Comment les responsables des opérations commerciales peuvent-ils utiliser les données pour prendre des décisions éclairées ?

When defining objectives, it's also beneficial to consider the following: 1. Ensure that objectives align with the organization's mission, vision, and values. This promotes coherence in overall goals. 2. Use clear and straightforward language to avoid ambiguity. 3. Assign responsibility for each objective to a specific individual or team. This enhances accountability and ensures clear lines of responsibility. 4. Rank objectives based on their importance and impact. This helps in focusing efforts on priority goals. 5. While objectives should be specific, allow for some flexibility to adapt to changing circumstances. By incorporating these elements, you create a robust framework for setting objectives that are realistic and strategic.

I agree, but you may want to first consider centralizing your overall governance. While it’s best to have each department and division set their own goals and targets, allowing each group to define data sources, definitions and other governance parameters creates the environment for perpetual “data wars”. We’ve all been in those meetings where the entire discussion is around who has the “right numbers”. The reality is, everyone may have the right numbers, and al of the numbers can still be different, even if all of the data comes from the same source. Setting a corporate standard for data governance will help prevent these debates and allow meetings to be focused on business decisions, not data wars.

Before delving into data analysis, set SMART goals aligning with business objectives: efficiency, quality, customer satisfaction, and profitability. Measure performance in hourly productivity, defect rates, NPS for satisfaction, and revenue/ROI for profitability. Identify data sources, collection methods (automated/manual), and storage standards (privacy/security). Validate data accuracy pre-analysis, ensuring integrity and compliance. This systematic approach enables actionable insights, informed decisions, and progress toward goals.

Business operation leaders can leverage data by analyzing trends, performance metrics, and customer feedback to make informed decisions. Utilizing data-driven insights helps them understand the current state of operations and identify areas for improvement, ultimately leading to more strategic and effective decision-making.

A definição das fontes de dados após a determinação de objetivos e métricas também é crucial, pois orienta a coleta de dados de maneira estratégica e intencional. Isso não só economiza tempo e recursos, evitando a análise de dados desnecessários, mas também garante a qualidade e relevância dos dados coletados. Essa abordagem assegura que cada passo no processo de análise de dados seja direcionado para gerar insights valiosos e decisões bem fundamentadas, maximizando o impacto e a eficiência das análises realizadas.

While the majority of the analysis we do tends to focus on finding and solving problems, we need to be aware of two other opportunities that exist in our data sets. First there are the successes. Many times we focus on identifying and solving issues and we forget that there are things we are doing right. Those things can be shared with other teams and departments and help us further improve and grow. The second is we need to be ready to be wrong. The best analysis I’ve done has been work that has proven that my assumptions were incorrect. These results bring to light some of the best opportunities for change and improvement in our businesses processes.

In line with my comment on defining targets and tools, I would stress to distinguish between data and indicators needed to monitor day to day business development, on the one hand, and more complex research and data analysis by specialized entities or departments. While a simple monitoring system will do for many SMEs, more complex systems are required for large companies and market leaders. Most companies can do with less sophisticated systems build on data generated by the business as it evolves.

Having proper data is crucial. I always teach my team to do appropriate data wrangling. Is my data in the correct format to answer my question? It's a massive exercise. We may consider bias that might exist in the data. After that, I suggest visualising what we have and, based on that, understanding what data we have, what else we need to collect and what data is missing. Another good approach before starting the analysis is creating hypotheses. How can I turn my question into a testable problem?

Uma vez que os dados estão preparados, é crucial aplicar métodos e ferramentas adequados para uma análise eficaz, visando extrair insights valiosos. Dependendo das características dos dados, como tipo, volume e complexidade, técnicas variadas como análise descritiva, diagnóstica, preditiva ou prescritiva podem ser empregadas. Ferramentas como planilhas, painéis, relatórios e softwares de visualização são essenciais para organizar, resumir e apresentar os dados de forma clara. O foco dessa etapa é identificar padrões, tendências, correlações e anomalias, fundamentais para compreender a situação atual, diagnosticar problemas, prever futuros cenários e sugerir ações estratégicas.

Introduce técnicas de 'minería de texto' para analizar los comentarios de los clientes y otras formas de datos no estructurados. A menudo, estos datos pueden revelar tendencias y patrones que no son inmediatamente evidentes en los datos cuantitativos, ofreciendo una comprensión más profunda de la experiencia y expectativas del cliente.

They are some options, a decision to make, and some risks that come with it. Needless to say, risk assessment and management are critical elements in decision-making for business leaders. Business leader can address those issues with some steps such as identify risks (internal and external factors), quantify and qualify risks (impact and likehood), risk mapping, setup priority, mitigation strategies along with their cost-benefit analysis, alternative scenarios, and regular reviews/feedbacks. Business leaders can make decisions that are not only well-informed but also resilient to potential challenges, through systematically assessing and addressing risks, contributing to the overall success and sustainability of the organization.

Ao avaliar opções e compensações em operações empresariais, líderes devem ponderar criteriosamente cada alternativa, considerando aspectos como custo, benefício, risco, impacto e alinhamento com objetivos e partes interessadas. É fundamental reconhecer as suposições, limitações e incertezas inerentes aos dados e análises, e validar as opções através de experimentos, simulações ou feedback. Esta etapa crítica permite uma tomada de decisão mais informada e estratégica, equilibrando eficácia operacional com as necessidades e expectativas das partes envolvidas.

Utiliza 'análisis de escenarios' para evaluar las diferentes estrategias. Esto implica crear modelos que muestren cómo diferentes decisiones podrían afectar a las operaciones, considerando factores como cambios en el mercado, nuevas regulaciones, o innovaciones tecnológicas. Este enfoque ayuda a prever las consecuencias de distintas decisiones antes de implementarlas.

That's why the hypotheses are essential. We can evaluate potential flows, options, and decisions based on data we have already visualised. In most cases, you realise you are missing data to finalise your decision, and you should start over the data wrangling. You can add patterns and additional variables that may affect the final result.

When evaluating options and trade-offs, consider factors such as costs, benefits, risks, and long-term impact. Identify available options and their pros and cons. Assess potential benefits against associated costs and risks. Analyze impacts on operations, customer experience, and future scalability. Consider alignment with long-term goals and seek input from stakeholders. Making well-informed decisions involves weighing these factors to maximize long-term value and align with strategic objectives.

This is likely our weakest area when it comes to leveraging our data and information. We need to remember that data and information have no intrinsic value but only provide returns on our technology investments when we make decisions and take action. If you’ve invested in a data warehouse, new visualization tools, and teams of data scientists, and you don’t see an ROI, check first to see how the tools are being used, what decisions are being made, and what action is being taken. If everyone is talking about the numbers but no one is acting on the numbers, you are spending technology dollars, not investing.

A comunicação e implementação eficazes de decisões são cruciais após a escolha da melhor opção para as operações de uma empresa. É essencial utilizar uma linguagem clara e concisa, apoiada por visualizações de dados e técnicas de storytelling, para transmitir a lógica, as evidências e as consequências das decisões tomadas a todos os envolvidos, incluindo equipe, gerentes e outras partes interessadas. Além disso, é importante definir claramente as funções, responsabilidades e prazos para a execução das decisões, mantendo um acompanhamento rigoroso dos resultados e feedbacks. Essa abordagem permite ajustes necessários e promove o aprendizado contínuo a partir dos sucessos e fracassos experimentados no processo.

Fundamental change management principles are important for the data driven decision making process and implementation of KPI’s & PPI’s. ADKAR model is my favourite tool: Raising Awareness, Desire to change, Knowledge & Ability to make it happen, and Reinforcement.

Effective communication and implementation of decisions involve clear articulation of the decision and its rationale, engaging stakeholders, developing a detailed implementation plan, maintaining open lines of communication, adapting the plan as needed, and celebrating achievements. Engaging stakeholders early fosters buy-in, while open communication and adaptability ensure clarity and collaboration throughout the implementation process. Recognizing efforts reinforces the value of effective communication and implementation.

You know how they say a picture's worth a thousand words? In business ops, a solid graph or chart is like that. It makes the complex stuff simple and shows why we're making a certain decision. And about telling the story – it's all about connecting the dots. We take a journey from point A to B with data. When we share this story, it helps everyone get the whole picture and why we're heading in a certain direction. Once everyone gets the 'why', we need to focus on the 'how'. It's all about setting clear tasks and deadlines. Who does what and by when? Getting this straight helps everyone stay on track and avoids any mix-ups.

In my experience, one of the best ways to both surface and foster open debate around your data is making sure your team members are proficient in data analysis and can effectively use the data tools in your stack. Organizations can’t have a bottleneck with a Data or BI team. It’s often business leads or analysts that can QC the data as well, and act as a feedback loop back to engineering regarding how data is captured and stored.

Agreed. I have often found engaging the team helps to identify or expose conditions or situations which you were unaware and not reflected in your data.

Para fomentar uma cultura orientada por dados nas operações empresariais, é essencial cultivar um ambiente onde a alfabetização de dados, a curiosidade e a colaboração sejam valores centrais. Incentivar a equipe a questionar, explorar dados, compartilhar insights e testar novas ideias é crucial para manter um processo de tomada de decisão baseado em dados. Além disso, prover treinamento adequado, ferramentas e suporte necessário para que todos possam acessar, analisar e utilizar os dados de maneira eficiente é fundamental. Celebrar as conquistas impulsionadas por dados e aprender com os erros cometidos nesse contexto também são práticas importantes para reforçar e sustentar essa cultura orientada por dados na organização.

Promueve 'hackathons de datos' y talleres internos donde los empleados puedan trabajar en proyectos que utilicen datos para resolver problemas de la empresa. Esto no solo desarrolla habilidades en el manejo de datos, sino que también fomenta una cultura de innovación y experimentación basada en datos.

The most important issue in building a data-driven culture is reducing the fear among employees that data might show flaws in their performance. One has to understand that business is like in the operation room: even the case that a patient dies in a simple intervention, leaves big lessons for improvements.

Business Operations Leaders leverage data by analyzing key performance indicators (KPIs) to identify trends, assess efficiency, and make informed decisions. They use data-driven insights to optimize processes, allocate resources effectively, and enhance overall operational performance. Instead, predictive performance indicators are metrics that forecast future outcomes based on historical data and trends. Examples include inventory turnover, obsolete inventory.. By analyzing these indicators, and developing provisions, businesses can anticipate potential issues, make proactive adjustments, and optimize strategies for better overall performance.

They harness data for informed decisions through strategic analysis. They begin by identifying key business metrics aligned with objectives. Efficient data collection, using advanced tools, helps gather relevant information. Analyzing trends and patterns unveils insights crucial for decision-making. Integrating data-driven insights into regular processes enhances operational efficiency. Continuous monitoring and updating of data ensure relevance. Visualization tools simplify complex information, aiding in communication. Establishing a data-driven culture fosters proactive decision-making across the organization. Thus, leveraging data empowers leaders to make informed choices, optimize operations, and stay agile in a competitive landscape.

Utilizing data to make uniformed decisions are a great way to manage fairness and equality in an organization. However, it is also important as a leader, to look at factors that contributed to the data. Ensure the data has integrity and has not been tampered or manipulated with to drive the end result while failing to deliver on the purpose.

Method of communication is key. Some concepts to consider are: 1) The best forum to share the information (and should it be pushed or pulled). The “push” method may include distributing reports over email or presenting to a committee. The “pull” method may involve directing users to a self-service reporting tool where they must input their own criteria. 2) The appropriate audience for the data and what action is expected. This may include engaging experts to support with data analysis or ensuring that the right decision-makers are at the table to action those insights. 3) How frequently the data should be shared. It can be hard to spot trends if data is shared too often, but not often enough can result in missed opportunities.

Incorpora un enfoque de 'gestión del cambio' en la implementación de decisiones basadas en datos. Reconoce que la adopción de nuevas estrategias basadas en datos puede requerir cambios en la cultura organizacional o en los procesos de negocio, y planifica cómo gestionar estos cambios para asegurar una transición exitosa.