Enterprise data warehouses have become almost sacred for many organizations. Standards, best practices, and processes are emphasized so much that the original purpose of these systems is often forgotten. Is the goal to strictly adhere to data warehouse standards, or is it to meet the needs of business units? This is where organizations often lose sight of their true objectives.

According to Gartner, 60% of large-scale enterprise data warehouse projects take longer than planned, and over 50% fail to fully meet initial business requirements. These figures highlight how easily data warehouse projects can deviate from their intended goals.

Adapting to New Technologies

The rapid evolution of digitalization has transformed the data landscape. Cloud-based solutions, real-time data processing platforms, data lakes, and many other new technologies enable us to use data more agilely and efficiently. However, organizations that treat the enterprise data warehouse as untouchable often resist these innovations. As a result:

• Business units seek flexibility through third-party tools.
• The data warehouse falls behind business processes.
• The focus on “preserving standards” overshadows the real needs of business units.

Forrester reports that data lakes have accelerated data access processes in 72% of organizations, but this rate drops to 35% for those strictly adhering to traditional data warehouse standards. Similarly, IDC’s 2023 report shows that companies with modernized data analytics infrastructures meet business needs 80% faster than those relying on legacy systems.

Trapped by Standards

Standards and best practices are essential — they ensure organization and sustainability in projects. However, applying them rigidly should never be the ultimate goal. The purpose of a data warehouse is to deliver the right data to the right people at the right time. Standards are valuable only as long as they serve this purpose.

McKinsey’s “Agility in Data Management” report reveals that excessive reliance on standard processes extends adaptation time to new business needs by an average of 40%. This insight helps explain the growing gap between business units and technology teams.

The Solution: Refocusing on the Purpose

The goal is not to criticize enterprise data warehouses but to unlock their full potential. Here’s how:

1. Redefine the Purpose: The data warehouse is a tool to meet the needs of business units — not a barrier.
2. Be Open to New Technologies: Embrace alternatives like data lakes, hybrid architectures, and cloud solutions.
3. Develop Agile Processes: Maintain flexibility while implementing standards. The approach of “quick solutions first, then optimization” builds trust with business units.
4. Involve Business Units: Collaboration between technology teams and business units eliminates unnecessary processes.

Deloitte’s “Data Modernization Strategies” report shows that companies modernizing their data infrastructure see a 85% increase in business unit satisfaction. Gartner also notes that businesses investing in modern data solutions handle requests from business units 50% faster than with traditional systems.

In conclusion, instead of turning data warehouses into sacred texts, we should view them as agile tools designed to meet the ever-changing needs of business units. After all, our real goal is not preserving standards but ensuring the success of our business units.