Data Warehousing is Not a Strategy: How to Maximize ROI from Your Warehouse Investment | RudderStack

By neub9
3 Min Read

The power of modern data warehouses cannot be overstated. Cloud-based warehouses offer unprecedented scalability and flexibility, contributing to remarkable business outcomes. Gartner/Tellius data reveals that data-driven companies can achieve up to 19x profitability, are 23x more likely to acquire new customers, and can experience a 20-30% EBITDA boost.

However, many companies struggle to realize such returns on their warehouse investments. One reason for this is the tendency to focus solely on perfecting data storage and overlooking the bigger picture. Merely storing data in a warehouse, while valuable, does not automatically translate to a business advantage. It’s important to ensure that the data and insights from the warehouse are accessible to business users and used for strategic action.

Creating collaboration between data and business teams presents a challenge. Data teams can be inclined to stay within their roles and consider their job done once data pipelines are built. However, to position the team as a value creator, it’s essential to bridge this gap and empower business users to leverage warehouse data for decision-making.

By strategically approaching this, it’s possible to transform the perception of the data team from a cost center to a business engine. The key lies in bringing insights derived from data to decision-makers, translating them into game-changing actions.

To facilitate this transition, a shift from working in silos to working cross-functionally is necessary. Data warehousing should be viewed as a means to achieve competitive business advantage, rather than an end in itself. Implementing strategic processes and fostering collaboration between technical and business teams are crucial to creating valuable datasets and analytics models.

A role that has emerged to bridge the gap between data and business teams is the analytics engineer. This hybrid role combines the technical skills of a data engineer with the analytical skills of a data analyst, providing alignment between teams and contributing to higher business value projects.

Practical steps to improve data culture and unlock valuable business insights include prioritizing curated and unified views of the data, fostering internal team alignment, introducing analytics engineering roles, and establishing feedback loops. By aligning people, processes, technologies, and skill sets, companies can significantly increase the return on investment in their data warehouse and turn it into a powerful business tool.

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