Renata Mello Feltrin, Executive Director Latam CI&T and LinkedIn Top Voice, is a veteran in the field of technology and innovation with over two decades of experience. She has led innovation and digital transformation strategies across various sectors, including finance, retail, consumer goods, entertainment, manufacturing and healthcare.
The ST-One team invited the executive to comment on the role of data intelligence in the digital transformation of industries, the benefits and challenges of its implementation, and how constant renewal is essential for the success of established corporations.
Renata emphasizes the importance of organizing the data pipeline and developing a data-driven management culture. She highlights that “industries typically generate a lot of data, but they don’t use it in a structured way. If you don’t organize the data into an analytical architecture so you can use it, you don’t have the foundation to start leveraging data science technologies.” For her, “it’s crucial that leadership learns to view the process with an intent for continuous improvement and to ask the right questions, seeking out gaps. Data will be great allies in providing quick and accurate answers that will lead to evolutionary decisions.”
Renata also addresses the main benefits of applying data science technologies in the daily operations of industries. According to her, “what you don’t see, you don’t have. And what you don’t measure, capture, analyze, you don’t truly see. And once you have this and see with more precision, you can identify gaps for improvement and opportunities for gains, enhancing the entire process.”
Check out the full interview:
How do you see the role of data intelligence tools in industrial digital transformation?
Renata Mello Feltrin: Fundamental. Without clear data providing accurate insights into improvement opportunities, it is not possible to make truly significant gains in efficiency, which is the first benefit for those who bring data into the practice of data-driven management. Then, in a second phase, combining these data with others, often external, enriches insights and enables more intelligent and predictive analyses, leading to real growth outcomes.
What are the main benefits that industries can obtain by applying data science technologies to their daily operations?
Renata Mello Feltrin: First, as I mentioned, efficiency. What you don’t see, you don’t have. And what you don’t measure, capture, analyze, you don’t truly see. Once you have this data and see it more precisely, you can identify improvement gaps and gain opportunities, enhancing the entire process. Then, predictions based on intelligence models can lead to significantly better and more precise decisions, such as production planning, logistics routing, and storage models, etc. The entire process and supply chain benefit from a data-driven model, which stems from the application of data science technology to daily processes.
Could you share an example of how data intelligence improved operational efficiency in a project you worked on?
Renata Mello Feltrin: Of course. I’ve been involved in various projects in this area. One of them, for a large consumer goods manufacturing, led to the development of a predictive model for product mix distribution, considering production and sales histories enriched by geographic and consumption data. This resulted in an 8.5% sales uplift in the first predictive analysis test. This is just one example; there is a wealth of opportunity throughout the entire production and distribution chain for this type of solution.
What are the main challenges industries face when implementing technologies that integrate data intelligence into their operations?
Renata Mello Feltrin: First, organizing the data pipeline itself. Industries typically generate a lot of data, but they don’t use it in a structured way. If you don’t organize the data within an analytical architecture for use, you don’t have the foundation to start leveraging data science technologies. The second point, equally important as the first, is developing a data-driven management culture. Especially, leadership needs to learn to view the process with an intent for continuous improvement and learn to ask the right questions in search of gaps. Data will be key allies in providing the quick and accurate responses that lead to evolutionary decisions.
How do data intelligence technologies, such as IoT and Big Data, contribute to more assertive decision-making processes?
Renata Mello Feltrin: There are many aspects. For example, there’s the real-time data collection issue. With IoT sensors and devices, it’s possible to collect real-time data on operations, environment, machine health, and consumer behavior. This allows for a continuous and up-to-date view of operational conditions, which, combined with the collection and storage of large volumes of data from various sources, enables a more complete and accurate analysis of the available information. IoT data is also crucial for detecting anomalies, predicting equipment failures, and optimizing preventive maintenance, thus reducing costs and improving efficiency.
Regarding Big Data, it’s possible to identify patterns, trends, and correlations that would be difficult to detect manually. With this, machine learning and artificial intelligence algorithms can be applied to forecast future events and optimize processes. Moreover, analyzing large volumes of data helps identify potential risks and develop proactive mitigation strategies.
From a new business development perspective, which is a topic I’m very connected to and where these technologies are highly important: the combination of data generated by IoT devices and advanced Big Data analytics can reveal new business opportunities, and the development of new products and services that meet emerging market demands.
Do you believe that some types of industries can benefit more than others from the application of data intelligence technologies?
Renata Mello Feltrin: I believe all industries can benefit, but those with more scaled operations and complex logistical processes have immediate advantages in adoption due to the volume and complexity of the data they generate and the need for ongoing optimization and innovation in their operations. These include manufacturing, healthcare, financial services, logistics, energy, telecommunications, and retail.
How can data intelligence help industries become more sustainable and environmentally friendly?
Renata Mello Feltrin: There are many ways, particularly related to waste reduction. Real-time energy consumption monitoring, for example, can provide opportunities to identify anomalies and correct them quickly. Better management of natural resources, such as raw materials and water, is also possible. There is also the potential for using air quality and pollutant gas emissions sensors, enabling rapid interventions to minimize environmental impacts. Ultimately, better waste management, production planning, and regulatory risk management—the precise collection of necessary data can greatly help in complying with environmental regulations.
What skills do you believe are essential for professionals who wish to work with data intelligence in the manufacturing?
Renata Mello Feltrin: A mix of technical and interpersonal skills. It’s important to understand how machine learning and artificial intelligence systems work to help think about the data and correlations that can deliver the results sought by the manufacturing. Additionally, critical thinking and problem-solving, developing the ability to communicate insights and data analysis results clearly and concisely to non-technical stakeholders, teamwork skills, especially in multidisciplinary teams, and most importantly: curiosity and a desire for continuous learning. I would also add specific knowledge about the manufacturing they operate in, whether it’s manufacturing, healthcare, energy, etc.
How do you see the future of data intelligence in the manufacturing? Are there any emerging trends we should be aware of?
Renata Mello Feltrin: We’ve covered quite a bit already, but speaking of the future, without a doubt, AI and ML algorithms are becoming more sophisticated, enabling more accurate and predictive analyses. The evolution of machine learning, which automates the data modeling process, making it accessible to professionals with less technical experience, will also significantly accelerate the large-scale adoption of these technologies in the manufacturing.
Another very interesting topic is Digital Twins, for simulation and modeling applications. In other words, virtual replicas of physical processes that allow detailed simulations and modeling to optimize performance and maintenance. The application of this ranges from manufacturing to smart city management. Can you imagine the potential of this in the near future?