You're starting a new data warehousing project. What are the first steps you should take?

Define project objectives and scope, including data sources, user requirements, and expected outcomes. Assess existing data infrastructure and determine technical requirements for storage, processing, and analytics. Develop a data model and architecture design, considering scalability, performance, and security requirements.

Starting a new data warehousing project, first, understand the business requirements and objectives. Identify the key stakeholders and establish clear communication channels. Define the scope of the project and set realistic timelines. Identify the data sources and plan for data integration. Choose the right data warehousing architecture and technology stack based on your needs. Design the data model considering factors like scalability and performance. Plan for data quality, security, and privacy. Remember, a well-planned project is more likely to be successful and meet the business needs effectively.

First step has to be the business and technical goals. Understanding the scale, user persona, product vision, growth and type of interaction with the application would help in designing a robust design.

Commit to continuous improvement and optimization of the data warehouse to ensure ongoing alignment with evolving business needs and changing market dynamics.

Consider factors such as data completeness, validity, timeliness, and integrity, as well as any known issues or anomalies that may affect data quality.

Choose solutions that offer scalability, extensibility, and compatibility with emerging technologies to future-proof your investment and ensure long-term viability.

Choose appropriate extraction methods, such as full extraction, incremental extraction, or change data capture (CDC), based on data source characteristics and requirements.

Validate data quality and integrity within the data warehouse by conducting data validation checks, integrity constraints, and data profiling exercises. Ensure that data cleansing, transformation, and validation processes are functioning correctly to maintain high data quality standards.

1. Define goals. 2. Identify stakeholders. 3. Assess data sources. 4. Choose technology. 5. Plan architecture. 6. Secure funding. 7. Establish team. 8. Set timeline.

When you are starting a new Datawarehouse project or upgrading your DWH system which could also be your operational reporting, you must understand that there is some expectation that is not well defined or defined to a limit. The most important thing is to take ownership of the outcomes from the DWH as this is something you will be making available for the end users. If you limit yourself to the expectations defined by them, you will somehow end up fulfilling the needs but not exposing the full potential of a DWH. Rather, you must focus on delivering the best by analyzing the data, getting involved with stakeholders and identifying their problems along with minimum expectation, this way you will produce something of immense value.