Recent surveys suggest that AEC companies and facility managers are embracing digital transformation at record-breaking speeds. Partially fueled by the black swan event of 2020, digital transformation has become the table stakes of modern facility management and engineering. And it couldn’t come at a better time. In this article, we will discuss an approach to better metadata.
Back in 2019, McKinsey estimated that construction companies could see 15 percent cost reductions. And 6 percent productivity gains from simple digital transformation strategies. Facility managers saw nearly equal gains. Now, in 2021, those value drivers are being realized by thousands of companies across the globe. But digitization comes with its own set of challenges.
In a non-digitized world, engineering drawings were labeled and put into buckets, boxes, and flash drives. Now, those engineering drawings are digitized and sitting in the cloud. So how do you find the right document without those labels? After all, what’s the point of digitization if you’re bleeding time and revenue trying to find the right documents at the right time?
The answer to these woes is something called metadata. This helps you organize and explore your precious drawings. But there’s a problem. This game-changing data organization technique comes with a few pain points.
What is Metadata and Why is it Important?
While there are a few definitions, it’s easiest to think of it as data that describes other data. We like to use the leftover analogy. Let’s say that you cooked a nice big pot roast. You didn’t finish it all, and you want to store it in the freezer for leftovers. Easy enough! But when you open your freezer, you see a ton of other food containers you stored in the past. How will you know which container has your pot roast next time you open up the freezer? Obviously, you could look through each container in the freezer. But that’s time-consuming and energy-draining. Instead, you whip out a label and sharpie and write “pot roast 1/1/2021” on the container. That label is like metadata. It describes what’s in the container. Allowing you to easily find the right leftovers next time you take a peek in the freezer.
Now, let’s apply the freezer analogy to engineering drawings. When you store your engineering drawings in a big database, how do you find the right drawing at the right time? You use metadata. It describes the data in your drawings for you. So, instead of searching through every engineering drawing in the bin, you can input a quick search term and find the exact drawing you need.
Types of Metadata
There are three types of metadata that can be applied to engineering drawings (and data in general):
- Descriptive: This type is used to make data searchable. You could label an image in a drawing with tags, add titles to documents, as well as use keywords to discover drawings based on specific criteria.
- Structural: This type is used to organize data in a database. For example, you can add types and versions to engineering drawings to help you find the right one. You could also describe how a specific drawing is related to other drawings in your database.
- Administrative: This type is used to describe the administrative needs of drawings. For example, you can add permission levels, dates, as well as drawing types.
That’s it in a nutshell. Easy peasy, right? Or… is it?
Common Pain Points
We all hear about metadata as this magical, problem-solving data mechanic. But an unfortunate number of companies struggle with it. There are serious pain points hiding beneath the seemingly simple premise of metadata, including:
- Manual labor: Adding metadata to every engineering drawing is labor-intensive. You have to classify every image, categorize every series of drawings, and add robust, comprehensive, and uniform tags. It’s not easy. And it certainly eats away at your time. Worse yet, manual data entry introduces errors. Research suggests that the error rate for a single manually-entered spreadsheet hovers over 30 percent. Metadata mistakes can lead to serious headaches. You won’t be able to find the right document, and incorrect metadata can actually make document retrieval more difficult.
- Complexity: Remember those three categories? There are hundreds of types of metadata within each of those three categories. You need to apply all of them correctly on every single drawing.
- Departmental frictions: Chances are, your engineering drawings intersect multiple departments. Finding consistency between those departments in terms of metadata language is challenging.
- Governance: Metadata requires governance. Access privileges, compliance, and a bucket of other pain points come into play when you start trying to pool engineering drawings together under one roof.
Wait… so is it bad? It certainly seems like metadata is useful. But those problems seem pretty dire. What’s the solution?
echo + Metadata: A Match Made in Heaven
The secret to leveraging metadata correctly is automation. Manually entering it is a recipe for disaster. And trying to build ad-hoc metadata structures can leave gaps between departments that impact “searchability.” The act of data cataloging and tagging is notoriously time-draining. This can defeat the purpose of using metadata in the first place.
When you’re dealing with something as mission-critical and facility-impacting as engineering drawings, every mistake counts. You need pitch-perfect metadata structures, and you need those structures to be holistic and uniform. That’s why we created echo. Not only is echo the world’s most powerful engineering drawing platform. But it also automatically catalogs over 15 metadata points from every drawing uploaded into the system. In other words, echo streamlines drawing ingestion, digitization, and categorization all under one roof.
Are you ready to digitize your engineering drawings in a way that makes them easy to find, retrieve, and discover? Contact us. We’ll help you embrace the real value of digital transformation.