In the world of site selection, companies assess the pros and cons of various factors to identify the location that best fits their current and future business needs. The list may seem endless: labor availability, utility needs and costs, real estate options, tax climate, incentives, quality-of-life considerations, even an area’s weather patterns and the likelihood of natural disasters.

With the emergence of big data and the use of data analytics in all facets of business decision-making, the mechanics used to organize and evaluate the information that drives site selection decisions have taken a quantum leap . And the impact is only going to become further-reaching.

When we started working in the site selection industry, a paper-intensive request for proposal (RFP) drove the process. Through an RFP, we would ask for a bevy of information that state and local stakeholders would spend days and weeks gathering, resulting in a final deliverable totaling hundreds of pages.

Now the process moves much faster and in a much different fashion. With companies making decisions more quickly, the demand for information accessibility grows every year. RFPs still have their place, but because so much information is available online via various unrestricted databases, companies don’t need to rely on site selection consultants or state and local government officials to access basic information. They are able to obtain it themselves.

For more comprehensive searches, consultants and companies are able to access private databases and other paid research resources for information. These outlets allow for sophisticated, targeted searches, enabling companies to be especially precise in the data being searched.

Nowhere is this level of refinement more precise than in the retail sector. By digging deep into the characteristics of sites and cross-referencing those attributes with consumer and other demographic information driving a retailer’s business, companies are able to make site decisions with such acuity that it makes the old way of site selection decision-making seem embarrassingly antiquated.

Despite the advances fostered by data analytics, site selection is still greatly impacted by qualitative factors that fall outside the realm of electronic research or granular data mining. Various economic development publications issue quality-of-life surveys along with best and worst rankings proclaiming the top places to grow a business and areas to be avoided. Despite these lists, many decision-makers find softer, qualitative influences best evaluated through a process not driven solely by a clinical, dispassionate approach. In place of data analytics, CEOs and other executive team members physically visit sites and meet with state and local officials in person to assess quality-of-place considerations. They then cross reference this human experience with the sense of culture they seek to build within their companies and workplaces.

The use of data analytics may also fail to account for the human component of local site selection decisions. For example, if a project is on a tight timeframe, companies want to know how that deadline will be nurtured or impeded by the governmental bureaucracy indigenous to the area. Do local officials have a strong history of working collaboratively with companies, or do they bring their own wish list to a project that could kill the deal? These factors are hard to account for without live interactions and interviewing the individuals directly impacting those outcomes.

Nonetheless, the use of data analytics is here to stay, and its impact on site selection is sure to increase. These are big decisions companies are making. They need to find data just as big to help them make the best decisions.

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