Effective companies often make major investments to ensure their teams have the right tools, and know how to use them. We put a lot of thought into getting the right computers, useful software, and collaborative work spaces, and making sure they’re used effectively - but why don’t we put the same thought into our data?
It’s likely because it can be challenging to strike the balance between security/protection and transparency/collaboration. The Harvard Business Review did a study in 2016 that found the US spent $3.1 Trillion Dollars on bad data - a combination of incomplete, corrupt, offline, unavailable data that led to incorrect decisions, mistakes and added project work. Let that number sit for a minute… the entire industry of ‘big data’ was only valued at $136B at this time, yet the money lost for the United States alone on simply using the data incorrectly had a 20X negative impact. And this problem isn’t limited to the United States - this problem is experienced worldwide. This is particularly challenging in the engineering world because of the impediment it poses for executing work on time and with accuracy. Fortune reported that almost 80% of large companies reported an important strategic decision go wrong in the past three years due to "flawed" data.
Engineering companies big and small often experience challenges in accessing the right information, getting it to the right people, and knowing how to use it. This is costing engineering teams considerable time, and money that they need - but it doesn’t have to.
Here are four tips that all teams can put into action:
Before making any changes, you need to understand what data is important to your business, who needs that information, and how often they need it. Companies who are looking to store data for official records have much different needs than an engineering firm trying to manage 3D model data and correspondence.
Establish your teams needs in terms of the following four criteria:
One of the biggest problems with data is that it is not consistently structured, making it impossible to parse. Establish naming conventions, required data fields and a set of rules for your team that will empower them to use and reuse the data without the risk of losing it or using the wrong information.
If you are working on a technical project, emails are your enemy - they decentralize information, create copies and ultimately create silos that destroy transparency and efficiency.
All data should be clearly available to all of the stakeholders required with very little barrier to entry (besides the required security protocols). Information should be stored in a single shared location - so if you are modeling a bicycle frame, you should be able to quickly access analysis files, issues with the design and its latest status. Storing your data in one place, then managing your project in another is a recipe for miscommunication and inefficiency.
Often times the immediate reaction to “centralizing” your data is to find a massive platform that can store it all. In theory, this does “centralize” it but it often makes most of your data stored in ineffective ways that are hard to find and usually cumbersome. Using a traditional PLM tool to store all of your product’s data is great for history and records, but most users don’t like the hassle of PLM considering it still lacks the key simple collaboration they desire.
BeyondPLM blogger Oleg Shilovitsky wrote a great article about the challenges of PLM and collaboration and concluded “PLM applications are missing modern collaborative features allowing people to communicate across silos, teams and organizations.”
Find a tool that will host your data in a way that enhances your workflow and improves your team’s focus on collaboration instead of hindering it. Lightweight, cloud-based platforms can help your teamwork collaboratively, while managing your data effectively. Tools like Tableau can help teams better understand their metrics through engaging dashboards. Gradient empowers teams to collaborate during the design stage of a product with simple access to their 2D/3D data and a constant understanding of the existing issues, open actions and project status.
Data management is difficult. Choosing what to store, where to store it, who to share it with and how to use it is only the start. But by figuring out a platform that enhances the way people already work, you are going to get a much higher buy-in from your end-users and a greater value return on your investment.
What challenges do you experience with your data management and collaboration platforms?
Have thoughts or feedback? Email me at firstname.lastname@example.org