What is Big Data?
Let’s start with the basics. Big Data can be defined by three things—volume, velocity, and variety. It involves tremendous volumes of data from multiple data sources, often constantly streaming and seemingly never-ending. Take Google Reviews as an example. Google Reviews allows users to submit text and photos of places, services, or businesses they have used and interacted with.
Now think of how many places there are in the world and how many users there are. The amount of data constantly streaming into Google servers is mind-boggling—this is what is meant by velocity, essentially a measure of how fast data is coming in.
Industries such as retail, health, or insurance generate vast amounts of data every second and deciding on how to capture it all is a challenge. Data captured from sources such as sensors, social media, forms, and email make up the bulk variety of Big Data.
How does Big Data relate to geography?
Aspects of geographic information system (GIS) tools and mapping provide another dimension of context when dealing with Big Data. GIS, once exclusive to a desktop environment, has evolved into cloud platforms designed to ingest Big Data and can provide a good return on investment based on the decreasing costs of storage and servers. Small organizations can depend on scalable solutions and midsize organizations can afford to architect their own in-house cloud solutions, making Big Data and GIS accessible to almost everyone.
Many of the new sources of Big Data revolve around data collected by crowdsourcing and social media. The ubiquitous use of smartphone apps has made for an infinite stream of information collection and has opened up geolocation tools such as wayfinding, digital mapping, and finding points of interest to everyday consumers. A visual tool such as an interactive map of a retail store’s trade area can show consumer shopping patterns or travel times and how that relates to the neighbourhood demographic characteristics. While useful to analyze the customer base or drive business growth strategies, it can only provide a glimpse of what’s happening at any given moment. With Big Data, it is possible to create predictive models and provide an advanced view with data from online behaviours, social media, and trending behaviours. This could drive improvements in supply chain and logistics, ensuring positive customer experience and satisfying expectations.
Who’s combining Big Data and location intelligence?
Customer segmentation and consumer behaviour are the most common use cases for geospatial analytics in business. Apps like Strava, a fitness app, for example, can track cyclists and routes that users record. Based on a freemium model—anyone can use it for free, but can pay to access premium features—Strava can segment their audience based on user-provided information such as age, sex, bike type, level of physical activity, and types of activities and deliver marketing and promotional content based on engagement. It can also use the collected location data to provide the public with information on where people are cycling or doing activities visualized in a heatmap. Maps like this could aid in situations where urban planners need to decide to reconfigure or build new roads, optimize flow or determine whether existing paths are being used enough. On the flip side, visualized data like this can cause controversy especially in situations where the app is being used in places such as military bases.
Humanitarian projects and natural disaster emergency response are other areas where Big Data and location intelligence mesh strongly. Open data, crowdsourcing, and social media have become de facto sources when a large scale disaster hits. Satellite imagery is updated constantly and coverage over an area over time provides a spatiotemporal view. While previously reserved for GIS professionals who provide expertise in handling this type of data, the cloud platform and simply designed web applications allow the general public to engage. For example, when Malaysia Airlines flight 370 went down in the Southern Indian Ocean, several sites accessing map sources and satellite imagery were developed so that anyone could browse over the search area at various map scales and submit a location if there were any hints of debris found.
The retail industry is beginning to catch on to the value of combining Big Data and location intelligence. While GIS was prevalent in most companies, its use was often relegated to making pretty maps such as those that marked real estate opportunities or retail locations. But as location intelligence has gained in prominence, retailers have started to see analytics from a spatial perspective. Take, for instance, an example of a major fashion retailer in Canada. Their desire was to develop a model to help identify new store locations in Canada that would result in successful store openings. Using location intelligence, the retailer was able to identify neighbourhoods where the potential spend at this retailer would be greater than the average proportional spend within this category. As a result, the retailer went on to open several new stores, each of them outperforming historical store openings.
Do you have any more questions about Big Data and how it can be used with location intelligence? Contact us to chat more about it.
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