In part 1, we just looked at one person’s view of what Big Data means for corporations, then tried to apply it to real estate. I’ve had a couple of conversations about it with some very smart industry people since posting that, and am trying to wrap my head around the whole concept.
The first place to start, it seems to me, is to try to understand what Big Data is or means or even looks like when applied to real estate. I’m 99% certain that I’m missing a whole bunch of complexity that the more technical folks amongst the readership understand, and I hope y’all will chime in either here or elsewhere. Just let me know and I’ll link up to your posts.
Defining Big Data
The first challenge is to understand what Big Data even means. So I start with the usual suspects: enter Google-fu.
In information technology, big data is a loosely-defined term used to describe data sets so large and complex that they become awkward to work with using on-hand database management tools. Difficulties include capture, storage,search, sharing, analysis, and visualization. The trend to larger data sets is due to the additional information derivable from analysis of a single large set of related data, as compared to separate smaller sets with the same total amount of data, allowing correlations to be found to “spot business trends, prevent diseases, combat crime.”
And a bit further down, Wikipedia provides some examples:
Examples include web logs, RFID, sensor networks, social networks, social data (due to the social data revolution), Internet text and documents, Internet search indexing, call detail records, astronomy, atmospheric science, genomics, biogeochemical, biological, and other complex and often interdisciplinary scientific research, military surveillance, medical records, photography archives, video archives, and large-scale e-commerce.
- Walmart handles more than 1 million customer transactions every hour, which is imported into databases estimated to contain more than 2.5 petabytes of data – the equivalent of 167 times the information contained in all the books in the US Library of Congress.
- Facebook handles 40 billion photos from its user base.
- FICO Falcon Credit Card Fraud Detection System protects 2.1 billion active accounts world-wide. 
The first thing that strikes me is that if we took all of the property information in the United States, along with let’s say 10 years of historical information (listed, sold, DOM, etc. etc.), that’s probably approaching “Big Data” quantities… but I don’t know. Anyone with actual knowledge care to weigh in?
The second thing that strikes me is that “Big Data” is defined in some ways as a “cannot do”. That is, Big Data isn’t simply huge; it’s so huge that standard technologies and standard data techniques can’t handle it:
It’s not only the much bigger quantities of data, it’s also the rate at which it’s coming in and the fact that it’s mostly unstructured that are outstripping the ability of people to use it to run their organizations with existing methods, said Dale Wickizer, chief technology officer for the U.S. Public Sector at NetApp Inc.
“The IDC study also says that the number of files are expected to grow 75 times over the next decade,” he said, “and if that’s true, then a lot of the traditional approaches to file systems break because you run out of pointers to file system blocks.”
The literature of Big Data is full of such statements, that existing SQL-type databases (those would be the same types of databases that virtually every real estate company and brokerage and MLS run) can’t handle the volume or the speed, that existing techniques for data analysis would take months to complete a report on databases that are that ginormous, etc. etc.
The third thing that strikes me is that 99.99+% of the companies and organizations in real estate today do not have the technical personnel to handle this kind of cutting edge technology. And given how hot this area is, they won’t be able to recruit those people either. They’re not competing with other brokerages to find that Director of Hadoop Programming; they’re competing with Amazon and Google.
Okay, So My Head Hurts Now… So What?
I’m not a technologist, so this is all just licking the surface of the watermelon. I am, however, a consumer and a business strategist. So what I care about is what all this technology enables and the impact of that on businesses.
So let’s refer back to the Evans video. Evans says:
What Big Data does is to change [corporate strategy] again, by creating a horizontal view. And the horizontal view is transformative of how you think about the corporation…. Big Data is often bigger than the individual business unity. It’s often bigger than the corporation itself. Therefore, if you are to exploit the value of Big Data, you need to fundamentally to reorchestrate how it is that the various pieces of the business system fit together.
I’ve been puzzling over what this means, and I think I kind of get it.
Consider what very smart people think Big Data allows companies to do. McKinsey & Co. thinks Big Data is the “next frontier for innovation, competition, and productivity” and says things like this:
The use of big data is becoming a key way for leading companies to outperform their peers. For example, we estimate that a retailer embracing big data has the potential to increase its operating margin by more than 60 percent. We have seen leading retailers such as the United Kingdom’s Tesco use big data to capture market share from its local competitors, and many other examples abound in industries such as financial services and insurance. Across sectors, we expect to see value accruing to leading users of big data at the expense of laggards, a trend for which the emerging evidence is growing stronger. Forward-thinking leaders can begin to aggressively build their organizations’ big data capabilities. This effort will take time, but the impact of developing a superior capacity to take advantage of big data will confer enhanced competitive advantage over the long term and is therefore well worth the investment to create this capability. But the converse is also true. In a big data world, a competitor that fails to sufficiently develop its capabilities will be left behind.
Since this is a real estate oriented blog, I know most people are thinking about marketing, marketing, and maybe a little bit of marketing. Well, here’s one view on what Big Data means for marketing:
By themselves each data source provides some information, but not enough to produce any significant insights or breakthroughs. Imagine the power of linking and correlating these sources; and maintaining them with real-time updates. That changes the rules of the game. Two very popular uses of Big Data are customer insights and predictive analytics. They help to answer these kinds of questions:
- What real-time offers do prospects prefer?
- What are the best web pages to serve based on an identified prospect’s interests?
- What is the probability of closing a sale with a potential customer who just filled in a web form?
- What promotions work best at particular times of the day?
- What is the probability that a prospect targeted by a nurturing campaign will make a purchase in the next six months?
Marketers understand that their biggest challenge is communicating the right offer at the right time in the right channel to prospects or existing customers. To that end, digital interactions (email, SEO, paid search, display advertising, social media) with a target audience are far more valuable than offline equivalents. Those transactions, if corralled and used intelligently, enable businesses to build sustained relationships with while supporting behavioral modeling and probabilistic forecasting. [Emphasis in original]
Customer insights and predictive analytics can yield results like that? A company could know (or make very good guesses on) how likely a prospect targeted by a nurturing campaign will make a purchase?
Why, that sounds like fantasy!
Except it isn’t, even in the small data world we live in. Ever hear of Smartzip? Now imagine Smartzip connected to an actual cross-vertical Big Data engine.
But as Evans suggests, the Big Data in question is bigger than the company itself. Smartzip isn’t big enough to do Big Data the way Google and Facebook and Amazon can. So Smartzip as a company has to be thinking more along the lines of partnerships, alliances, and building on third party platforms (e.g., Social Graph) and figure out where it adds value.
Since the value chain itself hasn’t changed — i.e., real estate is still about a buyer making an offer on a property, getting it accepted, and then money changing hands along with title to the property — when Smartzip adds value, someone else has to lose it.
Evans sees the corporation evolving to the few Big Guys who can handle and manage Big Data (the Google, Apple, Amazon, etc. of the world) and a whole bunch of very small businesses: individuals, basically, and small teams.
I see that possibility the most in real estate, where individuals and small teams already dominate the value chain, together with big Web portals.
Let’s think about what that sort of marketing might look like, and where the values start to go in the next part.