Data literacy for managers: Why C-levels need to learn the language of data

The new reality: from gut feeling to data-based evidence

The days when important strategic decisions were made solely on the basis of experience and intuition are finally over. Successful corporate management in the 21st century is data-driven. But what does that mean in concrete terms? It means creating a culture in which hypotheses are validated by data, processes are optimized through analysis and new business models are developed on the basis of patterns and forecasts.

Many managers are facing a fundamental challenge here. As our latest report "On the road to AI excellence" shows, the most basic requirements are often lacking. The study, for which we surveyed over 1,300 companies, paints a clear picture:

• Fragmented data landscapes: Only 8% of companies state that their data structures are completely consistent and uniform. Over a third struggle with heterogeneous systems that act like isolated islands.
• Lack of success measurement: Over half (51%) of companies do not use any KPIs to measure the success of AI initiatives. No measurement means no control, no control means no sustainable improvement.
• Strategic gap: Although there is awareness of AI, only 21% ensure clear and close alignment between AI projects and the overarching corporate strategy.

These figures make it clear: The biggest obstacle on the path to digital transformation with AI is often not the technology itself, but the lack of data and strategy expertise at management level.

What does data literacy really mean for managers?

Data literacy is much more than reading Excel spreadsheets or understanding dashboards. It is a strategic mindset based on a solid understanding of the entire data value chain. For managers, several core areas can be derived from this that are crucial for a successful AI strategy.

1. understanding the foundation: Data quality and access (data foundation)

It all starts with the data. A manager must be able to ask the right questions:

• Where does our data come from?
• How clean, structured and reliable are they?
• Are there data silos that hinder the flow of information across departments?

Our study shows that many teams are still working with isolated data silos. Modern AI consulting therefore does not start with algorithms, but with the creation of a clean, accessible and secure database. Without this foundation, even the most advanced enterprise AI solutions are doomed to fail. It's about creating an infrastructure that serves as a solid launch pad for future innovations.

2. developing the vision: strategic objectives and KPIs

Data is only valuable if it is viewed in the context of the company's objectives. Managers must be able to bridge the gap between data potential and business strategy.

• What specific problems do we want to solve with AI?
• How do we define success? (e.g. cost reduction, sales increase, customer satisfaction)
• How do we measure progress (KPIs)?

There is often a big gap here. Many AI projects are initiated without clear, measurable goals and peter out as "interesting experiments". A data-competent leader, on the other hand, firmly anchors the use of artificial intelligence in the company in the overall strategy and ensures that every initiative makes a traceable contribution to success. This ranges from AI process optimization in administration to complex applications such as predictive maintenance in production.

3. shaping the culture: People, change and acceptance

The introduction of AI is 20% technology and 80% change management. The biggest hurdle is often the human one. Fears of job loss, skepticism towards new technologies and resistance to change are natural reactions.

A data-competent manager acts as the top cultural ambassador here:

• It communicates the vision and benefits of AI in a transparent and understandable way.
• It encourages curiosity and a "fail forward" mentality.
• It invests in employee training and empowerment.

In targeted AI workshops for companies, for example, can not only impart technical knowledge, but also initiate an open discussion about opportunities and risks. This is a crucial step in creating a culture in which data and AI are perceived as tools for support and not as a threat.

4. setting the guard rails: Ethics, safety and compliance

With great power comes great responsibility. The use of AI and the processing of large amounts of data require clear ethical and legal guidelines. Issues such as data protection (GDPR), bias in algorithms and the traceability of AI decisions are not sideshows, but key trust factors. Managers must ensure that their company acts responsibly and not only meets the necessary safety standards, but also lives them.

The path to data-driven leadership: where to start?

The theory is clear, but what does the first practical step look like? The journey to data literacy and an AI-ready company doesn't have to be complicated, but it does require a structured approach.

Step 1: Positioning - Where do you really stand?

Before you start a journey, you need to know your starting point. An AI maturity model provides an objective and comprehensive basis for analysis. It helps you to understand where your strengths and weaknesses lie - from the technical infrastructure to the corporate culture.

We at CorpIn have developed the CorpIn Hexagon Framework developed. It analyzes your company along six key dimensions that are crucial for a successful AI transformation. With our new CorpIn Hexagon platform you can even carry out this first step independently. Receive an empirically based assessment of your AI maturity level and compare yourself anonymously with over 1,300 other Swiss companies.

➡️ Start your AI maturity analysis on the Hexagon platform now.

Step 2: Build competence - Invest in knowledge

Data literacy does not fall from the sky. It must be built up in a targeted manner - at all levels, but especially in management. Tailor-made AI workshops are a highly efficient way to quickly bring management teams up to the same level of knowledge, identify common use cases and develop a common language.

➡️ Find out more about our practice-oriented AI workshops for managers and teams.

Step 3: Develop a roadmap - get a partner on board

Developing a comprehensive AI strategy is a complex task. An external partner can bring valuable perspectives, uncover blind spots and ensure that your strategy is built on a solid foundation and delivers measurable results. A professional AI consultancy accompanies you from the initial analysis through solution development to successful implementation.

Conclusion: data literacy is a matter for the boss

The ability to speak the language of data is the decisive meta-skill for managers of today and tomorrow. Companies whose leaders are data-savvy are more agile, efficient and innovative. They make better decisions, recognize market opportunities earlier and can make targeted use of the potential of artificial intelligence, be it in the implementation of LLMs in the company for better AI in knowledge management or in the comprehensive digital transformation with AI.

Getting there is a journey, not a sprint. But it is a journey that must begin now. Waiting is no longer an option.

Are you ready to take the next step?

At CorpIn, we specialize in accompanying companies in the DACH region on their journey to AI excellence. As a young, dynamic team of digital natives and experienced experts, we bridge the gap between business understanding and technological expertise.

💬 Let's get talking! Book a non-binding introductory meeting in which we can discuss your individual challenges and potential.

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