How Big Data Complements Information Management

Having been intensely paper-based for decades, the construction industry has developed sophisticated approaches to inter-company transactions, document management, and collaboration over the past 15 to 20 years.


Now, partly as a result of Building Information Modeling (BIM), the industry is also becoming more data-centric. However, even in leading BIM markets, the great majority of construction information exchanges still rely on processing large volumes of unstructured data.

 

There is a growing opportunity for project-oriented construction and property-related businesses to collate and drill into their data. By reaching beyond their structured data and interrogating related unstructured data (plus data from relevant external sources), organisations can identify previously hidden patterns, correlations, and anomalies. These can reveal inefficiencies and improvement opportunities, and maybe even unlock new value streams. Ultimately, the industry could be harnessing Big Data to help owners of capital projects get better, more cost-effective and more sustainably built assets.

 

The construction information challenge

 

Construction is synonymous with huge volumes of written and graphical information, amassed during the planning, design, and build processes, at handover, and during the life of the built asset through to its eventual decommissioning, dismantling, or demolition.

 

Much of this information will of course, be about the built asset itself (briefs, specifications, drawings, models, operation and maintenance manuals, service records, and so on). However, there will also be numerous information exchanges relating to the process of creating that built asset. There will be various contracts and agreements, insurance documents, estimates, schedules, orders, invoices, and numerous forms (transmittals, RFIs, change orders, an so on) to request, capture, and process information, as well as to provide records of who did what and when. In addition to this documentation, there will often be large volumes of correspondence.

 

Ultimately, the industry could be harnessing Big Data to help owner-operator clients get better, more cost-effective and more sustainable built assets.

 

Traditionally, all of that information exchange was paper-based. Depending on the age of the built asset and the associated information, some information will still be in paper form and some will be on computers or related storage devices. Paper may be stored in archive boxes or filing cabinets, while some documentation may be captured on microfilm. For some older buildings and other assets, written records may have been lost or destroyed altogether, or there may only be partial records. Digitised information may be stored on computer disks, removable hard-drives, USB sticks, or tapes.

 

The information may also be spread across several different organisations involved in the delivery of the project. Within a single organisation, that information may be spread across several different locations or departments. Even within single departments, information may be stored in multiple devices, often with numerous duplicates.

 

Another feature of the traditional project delivery process is that every company involved would need to manage and maintain its own records of what they did and when. As a result, each business would retain information archives, often duplicating some of what was contained in other businesses’ systems (the tendency of some email users to share copies of information indiscriminately with every colleague will also exacerbate this.)

 

The information management challenge does not end once a built asset is handed over to the owner-operator. As the asset begins to be used for its intended purpose, further operational information will be created to manage maintenance and repairs, to facilitate periodic refurbishment or extension projects, and – importantly – to relate use of the built asset to the business operations of the owner-operator. This information may also be generated for a much longer period of time; for example, design and construction may have taken two years, but the owner-operator may then be operating the facility for decades, and business operations may also generate much larger volumes of data.

 

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