There is a concept related to compromising quality in order to maintain deadline. Well, at present due to share presence of automation tools and other options, that concept is kind off obsolete. Now, if stakeholders demand results within short space, you have to deliver regardless of whatever resources or time constraints you are facing. This is why always be proactive and do all the tasks diligently without complacency so that these situations can be negated ??

We can start wd a clear understanding of what insights r needed.This helps focus your analysis on d most relevant data.We should focus on the most critical metrics that align with stakeholder goals. This allows you to deliver essential insights quickly without getting bogged down in details.We will implement tools and software that can automate data collection and preliminary analysis.This saves time and reduces human error& will Implement quick data quality assessments to identify any major issues upfront, which can prevent inaccuracies in your analysis. We will present initial findings quickly and use feedback to refine and expand the analysis. This helps stakeholders get timely information while allowing you to ensure accuracy over time.

In the field of data analytics, nothing can be rushed. Proper analysis requires sufficient time to deliver accurate insights to the business. However, to avoid last-minute pressure, analysts can take a few proactive steps: 1. Automated tools: Develop automated dashboards (in Excel/BI) using SQL, and share them with stakeholders. This allows them to track the progress of the project, process, or analysis in real time. 2. Create project timelines: Last-minute rushes often occur when clients/stakeholders are unaware of the current status, deadlines, and progress. Establish clear project timelines with task deadlines. 3. Be specific: Prioritize tasks based on Critical to Quality or Critical to Process factors, and share insights accordingly

I prioritize building a well-analyzed ETL process from the start to ensure a smooth workflow. Once that's in place, I focus on creating dashboards with proper data modeling, displaying key metrics in a matrix format with various filters and slicers. This approach allows me to quickly adjust to new requirements by simply applying filters, and having multiple tables in the same view makes it easy to cross-verify numbers. This combination helps balance speed and accuracy effectively.

Quando as partes interessadas querem resultados rápidos, é importante equilibrar velocidade com precis?o. Usar ferramentas automáticas, como Apache Airflow e Apache Spark, ajuda a processar grandes volumes de dados rapidinho, sem erros manuais. Mas n?o dá pra abrir m?o da qualidade, ent?o, validando os dados constantemente com ferramentas que conferem tudo no caminho, você garante que nada saia errado. Além disso, foque nas métricas que realmente importam e priorize o que faz diferen?a. Com essa abordagem, dá para entregar resultados de forma ágil e confiável, sem comprometer a precis?o nem perder tempo!