Vous avez beaucoup de données à gérer dans le développement de produits. Comment faites-vous pour organiser tout cela ?

1. Relevancy of Data is key 2.Targets should be accurately and precisely be defined. 3. Segmenting it according to your need 4. Benchmarking with defined thresholds 5. But don’t discard extra data because it will help you to drill down to explore opportunities for non targeted / irrelevant segments or benchmarks.

Organizing data in product development involves implementing a structured approach. Utilize project management software, establish a clear data hierarchy, and employ version control systems for code repositories. Regularly audit and streamline data processes to maintain organization and facilitate seamless collaboration among team members.

keeping your data neat and useful is key: 1. Figure Out What You Really Need: • Ask the Right Questions: What info will actually help you make better decisions? • Trim the Fat: Skip the data that doesn’t really add value. Keep it lean and mean. 2. Organize Smartly: • Categories & Tags: Group your data in a way that makes sense. Easy to find, easy to use. • Tools That Work for You: Find tools that fit your flow. Whether it’s spreadsheets, databases, or something fancier, make sure it’s something that the team will actually use. Remember, it’s not about having heaps of data; it’s about having the right data, right where you can use it. Keep it clear, keep it clean, and you’re all set.

Managing extensive data in product development is indeed a challenging but by defining the specific needs of the data we can stay organized and here's my perspective: 𝗣𝗿𝗶𝗼𝗿𝗶𝘁𝗶𝘇𝗲 𝗥𝗲𝗹𝗲𝘃𝗮𝗻𝘁 𝗗𝗮𝘁𝗮: It drives our decision-making and resource allocation. 𝐒𝐞𝐭 𝐏𝐫𝐞𝐜𝐢𝐬𝐞 𝐎𝐛𝐣𝐞𝐜𝐭𝐢𝐯𝐞𝐬: Targets guide our focused research and product development. 𝐒𝐞𝐠𝐦𝐞𝐧𝐭 𝐃𝐚𝐭𝐚: This strategic categorization helps tailor our product approach. 𝐁𝐞𝐧𝐜𝐡𝐦𝐚𝐫𝐤 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞: We measure against set standards, adjusting strategies when needed. 𝐑𝐞𝐭𝐚𝐢𝐧 𝐄𝐱𝐭𝐫𝐚 𝐃𝐚𝐭𝐚: This opens up potential for exploring future innovative opportunities.

Defining your data needs is paramount for effective organization and utilization. Clearly outline the type of data required, its purpose, and how it aligns with your goals. This ensures prioritization and prevents the collection of extraneous or redundant information. Establish specific goals and metrics, such as tracking user satisfaction, retention, conversion, or revenue, to measure progress and overall performance. Strategic identification and utilization of data contribute to informed decision-making and the successful evaluation of a product's success based on key performance indicators.

Selecting appropriate data sources is pivotal in the data organization process. Tailor your choices based on your product type and developmental stage, utilizing surveys, interviews, focus groups, analytics, experiments, or reviews as needed. Assess the quality, reliability, and validity of each data source, ensuring alignment with your specific needs and goals. For instance, employ qualitative data from user interviews to comprehend pain points and needs, while quantitative data from analytics can be instrumental in testing hypotheses and assumptions. Strategic sourcing ensures a comprehensive and reliable dataset, facilitating more accurate insights and informed

Consider using tools like Jira, Confluence, or Notion for organized data management in product development. Create a structured system for documentation, version control, and collaboration to ensure efficient handling of project data.

Pour organiser efficacement vos données, il est essentiel de définir vos besoins en matière d'information. Cette démarche commence par l'identification des objectifs commerciaux, la sélection d'indicateurs de performance clés, la compréhension des utilisateurs, et la garantie de conformité réglementaire. Il est crucial d'évaluer les sources de données, de déterminer le cycle de vie des informations, et de fixer la fréquence de collecte appropriée. La sécurité des données et la gestion des coûts doivent également être intégrées dans le processus. En établissant ces paramètres, vous assurez une collecte ciblée, évitez les données superflues, et maximisez la valeur des informations pour atteindre vos objectifs organisationnels.

É interessante ter atenção a qualidade e veracidade das fontes de dados utilizadas, ter alguém do time de negócios ou que tenha alguma vivência mais profunda do tema para verificar e contribuir com a opinião ativa sobre os números do negócio

Secure and efficient data storage is imperative post-acquisition. Select a storage solution matching data type, size, and frequency, ensuring protection, backups, and compliance with regulations. Implement a consistent naming and labeling system for files and folders to facilitate easy retrieval. Cloud platforms like Google Drive or Dropbox offer online storage and sharing, while databases like SQL or MongoDB facilitate data storage and querying. By prioritizing security and choosing appropriate storage solutions, you safeguard data integrity, accessibility, and compliance, contributing to a robust foundation for data-driven decision-making in your product development journey.

Não concordo com ferramentas de armazenamos de propósito geral para armazenamento de dados para fins projetos, ferramentas específicas como BigQuery da Google ou Redshift da Amazon são ótimas recomendações para segurança, disponibilização e performance de dados.

You should handle personal data carefully when using the cloud. You can use anonymization of personal data. Trade secrets also apply to confidential data.

Accurate data analysis is pivotal for informed product decisions. Employ suitable methods and tools aligned with your data type, goals, and questions, capable of handling complexity and volume. Validate results to prevent errors, biases, or false conclusions. Spreadsheets like Excel or Google Sheets are apt for basic calculations and visualizations, while advanced statistical and machine learning techniques can be executed using software like R or Python. Rigorous and precise data analysis ensures the extraction of meaningful insights, fostering a data-driven approach that enhances the efficacy of decision-making in your product development endeavors.

Um exemplo de ferramenta útil é o looker Studio da Google que você consegue conectar ao BigQuery ou também em planilhas Google para construir Dashboards e visuais interessantes, além de tudo é gratuita. Opções pagas e muito boas também é o Power BI e o Metabase, ambas tem versões gratuitas mas suas versões pagas valem muito a pena.

Communicate data analysis effectively by tailoring it to your audience. Engage in a collaborative, iterative process to understand their needs and shape your presentation accordingly. Utilize liaisons if necessary, like online reports or customer success teams, for insights. Ideally because it's been a collaborative process, there will be no surprises during the presentation itself. Focus on crafting a compelling narrative supported by the numbers. Simplify complex concepts to ensure clarity and see if you can pass a helpful benchmark for effective communication: 'Would my grandma understand this?'

In product development, maintaining organized data involves defining needs, selecting sources, securing storage, accurate analysis, and effective communication. Begin by outlining data requirements and establishing goals. Choose relevant, reliable sources and secure storage solutions. Accurate analysis, using appropriate tools, ensures meaningful insights. Communicate findings effectively using clear language and engaging visuals. This systematic approach facilitates informed decision-making and successful product development, aligning data management with business objectives.

Utilize métodos de storytelling com os dados e lembre-se que ser funcional é o objetivo. Lembre-se que quando se utiliza cores para uma comunicação o termo "menos é mais" faz todo sentido.

My way of communicating data and ideas to stakeholders: daily distribution of information to stakeholders about the status and dynamics of key metrics, anomalies and causes of anomalies. I’ve implemented it with Airflow.

To manage a large amount of data in product development, it's crucial to use project management tools and databases. I recommend platforms like Jira, Trello, or Asana for project tracking and databases such as MySQL or MongoDB to store and organize data in a structured manner. Additionally, maintaining clear documentation and establishing efficient workflows will facilitate organization.

Keeping data organized in product development is crucial for efficiency and success. Here are some strategies to consider: -Centralized Data Repository -Version Control -Standardized Naming Conventions -Data Access Control -Regular Data Audits -Use of Project Management Tools -Collaboration and Communication Platforms -Documentation and Wiki -Data Backup and Recovery Plan -Integration of Tools and Systems -Training and Guidelines -Feedback Loop

New and existing products have numerous components that require data collection and analysis at each step of the operation processes. We measure these features using documented, calibrated gauging at each operation step. It is evaluated for statistical process control and improvement to reduce variation. Our company has computer systems to that make this easy thru electronic entry and storage either by human entry or direct electronic entry with analytics each step and feature. We continuously improve the process from feedback loop.