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Kylie Davis: What makes big data so big?

Kylie Davis of CoreLogic explains the concept of 'big data' and what exactly makes it bigger than we think it is.

Every real estate agent has a database – even if it’s just the names of people you’ve formerly done business with sitting in your mobile phone. From old-school Rolodexes to high-end computer programs that automatically send emails and respond to queries, a database is an essential tool in every agent’s kit.

So when the conversation turns to the concept of ‘big data’, many agents think they’ve got that nailed. Big data is just a bigger version of a database, right?

Wrong.

Big data is a constant stream of billions (and more) of data points being analysed and collated in real time, with a degree of intellectual property that requires enormous computing power, algorithms, analytics and insights, being accessed in fractions of seconds. Overlay big data on top of your CRM database and you’ll receive extraordinary insights into your customers, as well as obtain the ability to identify new clients more accurately and before your competitors. But it’s unlikely to be a DIY project.

However, all big data starts with little data, and the collection of individual records; so what agents have in their CRMs is just the start. Here’s what makes big data so very, very big.

1. These days, we all leave a digital trace
If you’ve paid for something with a credit card today, logged onto Facebook, used your mobile phone, gone for a run, watched TV or even just sat in traffic – virtually every activity we do now creates a digital trace, according to The Economist.

A digital trace is the new phase referring to the stream of data that we constantly generate, created, tracked and monitored by the devices that are now an essential part of our lives. The gold lies in being able to hoover up these traces and transform them into valuable insights and actionable products.

2. Objects are no longer dumb
From fridges to thermostats, elevators to cars, our homes – even our toilets – devices that we previously thought of as innocuous and dumb are instead becoming data collection services, feeding more and more granular data streams into the internet.

It is estimated that self-driving cars will generate 100 gigabytes of data a second when they become mainstream. Tesla cars map the roads they drive on and, in California, have a detailed view of road quality. Inside our houses, Google Home and Alexa are capturing our search history, our music preferences, our shopping lists – and turning on Netflix, which in turn is monitoring our viewing choices.

Some fridges are now capturing food choices, Fitbits monitor our activity, while it’s predicted that toilets will in the future regularly undertake medical diagnosis. All this will generate valuable information for insurance companies that could affect our premiums and coverage levels.

If the collection of data from home heating and cooling devices means every home on the planet can reduce its energy consumption, the effects of greenhouse gases could be reversed and the types and value of properties’ common selling features could change significantly.

3. The machines are now learning (constantly)
While videos of bots lifting boxes capture the imagination, the real face of Artificial Intelligence is more boring but nevertheless astonishing. Algorithms churn through billions of data points in fractions of seconds, recognise predetermined elements and do the next required thing – or, alternatively, run through millions of probabilities and do the next best thing. The speed and scale is at a degree that no human brain could keep up with.

Remember when Facebook used to alert you to tag your friends in photos? Had you realised that Facebook’s image recognition technology means you don’t have to do that anymore? Netflix recommends viewing options, Amazon tells us what to read and voice recognition is replacing dashboards as computer interfaces, with us now able to tell the computer what we want, rather than type and search.

Google knows when you’re pregnant, Facebook knows if your relationship is on the rocks and CoreLogic can identify people whose behaviour is indicative that they’re about to list their house for sale. Artificial Intelligence is both converting the digital trace into automated workflows and accelerating its ability to predict what happens next.

4. Data is the new oil
While it’s cheap and simple for anyone to set up and run their own database, getting it to a big data level takes serious infrastructure in the form of computing power, programmers and rooms full of data scientists with PhDs.

The Economist described big data as “the new oil”, with companies such as Google, Facebook, Apple, Microsoft and Amazon – five of the most valuable companies in the world – being specialists in the field who have a “god’s eye view of activities in their own markets and beyond”.

Can real estate agents compete with these behemoths? The size and scale is such that even big tech-based real estate organisations, such as realestate.com.au, would struggle.

But understanding the principles behind the data – how it’s being collected, what’s being done with it, its capabilities and significance – helps real estate agents understand the importance of managing and monitoring their own databases with rigour, and provides insights into the value they should be extracting from their own CRMs.

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Kylie Davis

Kylie Davis is the head of content and property services marketing at CoreLogic. She spent nearly four years as Network Editor of Real Estate at News Corp Australia, creating a national desk of real estate reporters across more than 100 titles and training them in the use of data and market journalism. She has a 25-year career in media across News and Fairfax, and as a 20-something was the founder and publisher of hyper-local newspaper The Village Voice. Follow her on Twitter @KDavisCorelogic.

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