In the past couple of years, we’ve been hearing more and more about “big data,” “machine learning,” “predictive analytics” and how these new technologies are going to make a significant impact across every industry. But how likely is that to happen, and if it does, how big of an impact will they have in the real estate landscape?
We recently had the opportunity to sit down with Zac Ruiz, CEO and Founder of Salt.io – a data firm that specializes in commercial real estate – to get more insights on the matter. They focus on using data to grow business and customer values, finding win-win situations for all parties involved.
We picked Zac’s brain to see what he thinks of big data but, most importantly, on how important it is to use available data efficiently. Read on to find out more.
Q: Tell us a bit about your background and how you first discovered your passion for working with data.
I graduated from the University of Delaware in 1999 with a passion for software development. I worked in a number of industries, but my favorite job was collaborating with people who built financial models. I would be their “data person” and help source/wrangle data and deliver it to the modelers so they could work their magic. I would also help them make their models run faster and at scale. Eventually I was promoted to oversee a team of more than 20 people, where I learned that being a talented programmer doesn’t prepare you for leading others. I quit my job and founded Salt.io as an opportunity to take a step back and round out my game. We are back on track now, bringing a mix of leadership and technology to bear on pain points in commercial real estate and across capital markets.
I am obsessed with the feedback loop that enables learning by discovery. The feedback loop is when you try something super small, see if and how it works, glean a little bit of information, and then try something new. When this loop is executed successfully, confidence starts to build, and as confidence and iterations accumulate an amazing amount of learning happens! As a computer science major in college, my memories of learning technology are a jumble of books, classes, homework, tests, and projects. I never saw and understood the feedback loop so clearly until I started working in finance and I met scores of financial analysts who taught themselves computer science purely through experimenting with Excel.
Microsoft Excel provides the ultimate feedback loop and discovery based learning platform.
You start with data in cells and worksheets and regardless of your technical expertise, there are always new capabilities to discover in pursuit of maximizing the value in that data. Formulas, pivot tables, charts, macros, VBA, add-ins, and so on. An analyst can start with data that they understand intimately and then have an idea about what they want to do with that data and try it right there on the spot, see what happens, then have a new idea and try something else. All while sitting at their desk, 100% in control, so they can iterate thousands of times to create value with their data, while at the same time learn skills that make them invaluable in the knowledge economy. A year of doing that and the ambitious analyst often finds him or herself with the confidence and skill to program/code anything they want. When I saw this same story over and over I was hooked on the power of data and technology to drive change and growth.
Q: Can you tell us how Salt.io started and how it seeks to help out the commercial real estate industry?
When I started Salt, the tagline on my website was “Databases are where Data goes to die.” Build on the story in the previous question; what usually happens next? The analyst has built something awesome in Excel, maybe a commercial real estate valuation model. It’s so useful that the business wants to run it for 500 commercial office spaces or retail properties, something you likely can’t do in Excel quickly. Or they want five other people to use it, which raises another problem. So, in the pursuit of performance and scale, the natural course is to move the data out of Excel into databases and inside larger, more generic technical systems. The IT team needs to get involved, usually more software developers.
This is great in theory but the consequence of this migration is that the core feedback loop is broken. The analyst can no longer perform even one additional iteration of the feedback loop by themselves. An iteration that used to take an hour at their desk now may take 4 days, as they have to request read or write access to databases they don’t control or request code changes to systems they can’t see. They may literally never see their own data again in its full form. The analyst can no longer grow with their creation, and their creation may no longer be able to change with the pace of the industry/market/world. And so my interest is how to bring performance and scale to systems without these side effects. How do we preserve the feedback loop at all costs? Commercial real estate specifically is a great industry to play with these ideas, because everybody uses Excel, but at the same time they do crave performance, scale and governance.
Q: There’s hype about big data going around in a lot of industries. Do you think it has reached or impacted real estate so far?
I really don’t. I think real estate is too niche that it doesn’t make sense to be on the forefront of Big Data. I actually replaced “Databases are where Data goes to die” with “Small Data” as my second Salt tagline. Principles and learnings from big data work trickle down to all industries over time, however, I just don’t think big data is the primary concern in real estate yet, because most firms struggle with basic access to data, especially in CMBS.
Q: Tech nowadays can make or break a company’s success. Do you have any tips for trends real estate professionals should follow?
I am obviously a huge fan of real estate experts learning technology and being masters of their own data. While there will always be collaboration between finance experts and computer scientists, I think so many communication problems become easier as finance experts learn technology and computer scientists learn finance, and we blend into one team instead of two teams at opposite sides of the building.
I am also a fan of emerging alternative data sets; figuring out how new sources of data like mall foot traffic relate to the real estate story. I am pro-Microsoft Excel; I don’t think it needs to be wiped out but rather it should inspire the next wave of technology.
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