What do you do if logical reasoning fails to solve complex data problems as a data engineer?

I thoroughly review the data and problem requirements to identify any hidden patterns or insights that may not be apparent at first glance. I seek to learn new techniques and analytical approaches, such as machine learning or complex network analysis, that could be applied to the specific problem. I collaborate with team colleagues and experts in related fields to gain different perspectives and insights on the problem. If necessary, I seek guidance from experienced mentors or consultants to guide me in resolving the problem more effectively, I remain open to experimentation and iteration, testing different hypotheses and solutions until finding the most effective approach to solving the complex data problem

Failure is a stepping stone to innovation. Embrace setbacks as opportunities to learn, grow, and refine problem-solving approaches. Each stumble brings invaluable insights that pave the way for future breakthroughs.

Uma frase muito útil para meu dia-a-dia aprendida, por mim, foi: errar cedo, errar rápido para buscar a melhoria contínua. Sempre o fracasso é a melhor oportunidade para nosso crescimento porque precisaremos dar um passo atrás e buscar resolver dado problema buscando novos conhecimentos, conversas com stakeholders, fazer brainstormings, buscar em comunidades e alternativas que ainda não tinha considerado.

Don't navigate the labyrinth alone. Collaborate with peers, subject matter experts, and diverse perspectives. Collective brainstorming fosters creativity, offering fresh angles and innovative solutions to intricate data puzzles.

No mundo da tecnologia, não há maneira de avançar trabalhar ou avançar sozinho. Participe das comunidades quer seja no seu trabalho ou na internet para encontrar mais rápido soluções para seus problemas de negócio, de forma colaborativa.

Dive deep into the problem domain. Immerse yourself in comprehensive research, scouring literature, case studies, and data repositories for illuminating insights. Thorough exploration unveils hidden patterns and alternative methodologies.

Embrace an iterative approach to problem-solving. Systematically test hypotheses, refine models, and validate assumptions through rigorous iterations. Each cycle brings refinement, edging closer to the elusive solution.

Dividir para conquistar também impera na Engenharia de dados. Assim a cada ciclo poderá refinará os processos e modelos aproximando-se do raciocínio lógico mais completo.

Use e abuse de ferramentas e não se apegue a nenhuma ferramenta, porque as usamos para resolver problemas. Utilize e conhece as ferramentas já construídas para não precisar inventar a roda nem ficar à deriva, tentando criar soluções do zero que já existem. O propósito da engenharia de dados e a tecnologia é resolver problemas e não, validar nossa inteligência para dada hard skills.

Logical reasoning is best applied to data-related problems because the numbers never lie. My take is that logical reasoning does not fail to solve complex data problems. If that happens, it is probably due to one or more of the below reasons: - A lack of adequate knowledge or experience: the solution is to upskill. - A logical reasoning fallacy of the individual/s attempting to solve the problem: the solution is to get another perspective. - There is a difficult problem that hasn't already been solved by anyone in the Data domain yet: find a solution. It's either a business opportunity or a white paper. As for me, realizing that failure is part of the game and taking a walk clears my head. It often helps with the answer as well.