinitions mainly mirror companies’ survival strategies in the face of the stoppage of production chains, which occurred in the last few months. March, April and May 2020. It means that not all companies were at the same level, fundamentally when it comes to the organizational dimension.  

4. Data is for decision-making like...

(…) Digital processes stand for the use of Artificial Intelligence. Both make it possible to identify and act on more levels, exchange information flows, and learn with large margins of progression. They allow us to produce accurate, complex and sophisticated forecasts. Therefore, the tendency is for the replacement of traditional static processes by scalable solutions, in which data creates unique opportunities for learning and constant improvement in intelligent companies. 

For example: I am not interested in just fetching data from the machine. I must know that data is assigned to that work order, to that production order, to that product, to a certain time, and so on. This is to say that everything starts from the context and attributes that are added to the raw data, which must be properly catalogued for each process, allowing the algorithms to have the ability to make increasingly accurate predictions. If there is no automatic cleaning and validation of data, companies will not be able to calculate indicators and statistical analysis in a convenient way.  

5. At a time when data drives the evolution of science, training in this area is...

(…) A distinctive advantage of these professionals, able to help with the speed of responses that must be given to customers, propose innovative and economically attractive solutions through transparent management processes, and whose customers can follow them. That is, they articulate with the characteristics and fundamentals of the I4.0 digitization and are ready for the new transition of the Industry. 

Although the machines and systems manage to operationalize, automate and exponentiate a large part of the tasks, they work on a secondary level to support the workforce or to perform large-scale production tasks. All industries inevitably need people, namely data analysis professionals.  

6. In your opinion, what are the three professions of the future? 

This wave of digital transformation also reveals a need to define how human interaction with production processes will occur in all industries. Considering the growing needs of human resources for new jobs specialized in creative, analytical departments, advanced analytics based on AI, among others, I highlight the professions: 

  • Data analyst: a study carried out by an American consultant, Cognizant, pointed out 21 jobs of the future and in the first place appeared precisely the “engineer of useless data”. An engineer will be increasingly needed to ensure the separation of important data from secondary data and be able to learn from them new business directions.
  • UX voice designer: this new profession is very interesting. It will emerge with the growth of semantics and web 3.0. It brings us several challenges, including voice recognition algorithms and the actions they contain. Today we already communicate with Siri and Alexa systems and they perform what we ask them to do. Actions commanded by voice should also be a trend in the technological evolution of the Industry. 
  • Leader of business ethics: All professions of the future bring with them a set of ethical challenges. On the social side, we will never stop being people and acting as such. The fallacies of “robots will replace us” are not true, otherwise, we wouldn’t be doing anything here. On the technological side, it is necessary to combine ethics with social responsibility in the way of acting and working within organizations and for society. Technology must be person-centric and make responsible use of one’s individual space. 
7. A company that does not invest in data analysis and processing is the same 

(…) A company that is unable to respond to its growth and customer demands. Having the data but not having the goals of the statistical analysis is the same thing as having nothing. Big Data allows you to get to the context and answers that companies want to achieve. For example, the product specifications that the customer requires or ways to optimize and reduce production costs. 

Now, in most industries there is a huge challenge, which is the difficulty in managing the data of the different machines, which are not connected to a central management system, generally called MES (Manufacturing Execution System), to a certain extent because of the different communication protocols of each machine. So, only with an agile and efficient operational management model, where islands of information do not form, it is possible to have a reliable, safe and traceable data architecture, which allows the generation of new knowledge from the analysis of Big Data and, consequently, ensure real-time control and monitoring of operations and encourage innovation.  

Betting on Productive Innovation, with investments in digital transformation and the development of R&D projects, are the right way to respond to the various technical and scientific challenges associated with the creation of an integral and intelligent system for managing internal knowledge. 

8. What messages do you leave for students who opt for this training offer? 

With the investments of companies in the implementation of data networks and information systems, new professionals with training in mathematics are needed for the analysis and management of huge databases, usually identified as Big Data. 

I believe that this training option will allow the business fabric to retain emerging talent in terms of processing dispersed data. They will be responsible for assigning attributes to the data, as well as for understanding and relating the context of the information. 

Complementarily, the actions of these professionals, who in practice translate the knowledge implied in the data, will help to identify trends for companies to have an impact on their organizational context, in fields such as reducing waste, energy consumption and the carbon footprint, to name a few.

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