How to unlock the potential of artificial intelligence and data science

By 2026, more than 80% from software vendors to incorporate generative AI capabilities (GenAl). In this way, a new data paradigm will be consolidated. 

This is how he analyzes it Gartner, who also highlights that 741% of CEOs believe AI will have a significant impact on their industries.

Given the growing importance of AI strategies, we interviewed an international leader to learn about her vision and anticipate a future that will change organizations.

In this article we share an exclusive conversation with Catalina Herrera, Field CDO of Dataiku, a world-leading platform in artificial intelligence, which systematizes the use of data to improve results and partner of IT Patagonia!

We spoke to him about his personal experience and asked him how to simplify the implementation and use of generative AI. 

We also asked him about the most effective strategies to overcome the barriers and resistance that arise when faced with digitalization and digital transformation processes.

We also discuss the importance of predictive analytics in today's business management, and why it is urgent to close the data skills gap and the gender gap in STEM (science, technology, engineering and mathematics) professions.

Let's meet one of the world leaders in artificial intelligence 

To understand the relevance of those who stand out in the field of technology and innovation, it is important to know where they come from and the experiences that marked their personal and professional development.

Since she was young, Catalina's curiosity to understand how things worked led her to take apart more than one electronic device, despite the problems this caused at home. 

Although one of her high school teachers discouraged her from pursuing a career in engineering, Something inside her told her that technology was her path.This intuition and the courage to take on the challenge would be decisive for his future.

So he began a career in electronic engineering, despite comments from his family who questioned whether changing light bulbs was really a profession. 

Over time, Catalina discovered that engineering gave her new perspectives and helped her overcome her fear of mathematics. 

Upon graduation, she began her career as a manufacturing engineer at Texas Instruments. It was in that role that she truly began to understand the importance of data. 

Although he had not planned to dedicate himself to this area, he discovered that he had a great ability to understand data, both technically and at a business level. In this process, he also realized the value that it brought to the business in order to be 3 steps ahead and optimize production. 

This combination of skills allowed him to make the leap into the software industry and help other organizations leverage their data strategically.

“I was struck by the realization that, regardless of the industry, the need to analyze and manage data is essential, and professionals are required to ensure the proper and effective use of this information. This experience was decisive in my professional and personal career, marking the course of my career in the world of technology and data science,” Catalina shares.

Today, with a solid background that includes three master's degrees in technology and a specialization in data science, she has the opportunity to lead global projects and educate others about the transformative power of technology.

Digital transformation with machine learning, data analysis and AI

With over a decade of experience transforming data into actionable insights across a variety of industries, Catalina is recognized as a person who offers powerful inputs.

Not only in relation to how companies can leverage data to predict “what's next,” but also in how they can use them to drive meaningful transformation. 

“My role is to collaborate with global technology and business leaders, including Fortune 500 companies, to Boosting digital transformation initiatives through machine learning, data analysis and artificial intelligence,” he explains. 

With over 15 years of experience in data science, Catalina has applied her technical and industry knowledge to solve complex challenges in sectors such as energy, manufacturing, finance, and healthcare.

“My contribution to business leaders focuses on helping them understand how data can not only anticipate future actions, but also catalyze deep and sustainable changes in their organizations,” he says. 

In this sense, he tells us that a significant transformation implies Integrate generative AI and other cutting-edge technologies. In a scalable, ethical and strategic manner, aligned with your business objectives, and ensuring that these practices generate a tangible and positive impact.

As a former university professor, Catalina has the ability to connect with diverse audiences, making complex concepts easy, for both technical leaders and business executives. 

In addition, her commitment to education and the development of STEM talent has led her to join the board of High Tech High Heels, where he works for Inspiring and empowering young women in technology careers.

Keys to unlocking the potential of AI and data science in business

Catalina’s insight is particularly interesting, drawing on her vast experience in driving generative AI strategy and execution for enterprise deployments. 

Also for being a fervent defender of ethical artificial intelligence practices.

“To unlock the potential of AI and data science, companies must focus on building an inclusive culture, fostering collaboration, establishing strong data governance, and ensuring ongoing and responsible training for their teams,” says the expert.

In this regard, he emphasizes that in this way not only business success is ensured, but also a positive and ethical impact on society.

Another key aspect in relation to harnessing the potential of AI lies in the ability to simplify the implementation and use of generative AI.

As Catalina explained, this means adopting technologies that facilitate the orchestration of the artifacts and resources needed to deploy these applications at scale, without depending on a single model or infrastructure. 

“It is essential that teams have access to tools that allow them to integrate, compare and experiment with language models in a scalable, secure and efficient way,” he says. 

And adds that the above must occur within a framework that allows implement robust governance policies, which ensures that AI models are developed and used responsibly and in accordance with company procedures.

According to Catalina, “democratising access to AI fosters innovation within the organisation, as teams can develop creative solutions to business problems, aligning with strategic objectives and maximising the value of this technology.”

Main pain points in a digital transformation and AI process

A central aspect of the conversation with Catalina focused on the main pain points that arise in a digital transformation process.

Based on her experience as a field data director at Dataiku, the expert warns that These pain points are intensified when incorporating artificial intelligence. 

One of the main challenges is the lack of visibility and control, which can hinder effective data lifecycle management and AI model deployment. 

“To address this challenge, it is crucial to have centralized governance and monitoring and deviation detection tools, such as those provided by Dataiku with its governance approach and MLOps. This ensures that data is managed securely and that models are transparent and explainable in their operation,” he emphasizes.

Another common pain point is expensive and complex infrastructure, which is often tied to legacy systems that do not allow for easy integration of new technologies.

“Adopting a modular architecture and using platforms that offer flexibility in implementation, such as Dataiku solutions that support both cloud and on-premise operations, can help mitigate these problems,” says the expert.

For Catalina, a well-articulated digital transformation strategy must address these aspects to facilitate a smoother and more efficient transition to an AI-driven environment.

How to overcome barriers and resistance in digital transformation processes?

According to our interviewee's vision and experience, several effective strategies can be implemented to overcome the barriers and resistance that arise in the processes of digitalization and digital transformation.

1. Clear and constant communication

It involves informing all levels of the organization about the benefits and objectives of digital transformation is essential. 

Transparent communication helps mitigate fears and uncertainties, and keeps employees informed about how their work will be affected and how they can benefit from new digital tools.

2. Involve teams in the process

Involving them in planning and implementing the change helps reduce resistance. Their participation in the design and implementation of the digital process should be encouraged, allowing them to contribute ideas and express their concerns. 

Strategies such as the formation of interdepartmental committees or working groups can be useful in achieving this.

3. Training and ongoing support

Providing adequate and ongoing training is essential to empower employees to use new technologies. 

Not only does this increase teams’ competence and confidence with the tools, it also helps create a culture of acceptance for continuous change.

4. Gradual implementation

A phased approach to digitalization allows employees to progressively adapt to new tools and processes. 

This strategy helps ease the transition and reduces the impact of change on daily operations, thereby increasing acceptance of the new processes.

5. Support from management

It is crucial that the organization's management and leaders show a strong commitment to the digital transformation process. 

This includes leading by example and ensuring that management is aligned with the mission and vision of the change and is available to address any challenges that arise during implementation.

“By implementing these strategies, barriers and resistance to change can be effectively mitigated, promoting successful adoption of digitalization in the organization,” says the specialist.

The role of data and the importance of predictive analytics in a digital world

Catalina often highlights that data is not just a tool, but the backbone of modern business strategy.

Along these lines, he explains that, unlike tools that fulfill a specific role, Data provides a comprehensive and continuous view of every aspect of the business:

• They allow us to identify patterns, predict trends and personalize experiences in real time, becoming a key driver for innovation and competitiveness. 
• When strategically integrated, they help align all levels of the organization and promote a more agile, precise and adaptable approach, which is essential in a constantly changing environment.

As for the reasons that make predictive analysis a crucial aspect in today's business management, Catalina identifies the following:

1. It allows companies to anticipate future trends and behaviors, based on historical data and identified patterns. 

This facilitates more informed and strategic decision-making, improving the planning and optimization of internal processes. 

For example, companies can predict demand for products or services and adjust their production and logistics accordingly, avoiding losses due to excess inventory or stockouts.

2. It is essential for risk management and identifying business opportunities. 

By anticipating potential problems or changes in the market, companies can develop proactive strategies to mitigate risks and take advantage of new opportunities before their competitors. 

In sectors such as finance, predictive analysis is used to assess the probability of customer default. While marketing teams, for example, use it to Personalize campaigns and improve customer experience. 

“This type of analysis is a powerful tool for any organization seeking to remain competitive in a dynamic and challenging business environment,” he notes.

Urgent need to close the data skills gap

The urgency to close the data skills gap arises because, in a world increasingly driven by analytics and artificial intelligence, many organizations lack the trained staff to make the most of their data. 

This is how Catalina analyzes it, who adds that this lack of skills limits the ability of companies to innovate, optimize processes and make strategic decisions based on data, which directly affects its competitiveness.

According to the specialist, to begin to resolve this gap, it is key to:

• Optimize investments in diverse infrastructures (capacity, clouds, computing, among others), reusing artifacts developed by diverse teams.
• Promote accessible and scalable training programs within organizations.
• Enable technical team members and people from non-technical areas to develop data culture skills.
• Promote collaborations between companies, educational institutions and the public sector to drive initiatives that expand training in data and analytics.

“By democratising access to these skills, it is easier to create a data culture where every person, regardless of their role, can contribute to data-driven growth,” he concludes.

Evolving data strategies to maximize artificial intelligence 

During the interview with Catalina, another question arose, regarding How leading companies' data strategies are evolving. 

Not only to maximize AI capabilities, but also to ensure trust and scalability.

For the expert, leading companies are adopting a comprehensive approach that connects your teams with your technologies and your processes, combining:

• advanced technologies;
• sound governance policies;
• a culture of effective collaboration. 

These elements ensure that AI is used responsibly and efficiently, generating sustainable value for the organization.

Key strategies to close the STEM gender gap

As a member of the board of directors of High-Tech High Heels (HTHH), a philanthropic organization dedicated to inspiring and empowering young women in science, Catalina is committed to reducing the gender gap in STEM professions. 

Also by promoting women's vocations in careers in science, technology, engineering and mathematics.

To close the gender gap in these issues and boost young women's interest in these areas, the organization focuses on several key strategies, including:

1. Early education and mentoring programs

It is crucial to inspire interest in STEM careers from an early age. Mentoring and mentoring programs, where girls and young women interact with role models, can help them see these professions as accessible and exciting.

2. Partnerships with schools and universities

This involves establishing collaborations with educational institutions to incorporate inclusive STEM programs, as well as workshops and science clubs that help increase students' exposure to these disciplines.

3. Access to resources and scholarships

Facilitating access to funding and scholarships dedicated to women in STEM not only helps reduce economic barriers, but also sends a clear message about the commitment of industry and society to promote equal opportunities.

4. Awareness campaigns and culture change

It is important to work on initiatives that break gender stereotypes in STEM, through awareness campaigns. 

These actions must be aimed at changing the cultural narrative that often discourages young women from choosing these careers.

“By implementing these strategies, the HTHH chapter brings together several volunteers from different technology companies, who bring their experience to create a comprehensive support ecosystem that inspires, empowers and encourages the sustained growth of women in STEM, helping to close the gender gap in these professions,” explains Catalina.

Conclusion

Artificial intelligence has undoubtedly become a transformative pillar in the business world. 

To drive operational efficiency and data-driven decision making, tools such as machine learning and natural language processing enable businesses to: 

• automate repetitive processes;
• optimize supply chains;
• anticipate market trends. 

Furthermore, AI's ability to analyze large volumes of data in real time provides organizations with strategic insights that strengthen their competitiveness. 

In this sense, AI not only boosts productivity, but also redefines business models, opening up new opportunities for innovation and customization of products and services.