The analysis of issues in digital economy cases often requires managing massive datasets and providing highly elastic computing power for data analytics and econometric modeling.
Businesses in the digital economy regularly collect large amounts of data from their day-to-day business operations. This data may include details on customers, sellers, and purchase transactions that power algorithms such as recommendation engines. The analysis of issues in digital economy cases often requires managing these massive datasets and providing highly elastic computing power for data analytics and econometric modeling.
BRG’s investment in big data is one of our key differentiators and enables our data management experts to respond rapidly to meet on-demand data needs. BRG has supported projects involving multiple experts accessing the same underlying data, and we have worked with some of the most advanced data technologies available. Our data science work includes building artificial intelligence and machine learning models to support specific case activities.
Why Big Data Is Important
Big data projects involving digital businesses have unique characteristics. Not only are the number of transactions large, but the number of artifacts that a transaction contains dwarfs those of a nondigital business. A digital transaction, for example, may include hundreds of online activities carried out by a user before they made a transaction, resulting in billions of activities and transactions that need to be analyzed.
BRG’s Approach to Big Data
BRG’s approach focuses on active data management and optimal computational computing environments. Our services include processing data, hosting data, and providing elastic cloud-based computational environments for economics teams to perform their work. We utilize cloud services provided by Microsoft (Azure) and Amazon (AWS), and BRG professionals have expertise architecting and deploying projects onto their platforms. Our team is also experienced in using software tools including Google BigQuery, Snowflake, Microsoft SQL, STATA, AWS RedShift, Databricks, and SAS, as well as programming tools such as R and Python.
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