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You need to manage engineering data. What's the best tool for the job?

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As an engineering manager, you know how important it is to organize, store, and share your engineering data. Whether it's design specifications, test results, code snippets, or documentation, you need a reliable and efficient way to manage your data and collaborate with your team and stakeholders. But what's the best tool for the job? In this article, we'll explore some of the key features and benefits of different tools for engineering data management, and help you choose the one that suits your needs and preferences.

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Dr. Haysam Chafi

Technical Director at EMEBCI | PhD in Structures & Materials | INSEAD MAP | Leadership | Problem Solving | Critical…
Narendra D.

Engineering @ Razorpay | xCurefit | xMeesho

1 Data management platforms

One option is to use a data management platform (DMP) that provides a centralized and secure place for your engineering data. A DMP typically offers features like version control, access control, data validation, data integration, and data analysis. Some examples of DMPs are Azure DevOps, Google Cloud Platform, and AWS. A DMP can help you streamline your workflows, ensure data quality and consistency, and leverage cloud computing and analytics capabilities. However, a DMP may also require more technical skills, resources, and costs to set up and maintain, and may not be compatible with all your existing tools and systems.

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Aditya Roshan

Engineering Leader | Driving Excellence, Innovation & Scalable Solutions
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You can use following tools: 1. Looker Studio to centralise all the reports. 2. Git to have all your test cases 3. Jira to manage all the tech repeated backlog 4. You can also build a custom tool similar to OpenMetadata

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2 Project management tools

Another option is to use a project management tool (PMT) that helps you plan, track, and communicate your engineering projects. A PMT typically offers features like task management, time tracking, resource allocation, reporting, and collaboration. Some examples of PMTs are Jira, Asana, and Trello. A PMT can help you keep your team aligned, monitor your progress and performance, and manage your deadlines and deliverables. However, a PMT may not be able to handle complex or large-scale data, may lack some data-specific features or integrations, and may not be secure enough for sensitive data.

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3 Data collaboration tools

A third option is to use a data collaboration tool (DCT) that enables you to share and work on your engineering data with others. A DCT typically offers features like data visualization, data annotation, data feedback, and data sharing. Some examples of DCTs are Miro, Figma, and GitHub. A DCT can help you create and present your data in engaging and interactive ways, solicit and incorporate feedback from your team and stakeholders, and foster a culture of data-driven decision making. However, a DCT may not be able to store or process large amounts of data, may have limited data security or governance, and may depend on internet connectivity and compatibility.

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4 How to choose the best tool

When it comes to engineering data management, there is no one-tool-fits-all solution. To choose the best tool for your needs, you must consider the type, size, and complexity of your data; the frequency, scope, and purpose of your data usage and updates; the level of collaboration and communication you need with your team and stakeholders; the budget, time, and skills you have available for setting up and maintaining the tool; and the security, compliance, and reliability standards you need to meet. By taking into account all these factors, you can narrow down your options and select the tool that best fits your engineering data management needs.

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Narendra D.

Engineering @ Razorpay | xCurefit | xMeesho
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Choosing the best tool for engineering data management involves a thorough assessment of specific team needs, including data type, collaboration requirements, security, scalability, and integration capabilities. Evaluating features, considering usability, and aligning costs with value are key. Additionally, seeking user feedback, exploring trial versions, and assessing vendor reliability aid in making an informed decision, ensuring the selected tool effectively meets the team's requirements and enhances overall productivity in handling engineering data.

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Dr. Haysam Chafi

Technical Director at EMEBCI | PhD in Structures & Materials | INSEAD MAP | Leadership | Problem Solving | Critical Thinking | Chess | Piano & Keyboard
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In managing engineering data, I've discovered that sometimes the most effective tool is already at our fingertips – good old Excel! Excel is surprisingly versatile for data management. I use it to organize and analyze large datasets, create pivot tables for quick summaries, and even run complex calculations with built-in functions. Its charting capabilities turn numbers into visual stories, making data easier to understand and present. Sure, it might not have all the fancy features of specialized software, but for many tasks, Excel is a reliable, accessible choice that gets the job done efficiently. It's about harnessing the potential of this everyday tool to make engineering data management straightforward and effective.

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You need to manage engineering data. What's the best tool for the job?

1

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3

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5

1 Data management platforms

2 Project management tools

3 Data collaboration tools

4 How to choose the best tool

5 Here’s what else to consider

Engineering Management

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You need to manage engineering data. What's the best tool for the job?

1

2

3

4

5

1 Data management platforms

2 Project management tools

3 Data collaboration tools

4 How to choose the best tool

5 Here’s what else to consider

Engineering Management

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