Como você pode personalizar estruturas de pensamento crítico para sua organização?

First Identify the specific goals, challenges, and characteristics. then select a well-established critical thinking framework as your starting point. Examples include SWOT analysis, Six Thinking Hats, or the RED model (Recognize assumptions, Evaluate arguments, Draw conclusions). Establish a feedback loop to continuously improve and refine the customized framework. Monitor outcomes and adjust the framework as needed.

Na minha opinião, entender os objetivos é o primeiro passo crucial. É como definir um destino antes de iniciar uma viagem. Na prática, isso significa alinhar as análises de BI com as metas da empresa.

Start by aligning critical thinking frameworks with the specific goals and objectives of your organization. Clearly define what critical thinking means in your context and how it contributes to achieving overarching business goals. Tailor the frameworks to address the unique challenges and requirements of your organization.

Customizing critical thinking frameworks for your organization involves adapting established frameworks to align with your organization's unique goals, culture, and challenges. Here are steps to customize critical thinking frameworks effectively: Understand Organizational Context: Assess Current Practices: Identify Key Stakeholders: Select Appropriate Frameworks: Tailor Frameworks to Specific Needs: Integrate with Organizational Processes: Provide Training and Development: Promote a Culture of Inquiry and Learning: Establish Clear Expectations and Accountability: Iterate and Adapt Over Time:

To start, take a deep dive into your organization's mission, vision, and strategic objectives. This understanding will guide you in aligning the critical thinking framework with the broader direction of your organization. Next, assess the current landscape by evaluating existing critical thinking practices within your organization. This analysis will help you identify areas of strength and opportunities for improvement. Once you have a clear picture, define the key competencies that matter most to your organization. This could include problem-solving, decision-making, and creative thinking skills. And build your customized framework that shows the linkages and causalities.

In my experience it’s important to always define a strategy and not goals. A strategy towards competitive sustainable advantage that is unique to your firm.

Understanding the goals/problems that needs to be solved for is actually the most crucial aspect. As former McKinsey CEO Rajat Gupta says, a big chunk of a consultant’s time should ideally go in understanding the real problem.

In my experience, it's important to know what kind of information the organization deals with and to check the business needs aligned to the information provided by data analytics team, it's crucial to have data teams aligned perfectly with business needs

Esse tema ressalta a importância de estabelecer claramente os objetivos e metas antes de iniciar qualquer projeto, especialmente no contexto do Business Intelligence (BI). Ao definir as metas, perguntas-chave e benefícios esperados, é possível direcionar efetivamente o pensamento crítico e garantir que os esforços estejam alinhados com a visão estratégica da organização. Essa abordagem ajuda a orientar a tomada de decisões e a medir o sucesso do projeto de BI, garantindo um impacto positivo e significativo nos resultados comerciais.

Customizing critical thinking frameworks for your organization involves aligning them with your specific goals, culture, and challenges. Tailor frameworks by incorporating industry-specific examples, adapting language and terminology, and integrating company values.

Aqui, na minha visão, avaliar os dados é como peneirar ouro. É essencial entender a qualidade, relevância e integridade dos dados disponíveis.

In my experience this involves a cultural shift by acknowledging that data is the only version of truth. First things first, educate and bring data literacy. Then we start with identifying the needs where we need to include data based decision making. Then the source to provide that data. Needless to mention, a lot of work will be needed to make the data consumable. Collaborate, implement and improve together.

Evaluate the types of data your organization deals with and the specific challenges associated with it. Understand the nuances of the data sources, formats, and quality. This assessment provides insights into the critical thinking skills required to analyze, interpret, and make informed decisions based on the organization's data landscape.

Data is seldom complete, BI teams must evaluate the eco system of platforms that generate information. Consider manual (written) data points that may require data entry, whether by manual entry or using OCR automation to complete your dataset. The correct data transformation rules need to be applied to prepare the data for ingestion and analysis.

En las organizaciones de más de 20 años de existencia es muy difícil la adopción de un departamento de BI, claramente depende de si tu producto o estrategia va de la mano con análisis de datos. El obtener datos de costará un poco de dinero, pero analizar y depurar los datos de calidad para la toma de decisiones de costará una inversión cuantiosa que bien documentada te trae un mayor margen de utilidad o ROI. Si el producto o servicios hacen sentido tener datos para generación de estrategia, lo mejor es armar un equipo de BI.

Assessing your data is essential when customizing critical thinking frameworks for your organization. Evaluate the quality, relevance, and accessibility of available data sources to ensure they support informed decision-making. Identify any gaps or inconsistencies in the data that may hinder critical analysis. By understanding the strengths and limitations of your data, you can develop frameworks that leverage reliable information and address any deficiencies to enhance the effectiveness of your organization's critical thinking processes.

Esse tema destaca a importância de avaliar a qualidade, quantidade e relevância dos dados antes de iniciar qualquer análise ou projeto. É essencial considerar a confiabilidade, precisão e completude dos dados, além de avaliar como estão relacionados aos objetivos e perguntas do projeto. Organizar, limpar e preparar os dados adequadamente é fundamental para garantir análises precisas e insights significativos. Ao avaliar os dados, é possível identificar pontos fortes e limitações das fontes e métodos utilizados, permitindo melhorias contínuas e tomada de decisões informadas.

In my experience we need Data Team for each business line to clean and transform raw data because all the data may not be needed for our analysis. Another question to understand is that it’s not east to have a daata driven strategy as you not be capturing the correct data. The corporate level leaders should define the measurement across the organization so these are standardized across the firm. Some data points are not to be reported in real time so please do not build a technical debt by collecting data that is not needed and costly to hold.

Identify and integrate critical thinking tools that align with your organization's needs. This could involve selecting analytical software, visualization tools, or decision-making frameworks that enhance critical thinking capabilities. Ensure that these tools are compatible with the nature of your data and support the analytical processes relevant to your goals.

Choosing the right tools is paramount when customizing critical thinking frameworks for your organization. Select tools that align with your organization's goals, data infrastructure, and team's expertise. Consider whether you need data visualization software, statistical analysis tools, or decision support systems. Evaluate the usability, scalability, and integration capabilities of each tool to ensure they meet your organization's specific requirements. By carefully selecting tools, you can empower your team to apply critical thinking effectively and derive actionable insights from data.

Enfatiza a importância de selecionar as ferramentas e técnicas adequadas para análise de dados. É crucial considerar o tipo de dados disponíveis e o tipo de análise necessária para escolher as melhores ferramentas e software. Além disso, é essencial pensar em como visualizar, comunicar e apresentar os dados e insights de maneira eficaz. Ao escolher as ferramentas certas, é possível otimizar o processamento, interpretação e geração de relatórios de dados, facilitando a tomada de decisões informadas e a comunicação efetiva dos resultados.

In my experience select a few tools and standardize them across the firm. Another approach is look at data horizontally. Financial analysis will show lost customers data, then analyze the interaction metrics across channel’s to find a correlation. This is hard work and you will build reporting across ERP system and customer interaction data collection points. Financial analysis will show lost customers data, then analyze the workforce management metrics to find a correlation. This is also hard work and you will build reporting across ERP system and systems that support average handle time for each interaction. You may find loopholes to improve a process.

Once you understand your data, your strategic objectives and most importantly, your target audience (users) you must select the right tools to analyse and present your insights. There is no single tool that works across all organizations. Consider the executives and ensure that the data will be composed in formats that will be widely understood.

Para analizar datos puedes buscar lo que tu presupuesto este preparado para pagar, la recomendación siempre será asesorarse con expertos para conocer cuales herramientas y procesos son los mas adecuado para tu modelo de negocios. Para documentar / adquisición de los datos depende primero del tamaño de la información, puedes pasar desde excel (antigua herramienta de datos y que todavía se usa para tener y analizar datos), Hadoop (herramienta para analizar grandes cantidades de datos) etc.

A aplicação de lógica, na minha perspectiva, é o coração do pensamento crítico em BI. Isso envolve interpretar dados, fazer conexões e tirar conclusões. Por exemplo, se os dados mostram um pico de vendas em certos períodos, busque entender o porquê. Pode ser uma promoção eficaz ou uma tendência sazonal.

Destaca a importância de aplicar lógica e raciocínio aos dados e insights obtidos. É fundamental testar suposições, hipóteses e conclusões, bem como evitar vieses, falácias e erros de raciocínio. A validação, verificação e apoio das alegações com evidências são essenciais para aumentar a credibilidade, precisão e clareza das análises. Além disso, desafiar, criticar e melhorar os argumentos contribui para aprimorar a qualidade das conclusões e tomadas de decisão. Ao aplicar uma abordagem lógica, é possível garantir uma análise robusta e confiável dos dados, promovendo uma maior confiança nos resultados.

A good analytics team lead sponsored by business will succeed. Don’t expect IT developers to do this alone. We need functional experts to make sure that the logic and analysis is correct. This evidence should point to some actionable items or parked as observations.

There are numerous established critical thinking frameworks that could be ported from other spaces. Some of them are classics like, for example Socratic questioning methods. Some others are more contemporary, for example, the process used by design thinkers to explore the problem and refine and critique solutions. Do not get stuck Into only one way you’re thinking about critical thinking.

Tener los datos compilados y presentables para analizar es solo el principio del análisis de los marcos de pensamiento crítico. Si conoces el mercado y puedes probar tus hipótesis, la mejor manera es documentar todo el proceso para analizar el éxito o el fracaso del mismo. "Una Sucesión de errores crean experiencia". Se debe crear la experiencia basada en datos.

Contexto é tudo. Em minha experiência, sem entender o contexto, os dados podem ser mal interpretados. Isso significa compreender o ambiente de mercado, a cultura organizacional e as tendências econômicas. Por exemplo, um aumento nas vendas pode ser impressionante, mas se o mercado cresceu ainda mais rápido, na verdade pode indicar uma perda de participação no mercado. Pegou a dica?

Para tomar las decisiones basadas en datos y experiencia, debes igualmente tener contexto de tu entorno. Recordemos que el entorno o el mercado es el campo de juego en el que estamos implementando nuestras estrategias; un estratega experimentado utiliza todas sus herramientas para crear la mejor manera de ganar. El contexto tanto del mercado como de los datos que tienes es la manera de cerrar todas las aristas de tu propuesta.

Logic where magic ends... Absolutely do you know why logic matters most Here is a detailed explanation I got an idea which was super fantastic I shared it with my friends they said it was amazing I started believing it so much without any hesitations But i forget to look logic behind it Let's say for example The idea was super But when it comes to execution we are facing a lot of troubles due to the lack of a simple logic that idea couldn't convert to app cause lot of policies and regulations So, make sure your logic make sense before you moving not only in the work you do But, also decisions you take!

Por fim, sempre há mais a considerar. Na minha visão, isso inclui manter uma mentalidade aberta para novos dados e tendências, estar atento à ética na manipulação de dados e continuar desenvolvendo habilidades analíticas. E não esqueça: a comunicação eficaz dos insights é tão importante quanto descobri-los.

It involves tailoring it to align with the company's goals, values, and unique challenges. Then, identify key skills and competencies that employees need to develop to support these goals. Integrate relevant critical thinking methodologies, such as problem-solving techniques, decision-making processes, and analytical approaches, into training programs and organizational practices. Encourage a culture that values questioning, evidence-based reasoning, and creative problem-solving. Regularly evaluate the effectiveness of the framework and make adjustments as needed to ensure it remains relevant and impactful for your organization's success.

You may use an agile approach to customise the framework, i.e. some elements of the framework are static and some are dynamic. This will reduce the efforts of customising everything For example the elements where you need uniformity throughout the organisation need not be customised. Things like ethics, value, branding, etc. Others like recruitment, departmental ways of working, conflict resolution,etc. can all be dynamic

Remember in today’s times, a most data is in the cloud and not on premise. Your vendors may not be providing it to you in the frequency and ways in which your firm needs for accurate decision making. Read the fine print before choosing vendors. You have to build visuals from multiple systems so align the data analytics projects to strategy and all measures that are needed must be defined. Be agile and not reactive. Always remember that the goal is sustainable competitive advantage in the industry. If the company strategy changes the analytical insights should also change so make sure the leaders are communicating the measures and expectations to the lowest leverage. Without a clear direction, the insights will be incorrect.

An example I’ve seen studies the lowest common denominators in workflows related to the highest value to the company. To make money. This is simply the inputs (what comes from the customer) and the output (final product). Studying the data in between, from beginning to end, can highlight bottlenecks to resolve needing vital critical thinking improving profits. Reminding of the company’s critical thinkers of the highest priorities helps focus the team toward success.

I also strongly recommend to start exploring the ability of large language models, especially when applying some sort of structured thinking process, to support human critical thinking for example, by critiquing answers and exposing potential logical fallacies

Data projects should be approached using an agile methodology. Select a high impact problem that can be solved and leverage analytics to aid in decision making. Once the organization understands the importance/value of that solution it becomes easier to build out other solutions for other high impact problems.