Aug 28 2014

Are asset owners and the construction industry really ready for ‘Big Data’?

Big Data While the management and IT press are talking energetically and knowledgeably about ‘Big Data’, it is a term that is less well understood and less frequently discussed in construction circles. This may, in part, be due to the current fascination with building information modelling (BIM) where the design and construction of built assets is increasingly being undertaken digitally, and where the associated model files can be large in size.

BIM is not ‘Big’
But they are not big – at least, not in the way that we define ‘Big Data’. Compared to the 2560 terabytes of data that retailer Walmart collects every hour by capturing customer transactions most BIM data is actually very small. Sectors such as retail and banking alongside fields such as Pharmaceuticals and Mapping/Remote Sensing have embraced an ever growing range of data sources from mobile and point of sale devices to satellites and UAVs to become prodigious producers and gatherers of data.

However, Big Data is not just about having a huge store of data; it is concerned with being able to access analyse and leverage the information in a timely manner to deliver real world intelligence and business benefit. You are likely to hear Big Data defined in terms of the four Vs:

  • Volume: This is purely the amount of data the ‘big’ part
  • Velocity: Processing and analysing the data in a timely manner
  • Variety: Including data from a range of sources to increase business value
  • Veracity: The integrity of the data, you can have the best analytics package in the world but if you put poor quality data in you will get poor results out

Big Data can include both structured and unstructured data. Again, BIM is largely about structured data (based on data models, defining fields and types of data, with data held in fixed fields, and so easily entered, stored, queried and analysed), but a typical construction project also generates additional volumes of unstructured data – captured in photos, graphics, videos, audio (including voicemails), web-pages, PDFs, PowerPoints, email content, Word documents, OCR-scanned documents, etc.

BI and ‘Big Data’
As data volumes have grown over recent decades, businesses have applied various management applications. These range from simple search and indexing tools, through information (lifecycle) management tools to document management systems (including collaboration platforms). But increasingly sophisticated technologies are also being applied, including data integration tools, business intelligence (‘BI’) software, and ‘Big Data’ analytics.

Most of these are commonly understood, but it’s perhaps worth distinguishing between BI and ‘Big Data’ tools:

  • Briefly, business intelligence tends to use mainly structured data with high information density to generate descriptive statistics which provide measurements or show trends (at CONJECT, for example, we have begun to offer BI insights, by enabling customers to interrogate their project and asset portfolios and analyse the performance of their project teams, the efficiency of their processes, etc).
  • ‘Big data’, by contrast, uses inductive statistics to infer laws (regressions, nonlinear relationships, and causal effects) from large data sets to reveal patterns, relationships or dependencies and to perform predictions. Moreover, ‘Big data’ analytics is often primarily focused on much bigger volumes of un- and semi-structured data.

‘Big Data’ and the built environment
Genuine ‘Big Data’ analytics, therefore, often reaches beyond the ‘silo’ of an individual organisation; it requires the collation and processing of substantial volumes of unstructured data, often from multiple sources; and it will combine various data and text mining, data optimisation and search techniques.

Increasingly, owners and operators of large-scale, multiple built assets will be looking to harness the immense volumes of data they generate from their operations. In the near future every individual component of a building, infrastructure network or any built asset will be fitted with sensors, collecting data each second on performance, behaviour & status. Built assets will generate vast quantities of data for analysis; marking the arrival of true Big Data.  To return to our BIM discussion, only a fraction of this data will concern the design and construction of the physical asset; much, much more will concern the day-to-day operation and use of those assets (energy use, employee behaviours, customer transactions, whole life costs, etc.).

And these assets form parts of bigger infrastructure systems – so-called Smart Cities – within which we can apply ‘Big Data’ analytics to look at both hard and soft infrastructure requirements that underpin efficiency, profitability and quality of life. This is one reason why the UK government, for example, is encouraging ‘open data’ approaches to make government-funded data more freely available in forms that allow creation of new information services and products.

In the next post in this series, we will look in more detail at how construction businesses and built asset owners can extract value from BI and ‘Big Data’.

About the author

Steve Cooper

Steve Cooper is Managing Director of Conject Ltd. He has spent over 25 years within the construction and engineering software markets, successfully running sales and marketing teams. He spent a number of years at SAP within their E&C practice, set up and managed a distribution channel in Asia Pacific for a division of Misys and ran a sales and marketing team within CSB COINS. In 2000 he gained an MBA from Henley Management College.

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