Being “first to market” with a new solution can often mean the difference between success (an IPO or SPAC deal) or also-ran status. That means finding the right partners—particularly real estate data providers—that understand your concept and can engage quickly is critical. At the same time, the quality and freshness of the data takes an increased importance when you’re putting your balance sheet, or investors’ capital, on the line.
So, how can you ensure that you have the best data at the right time? The answer is “old school.” Conduct smart and thorough due diligence that probes the critical aspects of one data intelligence source versus another. Then when your ready, take our free data-bakeoff challenge.
Here’s a sample set of questions to consider when you’re choosing a data and analytics provider:
- What is the breadth and depth of data available and can it be tailored to your needs?
- How fresh is the data? For example, what’s the lag time between the actual date of a transaction and when that data can be fed into your platform or updated in analytic modeling workflows?
- What kind of unique datasets are available to you that another competing data provider can’t provide without delaying data delivery? Examples include HOA data, active listings, liens or spatial offerings, like First American’s True Rooftop.
- How much experience in managing and curating massive property and ownership datasets do the companies have? One or two years? Or decades?
- Is the data provider just harvesting and combining data or are they taking the “hard steps” to verify it is curated properly and is consumable in the form that you need?
- Will the provider actively engage with you to create and investigate new possibilities that you may not have considered?
- Finally, is the provider’s data accepted in the industry as suitable to support critical business functions?
The next step, after you’ve addressed your core questions with each provider, might be a “champion/challenger” evaluation or “data bake-off” to measure performance and determine data quality.
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