Vous dirigez une équipe dans le cadre d’un projet d’envergure. Comment pouvez-vous utiliser les données pour prendre les meilleures décisions ?

From an analytical perspective, leveraging data to clarify problems and set goals establishes a solid foundation for evidence-based decision-making. Data provides insights into the current state of affairs, enabling organizations to identify patterns, trends, and potential gaps that may impede progress. By systematically collecting and organizing data, businesses can not only determine the root causes of issues but also quantify the impact of their initiatives. This approach ensures that goals are realistic and measurable, as they are informed by empirical evidence rather than subjective assumptions.

La ventaja de manejar proyectos mediante analisis de datos, al menos para mi radica en lo siguiente: Lo que no se puede medir no se puede controlar. Lo que no se puede controlar no se puede gestionar. Lo que no se puede gestionar no se puede mejorar. Facts#

Data collection and problem definition are two different topics, the second following the first one. You cannot define the problem with certainty if you don’t have data supporting it (you might just assume it). Data collection divides itself into two categories: direct source and indirect source. A direct source is one you collect yourself by gathering numbers for example with dashboards, surveys or interviews; and indirect source is when you use others’ findings to support your research.

Define the problem by data driven to know exactly the problem and classify the problem by risk or impact. Clean the data anomaly by identifying the issue at the time

Alright, let's kick things off with a bit of clarity! The first step on our journey is to get super clear on the problem we're tackling or the awesome goals we're aiming to smash with our project. And guess what? Data is our trusty sidekick for this adventure! It's like our detective partner, helping us understand what's happening right now, figure out what's causing any hiccups, and set some super achievable goals. So, how do we get our hands on this magical data? Well, it's all about gathering the good stuff from different places like surveys, chats with folks, reports, and even databases. Once we've got our data treasure trove, it's time for a little spring cleaning!

Now it's time to crunch those numbers! Take a deep dive into the data you've gathered, using the right methods and tools. Analysis uncovers all sorts of goodies like patterns, trends, and relationships that guide your decisions. Bust out techniques like stats—descriptive, inferential, predictive—or visual aids like charts and graphs to get the scoop. But don't stop there—use your noggin to interpret the data, test out your assumptions, and see if your hypotheses hold water.

Data Mastery Journey (1) Context & Domain Knowledge: Prioritize understanding the broader context before analyzing data. Question its alignment with the problem. Uncover unexpected insights for effective problem-solving. (2) Advanced Analysis: Go beyond basics in data analysis. Employ correlation, regression, or clustering for deeper insights. Conduct A/B tests for variable isolation. Expand tools beyond Excel, explore Python, R, Machine learning and LLM for enhanced capabilities. (3) Continuous Learning: Guard against bias, actively seek contrary data, and remember data isn't static—continuously re-analyze to adapt to new information and strategies. Stay current in data practices, engage in the data community for learning and sharing.

Sure thing! Picture this: after defining your project's goals, it's time to unleash the data wizard within you and analyze the collected information. Dive into various statistical techniques—descriptive, inferential, predictive—and wield visualizations like charts and graphs. It's not just about numbers; it's about uncovering patterns and relationships. Think of it as crafting a data masterpiece. But here's the kicker—don your thinking cap, challenge assumptions, and test hypotheses. This analytical journey is where you discover the insights that become the compass guiding your team to the best decisions for your project.

In this stage you have to collect the relevant data and information related to the problem. this could include internal (sales figure, customer feedback etc)and external data. (market research, industry reports etc) If you have analyzed the data before starting work you get the overall image of work. It's like you want to play but before you start You measure much boundary distance, pitch report etc it helps you a lot during play.

Para mejorar los datos y tomar decisiones informadas en un equipo, considera: 1. **Calidad de los datos:** Asegúrate de que los datos sean precisos, completos y actualizados. 2. **Normalización:** Unifica la estructura y formato de los datos para facilitar su comparación y análisis. 3. **Eliminación de duplicados:** Evita redundancias para evitar sesgos y errores en los análisis. 4. **Validación:** Verifica la integridad de los datos mediante pruebas y validaciones periódicas. 5. **Recopilación consistente:** Establece protocolos para la recopilación de datos, garantizando coherencia y fiabilidad. 6. **Seguridad de los datos:** Implementa medidas para proteger la confidencialidad y la integridad de la información.

Strategic Data Communication (1) Adapt Decision Support: Tailor your communication style to your manager's preference. Address their concerns and offer solutions. (2) Prepare for Challenges: Anticipate objections and ensure your analysis passes the 'So what' test, making your insights clear and impactful. (3) Energize Message: Infuse passion into your data presentation, using analogies to simplify complex findings and make your insights relatable. (4) Engage Audience: Seek feedback and involve your audience in solutions, fostering an interactive and collaborative data presentation. (5) Effective Communication: Provide concise key insights and follow up to ensure understanding and address challenges, building trust and collaboration.

Now it's time to spread the word! Share the juicy results of your data analysis with your team and other stakeholders using simple, straightforward language and formats. This helps you break down your findings, back up your suggestions, and win over your audience. Get creative with your methods—reports, presentations, even storytelling—and make sure they fit the purpose and the crowd. Don't forget to throw in some context and evidence, and be ready to tackle any questions or feedback that come your way.

Once you have identified your target audience, be sure to use the right format for communication, be clear and concise using visuals, such as charts, graphs, maps, or tables, can help you illustrate your data, highlight key points, and make comparisons.

You should always make sure that the data speaks to the “so what” and also the future state of things. Random data presentations often sparks more questions and takes the presentation or socialization out of scope.

In this stage you have to show or present your result to someone. This is important because if you have done all the things nice but not present well this goes your hard work and all into waste of time. Results can be presented in any format like reports, trends, patterns, charts etc. Communication skill is always the top most in any skill you have

Let's team up! Data isn't just for decision-making—it's a powerhouse for collaboration too. Use it to divvy up tasks, lay down expectations, and keep tabs on how everyone's doing. With data in hand, you can paint a picture of where we're headed, get everyone on the same page, and build a vibe of growth and betterment. Plus, it's handy for giving and getting feedback, smoothing out any bumps, and throwing a party when we hit those milestones.

I think i should Utilise analytical tools and techniques to analyze the data effectively. This may involve statistical analysis, data visualization, trend analysis, predictive modeling, and other methods to gain insights from the data. Also, Collaboration is crucial for making informed decisions so i should involve my team. Encourage open discussions, brainstorming sessions, and sharing of perspectives and insights.

Absolutely, data serves as a linchpin not only for decision-making but also for fostering collaboration within the team. Leveraging data enables the assignment of roles and responsibilities with clarity, setting expectations and deadlines grounded in realistic insights, and tracking and measuring performance against tangible benchmarks. This data-driven approach is pivotal in creating a shared vision, aligning the team's efforts, and nurturing a culture of continual learning and improvement. Furthermore, data acts as a conduit for soliciting and providing feedback, facilitating conflict resolution with evidence-based discussions, and celebrating successes as milestones are achieved.

The success of most projects is dependent on team effort. However, a leader is required to create an environment that encourages collaboration and shared ownership for team effort to be harnessed. Clearly communicate the project to team members and request for feedback. Listen actively while they provide feedback and implement changes where necessary.

Beyond decisions, data is a collaboration powerhouse. It assigns roles, sets clear expectations, and tracks progress, creating a shared vision and aligning efforts. Use data to build a culture of learning, fueled by feedback, conflict resolution, and celebrating achievements.

Data is the fuel for continuous learning and growth. It empowers you and your team to assess your project's impact, pinpoint strengths and weaknesses, and chart a course for improvement. By reflecting on data-driven insights, you can solidify lessons learned and apply them to future endeavors. It's also a springboard for upskilling, as data unveils knowledge gaps and opens doors to exciting challenges and opportunities. In essence, data transforms you and your team into perpetual learners, ever evolving and refining your expertise.

Time to soak up some knowledge! Data isn't just about numbers—it's a goldmine for learning and growth, both for you and your team. Dive into the data to see how your project panned out, spot what worked and what didn't, and pinpoint where we can level up. Reflect on the journey, jot down those lessons learned, and stash them away for next time. And hey, use that data to sharpen your skills, sniff out new adventures, and keep on pushing boundaries.

A sureshot way of learning from data is to keep an open approach so that the findings are correlated with reality and what we know many times intuitively is shown in numbers and patterns...

Das Sammeln und Analysieren von Daten ist entscheidend für den Erfolg von Projekten und Unternehmen. Daten helfen dabei: 1-den Erfolg zu bewerten 2-Stärken und Schwächen zu identifizieren 3-Erfahrungen zu reflektieren und Erkenntnisse festzuhalten 4-zukünftige Projekte zu verbessern 5-das Team auf dem neuesten Stand zu halten, und neue Chancen und Herausforderungen zu erkennen. Durch die systematische Nutzung von Daten können fundierte Entscheidungen getroffen und der langfristige Erfolg von Projekten sichergestellt werden.

Modern AI algorithms can clean data to enhance data quality, identify, predict and synthase missing values to ensure data integrity. Accessible, structured and formatted data kept together with its context provide additional value to any business processes. Here there is a transition to forecasting, analysis of possible scenarios, resilience and sustainability.

If you have done anything like reports prepared you have to keep data in mind because when you present your result to someone it will help you a lot. Just like if you are playing cricket bowler bowl bouncer you need to remember that you should pull the bat in this case

Maintaining a data-centric approach throughout the project lifecycle is essential. This means regularly revisiting your data analysis, updating it with new information, and adjusting your strategies accordingly. It also involves staying abreast of new data analysis techniques and technologies that could enhance your decision-making process. Cultivating a data-driven mindset within your team encourages a more analytical approach to problem-solving and innovation.

Os dados são fundamentais para a tomada de decisão em todo projeto, e através deles podemos responder a 3 perguntas que são premissas para a garantia do projeto desde o planejamento, execução e entrega: - O que queremos? - Como queremos fazer? - Qual recurso necessário para fazer? Quando os dados nos entregam as informações que precisamos, podemos ter as 3 respostas, e através delas criar parâmetros a serem seguidos de forma clara e objetiva. Após definirmos os parâmetros, podemos criar um plano de ação com os responsáveis por cada parte do projeto, definindo metas, equipes, recursos necessários, as variáveis, os conflitos e principalmente os prazos, dessa forma temos tudo o que é preciso para garantir a entrega do projeto com sucesso.

Keeping organized and clean data can improve the analysis process, and enhance the future results using the same data by many different tools.

A inteligência artificial (IA) desempenha um papel crucial na gestão de dados, especialmente quando se trata de lidar com grandes volumes de informações que estão em constante mudança. A IA pode ajudar a automatizar processos de atualização e garantir a precisão dos dados, identificando tendências, padrões e anomalias em tempo real. Além disso, sistemas de IA podem ser implementados para monitorar continuamente fatores externos e internos que afetam os dados, fornecendo insights valiosos para tomadas de decisão mais informadas. Em relação à segurança e ética dos dados, a IA pode auxiliar na detecção e prevenção de violações de privacidade, garantindo o cumprimento de regulamentos e padrões éticos.

Data is useless if you leave common sense on the door. I've seen many "data driven" projects lose themselves to the complexities and premises of numerical analysis and losing the grip on the final goal by not keeping a "sense check" filter at hand. As basic as it may sound, its necessary for leaders and team members to be asking themselves if the progress/focus/spins of a project is making sense, is it helping to achieve the big goal? Is it cost efficent? Is it worth it?

Other things to consider are; 1. Learn and adjust: After the project is completed, conduct a thorough analysis of the outcomes. Identify lessons learned and areas for improvement in data collection and decision-making processes. 2. Risk Analysis: Use data to assess and mitigate risks associated with the project. Identify potential challenges early on and develop strategies to address them.

I will communicate with my team. Will discuss with them about the project. Will read data and will take suggestions from the team. Will consider the best one and Will work on the project.

Another critical point is to know your audience, know who you are presenting to, make your visualization simple and easy to understand. Make it aesthetically pleasing to the eye. Understand the data well and you are good to go.

Fostering a culture of data-driven decision-making and encouraging robust analytical practices, teams can harness the full potential of data to navigate complex projects with clarity and precision.