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How to implement a Data innovation strategy for the healthcare sector

Reading Time: 3 minutes
The evolution of Data Science applicable to healthcare has been undergoing a major acceleration in recent years. Data has been applied robustly in this field for some time now, but with the development of increasingly advanced techniques in machine and deep learning and others more related to AI, such as natural language processing (NPL), the evolution of the value obtained from data is becoming exponential.
It is complicated to have all the necessary profiles in each phase of a project of this type since they have, by their very nature, a clear multidisciplinary component. In this sense, the support of Talent As a Service models to have the right talent at the right time is key.
Another fundamental element is to know which phases must be undertaken so that innovation in the healthcare data strategy delivers the necessary value in a safe way; all of them are important and necessary.

Define Clear Objectives and Goals

  • Establish what you want to achieve with your analytics strategy: improve decision making, enhance customer experience, or develop new products.
  • Align these objectives with the overall business strategy to ensure cohesion and relevance.

Build a Qualified Team

  • Hire the Right Talent: Form a multidisciplinary team with the key profiles identified above: Data Scientists, Data Engineers, Data Analysts, NLP Specialists, Machine Learning Engineers, Explainable AI (XAI) Specialists, Federated Learning Experts and a Data Science Project Manager.
  • Continuous Training: Invest in continuous training and development to keep the team updated with the latest trends and technologies (Analytics Insight)​ (MyGreatLearning).
  • In this phase, having a talent partner (TaaS) that reduces the difficulties and risks to a minimum may be the best option if you do not have all the necessary talent in the organization. In addition, continuous training and qualification will be guaranteed.

Invest in the Right Tools and Technologies

  • Data Infrastructure: Establish a robust data infrastructure using tools such as Hadoop, Spark and cloud storage solutions to handle large volumes of data efficiently.
  • Advanced Analytics and Machine Learning Platforms: Use platforms such as Azure ML, TensorFlow, PyTorch and Scikit-learn to build and deploy machine learning models (Yale School of Medicine)​ (McKinsey & Company).
  • Data Visualization Tools: Implement tools such as Tableau and Power BI for effective data visualization and reporting.

Develop a Data Governance Framework

  • Establish policies and procedures for data management, including data quality, security and privacy.
  • Implement compliance measures to adhere to relevant regulations and standards, such as the GDPR for data protection and the National Security Scheme (mandatory in the Spanish public administration).
  • This phase is absolutely fundamental in a sector such as the pharmaceutical industry, where the high sensitivity of data is an element to be taken into account (SpringerLink).

Implement Agile Methodologies

  • Use agile project management techniques to ensure flexibility and iterative progress. This allows for continuous improvement and rapid adaptation to changing requirements ​ (McKinsey & Company)..
  • Regularly review and adjust strategy based on performance metrics and feedback.

Leverage Advanced Analytics and AI

  • Integrate machine learning and AI to gain deeper insights and automate decision-making processes. Focus on Explainable AI to ensure transparency and confidence in your models (Yale School of Medicine)​ (SpringerLink).
  • Explore federated learning to improve privacy and security while effectively utilizing distributed data sources (McKinsey & Company). When developing a data strategy in an industry as heavily regulated as healthcare we must assess and manage any potential risks.

Foster a Data-Driven Culture

  • Promote a data-driven mindset throughout the organization by fostering data literacy and making data accessible to all relevant stakeholders.
  • Use storytelling with data to effectively communicate insights and promote informed decision making (Yale School of Medicine).

Monitor and Evaluate

  • Continuously monitor the performance of the data strategy against defined objectives and KPIs.
  • Use feedback loops to refine models and processes, ensuring continuous improvements and innovation  (McKinsey & Company).

Scale and Innovate

  • As the data strategy matures, explore new areas of innovation such as Edge Computing, real-time analytics and Data-as-a-Service (DaaS) models.
  • Keep the company abreast of emerging trends and technologies to remain competitive and forward-looking  (MyGreatLearning)​ (McKinsey & Company).
 
This sequence of phases will allow us to innovate and maximize the value of our data with an adequate risk control, obtaining key support in decision making.
 

References

10 Data Science Papers for Academic Research in 2024 .- Analytics Insight (2023)
Latest Trends in Data Science 2024 .- GreatLearning (2024)
What Does Natural Language Processing Mean for Biomedicine? .- Yale School of Medicine (2023)
Natural language processing in healthcare .- McKinsey & Company (2018)
Natural Language Processing for Health-Related Texts .- Sprinkler (2021)
Natural Language Processing in Health Care and Biomedicine .- Sprinkler (2013)
Antonio Castaño

Autor Antonio Castaño

Understanding Talent-as-a-Service (TaaS): A Brief Guide

Reading Time: 2 minutesIn our dynamic world, adapting quickly and efficiently to changing needs is ever more important. Talent-as-a-Service (TaaS) is an innovative approach that addresses these needs by providing flexible access to talent, on-demand. Let’s explore what TaaS is and why it’s becoming a game-changer for a lot of organizations, including those in the pharmaceutical industry.

What is Talent-as-a-Service (TaaS)?

TaaS is a model where businesses can access a global pool of skilled professionals as needed, without the complexities and commitments of traditional employment. Similar to how you might subscribe to software services, TaaS allows companies to “subscribe” to talent, bringing in experts for specific projects or timeframes.

Key Features of TaaS:

  • Flexibility: Companies can scale their workforce up or down quickly, responding efficiently to project demands or market changes without the long-term commitments of hiring full-time employees.
  • Cost Efficiency: TaaS eliminates many overhead costs associated with permanent hires, such as benefits, training, and unused labor, making it a cost-effective solution.
  • Access to Expertise: With TaaS, organizations are no longer geographically limited and can tap into a diverse pool of top-tier talent from around the world. This access is particularly beneficial for industries requiring specialized knowledge that may not be available locally.
  • Simplicity and Speed: The TaaS model streamlines the hiring process. Companies can bypass the lengthy and often cumbersome steps of traditional recruiting, speeding up project kick-offs and reducing downtime.

Why TaaS is a Strategic Advantage:

For businesses in fast-paced and innovation-driven sectors like pharmaceuticals, TaaS offers a strategic advantage by allowing them to remain agile and competitive. They can quickly bring in experts for the duration of a project or scale their teams based on the current pipeline and R&D needs.

Embracing TaaS:

At Quodem, we specialize in connecting pharmaceutical companies with top-tier talent tailored to their specific project requirements. Our TaaS solutions ensure that you have the right skills at your fingertips, ready to deploy as your business evolves.

Ready to explore how TaaS can transform your business?

Contact us to learn more about integrating Talent-as-a-Service into your strategic operations. Let us help you navigate the complexities of talent management with ease and expertise.
Rafael Palomino Rodríguez

Autor Rafael Palomino Rodríguez

Talent Acquisition in Pharma: Navigating Uncharted Waters

Talent Acquisition in Pharma: Navigating Uncharted Waters
Reading Time: 3 minutes

As the pharmaceutical industry sails into the horizon of 2024, it faces a sea of transformation, stirred by the winds of technological innovation and the currents of a global health crisis. The quest for talent has become the epicenter of strategic discussions, as companies strive to navigate these uncharted waters successfully. Drawing upon insights from industry analyses, including the critical perspectives offered in the “State of the Global Workplace: 2023 Report” by Gallup, this exploration delves briefly into the intricate dynamics of modern talent acquisition within the pharmaceutical realm.

The Evolving Landscape

The sector’s landscape is marked by rapid technological advancements, a paradigm shift towards patient-centric approaches, and an increasing emphasis on digital and data-driven methodologies. This evolution demands a workforce that is not only scientifically adept but also versatile in navigating the digital realm, underscoring the importance of attracting and retaining the right talent.

Key Trends Shaping the Future of Pharma Talent

  1. The Ascendancy of Candidate Empowerment: Today’s candidates are not passive participants but key players who often dictate the terms of engagement. This shift necessitates a reevaluation of recruitment strategies, focusing on what organizations can offer to attract the cream of the crop. The emphasis on pre-boarding experiences and fostering a sense of belonging from day one becomes crucial in retaining top talent.
  2. The Renaissance of Corporate Culture: The pandemic has fundamentally altered workplace cultures, spotlighting the necessity for flexibility, work-life balance, and mental health support. Organizations that adapt to these changes, fostering a culture of trust, inclusivity, and support, are more likely to attract and retain talent in a post-pandemic world.
  3. Strategic Alliances with Talent Acquisition Specialists: As the talent gap widens, the role of experienced talent acquisition partners becomes indispensable. These specialists bring a nuanced understanding of the market and access to a diverse talent pool, essential for navigating the competitive landscape of pharmaceutical recruitment.
  4. AI and Big Data in Recruitment: The integration of AI and big data analytics into the recruitment process offers a transformative approach to talent sourcing. By enabling a more sophisticated analysis of candidate pools, organizations can streamline recruitment, enhancing efficiency and effectiveness.
  5. The Untapped Potential of Passive Talent: The industry’s reliance on passive talent—those not actively seeking new opportunities but open to compelling offers—underscores the need for proactive engagement strategies. Leveraging data analytics to identify and attract these individuals can significantly expand the talent pool.
  6. Digitalization and Data Science Demand: The surge in digital health technologies has created a burgeoning demand for professionals skilled in data analytics, bioinformatics, and digital health. This trend highlights the critical role of specialized skills in driving forward the industry’s innovation agenda.
  7. Flexibility and Remote Work Dynamics: The pandemic-induced shift towards remote and hybrid work models offers both opportunities and challenges. While it enables access to a broader talent pool, it also necessitates strategies to maintain engagement and collaboration in a dispersed work environment.
  8. The Imperative of DEI: Diversity, Equity, and Inclusion (DEI) initiatives have gained momentum, recognized for their role in fostering innovation and understanding diverse patient needs. Organizations committed to DEI principles are likely to stand out as employers of choice.

The Voyage Ahead

As we delve deeper into the intricacies of talent acquisition in the pharmaceutical industry, several strategies emerge as navigational aids in this journey:

  • Building a Strong Employer Brand: In a candidate-driven market, a strong employer brand that highlights an organization’s values, culture, and commitment to employee development is key to attracting top talent.
  • Investing in Employee Experience: From the recruitment process to career development and retention strategies, investing in a positive and enriching employee experience is critical.
  • Embracing Technological Innovations: The effective use of AI, big data, and digital tools in recruitment and talent management can provide a competitive edge, enabling more efficient and targeted talent acquisition strategies.
  • Fostering a Culture of Continuous Learning: Encouraging and facilitating continuous learning and adaptability among employees can help organizations stay ahead in a rapidly evolving industry.

Conclusion

The journey of talent acquisition in the pharmaceutical industry is complex, requiring a multifaceted approach that balances traditional human resource practices with innovative strategies tailored to the unique challenges of the sector. As organizations chart their course through these turbulent waters, the focus must remain on adapting to the changing tides, embracing innovation, and fostering a culture that attracts, nurtures, and retains the brightest minds. In doing so, the pharmaceutical industry can continue to thrive, driven by a workforce that is not only skilled but also committed to advancing healthcare for all.

Autor

Is My Job at Risk?

Is my job at risk?
Reading Time: 3 minutesThe question of whether our job is at risk has been recurrent throughout history. As technology and society advance, professions evolve, with some disappearing and others emerging. This phenomenon is not new; it has been happening for centuries, although now the pace of change has drastically accelerated.

Historical Evolution of Professions

A hundred years ago, the job landscape was very different from today. Professions like artisans, farmers, and blacksmiths were common and essential to the economy and daily life. According to the U.S. Bureau of Labor Statistics, approximately 85% of the professions that existed in 1900 have either disappeared or been significantly transformed. These changes are due to mechanization, electrification, and more recently, digitalization and automation.

The Speed of Change

What distinguishes the modern era from previous ones is the speed at which these changes occur. The digital revolution and artificial intelligence (AI) are accelerating the creation and disappearance of jobs at an unprecedented rate. While technological changes in the past could take decades to transform the labor economy, today these changes can happen within a matter of years. For example, the automation of processes through AI and robotics is replacing manual and repetitive jobs in factories at a rapid pace.

Jobs at Risk from the Rise of AI

AI and automation are putting numerous jobs at risk, especially those involving repetitive and routine tasks. According to a report by the McKinsey Global Institute, up to 45% of work activities could be automated using existing technology. Among the most vulnerable jobs are:
  • Operators and Assembly Line Workers: Automated production lines are replacing many of these workers.
  • Customer Service: Chatbots and AI systems are taking over support and customer service tasks.
  • Accountants and Auditors: Advanced accounting software and AI are performing many of the routine tasks of these professionals.
  • Drivers: With the development of autonomous vehicles, the need for human drivers may significantly decrease.
On the other hand, professions that require creativity, critical thinking, and complex interpersonal skills are less likely to be replaced by AI in the short term. This includes doctors, teachers, artists, and scientists.

Adapting to Change: Keys for the Worker of the Future

In this scenario, it is crucial for workers to adopt a flexible mindset and be willing to adapt to changes. Here are some important aspects to consider:

Continuous Education and Skills Update

Training and skills updates are essential to stay current in the job market. Participating in continuing education courses, certifications, and professional development programs can help workers acquire new skills that will be in demand in the future. Areas like programming, data management, and knowledge in AI and robotics are especially valuable.

Developing Interpersonal and Creative Skills

While many routine tasks can be automated, interpersonal and creative skills are more difficult for AI to replicate. Developing skills in communication, leadership, problem-solving, and creativity can provide a competitive advantage in the job market. These skills are highly valued in roles that require collaboration, innovation, and team management.

Adaptability and Growth Mindset

Adaptability is one of the most important competencies in a constantly changing work environment. Adopting a growth mindset, which involves seeing challenges as opportunities to learn and grow, can help workers adapt to new situations and job roles more easily. The willingness to learn and take on new challenges will be crucial for thriving in the future.

Entrepreneurship and Self-Employment

With the disruption of many industries, entrepreneurship and self-employment are becoming increasingly viable options. Creating and managing your own business can be a way to secure your future employment, taking advantage of emerging market niches and needs. Additionally, digital platforms have facilitated access to resources and global markets, making entrepreneurship a more accessible option.

Conclusion

The world of work is constantly evolving, and the rise of artificial intelligence and other emerging technologies is accelerating this process. While it is natural to feel concerned about job security, it is important to remember that throughout history, new professions have always emerged to replace old ones. Adapting to these changes through continuous skill updates, developing interpersonal and creative competencies, and adopting a growth mindset can help workers successfully navigate this dynamic job landscape.
Rafael Palomino Rodríguez

Autor Rafael Palomino Rodríguez

Beyond Technical Know-How: The Critical Role of Soft Skills in Career Growth

Beyond Technical Know-How: The Critical Role of Soft Skills in Career Growth
Reading Time: 3 minutes

As we progress in our careers and take on greater responsibilities within our organizations, it becomes evident that professional skill development is just as crucial as technical competence. While we often associate training with acquiring skills and knowledge directly related to the technical performance of our job, the reality of daily work requires a different set of skills. These include managing diverse workgroups with varying profiles and expectations, developing professional networking, handling difficult clients, communication and interpersonal skills, complex negotiations, and adapting to changing environments. This set of skills is commonly referred to as “soft skills.”

Understanding Professional/Soft Skills

Soft skills encompass competencies related to personal behavior, social relationships, leadership, and emotional management. They enable us to interact and manage our environment effectively. On the other hand, technical skills, known as “hard skills,” are the specific technical abilities required to perform a job. Mastery of technical aspects is essential for any career: a doctor must diagnose accurately, an architect must design creative and functional spaces, and a marketing professional must understand and segment their audience, manage a budget, or develop a marketing plan. However, these competencies, while necessary, are not always sufficient for a successful professional career. We all know individuals who, despite being technically proficient, are stalled in their careers due to their inability to relate optimally with their environment or to leverage their strengths and achievements.

The Shift from Hard to Soft Skills

We are often hired for our technical abilities but grow in our careers through our professional skills. As we accumulate years of professional experience, technical competencies gradually give way to the need to develop other types of soft skills directly related to higher responsibilities, such as team management, leadership, change management, and strategy development. Interestingly, while hard skills are acquired systematically and procedurally through academic life and continuous training, professional competencies are often learned (if at all) through practice, typically in a haphazard manner, late, and usually after making many mistakes due to not having internalized them when assuming responsibilities where their use was critical.

Key Soft Skills

The list of potential non-technical skills to consider in our professional development is extensive, especially given the increasing variability of professional profiles in an ever-more specialized world. However, a preliminary categorization to help distribute this broad list might include:

1. Skills Related to Our Attitude Towards Work:

– Innovation and Entrepreneurship: Constantly seeking to change our environment.

– Professionalism and Integrity: Being a good professional requires being a good person.

– Creativity: Thinking outside the box to find new solutions.

– Growth Mindset: Continuously seeking to learn and improve.

2. Skills Related to Our Future Expectations:

– Clarity of Professional Goals: Knowing what we want professionally.

– Acting in Accordance with Future Goals: Aligning today’s actions with tomorrow’s expectations.

– Wealth vs. Influence: Understanding whether we aim to be rich or to have significant influence can guide our career decisions and reduce frustration from unmet expectations.

3. Skills Related to Managing Our Environment:

– Networking Development: Building a network of contacts with the principle of “giving without expecting, receiving without forgetting.”

– Communication: Effectively conveying ideas and listening to others.

– Negotiation and Difficult Situations Management: Handling complex interactions smoothly.

– Adaptability: Adjusting to a constantly changing environment.

The Evolving Workplace and the Need for Continuous Learning

A generation ago, the functions and competencies associated with job positions remained static for many years. Today, new competencies, challenges, and skills are constantly required, necessitating continuous evolution. Our ability to manage change and adapt is arguably the most critical aspect to consider in our professional training. In a rapidly shifting professional landscape, those who can evolve and adjust their skill set in response to new demands will be the most successful.

In conclusion, while technical skills form the foundation of our professional capabilities, it is the soft skills that enable us to grow, adapt, and excel in our careers. A balanced development of both sets of skills is essential for achieving long-term professional success and personal fulfillment.

Rafael Palomino Rodríguez

Autor Rafael Palomino Rodríguez

AI revolution, redefining and reinventing tech talent (part 1)

Revolución IA / IA Revolution
Reading Time: 4 minutes
According to the most serious market studies, a new professional framework for the development of AI is already being defined, although this process is far from complete and we need to be very vigilant, as the point of stabilization is only just on the horizon. This fact, in a sector such as pharmaceuticals, which is one of the fastest developing sectors for AI, has created a pressing need for a better understanding of the context surrounding the impact on the talent required for its implementation.
The rise of artificial intelligence (AI) has led to the creation and redefinition of several specialized professional profiles within the IT field, each with specific roles in the market.
Some are newly created, the most specific to AI, and others are redefined and deepened within existing profiles, almost always linked to an element that is very close to AI: the processing and analysis of data in all its aspects.
The symbiosis between AI and Data Science is such that it is sometimes difficult to draw a clear boundary; analyzing the skills of each profile involved is a good way to start.

Data Scientist

Data Scientists are experts in analyzing and processing large volumes of data to extract valuable information to guide strategic decision making.They use machine learning techniques and statistical analysis to build predictive models and present understandable results to business stakeholders.
Functions:
  • Analysis and processing of large volumes of data.
  • Creation of predictive and machine learning models.
  • Data interpretation for strategic decision making.
  • Data visualization and presentation of results to stakeholders.

Data Engineer

Data Engineers are responsible for designing and building systems that facilitate the efficient processing and storage of large amounts of data. Their work is crucial to ensure that data is available and of high quality for analysis and modeling.
Functions:
  • Designing and building data processing systems.
  •  Integration and management of databases and data lakes.
  • Creation of data pipelines for analysis and modeling.
  • Data quality assurance and data availability.

Data Analyst

Data Analysts are in charge of extracting and analyzing data to obtain insights to support decision making. They create reports and dashboards that help identify trends and patterns, providing key information for business strategies.
Functions:
  • Extracting and analyzing data for actionable insights.
  • Creation of reports and dashboards.
  • Supporting data-driven decision making.
  • Identification of trends and patterns in data.

Machine Learning Engineer

Machine Learning Engineers specialize in the development and implementation of algorithms and models that allow machines to learn from data. Their focus is on optimizing and deploying these models in production environments to solve complex problems.
Functions:
  • Development and implementation of machine learning algorithms and models.
  • Optimisation of models for performance and scalability.
  •  Implementation of machine learning solutions in production.
  •  Maintenance and continuous improvement of AI models.

AI Engineer

AI Engineers are dedicated to developing artificial intelligence systems that emulate human behavior. They use advanced technologies such as natural language processing and computer vision to create innovative solutions that are integrated into products and services.
Functions:
  • Development of AI systems that mimic human behavior.
  •  Implementation of technologies such as natural language processing (NLP) and computer vision.
  • Integration of AI into products and services.
  • Collaboration with other technical and business teams for the implementation of AI solutions.

AI Researcher

AI Researchers focus on exploring new techniques and algorithms in the field of artificial intelligence. Their work includes publishing scientific papers and collaborating with academic institutions to advance the understanding and application of AI.
Functions:
  • Conducting advanced research in new AI techniques and algorithms.
  •  Publication of scientific papers and presentation of findings at conferences.
  • Collaboration with academic and research institutions.
  •  Exploration of new applications and emerging technologies in AI.

AI Ethics Specialist

AI Ethics Specialists assess the ethical implications of artificial intelligence systems. They develop policies and guidelines to ensure the responsible use of AI, promoting transparency and fairness in its application.
Functions:
  • Assessment of ethical implications of AI systems.
  • Developing policies and guidelines for the responsible use of AI.
  • Monitoring compliance with ethical standards in AI projects.
  • Promoting transparency and fairness in the use of AI technologies.

Chatbots and Virtual Assistants Developer

They create programs that interact with users using natural language.They implement natural language processing techniques to improve language understanding and generation, integrating these systems with various platforms and services.
Functions:
  • Design and programming of chatbots and virtual assistants.
  • Implementation of NLP techniques for language understanding and generation.
  • Integration of chatbots with existing platforms and services.
  • Continuous improvement of interaction and user experience.
 

AI Robotics Engineer

AI Robotics Engineers develop intelligent robots with autonomous capabilities.They integrate computer vision and machine learning systems into robots, programming autonomous behaviors and validating their performance in real environments.
Functions:
  •  Development of intelligent robots with autonomous capabilities.
  •  Integration of computer vision and machine learning systems in robots.
  • Programming of autonomous behaviors and decisions in robots.
  • Testing and validation of robots in real environments.
The change we are experiencing in the world of data thanks to AI, and the need to rethink or reinvent many of the technical profiles needed in the Pharma sector to implement technological innovation. is forcing us to adapt to our organizations.
In this context of change and growth, the needs for new professionals who know how to adapt and surf in change convert companies dedicated to attracting talent in the ideal partner to get flexibility and adaptability necessary, minimizing the risk of creating internal structures in our organization that may become obsolete before their time.
In a second article we will take a closer look at how, in the context of formal training, AI is redefining the training of specialized talent and in which certification or auditing framework we can start moving to make sure we are moving in the right direction.

Bibliography

Antonio Castaño

Autor Antonio Castaño