Here's how you can successfully deliver BI projects on time and within budget.

The most important thing is to estimate the project as accurately as possible. Take some time to analyze what you have at hand, understand expectations, and provide the estimate (not guestimate). Next, work with an agile methodology. Break the project into sprints and deliver parts of the project incrementally. This way, you can get quick feedback from stakeholders and ensure you’re always aligned with their expectations. If you're working with a team, make sure the right people are working on the right tasks. For example, someone might be great at DAX, while another excels at visualizations.

To me BI projects should deliver improved decisions. One of the best approaches I have learned to define scope is to focus the project on decision-making by asking and getting answers to the following three questions: 1. In a perfect world, what decisions would you like to make (about customers, products, operations, etc)? 2. What data / information would allow you to make those decisions? 3. Where can we get that data / information? This focuses the project on the desired outcomes (better decisions) rather than report formats, tools and the other "how" elements that often bog down projects. The team then uses agile / iterative methods with early visual prototypes to deliver the desired outcomes.

The most common ways i would think of are Develop a detailed project plan with realistic timelines and budget estimates. Include buffer time for unforeseen issues. Use an agile approach to manage the project, breaking it into smaller, manageable sprints with regular reviews and adjustments. Allocate the right resources and ensure your team has the necessary skills and tools. Monitor workloads to avoid burnout and ensure productivity.

É muito importante desde o começo do projeto de Business intelligence alinhar exatamente as expectativas que o cliente pretende alcançar como objetivos, ter claro cada métrica de sucesso do projeto, a partir destes objetivos é que devem ser traçados o plano de escopo do projeto desde a obtenção dos dados até a Entrega do design levando em consideração o usuário final que pode ser o analítico operacional ou estratégico.

Sempre que possível (isso depende do tamanho da estrutura da empresa), colocar cada profissional, com sua devida expertise, em cada etapa do projeto. Quem irá capturar dados, enriquecer os dados, desenvolver análises, gestor de projetos, desenvolvimento de layouts. Mas nem sempre se terá acesso a grandes equipes.

Dependendo do nível do projeto é importante que a equipe seja composta por mais de um profissional, em muitos dos casos se faz necessário um arquiteto e engenheiro de dados, um designer, especialistas em UX e UI, além é claro do analista de bi, para a Entrega de um projeto sustentável automatizado e com um nível de qualidade profissional.

While my experience lies primarily in the business development realm, a valuable lesson in team assembly comes from my time as a Trainee Manager at Hakkasan, a renowned Cantonese restaurant in London. The restaurant thrived because of its well-oiled team. Front-of-house staff seamlessly interacted with the chefs, ensuring a smooth dining experience. But it wasn't just about individual roles; success stemmed from a deep understanding of each other's functions. Like Hakkasan's chefs & servers, a BI team thrives on collaboration. Data analysts ("chefs") prepare info, but business needs context ("sommeliers"). Clear communication between both ensures data translates into actionable insights for the business.

O Planejamento é crucial para trabalhar prazos e expectativas de projetos. Com energia gasta em um bom planejamento e execução de projeto os resultados serão muito mais positivos.

From P.P.: As Sr. Consultant, a recent BI project initially floundered due to a lack of planning. We swamped ourselves with data, leading to delays and frustrated clients. We course-corrected by meticulously planning, setting clear goals, and streamlining data collection. This transformed the project, yielding actionable insights that fueled data-driven decisions and improved outcomes. The lesson learned; meticulous planning is crucial for successful BI implementations.

Managing data can be nuanced. I often encounter organizations that want to get their data "right" before they will even start a BI project. I offer these two pieces of advice: 1) There are worthwhile projects for which your data - in its current state - is good enough 2) Use these early projects to master your data governance, cleanliness and quality processes. In other words don't let the current issues with the data stop progress and data management maturity. This approach becomes even more important when we consider AI projects. There are solid projects for which the current data and data condition are adequate. Then use those projects to improve the other AI readiness elements and processes.

Totally agree. I have always said we can do something now while we work on the bigger picture. And it pretty much all cases it is right.

Controlling costs is essential for staying within budget. I closely monitor expenses and resource utilization throughout the project. Implementing cost control measures, such as budget tracking and variance analysis, helps prevent overspending and ensures financial discipline.

Eu sempre começo com o MVP para que o cliente tenha uma entrega parcial e a partir deste possa ser modelados o projeto completo e entrega final com mais assertividade para o cliente.

In addition to the above points, fostering open communication and collaboration within the team is crucial. Encouraging team members to share updates, challenges, and insights promotes a proactive problem-solving approach. Additionally, continuously evaluating project performance and learning from each project helps improve future project delivery.