challenge
Sparq needed a better, more analytical way to efficiently target the most promising American cities to establish a local presence. When comparing potential cities for a new development center, there are thousands of inputs to consider, including demographics, technical skills availability, wages, costs of living, quality of life and the prevalence of higher education institutions, just to name a few. Sparq needed to quickly analyze large amounts of data from both public sources and proprietary data not available for public purchase. Sparq’s growing success in providing onshore digital engineering talent required them to speed up the process of identifying and opening new locations to meet fast-growing client demand. Time was of the essence.
solution
Our team took on the challenge of using a unique combination of results from both data mining and machine learning approaches applied against purchased third-party data to evaluate tens of thousands of data points across 59 mid-sized cities. Sparq designed a similarity mining algorithm to check for similarity percentages between city statistics and implemented a clustering algorithm to examine similarities between cities based on their statistics. Using Power BI to visualize the algorithm results helped the executive team to query the data with different weighting scenarios and quickly assess the best possible cities in which to make investments based on head-to-head comparisons. The application was built in AWS using Terraform for environment setup, React for front-end, DynamoDB for data and .NET for back-end.
results
By overlaying the data mining and machine learning approaches, we were able to drill down on the most appropriate cities with more accuracy, allowing Sparq to increase the likelihood of success and achieve an outsized return on investment. These approaches helped Sparq leadership select two new development center locations in Buffalo, NY and Baton Rouge, LA. The entire development process took less than two months compared to six months in previous site selection processes.
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