The Data Economist - Blog (ENG) | Establishing sustainable "Data Inspired & Digital Culture"

The MIT study The GenAI Divide shows that only a fraction of GenAI pilot programmes deliver real economic impact – the majority stagnate.

The MIT initiative's research reveals a clear divide: ‘About 5% of AI pilot programmes achieve rapid revenue acceleration; the vast majority stall, delivering little to no measurable impact on P&L.’ The greatest successes are not in marketing and sales, but in the back office. The results also show that purchased solutions and partnerships are more successful than in-house developments and that adoption is more successful when line managers drive the implementation. In this interview, the findings of the study are classified and linked to my own perspectives on modern data strategy, in particular on the role of governance and data & AI literacy as enablers.

Data Cleansing vs. Data Observability

Data cleansing is not a solution, but rather a symptom of a poorly designed data strategy and inefficient, poorly functioning processes. Many companies invest considerable resources in continuous cleansing measures – without permanently eliminating the actual causes of poor data quality. But if you just keep cleaning without plugging the leak, you'll never be able to work dry.

The modern architecture for data- and AI-oriented companies.

Introduction

The increasing importance of data and artificial intelligence (AI) is challenging companies to realign their data management not only technically, but also organisationally and strategically. The Data Management Model 5.0 offers a contemporary, holistic framework that maps the integration of data, governance, AI and value creation in a clearly structured overall picture.

Why I have been fighting for true data industrialisation for over 15 years

Data governance – the sleeping giant of our time – is nothing less than the architect of a sustainable, data-industrialised world. I have been intensively and passionately dedicated to this topic for over 15 years, even though many thought I was crazy to tackle such a supposedly unpopular issue back then. As an advisory board member of DATAGOVKON, I am actively committed to raising awareness of its strategic importance. Even back then, it was clear to me: how can data-driven industrialisation be successful without robust standards and binding architectures?

Why Digital AI-Driven Ecosystems of modern companies are superior to traditional companies with one-dimensional products

The limits of one-dimensional products

Traditional companies, especially those from classic industries, often develop products that solve specific, isolated problems. These one-dimensional products usually offer solutions that are tailored to a specific requirement without providing a broader perspective or integration into other areas. These products follow a linear development and an isolated data flow that offers little opportunity for feedback and improvement.

As a result, such companies are often rigid and inflexible when it comes to adapting to constantly changing market conditions and customer needs.

Automation, AI and blind spots: Why data quality is more important than ever

In an age when customers expect more than just a product or service, data-driven customer service is at the heart of a successful business. Data governance plays a crucial role in this. It is the foundation on which 360-degree customer service is built, ensuring that the various departments of the company – sales, marketing, customer service – all access the same high-quality and consistent data.