For many years, companies have heavily relied on data warehouses and high-quality data models to make decisions. These centralized data stores are still seen as crucial for consolidating and organizing information from multiple sources. However, in today's AI-driven world, this traditional approach is becoming an obstacle.
The Data Warehouse Trap
Focusing too much on building the perfect central data warehouse is a dead end. Even though the intentions are good, this "better data mousetrap" mindset will prevent you from taking full advantage of AI's potential.
AI algorithms don't need perfectly curated data - they thrive on raw, unfiltered data that reflects the complexity of your processes and organization.
Many companies still operate under the assumption that they function as a series of linear, end-to-end processes. This perception has influenced how we prepare data, resulting in us carefully documenting, measuring, and optimizing these processes.
But AI challenges this established truth: Companies are not just processes, but complex value networks.
Within these networks, results depend on decisions and metrics that are intertwined across your company. Viewing your operations as standalone processes is an oversimplification that does not mirror reality.
Embrace the Value Network Paradigm
To unlock AI's transformative power, you must unlearn outdated thinking:
Decentralize data handling close to business domains instead of treating the central data warehouse as the sole source.
View data as a living, pulsating stream that drives AI initiatives for real-time, agile decision-making.
Shift your perspective from process-oriented to a value network view.
Identify critical decisions and metrics that drive results and improvements, and assign accountability accordingly.
Only by unlearning ingrained notions about central high-quality data models and end-to-end processes can you leverage AI to remove limitations, increase efficiency, and accelerate the pace of innovation.
The path forward is clear:
Break free from traditional mindsets and embrace the value network paradigm. Only then will you position your company for growth and success through data and AI!
One thing is to embrace the ever changing world, but it is not going to help us if we decide to unlearn everything we already know about our data already. Even AI depends on continuously storing and churning information and building up on its experience.