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You're managing a data warehousing project. What can you do to ensure it stays on time and within budget?

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Data warehousing is the process of collecting, integrating, and storing data from various sources for analysis and reporting. It can help organizations gain insights, improve decision making, and optimize performance. However, data warehousing projects can also be complex, costly, and time-consuming. How can you manage them effectively and avoid common pitfalls? Here are some tips to help you keep your data warehousing project on track and within budget.

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Nancy Rausch
Siraj Siddiqui

Cloud Adoption | EDW/ETL Modernization | Capability Building
Divya Srivastava

Top Voice Data Warehousing | SnowPro Certified| Data Engineer | Datawarehousing | ETL/ ELT | Ab Initio | Linux

1 Define the scope and requirements

One of the first steps in any data warehousing project is to define the scope and requirements clearly and realistically. You need to understand the business goals, the data sources, the data quality, the data models, the data transformations, the data storage, and the data delivery. You also need to identify the stakeholders, the users, the roles, and the responsibilities. By defining the scope and requirements upfront, you can avoid scope creep, miscommunication, and rework later on.

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Siraj Siddiqui

Cloud Adoption | EDW/ETL Modernization | Capability Building
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Very well covered! In my experience, initial technical execution planning to optimise the resources and skills, scalable infrastructure to ensure it offers vertical and horizontal scalability in testing, and test planning that includes test data requirements/preparation/availability and scope of testing is finalised.

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Divya Srivastava

Top Voice Data Warehousing | SnowPro Certified| Data Engineer | Datawarehousing | ETL/ ELT | Ab Initio | Linux
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Defining the scope realistically is very important in a Datawarehousing Project as it is going to be the deciding factor for resourcing, delivery timelines and costing estimation. Next step is to break down the tasks and set goals within realistic timelines. Involve team members early on in the planning and there will be no excuses when the final project schedule is set. The surest way to ensure project is within budget and timelines is to have a contingency plan for each phase and milestone. Be prepared as variables and conditions change all the way through Project . Communication is another key factor which can help in setting clear goals, expectations and feedback.

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Rafeeq B.

Manager of Enterprise Data Warehouse, BI and AI | Data & Analytics | Healthcare IT & Quality | Digital Transformation | Enterprise Data Architect | Azure, AWS and Google Cloud | FinOps | CSTM | CSM | CSPW | PMP
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Define clear scope and objectives to prevent scope creep that can lead to delays and cost overruns. Engage stakeholders early and regularly to ensure the project aligns with business requirements to help manage expectations. Having a right mix of skills and resources (Architects, Business Analysts, ETL Developers, BI developers and Project. Managers) allocated to the project is important. Regular code reviews, data validation and most important is testing. Regularly monitor the project budget against the actual spend which helps to have control how budget is spent. Review project progress against the plan. Maintain open and transparent communication with the project team and stakeholders about progress, challenges and changes.

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Naveen Kaul

AI/ML Engineering Leader | Expert in building high-performance inclusive teams, driving Business Growth | Data-Driven Culture Champion | Director-Level Strategy & Innovation
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In my experience scope is the first step in winning the long battle of staying successful. Because with brining in data you never know the surprises that may be in store when you actually start to bring the data into a data warehouse. Best bet is to make sure that requirements are documented and signed off by stakeholders before beginning to plan the execution of the work. It’s also important to set the right expectations with stakeholders from the project kick off to avoid any scope creep or requirement misses.

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2 Plan and prioritize the tasks

Once you have the scope and requirements, you need to plan and prioritize the tasks for your data warehousing project. You need to break down the project into manageable phases, milestones, and deliverables. You need to estimate the time, cost, and resources for each task. You need to assign the tasks to the team members and track their progress. You also need to prioritize the tasks based on their importance, urgency, and dependency. By planning and prioritizing the tasks, you can ensure that you deliver the most value to the business in the shortest time.

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Naveen Kaul

AI/ML Engineering Leader | Expert in building high-performance inclusive teams, driving Business Growth | Data-Driven Culture Champion | Director-Level Strategy & Innovation
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Next step is planning and being agile is one of the keys. Breaking down the project into independent features that can be developed and tested and moved to production when ready makes it easy to realize the benefits early.

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3 Communicate and collaborate with the team

Communication and collaboration are essential for any data warehousing project. You need to communicate and collaborate with your team members regularly and effectively. You need to share the project vision, goals, expectations, and feedback. You need to use tools and platforms that facilitate communication and collaboration, such as email, chat, video conferencing, project management software, and data warehousing tools. You also need to foster a culture of trust, transparency, and accountability among your team members. By communicating and collaborating with your team, you can improve the quality, efficiency, and satisfaction of your data warehousing project.

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Nancy Rausch
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Internal team communication and collaboration are essential at this stage. Equally as important however is collaboration with external stakeholders. It is important to understand the requirements as clearly as possible, and oftentimes projects need additional guidance or clarification as they get underway. Collaboration at all levels in the beginning stages of the work, both internally and externally can make a big impact in the overall success of the project.

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4 Test and validate the data

Testing and validating the data are crucial steps in any data warehousing project. You need to test and validate the data at every stage of the data warehousing process, from data extraction to data loading to data analysis. You need to check the data for accuracy, completeness, consistency, and reliability. You need to use tools and methods that automate and simplify testing and validation, such as data quality tools, data profiling tools, data comparison tools, and data visualization tools. You also need to involve the users and stakeholders in testing and validating the data. By testing and validating the data, you can ensure that your data warehousing project meets the business needs and expectations.

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5 Monitor and control the project

Monitoring and controlling the project are vital steps in any data warehousing project. You need to monitor and control the project performance, risks, issues, and changes. You need to measure the project progress, quality, cost, and value. You need to use tools and metrics that help you monitor and control the project, such as dashboards, reports, indicators, and alerts. You also need to take corrective actions and preventive actions when necessary. By monitoring and controlling the project, you can ensure that your data warehousing project stays on time and within budget.

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6 Learn and improve from the project

Learning and improving from the project are important steps in any data warehousing project. You need to learn and improve from the project outcomes, feedback, and lessons. You need to evaluate the project success, challenges, and opportunities. You need to use tools and techniques that help you learn and improve from the project, such as surveys, interviews, reviews, and audits. You also need to document and share the project knowledge, best practices, and recommendations. By learning and improving from the project, you can enhance your data warehousing skills, experience, and reputation.

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7 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?

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You're managing a data warehousing project. What can you do to ensure it stays on time and within budget?

1

2

3

4

5

6

7

1 Define the scope and requirements

2 Plan and prioritize the tasks

3 Communicate and collaborate with the team

4 Test and validate the data

5 Monitor and control the project

6 Learn and improve from the project

7 Here’s what else to consider

Data Warehousing

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You're managing a data warehousing project. What can you do to ensure it stays on time and within budget?

1

2

3

4

5

6

7

1 Define the scope and requirements

2 Plan and prioritize the tasks

3 Communicate and collaborate with the team

4 Test and validate the data

5 Monitor and control the project

6 Learn and improve from the project

7 Here’s what else to consider

Data Warehousing

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