What do you do if your engineering data management tools are too complex?

Get back to the basics: Focus on the 20% of your data that will drive 80% of business value and make sure you do that well. Meaning, you understand where it's coming from and underlying assumption, your data is reliable and accurate. My second part will be unpopular among the folks who say "information is currency" and "data is cheap", I recommend to minimize the remaining 20% at the collection point: often it adds work, maintenance and noise.

Assess the objectives of your ED and try to figure out ways to break it down into agile component, that will shade some light into out of the box solutions prior to draw conclusions.

If your engineering data management tools are too complex, causing challenges for your team, consider the following steps to address the issue: Evaluate User Experience, Simplify User Interface, Provide Training and Support, Customize Configuration, Integrate with Existing Systems, Seek Feedback and Iteration, Promote Adoption and Best Practices, Consider Alternative Solutions. By taking these steps, you can address the complexity of engineering data management tools and improve the overall user experience for your team, leading to increased efficiency and productivity.

First identify your needs to meet your requirements to deliver effectively. See how you can achieve your objectives by keeping it simple.

Knowing the end result is important to work backwards and understand the need for engineering data management. If the goal is inline with the complexity of the engineering tool then the only way out would be to accept the complexity and work on training to ensure quality and compliance. However most of the times we realise that the result set for the end target can be achieved by having a lot lesser complexity and thus the task becomes much more easier and thus the added complexity of the engineering tools also gets reduced dractically

Simplifying the engineering management standards are very important in making sure that no complex data in the management system can bring headache in analysis

The article covers good width of elements to be considered for such implementations which could already be complex. I would give priority to “process simplification” and then “training of teams” for a successful implementation. One additional point which I would like to add is to keep in mind the pace of rate of change of technology and chose a forward looking solutions. For example, consider potential impact of AI on product life cycle process and hence impact on data collection, retrieval and usage during the product life cycle.

Proceed by assessing the array of engineering data management tools at your disposal. These encompass CAD software, PDM systems, PLM platforms, cloud services, and collaboration tools. Each tool boasts unique merits and drawbacks contingent upon your requirements, financial plan, and inclinations. It's essential to scrutinize features, expenses, compatibility, scalability, and user-friendliness across different options. Seek out feedback, endorsements, and demonstrations from fellow users to gain insights. Additionally, consider the integration and customization capabilities of these tools, ensuring they align with your current workflows and systems.

Go online, whatever tool you pick realize you are going to have to learn and teach it to others. If you can’t learn it, then you can’t teach it. It won’t be used.

Evaluate based on the customer requirements and complexity of data, at the same time the simplified architecture is reusable.

Building on simplified workflows, it's crucial to remain flexible and receptive to feedback from your team. Regularly review and adapt your engineering data management processes. - encourage open dialogue - add a review and retrospective sessions - assign a person for improvement actions - share updates to workflow and shape them for the current needs (practical needs, not theoretical)

As an engineering manager, simplifying workflow amidst complex data management tools is key. Alongside designating clear roles, standardizing processes, and embracing automation, integrating tools seamlessly, prioritizing essential features, and fostering ongoing training are vital steps. Collaboration with IT and software providers ensures sustained efficiency.

I have always believed that the tools made available to you to perform your duties, should work for you and not you work for your tools. If you are the decision maker that is purchasing a accounting or manufacturing program,make sure that you have buy in from all team players before YOU force an impractical works tool on them.

No matter what, I always try to simplify simply simply .. until we reach the level of quality or success needed to proceed. And only then, do I take the next step. Everything in small but confirmed steps. This may seem like the long route, but it's the most reliable way to guarantee results every time.

The most common problem of data management is due to complex and obsolete procedures. We must to think in simplification and introducing in the digital world, really evaluate the real issue, we can to we can take advantage the most of our team's man hours simplifying the process avoiding lost time in things and procedures that do not add value.

Working as a team is always the best approach, keeping everyone on the loop and making sure you are training your team members will boost your team's efficiency. Believe in what your colleagues can accomplish, sometimes you only need to spark the fire and they surely will come back with invaluable contributions from which you can always learn.

If engineering management tools are too complex, simplify them based on user feedback, provide simplified team trainings, explore alternatives, and ensure regular updates for ongoing improvement. Technology is evolving day by day and you need some time to get acquainted with those tools through proper understanding, discussion and self trainings.

A mi me ha sido de utilidad desarrollar sistemas para preservar el conocimiento dentro de la organizacion, lo cual lo he conseguido creando sistemas de estandarizacion para que el personal pueda de manera simple y sencilla asimilar el conocimiento y como una herramienta mas para transmitirlo.

Nothing is complicated!!! A data management system developed in such a way that it will simply your efforts to identify correct drawings, documents or any part numbers. For this a Manager should develop a proper SOP and develop or train team members to follow the same SOP. Once the process is simplified as per SOP it will be ease to use and friendly to everyone. Now a days most of the very old companies are struggling to simplify their PDM and PLCM with this process. I did the same and got success.

Training your team is essential as your engineering ecosystem expands. Organize a "product day" every two weeks or once a month, and choose one or two tools to provide knowledge transfer or training. Ask your team and stakeholders for feedback on choosing the tools they need to learn about. Share the videos and create a repository to train newcomers. This will create a learning culture and encourage your team to voice their opinions to improve the process.

Keep it simple and over communicate the objectives and measurable goals. This allow you to understand how your organization is performing. A good key performance indicator (KPI) should help you and the team understand if the strategies you are using are taking you toward your goals!

This is probably the most critical advice. A tool that is too complex is impeding the potential value you should be deriving from it. With the technology landscape constantly and quickly evolving, and so should the ease of use of your data management tools. If not, consider feedback to the vendor or consider other options.

First of all, gotta understand that if tools are too complex, no one is going to use it, and you're better off not having tools at all. So distill the essence of your team down into what the metrics are. What are the 3 main things that help make forward progress? What is measurable and can hold everyone accountable? Find the appropriate tools, and perform regular trainings and workshops to ensure the tool is being used to its max potential. Data-driven is good. Human-first is better.

Continual improvement is required for modern business to function effectively. However, improvements can result in loss of efficiency over time as a process out grows its tool. Have a pathway in front of you to adapt and grow. Whatever you choose to do will require constant support.

Any data management tool needs to be fit for purpose. If it is too complex, it will be mis-understood and inevitably ignored (or worse still, given lip service.) Training on any management tool is also essential as it gives all users the required skills to use the system depending on their requirement’s. In the military, keeping it ‘simple stupid’ usually works…..

There needs to be a culture developed of not being fearful of getting help from experts in the field throughout any organization. At the same time, no one can be an expert at everything so fostering an environment of "say no to your ego" might help in this endeavor.

Understand what does it mean complexity data then work it out with our subject matter expert, internal and/or external. Oswaldo H.

Most oftentimes, the expert can provide help only the generic process. In my experience, the expertise resides in the actual users themselves as the subject matter, expert, as most processes are non-generic, and the outside expert would not be able to understand and identify where the improvement needs to be.

See the whole of the picture Represent data in graphics Avoid analyzing the very small data that maybe doesn’t add great value to the results

Put hands on both temples and rub said temples with said hands. Go for a walk around the office or possibly outside if the weather is ok

When dealing with complex data management tools, it's essential to prioritize checking your crucial business datapoints first. Running a prototype and gathering feedback from stakeholders before a mass release is crucial. Following Agile practices can be immensely helpful, allowing you to customize the tool according to your business needs by the project's end. Avoiding a mass release without proper testing and feedback is highly recommended.

Even with the most complex tools, there is always one person or one team that has spent (or is about to spend) a lot of time doing some things over and over again. You can "templatize" and automate these repeated tasks and processes so that you can save time when other people needs to do the same things. For example, if analysts want to generate dashboards on revenue for a particular demographic and it takes multiple steps to create that, you should be able to create a single-click button that uses a template and that would just ask some parameters such as date ranges and type of user to go through all the necessary steps to create that report.

For the tools to be effective, the processes developed must be simple and precise to the objectives and set the standard template that is understood by everyone using it. Automate the workflow stages to the best possible. Adapt agile methodologies for a constructive & collaborative result.