You’re working on a data warehousing project. How can you prioritize tasks to ensure success?

Clearly define the goals and objectives of the project. Identify key stakeholders and their requirements. This will help you prioritize tasks based on their importance and impact on the project's success.

Clearly outlining the project's scope is a critical initial phase. Failing to do so may result in addressing non-existent issues, encountering scope creep with indefinite project boundaries, and inefficient allocation of resources. Additionally, the scope establishes essential elements such as use case, methodology, priority, and the definition of project completion. Accurate estimation of costs is contingent upon a well-defined scope, preventing unnecessary expenses.

Clearly state what the data warehousing project aims to achieve, including how it helps with business goals. Figure out what types of data the project will use, where it comes from, and if any changes or cleaning are needed. Understand what the people using the data warehouse need, like specific questions, reports, or analytics. Set up a schedule for the project, including important dates. This gives a clear plan and helps track progress. Explain how important people will be part of the project. Define their roles to make sure everyone works well together. Think about how the data warehouse can grow and what might be needed in the future. Make sure the project fits with long-term business plans.

Define the key performance indicators (KPIs) that will be used to measure the success and effectiveness of the data warehouse. These KPIs should align with the broader business objectives and provide actionable insights.

It all starts from Business Requirement or on what dimensions they want to visualize the data . The attributes in our facts and the corresponding description is vital . So , to start with we need to generate a model ( Dimensional Data Model ) , identify the key attributes and relationships . We need to have questions to the business or BI based on the Model and tune it accordingly .

Now we have to finalise a Modelling pattern . This could be Star or Snowflake or Hybrid or even a Data Vault . We have to analyse the expected expansion for the tables or attributes in future . The selection of tools and technology - In this rapidly changing world , we have to be very specific and focus on pluggable Architecture as we keep on changing the tools because of easy of use and financial reasons . Data Preservation should also be planned like the Replication strategies etc . The Architecture should be flexible in terms of scalability and performance .

Select a modeling method (star schema, snowflake schema, data vault, or dimensional modeling) that suits the project's goals. Evaluate if the chosen setup can handle growing data volumes and user demands as the project progresses. Assess how quickly the data warehouse needs to process queries and the efficiency of loading data. Choose the right data processing technologies, such as relational databases or distributed computing platforms, that align with the project's needs. Plan how the data warehouse will work with current systems, ensuring compatibility with tools like business intelligence and reporting systems. Evaluate the budget and available resources, selecting an architecture that fits financial constraints.

Select the most suitable data warehousing architecture based on your project requirements, such as the traditional ETL (Extract, Transform, Load) approach or ELT (Extract, Load, Transform) approach. Consider factors like scalability, performance, and cost.

Define data access methods, considering how end-users will interact with the data warehouse. Whether through SQL queries, OLAP (Online Analytical Processing) tools, or other interfaces, ensure that the chosen methods align with user preferences and needs.

Create a detailed project plan outlining the tasks, timelines, dependencies, and resources required. Prioritize tasks based on their dependencies and critical path to ensure smooth execution.

Since at this point we have finalised the Architecture , the tools etc . Now we have to split the project into multiple parts and an arrangements of work in an order . Estimation should be done for all the components in terms of resources and time . A popular way is to create Jira stories and group them as per sprints .

Plan for thorough testing and quality assurance processes. Define testing criteria, methodologies, and acceptance criteria to ensure that the data warehouse meets the specified requirements.

Focus on implementing the ETL process to extract data from source systems, transform it into the desired format, and load it into the data warehouse. Prioritize tasks based on the volume and complexity of the data.

Now the time for development . We should adhere to the best practices involved in the development of ETL frameworks . Data Quality , Governance and Exceptions should be well defined . Make sure the whole team follows a common pattern , it could be nomenclature or testing strategy or any Acceptance criteria . Documentation should be part of every process .

Conduct thorough testing and validation of the ETL processes. Perform unit tests, integration tests, and end-to-end tests to identify and rectify any issues before deploying the processes into a production environment.

Once the data is loaded into the data warehouse, prioritize the development of reports and dashboards based on their importance to the business. Consider the needs of different user groups and prioritize accordingly.

Utilize suitable data visualization tools like charts, graphs, tables, and maps to effectively convey information in a clear and captivating manner. Adhere to the principles of data storytelling, including identifying the audience, constructing a narrative, and selecting key messages. Creating reports enables you to articulate the discoveries, recommendations, and actions derived from your data.

Provide training sessions and documentation to help users understand how to navigate and interpret the reports. This is particularly important for users who may be less familiar with the chosen data visualization tools.

The BI team should follow the best principles storytellings and presentation . The Dashboards should reflect the actual requirements that was finalised . The choice of BI tools is also important but that we be finalised before only . With the latest tech stacks , customised dashboards should be prepared which details the real time analytics as well (if requested).

Continuously evaluate the performance of your data warehousing project against the defined goals and objectives. Identify any issues or bottlenecks and prioritize tasks to address them.

Data Quality: Ensure that data quality is maintained throughout the project. Prioritize tasks related to data cleansing, validation, and monitoring. Security and Compliance: Prioritize tasks related to data security and compliance with relevant regulations (e.g., GDPR, HIPAA). Implement measures to protect sensitive data and ensure regulatory compliance. User Training and Support: Prioritize tasks related to user training and support to ensure that end users can effectively use the data warehouse and reports. Change Management: Consider the impact of changes on the project and prioritize tasks related to change management to minimize disruptions. Continuous Improvement: Prioritize tasks related to continuous improvement.