Making sense of big and small data for destinations

More and more we are hearing that big data has all the answers that destinations are looking for when trying to understand what visitors are doing, where they are going, how much they are spending and so on.  This sounds enticing and useful.  But is it?

Firstly, what exactly is big data?  Put simply, big data refers to large complex data sets so voluminous that traditional data processing software just can’t make sense of them.  Unlike traditional data, which is structured in its format (think spreadsheets – even really big ones!), big data is usually highly unstructured containing things like text, video and movement.  A good example is social media comments, shares and posts.  However, for this data to become meaningful, complex analytics are required; these are usually time consuming and often expensive.  Critically, this is where the data can be misinterpreted and can diminish in its reliability, so it is vital this part of the process is done right.

By contrast, small data isn’t so-called because there is not a lot of data in a small data set – there could be hundreds of thousands (even millions) of records, but the data is in a structured format, and can as a consequence be analysed by humans rather than machines.

There is a tendency for destinations to focus on big data before addressing the basics of managing small data, in part as it appears to be an easy and enticing solution.  Clearly big data is becoming more important and cannot be ignored, but for destinations it is not the most pressing issue.  Making the most of small data is likely to deliver greater value for a number of reasons, such as:

• Being easier to understand: big data datasets can be very complex and therefore understanding what they represent can be difficult.  The analytical algorithms within them are usually “black boxes” and so have to be trusted – small data tells the story by itself.
• Being more actionable: big data can be used to identify patterns and trends but it can be difficult to use that information to make actionable decisions.  Small data is more focussed and targeted, making it easier to use to make decisions that will have real impact and subsequently measure that impact.
• Being more affordable: the cost of storing and analysing big data, or buying it from third parties, can be prohibitive for many destinations.

In short, quality CAN beat quantity!

So let’s get a bit more practical and leave the theory behind.  When we work with destinations helping them to track tourism and develop online systems to do this, we initially focus on the small data.  Typically these include:

• Accommodation utilisation: using several techniques including collecting the data directly from accommodation establishments, or using data from booking platforms such as Airdna.  Typically these include occupancy, average length of stay, average daily rates, and forward bookings.
• Transport movements: passenger movements by relevant transport modes (typically rail, bus and air) can be captured through engagement with local operators and can include passenger numbers between and origins and destinations.
• Car parking: using automated payment systems such as RingGo and PaybyPhone it is possible to track the number of car parking sessions and expenditure by the hour.
• Visits to attractions: usually collecting this directly from attractions or using automated visitor counters and payment machines that track visitor numbers, gate receipts and additional spend in shops/cafés.
• Events: capturing the number of persons attending events (concerts, festivals, etc) may require a system of manual counting, or if ticketed, can be sourced from ticket vendors.
• Environmental data: using sensors to measure the quality of air and water, and noise levels, the impact of tourism on areas can be measured.
• Traffic counts: using automated counters, the number of vehicles by type (motorbike, small car, large car, lorry, etc) can be counted entering and/or leaving a destination.
• Digital marketing: tracking social media and website engagement through their own analytics systems, such as Google Analytics and Meta Analytics.
• Tourism information centres: tracking visitor numbers and expenditure, and sometimes additional activities such as bookings can be achieved through automated counters and till systems.
• Visitor surveys: deploying QR code (or similar) self-completion surveys across a destination at key locations provides an opportunity for ongoing data collection of visitor and trip characteristics.
• Non-visitor surveys: utilising panel surveys, ongoing online surveys of consumers who are not visiting destinations can be undertaken and tracked, giving insight into non-visitor sentiment
• Weather: average temperatures, rainfall and sunshine hours at a large number of weather stations across the UK, as well as forecasts of the same can be sourced from the Met Office.

Despite the availability and importance of the small data, there is a place for big data when tracking destinations, with mobile phone, social media and card payments offering the greatest scope measuring tourist movements, viewpoints and expenditure as follows:

• Mobile phone data: used to track the movements of tourists and residents, generating data such as numbers of visitors, dwell time, place of residence, and whether they are new or repeat visitors.  Many different systems are available for purchasing this data, all utilising different techniques and algorithms, so data is not comparable between providers.  Some of the most popular include Place Informatics, Geosense and Near.
• Social media data: used to track the conversations of visitors and identify trends related to their interests and preferences.  This is often interpreted as visitor sentiment, identifying positive and negative hot topics.  Several options are available for purchase such as Tourism Sentiment Index and Data Appeal.
• Payment card data: facilities offered by MasterCard and Visa provide data that tracks the spending habits of tourists, such as average spend by sector (accommodation, food and drink, retail, etc), length of stay (based on first and last instances of card use), and average transaction value.  Systems are offered by MasterCard Data Services and Visa although only for non-European card users due to GDPR regulations.

These can provide some useful insight into visitor movement, sentiment and spend.  However, the limitations are relatively obvious.  Mobile phone data only tracks people carrying a mobile phone, and it must have Bluetooth and/or Wi-Fi turned on, and counts them twice if they have two phones.  Separating visitors and residents is not always easy to do.  Social media sentiment can get confused by discussions that arise around events not related to tourism such as political debates or the local football team’s performance!  Payment data only records spend on the tracked credit cards – cash is ignored, as is spend on those cards not being monitored.

There is another key aspect to the small data – big data discussion, and that is engagement with the sector.  Big data is faceless, whilst small data typically is sourced from owners or generators of it in the destination.  This creates an important link.  One of the key aspects of tracking tourism in a destination is the involvement and collaboration with as many stakeholders as possible, in particular the private sector.  Whilst the Destination Management Organisation (DMO) is typically the driver for setting up and maintaining a destination tracking system, the private sector is an important user.

The most successful systems we have set up are the ones whereby the DMO has engaged successfully with the private sector and made them feel part of the system – a two-way sharing of data.  Relying on big data sources and establishing a system that does not interact with other stakeholders will never truly get its finger on the pulse of a destination, or represent what is happening there.  As a consequence it will have less value.  Finding all those small data sources that tell the story of a destination is what we specialise in, bringing them all together to tell the story of that destination.  Layering big data on top of this, where relevant and appropriate, can embelish that story.