May 29 2015

How to make better use of all your data – ‘Big’ or otherwise

CDEIt is missing a trick to think that using data analysis tools to produce actionable information is solely within the purview of major asset and infrastructure owners.  Certainly, the hype and challenges around Big Data are more impressive when discussed with reference to large organisations with portfolios of built assets and complex operational challenges.  Global businesses in finance, retail, transport and utilities, for example, now routinely capture millions of data packets a day, and store the resulting information (sometimes measured in Petabytes) in data warehouses, where it is interrogated to extract business intelligence (BI).

Big Data is a blanket term relating to volumes of information that are very large, and too complex to be analysed by conventional database tools and processing capabilities.  Big Data challenges may involve hundreds of computers working across multiple server farms crunching their way through millions of files.

However, decision-making can be improved for companies of all sizes through the better utilisation of data.


Data challenge – What questions do you want to answer?

The starting point must be to set the questions that require answers.  Next, review existing data sources to determine where the data will come from to answer these questions, and to reveal the data gaps that need to be filled.


Data challenge – Add information in Word docs, PDFs to the data set 

A file may not necessarily contain structured data (building information modelling files are largely structured, of course, but a typical construction project will still involve masses of text and graphic-based data).  More often than not, the data challenge for a property owner, construction contractor or a consultant may require data to be extracted from unstructured or semi-structured files (for example: word-processed correspondence and reports, emails, photographs, graphics, presentations, PDFs etc). Data may need to be scanned and indexed.  Only then can analytics tools start to identify anomalies, patterns or relationships, and deliver usable business insights.


Data challenge – Integrating the existing multiple sources of data

Many medium-sized businesses have multiple internal data repositories. For example, a contractor business may want to get a more detailed picture of the performance of its supply chain partners across multiple projects.  It may have a wealth of information in its ERP system alongside project-related documentation including contracts, specifications and related correspondence in a collaboration platform, as well as a host of other information held by HR, legal, sales and marketing teams.

To integrate multiple data repositories and produce additional intelligence is typically a significant undertaking.  It requires a systematic and structured approach that aligns use of terminology, agreeing a data dictionary, and using it religiously across all teams involved.


Data challenge – Adding new data   

When a company has already integrated multiple data sources what they may want to do is add new, additional data to further enhance the quality of the intelligence. So, it may commission a Big Data analytics specialist to import and digest the contractor’s own data, and then combine it with third-party data.  This may come from other supply chain partners, it may be commercially sourced, and it may be publicly available as open data.  In our hypothetical example, the contractor may incorporate data from Companies House, from court records, from credit rating bodies, from media organisations, from social media, etc.

And the data may not just be historic, it may also be created contemporaneously – the ‘internet of things’ (see our previous post on this topic) enables systems and components incorporated into built assets to stream data, which may be used to assess the performance of the items delivered by a supply chain partner.

Data analytics for SMEs

Perhaps most important of all, an SME need not invest in creating an internal data analytics capability.  Just as industry clients and construction businesses work with Software-as-a-Service providers to manage their project and programme delivery processes, they can outsource their Big Data project to an external specialist, who in the space of a few weeks can combine all the data and provide them with visualisation and dashboard tools to help them extract the desired insights.


CONJECT provide extensive BI functionality allowing organisations to analyse project and programme data, helping to improve decision-making and adding value to their projects. For more information see our Business Intelligence solution.

About the author

Michelle Mason

Michelle Mason leads the UK and MEAP Marketing team, with far too many years in B2B marketing to mention. A CONJECT newbie, Michelle is eagerly climbing a steep learning curve.

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