What do you do if your data warehousing decisions are not aligned with organizational goals?

1 Identify the root cause

In order to address the misalignment between data warehousing decisions and organizational goals, it is necessary to identify the root cause of the problem. This could be due to a lack of communication between data warehousing teams and business stakeholders, unclear or changing business requirements, an inadequate or outdated data warehousing architecture, insufficient data quality or governance, or limited resources or skills for data warehousing development or maintenance. To gain a better understanding of the root cause of the misalignment, and how it impacts data warehousing performance and outcomes, one can use methods such as interviews, surveys, audits, or analytics to gather feedback and insights from different perspectives.

2 Align the data warehousing strategy

Aligning the data warehousing strategy with organizational goals is essential for achieving consistent and compatible outcomes. To do this, you should define and communicate the vision, objectives, and benefits of data warehousing for the organization, as well as establish and follow standards, policies, and best practices. Additionally, it is important to prioritize and plan projects and tasks based on business value and impact. Furthermore, you need to involve stakeholders such as business users, managers, analysts, or executives in the data warehousing strategy formulation and execution. Lastly, documenting and sharing the strategy and progress with the organization is key.

3 Implement the data warehousing solutions

The final step is to implement the data warehousing solutions that support the organizational goals, and deliver the expected results. This includes designing and building the data warehousing architecture and models that meet the business requirements and specifications, as well as integrating and transforming data from various sources and formats into the data warehouse. Additionally, you need to ensure data quality, accuracy, and completeness in the data warehouse, provide data access, security, and privacy in the data warehouse, and develop and deploy data analysis and reporting tools and applications that use the data warehouse. To do this successfully, you should apply best practices and techniques such as ETL, dimensional modeling, star schema, or OLAP. Finally, it's important to test and validate the data warehousing solutions before and after implementation.

4 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?