Triangulating Your Data Solution
Which are the commercial buildings?
What are the heights of the buildings in the city?
What addresses are in the building?
Easy to answer questions, right? As BuildingFootprintUSA creates high quality, richly attributed building footprint data, these are the questions that we hear. On the surface, each of these questions sounds simple until you dig a little and find the intention behind each question. We find that the same question can actually be looking for different answers depending the intent behind it; how the question is asked can lead to completely different solutions that will solve the problem.
Creating data solutions that answer questions such as these requires creativity. Frequently, BuildingFootprintUSATM data solutions use not one, not two but as many as three different datasets. We build data solutions for our customers that triangulate an answer from multiple data sources.
Let’s look at an example.
Which are the commercial buildings?
When someone asks us this question, we answer with a question. Do you mean any commercial buildings? Do you mean retail? Do you want to identify any building that has any type of business, even a home-based business? Do you mean zoned commercial?
To develop data solutions that answer these questions (and more), we triangulate:
- The foundation is business list data from Infogroup, which models all business activity across business classifications in the USA
- We use property usage and zoning codes that come from assessor data
- U.S. Postal Service residential & business delivery indicators contextualize the activity associated with each address
With this stack of data (and other data that indicates commercial activity) we can answer virtually any question, or we can answer the same question – can you show me the commercial buildings? – in virtually any way.
Trusted Partners Triangulate Solutions
To solve these hard problems, why are we triangulating across datasets? There are (of course!) three reasons why we do this.
3. No dataset is complete and up-to-date. Every data provider should be transparent about the completeness and currency of its data. With that transparency, you will quickly see how every dataset has gaps. Triangulating among more than one dataset can help to fill those gaps.
2. Multiple datasets can each contribute to answering your question. For example, you may ask a question that requires complex answers drawn from multiple datasets – “show me all buildings that have at least one business AND that the U.S. Postal Service believes have business-related addresses”
3. Multiple datasets can be cross-referenced to validate answers to important questions; when your business is making big decisions using data solutions. It’s important to verify the results.
Need another example?
When someone asks “What are the heights of the buildings in the city?,” they could mean:
- the number or floors or stories – they could be analyzing real estate valuation by floor
- the physical height of the building – they could be planning 5G coverage and need to understand how building height impedes wireless signals
- the building’s height above sea level – they are in emergency management and need to understand how a signal from a device translates to a location in the building
There isn’t a single data element that is going to answer all of these questions. Even the datasets that can answer just one of these questions are not complete. For example, we use assessor data to determine numbers of floors; this data can be incomplete and can require supplemental sources such as height to model out the number of floors in buildings.
Picking the right data solutions company
Businesses are seeking better solutions for long-standing problems and striving to solve previously intractable solutions. We have moved past the point where the majority of problems can be solved with simple data solutions. When your business analysis gets complex, what should you look for in a data solutions company?
A great data solutions company will:
- make you aware of the ingredients that could be used to solve your problem
- describe the limitations of these ingredients – completeness, currency, and fit for purpose
- design a data solution working with you that blends different components together to solve your problem
Our goal is to be the company that is asking the right questions to fully understand your business problem. We are building a shelf of data ingredients that we can blend and use in combination to solve your problem. We do this all in a manner that makes it easy to use our solutions – easy to license, easy to use, easy to get value. Start a dialogue with us about how a new data solution can solve your challenging business problem.