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Visualising the data


Big data must be better understood to deliver the biggest returns, says Geoff Keal of TerraQuest

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Visualising the data

Big data analytics is the process of examining a range of data sources, be they large or small, complex or simple, presented in multi-layered datasets to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information. The findings can lead to more effective marketing, new revenue opportunities, better customer service, improved operational efficiency, competitive advantages over rival organisations and other business benefits, ultimately providing or adding value to service propositions.


But what is meant by ‘big data’? Arguably it cannot just apply to the proliferation of large and diverse data sources. It must surely also extend to the effective access and application of data which can deliver ‘big solutions’, ‘big outcomes’ and ‘big benefits’ to businesses and the public it serves. What is considered big data varies according to the perception and capability of the user to realise its true value. The impact of ‘big’ should therefore be more appropriately applied to impact and benefit derived from how data is presented and consumed.


Different sources

Today’s data comes from multiple sources, much of which is offered freely having been derived from transactions undertaken by governmental bodies and local authorities as well as property, financial, or personal transactions. This data is delivered in a variety of media and is anonymised, making it challenging to relate to textual data held in silos.


Viewing of big data in itself provides a level of insight but it is the results derived from the analysis of it where the real benefit resides. It is the value that can be derived as a result of integrating and joining up with your own corporate data especially within visualisation software such as a geographical information system (GIS).


These empowering technological environments can reveal trends and influences on a business which are not readily apparent or easily understood from silobased datasets. A GIS will display the information within its geographical or ‘spatial’ context, providing a rich picture which rows and columns cannot replicate, and provides a credible and visual evidence base for key decision-making.


As previously observed, the social housing sector in particular has been slow to appreciate the power and business benefit that can be derived from the use of big data within a GIS technology which arguably emerged in the mid-1980s (Mapping the future, November 2012).


Many government bodies and local authorities are now deploying systems and services which can capitalise on the wealth of information they each generate, as well as utilising data available via external agencies. Once this data is integrated with their own systems it adds significant value and benefit to the people it supports and the services they provide. The effective and efficient delivery of services at a time of austerity has never been more essential. Activities such as asset management are being transformed through the centralisation of data management and processing in support of key departments such as housing, planning, care, highways and environment.


Duplication of effort, and therefore cost, is reduced through a shared and more comprehensive use of data, a more strategic approach to information management and a combined approach to delivering services. Modern local authorities utilise GIS, which helps contribute in a fundamental way to the management of services delivered by them.


Reducing effectiveness

A limited understanding and application thereof significantly reduces the influence and effective management and hinders the delivery of great service at best value. The key influencers need to understand more of the benefits of big data systems and focus less on the cost.


Perhaps one way of influencing this mindset further and raising the profile and benefits of big data is to make data and GIS a formal element of the geography curriculum.


A current and simple example of a business application capitalising on the above approach is the decision-making process involved in reviewing potential sites for housing development and the information required to inform and support the development decision.


In recent years the decision-making process to locate development, especially social housing, has changed and can now be described in spatial queries which can be used to evaluate potential land to include access to schools, bus routes, commercial areas, employment and suitable support for the potential occupants. This process is delivered by spatial analysis of a range of big data drawn from various sources including: existing resource locations, public transport routes, employment characteristics, environmental factors, proposed commercial and retail developments and many more datasets.


This evaluation also provides a method of scoring proposed sites, and informing the business case for each development. To allow successful analysis we must ensure the data is suitable for the questions being asked. Data needs to be qualified (metadata), maintained and made available in the right formats to support integration and effective usage, for example fit-for-purpose data.


Fit-for-purpose data delivers answers to queries and provides better-informed decisions quicker and with more confidence, as it is based on the best available and current information. Metadata provides vital information regarding the genesis of data and establishes a basis for evaluating the relative strengths and weaknesses of each element involved in whatever problem is to be solved by enabling a weighting to each dataset to qualify the results generated.


With the increased access to information, especially relating to individuals, there is an increasing responsibility to securely manage and store information.


Geoff Keal, managing director, TerraQuest


This opinion piece was written independently, but first appeared in a chapter sponsored by Mears


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