Voici comment utiliser efficacement l’analyse des données dans les processus de résolution de problèmes.

Data Analysis has to be a structured process. If there is no structure, you might as well be beating about the bush. It starts with a why. Why are we doing this? Define your objective(s) clearly. It could be a macro-level objective (increase revenue), or something more ground-level (improve CTR of a certain feature). Once you have the goals clearly articulated, the rest of the steps should systemically follow. One tip here could be : you often start with a problem statement. Articulating it clearly and exhaustively is a good point to start. Ideally you should also be ready to formalize a few hypotheses at this stage.

Before embarking on a data adventure, you need a clear map. Imagine you're solving a complex puzzle; you must first understand the complete picture you're trying to create. What are you trying to achieve? Are you looking for a missing piece, or do you want to build a masterpiece? Your goals are your compass, guiding you towards the right data and the best solutions. Just like a treasure hunter needs a map, you need clear objectives to unearth valuable insights from your data.

Comece por definir seus objetivos gerais para a análise de dados. O que você espera alcançar com essa iniciativa? Qual impacto você deseja gerar na organização ou projeto? Certifique-se de que seus objetivos de análise de dados estejam alinhados com os objetivos estratégicos da organização. A análise deve servir como um instrumento para alcançar metas maiores e contribuir para o sucesso geral da empresa.

Data analysis is pivotal in problem-solving across domains. Central to this process is the initial step: defining the problem. A clear problem statement sets the foundation for collecting, analyzing, interpreting data, implementing changes, and monitoring progress. It ensures that insights gleaned are relevant and actionable. Moreover, diverse perspectives within teams enrich problem definition, leading to more comprehensive solutions. Thus, the define stage is critical for successful continuous improvement initiatives.

In research, this is basically to have a clear purpose/aim. Then objectives on how the aims will be achieved. It is important to set SMART Goals

A key lesson I’ve learned is the importance of integrating operational data and customer experience for effective analysis. It’s not just about focusing on internal metrics like response times and issue resolution; it’s also crucial to gather and analyze direct customer feedback. This approach allows for a more comprehensive understanding of customer needs and expectations, which is essential for improving satisfaction and retention.

Extração de Dados: Utilize ferramentas e técnicas adequadas para extrair dados de diferentes fontes, como bancos de dados, APIs ou arquivos de texto. Pesquisas: Realize pesquisas online ou presenciais para coletar dados diretamente de clientes, funcionários ou outros stakeholders relevantes. Monitoramento: Monitore websites, redes sociais ou outras plataformas online para coletar dados em tempo real. Entrevistas: Conduza entrevistas com especialistas ou indivíduos com conhecimento específico sobre o tema da sua análise.

The importance of data collection cannot be overemphasised. Getting a suitable sampling technique will help to get the right amount of dataset that will be sufficient for the data analysis

If you do not gather data, you can’t get the result that you seek, as a matter of fact, there will be no materials to analyze.

Utilize técnicas de análise descritiva para resumir e caracterizar os dados coletados, como medidas de tendência central (média, mediana, moda), medidas de dispersão (variância, desvio padrão) ou distribuições de frequência. Realize a EDA para visualizar os dados de forma gráfica e interativa, identificando padrões, outliers e relações entre as variáveis. Utilize ferramentas como histogramas, gráficos de dispersão, box plots e dendrogramas. Empregue técnicas de análise estatística inferencial para testar hipóteses, estimar parâmetros populacionais e fazer inferências sobre a população a partir da amostra coletada. Utilize testes t, ANOVA, qui-quadrado ou regressão linear, por exemplo.

The most important thing is Analysis of data. To make the right decision, your data must be analysed effectively with zero or no errors. Recheck the data and proceed ahead.

Triangulação de Resultados: Comparar e contrastar os resultados da análise com diferentes fontes de dados, pesquisas e estudos relevantes para aumentar a confiabilidade dos insights. Validação com Especialistas: Consultar especialistas na área de estudo para obter diferentes perspectivas e validação dos insights extraídos da análise. Testar Hipóteses: Testar hipóteses e explicações alternativas para os resultados da análise, buscando refutar ou confirmar suas conclusões. Visualização de Dados: Utilizar ferramentas de visualização de dados para criar gráficos, tabelas e dashboards que facilitem a compreensão dos resultados e a identificação de padrões.

Quantitative data collected from questionnaires, surveys, experimental stidies etc usually uses statistical analysis while qualitative data colllected from intervkews, obseevations etc utilises content analysis

Interpretation is about deciphering or conveying something in order to gain a deeper insight or realisation. Here we can explain the intention of the data analysis in a well-founded way.

When we implement solutions, we bring the energy of change and progress into our reality. It is an act of manifestation and transformation in which we bring our intentions into the world and put them into action.

Data is like a puzzle that can help you solve life's challenges. By gathering clues, piecing them together, and understanding the big picture, you can make smart choices. Whether you're trying to improve your work, find a better product, or figure out what people want, data can guide you. Just remember to test your ideas and see if they really work! Data is a problem-solving tool. By analyzing information, you can make smart decisions to improve things.

Don't just set your plan in motion and forget about it. The real test starts now! Keep a close eye on how things are unfolding. Is your plan working its magic? If not, don't worry, it's okay to change course. Remember, learning and adjusting are part of the journey. Let's see where this adventure takes you!

Monitoring results (quantitatively and qualitatively is) is essential to maintaining the long term growth of the solution. Maybe some things work and others don’t, which is ok. Use data again to isolate which strategies are working well and which ones aren’t working well, and then potentially change the process accordingly. This is something one can do continuously.

One thing I love about data analytics is the data cleaning process by Preprocessing the data to handle missing values, outliers, and inconsistencies. Making it crucial for ensuring the accuracy of the analyses

A big thing is to make sure one can analyze the data to the actual solution or context. There’s a difference between pointing out a pattern or a result in the data and explaining how it’s actually important to the context of the problem or goal. Always contextualize the results.

Think of data as your wise friend. It can offer great advice, but it's not the only smart person in the room. Trust your gut sometimes, and don't be afraid to ask for help when things get tricky. Remember, even small steps can lead to big discoveries. Most importantly, keep learning and growing!