How can you manage product data across multiple systems in product engineering?

My starting point would be a clear definition of the type(s) and volume of data you need to manage, what the use case(s) are for that data, and how it will be consumed. Having those will then enable you to define the data model(s), infrastructure and storage requirements, security posture, tools and QA policies to deliver the product data strategy.

Data is money, just like you carefully manage where you put your money, and how you plan your finances, it is key to plan where the data would be stored, how it is managed and transported between systems. Ultimate goal of data is to produce information that is useful to a specific persona who is consuming the product. This means that the data needs to be processed/transported and stored in the most efficient manner to meet the end goals of the system in terms of cost, efficiency and insert/fetch latency

Managing product data across multiple systems in product engineering requires careful planning and the use of specialized tools and practices. Here are some key steps to manage product data effectively: 1. Data Consolidation 2. Data Quality Management 3. Version Control 4. Access Control 5. Data Security 6. Data Analytics By following these steps, product engineering teams can effectively manage product data across multiple systems, ensuring data consistency, accuracy, and security throughout the product lifecycle.

In my opinion, managing product data across systems in product engineering involves centralizing data in a repository and integrating systems for seamless exchange. Standardize formats and employ Master Data Management (MDM) for consistency. Version control tracks changes, aiding revision management. Data governance establishes policies for creation, access, and quality. Metadata management organizes attributes for retrieval. Quality assurance ensures accuracy. Collaboration tools foster teamwork. Continuous improvement refines processes. These strategies promote efficiency, accuracy, and consistency in managing product data, enhancing decision-making and innovation.

The main challenge is to transform "data" into "information"! We have vast amount of data, but very little conclusions or useful information out of it! - Beware of correlations! - Strive for further supportive data - Simplify the presentation format so everyone can relate to this data. - Avoid qualitative (e.g. words like "big", "large") and stick to quantitively conclusions (e.g. "we see x% increase/decrease") - Criticize every conclusion. Never end a discussion with unanimous agreement! Must discuss at least one opposing idea!

From my experience, selecting the right tools is crucial for effective product data management. This means choosing software and systems that not only cater to current needs but are also scalable for future requirements. I've focused on tools that offer robust data integration capabilities, user-friendly interfaces, and strong support for data security and compliance.

To manage product data across multiple systems in product management, consider implementing a Product Information Management (PIM) system. PIM centralizes product data, ensuring consistency across platforms, and facilitates efficient updates. Integration with other tools, data mapping, and regular audits help maintain accuracy and coherence. Regularly assess and optimize your data management processes to adapt to evolving business needs.

Selecionar plataformas de gerenciamento de dados centralizadas que ofere?am recursos de integra??o, interoperabilidade e seguran?a. Além disso, é importante considerar a escalabilidade das ferramentas para lidar com o crescimento futuro dos dados e garantir que atendam às necessidades específicas da organiza??o e dos processos.

1. Assess the type, volume, and complexity of your product data. 2. Consider integration, compatibility, security, and compliance requirements. 3. Evaluate usability, scalability, and user experience of potential tools. 4. Compare costs and benefits to determine the value proposition. 5. Select tools that best align with your product engineering needs and objectives.

In my opinion, selecting the right tools for managing product data hinges on several key factors. Firstly, consider the type, volume, and complexity of your data. Next, prioritize seamless integration, robust security, and compliance. Opt for user-friendly, scalable solutions that offer the best value for your specific needs in product engineering.

In my opinion, defining and implementing clear product data workflows is essential for efficient product management. Documenting and communicating these workflows ensures consistency and accountability within the team. Regular monitoring and measurement help identify and address any bottlenecks or errors, enhancing overall productivity and data quality.

Define an effective product data workflow by grasping data ownership, approval requirements from creation to updating, and comprehending the flow and consumption within the system.

In my work, establishing clear and efficient workflows for product data has been key. This includes defining processes for data creation, approval, storage, and dissemination. I’ve implemented workflows that ensure data consistency and integrity across different systems, which is essential for accurate product development and engineering.

Definir processos e procedimentos para coleta, armazenamento, integra??o, análise e compartilhamento de dados entre os diferentes sistemas utilizados. Os fluxos de trabalho devem ser projetados para garantir a consistência, precis?o e seguran?a dos dados, além de facilitar a colabora??o entre as equipes e garantir a conformidade com os padr?es e regulamentos aplicáveis.

1. Document and communicate clear and consistent product data workflows. 2. Define steps and actions for creating, updating, reviewing, and approving product data. 3. Monitor and measure workflows to identify bottlenecks and inefficiencies. 4. Implement mechanisms to resolve errors and improve workflow efficiency. 5. Ensure alignment of workflows with product data strategy and objectives.

Maintaining high-quality product data has been a priority in my experience. This involves regular audits, data cleansing, and validation processes. By establishing standards for data accuracy and completeness, I've helped ensure that the product data remains reliable and useful for decision-making and operational processes.

Estabelecer padr?es de qualidade de dados, realizar verifica??es regulares de integridade e precis?o, e implementar processos de limpeza e enriquecimento de dados conforme necessário. Além disso, é importante garantir a seguran?a e a privacidade dos dados, implementando medidas de controle de acesso e conformidade com regulamentos de prote??o de dados, como o GDPR.

Effective collaboration and communication have been vital in my experience with managing product data. This means ensuring that all stakeholders, from engineering to marketing, have access to the necessary data and understand its implications. Regular meetings and updates, along with shared platforms for data access, have facilitated better teamwork and decision-making.

Estabelecer canais de comunica??o claros entre as equipes que lidam com dados de produtos ajuda a garantir a consistência e alinhamento nas informa??es. Plataformas colaborativas e ferramentas de comunica??o facilitam a troca de conhecimento e a resolu??o eficiente de problemas relacionados aos dados. Além disso, promover uma cultura de colabora??o incentiva a participa??o ativa de todas as partes envolvidas, promovendo a integra??o de dados e a eficácia nas opera??es.

In my experience, no matter how much you maintain and communicate, if people don’t understand your data and connections between different objects, the data is useless. Create visuals and training material that is easily understood by your customers and users. Make sure to use the voice of the customer to deliver a system that works not only for you, but your organization. Building a system that interconnects data among different platforms and objects can be hard to maintain, automation is your best friend in these scenarios, so planning out the object data’s workflow and ensuring you don’t need to search item by item, but are also able to connect it, in order to be able to further drive analysis and reports by object-data relationships.

Establish standardized data formats and structures for product information. Define and enforce data governance policies to maintain consistency in naming conventions, units of measurement, and other data attributes.

Managing product data across systems involves establishing data standards, integration protocols, and workflows to ensure consistency and accuracy throughout the product lifecycle. Integration between systems enables seamless communication and data exchange, improving collaboration and decision-making across departments. Examples of systems involved in managing product data across multiple systems include 1. Product Lifecycle Management (PLM) software 2. Computer-Aided Design (CAD) software 3. Enterprise Resource Planning (ERP) software 4. Manufacturing Execution Systems (MES) 5. Customer Relationship Management (CRM) software

Managing product data across multiple systems in product engineering can be a complex task. Here are some strategies that can help at high level: 1. Centralized Data Management: Store all product data in one accessible location. 2. Data Integration Tools: Use tools that sync data across systems. 3. Standardization: Ensure data format and structure are consistent across systems. Anticipate change in a complex environment. 4. Data Governance: Control data access and modification. 5. Regular Audits: Check for data inconsistencies or errors regularly. The goal is to ensure that the right data is available at the right time to the right people. This can help improve decision-making, reduce costs, and increase efficiency in product engineering.

Beyond these steps, my experience has taught me to continuously monitor and adapt to technological advancements and changing business needs. This includes staying updated with new data management trends, training team members on emerging tools and practices, and regularly reviewing and updating the data management strategy to ensure it remains effective and relevant.

El tema resulta algo contradictorio, si no se tiene claridad sobre la cadena de valor del producto o servicio, que vas a buscar?, y para que?, si no entiendes lo que se debe corregir o lo que puede o no estar fallando que vas a buscar, debes conocer la cadena de valor del producto y es eso lo que debes monitorear, en cuántos sistemas hagan parte de la misma, inicia en la oferta, y esculcar el CRM, verificación de las ventas, y válida el sistema de facturación, tienes dudas del ingreso, indaga, en contabilidad en recaudo en cartera, pero para esos debes conocer la cadena de valor, y llega a un proceso de auditoria donde valores el pareto, no te quedes en las desviaciones "normales o esperadas", busca lo relevante.