To analyze data successfully, it must first be prepared successfully. Poor quality data creates poor results. Worse yet is data that takes too long to collect and clean because it is too big or too foreign.
To compete effectively, business users must be able to rapidly produce and present accurate, concise, and compliant information. Whether the analytic discipline is diagnostic, descriptive, predictive, or prescriptive, time-to-visualization matters.
Data franchising is the 2003 term coined by Richard Sherman of Athena Solutions to refer to the staging or packaging of large data sets into clean, usable chunks for decision-making, particularly through business intelligence (BI) and analytic software.