How do you define data analytics problems for different industries?

Defining data analytics problems for different industries involves understanding the industry, identifying business objectives, determining data collection and sources, data preprocessing, defining features, formulating the problem, selecting appropriate models, selecting evaluation metrics, ensuring data privacy and compliance, implementing and monitoring the solution, establishing a feedback loop, communicating results effectively, considering scalability, staying up-to-date with industry trends, acquiring industry-specific expertise, and measuring the impact of the solutions on the industry's bottom line or objectives.

Start wit the strategic objectives of the business. What does the business want to achieve in the long term. What is the vision. We need to make sure that technological projects support these strategic objectives and don't stand on their own. The same applies with data analysis. Once the vision is clear we can define how data analysis fits in. What are the problems we want to solve? What are the key insights we need to support decision making?

Understanding the Industry and the object of monetization is a must that come without saying, and there the question if what metrics should be used to measure success based on stages, revenue? User acquisition? Download? This is something that data leaders must advise to management about the proper measurement of success especially to avoid vanity metrics.

I agree that knowing the objectives of the business is very crucial when trying to define the problem. Defining the problem is usually the first step in the data analysis life cycle. You need to clearly define the problem as this will act as a guide when you move on to the other phases of data analysis.

No problem can be solved without understanding it and to understand the problem there is need for interview session with various stakeholders in the industry. Get to know when it has started and what are the various strategies they have tried so you won't be implementing a failed strategy. After this, get to know the stakeholders, their skills and level of experience. It won't sound good to deploy a strategy in that industry no one can implement. Know their strength, if there is need for T&D across the system, that will commenced immediately and before deployment they would have been skilled. After deployment too, there is need for continuous improvement and monitoring as the world involves so there will be need for updates.

Defining the problem is the most intricate part of the whole data analytics procedure. Infact, this is a nightmare for very many data scientists. The secret to navigating this challenge is to have a thorough understanding of all the data analysis concepts.

In a strategy room filled with decision-makers, you're the bridge between complexity and clarity. Your task is to condense their chip shortage challenge into a precise problem statement, the cornerstone of your analytical voyage. Extract their concerns about delayed production and the scramble for chips. Your crafted problem statement echoes their urgency, "Minimize production disruptions caused by chip shortage by optimizing chip allocation and inventory." This statement becomes your guiding star, leading your analysis toward a tangible solution that fits seamlessly within their business context.

Para enquadrar efetivamente um problema, come?amos com o entendimento do negócio. Isso envolve ouvir atentamente as partes interessadas e traduzir suas necessidades em objetivos analíticos. Seja para otimizar processos, aumentar vendas ou melhorar a experiência do cliente, cada setor exige uma abordagem única. A partir daí, definimos métricas claras e coletamos dados relevantes. A instru??o do problema deve ser específica, mensurável, alcan?ável, relevante e temporal (SMART). Com esses critérios, podemos n?o apenas resolver o problema atual, mas também estabelecer um precedente para futuras análises. Lembre-se, uma instru??o de problema bem definida é mais do que uma pergunta; é um mapa para descobertas significativas.

Amidst the vast enterprise expanse, data takes center stage. The initial challenge: handpick the right threads – order histories, chip supply, production schedules, transit time. In black swan events, seek recent and relevant data. Gather data from diverse sources – chip suppliers, production records, inventory logs. Uncover patterns, revealing chip shortage impact on production and profits. Data exploration unveils insights that guide strategic solutions, ensuring the enterprise maneuvers through challenges adeptly.

A explora??o de dados revela insights valiosos, identificando padr?es e tendências ocultas. Utilizamos análise exploratória, visualiza??es e estatísticas para entender melhor os dados. Correlacionar conjuntos de dados desvenda rela??es e influências, como o impacto das campanhas nas vendas. A explora??o n?o é só sobre respostas, mas sim sobre perguntar certo.

Now we have to choose the analytical approach that optimizes chip allocation and streamlines production amidst the shortage. Weigh options – predictive models for chip demand, optimization algorithms for production scheduling, and simulations to test allocation scenarios. Select methods that resonate with their unique challenges. Armed with the right analytical arsenal, craft solutions that takes cares of priorities such as profitability, market share, contractual obligations and goodwill despite chip shortages.

Antes de mergulhar nos dados, é fundamental definir o objetivo. Queremos prever tendências futuras, identificar segmentos de clientes ou otimizar opera??es? Cada objetivo requer uma abordagem diferente. Selecionar a abordagem analítica adequada é crucial para alcan?ar insights precisos. Definir o objetivo e entender os dados disponíveis orienta a escolha de métodos. A experimenta??o e adapta??o contínua das técnicas s?o essenciais para responder perguntas e impulsionar inova??es.

I think communicating our results after analyzing data is one of the hardest thing to do. Hence, it should be given a lot of time. To communicate your results you need to breakdown your language to the level where the stakeholders can understand. You have to do away with jargons and be be able to engage the stakeholders and/or users.

When communicating findings with non-technical team members, it is very critical for data analysts to be clear and concise. Doing this will help stakeholders to make timely critical decisions. It is also very important to consider the best suitable visualization. Although, how well you do this, depends on your level of understanding of various visualization tools.

Convey findings, insights, and strategic solutions that ignite understanding and inspire action. Craft compelling reports, dashboards, and visualizations that transform data into a narrative. Utilize correct metrics to monitor and parameters to control. Illustrate how optimizing chip allocation and production scheduling could mitigate the effects of the shortage. Your communication becomes the spark that ignites proactive strategies. Lead the conversation for the path in future, identifying triggers for secondary risk, roadmap for data strategy.

Comunicar resultados vai além dos gráficos; é criar uma narrativa. Histórias com exemplos reais, visualiza??es que simplificam e insights acionáveis s?o essenciais. Promova um diálogo aberto para inspirar a??o e mudan?a.