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How can a data warehouse project manager effectively manage an agile project?

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Data warehouse projects are complex, dynamic, and often involve multiple stakeholders and sources of data. To deliver value to the business, data warehouse project managers need to adopt an agile approach that allows them to respond to changing requirements, prioritize features, and collaborate with the team and the users. In this article, we will explore some of the best practices and challenges of managing an agile data warehouse project.

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1 Agile principles

Agile is a philosophy and a methodology that emphasizes delivering working software in short iterations, adapting to feedback, and empowering the team to make decisions. Agile principles can be applied to data warehouse projects by breaking down the scope into smaller, testable, and valuable units of work, such as data models, ETL processes, reports, and dashboards. These units of work are prioritized in a backlog and delivered in sprints, which are time-boxed periods of development and testing. At the end of each sprint, the team demonstrates the results to the stakeholders and collects feedback to improve the next sprint.

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Swapnil Surushe

GCP Certified Data Engineer | AWS Certified Solution Architect | 2 x GCP Certified Professional | Building a community with 4k followers on LinkedIn | SQL 5 ? on HackerRank | Python 4 ? on HackerRank.
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To manage an agile data warehouse project effectively, the project manager should prioritize clear communication, flexibility, task prioritization, continuous improvement, and proactive risk management.

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2 Agile roles

An agile data warehouse project requires clear roles and responsibilities for the team members and the stakeholders. The product owner represents the business needs and user expectations, defines the vision and goals of the project, and prioritizes the backlog items. The scrum master facilitates the agile process, removes impediments, and coaches the team on best practices. The development team designs, builds, tests, and deploys the data warehouse components while collaborating with each other and the product owner. They must also self-organize their work. Finally, stakeholders provide input, feedback, and validation for the project to support adoption and use of the data warehouse.

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3 Agile tools

An agile data warehouse project requires effective tools to support communication, collaboration, and documentation. Common tools for this purpose include a project management tool, such as Jira or Trello, to track the backlog, sprint progress, tasks, and issues; a data modeling tool, such as PowerDesigner or SQL Developer Data Modeler, to design and document the data warehouse structure and data flows; a version control tool, such as Git or SVN, to manage code changes, branches, and merges; a testing tool, such as DBUnit or Pentaho Data Integration, to verify data quality, performance, and functionality of the data warehouse components; and a reporting tool, like Tableau or QlikView to create and share reports and dashboards.

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Ritabrata Sanyal

Digital Supply Chain Management | O9, Kinaxis, Tableau & SAS Certified | Certified Scrum Master (CSM)
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In data warehousing, agile tools streamline processes, ensuring adaptability and efficiency. Key tools include JIRA for task tracking, fostering transparency and collaboration. Trello offers visual project management, ideal for organizing tasks and timelines. Confluence aids in documentation, centralizing project knowledge. Data modeling tools like ER/Studio enable quick iterations of warehouse schema. Continuous integration tools, such as Jenkins, automate testing, ensuring data integrity. These agile tools, integrated into a data warehousing project, enhance team productivity, foster collaboration, and ensure timely, quality deliverables.

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4 Agile challenges

As a project manager of an agile data warehouse, you will need to address a variety of challenges. These include managing the scope and expectations of stakeholders who may have different or conflicting needs, ensuring the data governance and security of the warehouse, integrating the warehouse with other systems and applications, balancing the speed and quality of delivery, and handling technical debt and refactoring. All these tasks may involve multiple sources, formats, standards of data, require compliance with regulations and policies, involve different architectures, interfaces, and dependencies, and may affect the performance, maintainability, and scalability of the warehouse.

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5 Agile benefits

Despite the challenges, an agile data warehouse project can bring many benefits to the project manager, the team, and the business. These include delivering value faster and more frequently, improving the quality and reliability of the data warehouse, enhancing collaboration and satisfaction among the team and stakeholders, and increasing flexibility and adaptability. These benefits are achieved by focusing on the most important and useful features, applying testing and validation techniques, involving stakeholders in planning, development, and review of the project, embracing change, responding to feedback, and adjusting scope and priorities as needed.

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Fiyanshu Arora

Supporting and Providing BI Solutions at Charter Communications
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By slicing the big picture into manageable small sprints , keeping the tech & knowledge balanced and effectively reflecting on outcomes from each sprint

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Arpit Kumar Jain

BI ETL Developer @ Charter Communications | Technical Program Manager @ Cenveo | Data Integration, SQL Development | Talend and Spotfire Admin
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All the parties involved can realize in the earlier sprints if the planning and development is going as per the end-goal. This can also help in scope redefinement and adjusting future sprints and planning.

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6 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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Claudio Bonel, M.Sc.

Professor | Pesquisador | Cientista de dados | Engenheiro de Machine learning | Inteligência artificial | Business Intelligence | Python | SQL | Power BI | CSD | PhD Student
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Vou trazer meu ponto de vista e pode parecer polêmico, mas n?o é. Acredite! é apenas o meu olhar para dentro de projetos de DW. Gest?o de projeto ágil precisa ser "casada" com um modelo de desenvolvimento ágil. Hoje a engenharia de software apresenta alguns modelos incrementais que, inclusive s?o chamados de engenharia de software ágil. Essas comunh?o quando existe, me parece que os resultados s?o mais expressivos, havendo redu??o de riscos. Isso é um ponto que, inclusive, caberia um debate mais amplo, para que possamos ouvir diversas experiências. Dúvidas? Chamaí!

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How can a data warehouse project manager effectively manage an agile project?

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3

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6

1 Agile principles

2 Agile roles

3 Agile tools

4 Agile challenges

5 Agile benefits

6 Here’s what else to consider

Data Warehousing

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How can a data warehouse project manager effectively manage an agile project?

1

2

3

4

5

6

1 Agile principles

2 Agile roles

3 Agile tools

4 Agile challenges

5 Agile benefits

6 Here’s what else to consider

Data Warehousing

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