Turning Data & AI into Your Competitive Edge
As data and AI have evolved into a strategic asset for organizations, and are now an integral part of business operations, it is essential to build a skilled and professional data team. However, the ideal composition of such a team varies by organization and depends on its level of data maturity.
Key Roles and Data Maturity
The stages of data maturity within an organization range from ad-hoc and reactive data use to fully integrated, data-driven, and predictive decision-making. It is essential to work step-by-step toward higher maturity levels and, at each stage, assemble a team with the right expertise.
Initial Stage
- Core roles: Business Analyst, Data Engineer, Data Governance Manager
- Focus: Use case definition, data collection, cleaning, and governance framework
- Recruitment strategy: Prioritize candidates with strong SQL and Python skills and data management expertise
Growth Stage
- Core roles: Dashboard Developer, Data Architect, Database Administrator
- Focus: Dashboards, data modeling, and performance
- Recruitment strategy: Seek professionals with experience in visual design, data architecture principles, and database/query performance
Maturity Stage
- Core roles: Data Scientist, Data Product Manager, DevOps Engineer
- Focus: Scalability, automation, and AI/ML model deployment
- Recruitment strategy: Target data professionals with expertise in statistical modeling, machine learning algorithms, understanding of data business value, and DevOps experience
A Recruitment Strategy for Every Situation
Building a successful data team requires a strategic approach aligned with the organization’s level of data maturity. In the initial phase, it’s wise to focus on attracting versatile data professionals who can take on multiple roles—such as data analysts with strong SQL skills and basic Python knowledge. Hiring freelancers or professionals on a contract basis can provide access to specialized expertise without immediate long-term commitments.
As the organization grows in data maturity, it becomes important to invest in data engineers and data scientists with deep technical skills. Focus on a more specialized skill set and build a core team of in-house experts to establish a sustainable data advantage. Encourage upskilling and cross-training to develop an agile and broadly deployable team. With a well-thought-out recruitment strategy, organizations can effectively build and scale their data capabilities.
“In companies, AI & data is not a byproduct, but a strategic foundation. It enables teams to make informed decisions, understand user behavior, optimize processes, and continuously innovate in an increasingly competitive digital world.”
Marco de Nooijer
Founding Partner Starz in AI
Put data and AI at the center of your strategy
In a rapidly changing digital world, data is no longer merely supportive—it is a strategic necessity. Organizations that actively deploy data and artificial intelligence (AI) create a fundamental competitive advantage. By placing data at the center of their strategy, decision-making, customer insights, and product development, they can respond more quickly to market changes and customer needs. AI makes it possible to analyze this data at scale, discover patterns, and generate predictive insights that far exceed manual processes.
Organizations that embrace data and AI:
- Accelerate innovation through data-driven product development
- Improve customer satisfaction with personalized user experiences
- Optimize internal processes through automation and predictive analytics
- Reduce costs through more efficient use of resources and smarter decision-making
- Respond proactively to market trends instead of reactively chasing after the facts
Those who ignore data and AI not only risk falling behind, but also lose the ability to scale quickly, experiment, and remain relevant in an increasingly competitive market. Data and AI are no longer optional—they have become essential for those who want to emerge as winners in the coming years.
Turning Data & AI into Your Competitive Edge
Future-oriented skills in AI and data.
