As industries become increasingly reliant on data, the demand for professionals who can navigate the complex world of data and business strategy is surging. The traditional silos of expertise across the data lifecycle (Data Science, Analysis, and Engineering) are breaking down, giving rise to a new breed of professional: those with Portfolio skill sets. This trend represents a paradigm shift, emphasizing adaptability, interdisciplinary knowledge, and the ability to bridge technical and non-technical domains.
What Are Portfolio Skill Sets?
Portfolio skill sets refer to the integration of competencies from multiple disciplines. In the context of data, this means combining technical prowess in areas like machine learning, statistics, or data engineering with soft skills such as communication, problem-solving, and domain-specific business knowledge.
For example, a Data Scientist today is often expected to:
- Develop machine learning models (technical expertise).
- Communicate findings effectively to non-technical stakeholders (soft skills).
- Understand the industry context to make insights actionable and commercially impactful (domain knowledge).
Portfolio skill sets embody the ability to think holistically, connect disparate ideas, and apply them to real-world problems.
Why Are Portfolio Skill Sets Emerging Now?
Several trends have catalyzed the rise of Portfolio skill sets in the data domain:
The Expanding Scope of Data Roles:
Traditional data roles, such as statisticians or data engineers, are evolving. Employers now look for professionals who can understand the full data lifecycle—from ingestion and pre-processing to analysis and deployment.
Advances in Technology:
Tools and platforms like AutoML, low-code/no-code software, and visualization tools have lowered the technical barriers for non-specialists. This democratization requires professionals to adapt and learn complementary skills.
The Convergence of Disciplines:
In areas like healthcare, finance, and marketing, data professionals are expected to understand domain-specific challenges. For instance, a healthcare Data Scientist must grasp medical terminologies and ethical considerations to interpret and act on patient data effectively.
Focus on Decision-Driven Analytics:
Companies are prioritizing commercial impact over technical complexity. This shift underscores the need for data professionals who can translate technical outputs into strategic recommendations to either generate a cost saving or increase revenue.
How to Build a Portfolio Skill Set
Upskilling Through Education:
Online courses, bootcamps, and certifications provide opportunities to develop both technical and soft skills checkout LSE’s brilliant Accelerator programme run by FourthRev.
Cross-Functional Projects:
Working on diverse teams can help professionals build empathy for non-technical perspectives while improving their ability to explain complex concepts.
Mentorship and Networking:
Learning from professionals who already operate at the intersection of disciplines can offer practical insights and career guidance.
Continuous Learning:
As technologies and business needs evolve, staying informed about the latest trends in both technical and domain-specific areas is crucial.
The opportunities, however, are immense. Professionals with Portfolio skill sets are uniquely positioned to upskill peers, operate in a diverse, adaptable and future focused way, enabling senior leadership teams to see their data assets as a vehicle for value creation rather than a cost centre. Constantly evolving and flexing at pace, means you are protecting your worth and position within the organisation, whilst constantly vying for the next data innovation. It also dilutes the perception that data folk always have their own insular agenda, which is always ‘data centric’. Data communities have an opportunity to shift the mindset of their stakeholders so they are viewed as ‘business performance enablers’.
I believe Portfolio skill sets represent the future of our data industry. As the demand for adaptable, interdisciplinary talent grows, cultivating these skills will be vital for both professionals and organizations. By embracing this shift, we can unlock the full potential of data and data skills to solve complex problems and create value in ways previously unimagined.