Why Big Data Matters to Your BIM

In the last 10 years, there has been a data explosion in business. It’s estimated that due to cheap storage and the intersection of cloud technology, business data and information has grown by a factor of 50.

At the same time, silos of data are proliferating with cloud tools that address specific niche work functions. The outcome is that while there is an abundance of data, only 10-15% of that data is being used as part of critical decision making on your BIM projects.

Additionally, turning big data into viewable insights through BIM offers an efficiency that reduces project delivery time, and risk of errors, resulting in increased profit margins. For an industry where 35% of costs are accounted for by material waste and rework – even after the adoption of BIM – big data analytics will be the next game-changer. Bringing big data analytics into your BIM should be a top priority as you look to outpacing your construction competitors.

Building Information Management, BIM, is defined as the life-cycle management of the built environment supported by digital technology (NBIMS – NIBS). One of the advantages of BIM has been to leverage data through the different phases (plan, design, build, operate). To date, however, too much emphasis has been placed upon the 3D visualization component of BIM vs. collaborative construction delivery methods.

In fact, roughly 50% of the construction industry today is using BIM in a similar fashion to the way they did 10 years ago. This “BIM stagnation” has put them in a position where they are unable to leverage the data ocean around them due to outdated BIM workflows.

This is commonly evidenced by cross-product and cross-platform workflows. For example, often, to achieve a 5D estimating workflow (3D Visualization + 4D Scheduling + 5D Cost) two or more pieces of software are required. The danger in these cross-product, cross-data silo workflows is that it hurts your project efficiency and accuracy while you risk being outpaced by companies that have a big data strategy as part of their overall BIM strategy.

To start addressing this issue, the following are considerations:
1. Focus on Process – the construction delivery method drives success/failure more than any other single component. Collaborative construction methods such as Integrated Project Delivery (IPD) and Job Order Contracting (JOC, also referred to as IPD-lite) are examples of proven, transparent and performance-based approaches. Also, consider documenting your workflows and BIM processes you currently use. Once diagramed, you might be surprised to see the data conversions, interpolation, and potential data loss that happens in your BIM work.

2. Robust Ontology – the use of standardized terms, definitions and data architectures are critical to enabling transparency, collaboration and reducing waste. For example, the use of standardized cost databases, such as RSMeans, and associated Uniformat/MasterFormat … and eventual OMNICLASS frameworks as the foundation for development are some of several key considerations. Ask yourself, does your BIM implementation require proprietary file and data formats? These can quickly become bottlenecks that isolate data sharing and propagate cross-platform workflows. Instead, consider looking at industry standards like the IFC file format and open API libraries that become the foundation of your BIM implementation.

3. Develop a BIM/Data Strategy – A good BIM Execution plan should account for the data separately. It should have the following considerations:
• Do you need structured or unstructured data, or (ideally) a combination of the two?
• Can you achieve your goal with internal data alone, or do you need to supplement your company data with external data (for example, cost, weather data, supply chain, etc.)?
• Do you already have, or can you quickly access the data you need?
• If not, you need to set up a way to collect the appropriate data? What data collection method will you use? Identify areas where data translation occurs and mitigate.
• Clear identification of roles and responsibilities is key and some items may need to be tied to contractual language to assure it gets done by the right entities at the right time

4. Leverage Technology – technology is an enabler and not a solution. Technology can, however, cause disruptive change to fundamental business processes. It is critical to adopt technology that is in concert with core strategies and dismiss those that are in conflict. For example, open cloud computing platforms that promote collaboration, scalability, information permanence and reuse are enablers, while dated monolithic software programs and even traditional relational databases should be seriously evaluated.

5. Train and Retrain – Data science is not new, but it has changed with the proliferation of cloud data silos. It might be time to look at your BIM management and VDC staff, assess their strengths and offer certification in some of the online courses related to data management. These data facilitators should be asking themselves the questions: what data needs to be consumed and by whom? And how do I need to package that up so that it can be properly digested? The best talent in the industry doesn’t just understand the data, they know how to manage it to provide workflows and processes that deliver the right data, to the right individual, at the right time.

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Neal Niemiec
Neal is a 20-year veteran in providing technical outcomes to Fortune 500 companies in the AEC space. The AEC industry lead has a background in information technology and is an expert in providing integrated solutions to the hardest of infrastructure problems. Today he works for Hexagon as an AEC thought leader and revenue enabler.

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