The world of “business” most of us live in today takes the form of statistical data and logistical information pertaining to production, wealth, consumption, and other such matters in written form (whether in hard copy or digitized), organized in tabular formats populated by numbers. The latter take the form of numerals invented centuries ago in the Hindu-Arabic mathematical tradition that were first adopted in the West by early mercantile capitalists, especially those of the city-states of northern Italy. This data-recording and analytical tradition began in western Europe from around the beginning of the Renaissance.

This presentation takes us far from the Western world, but still during the time of the European Renaissance, to a world yet to be discovered by Westerners of the time – the Inka empire of pre-Columbian South America. The Inka recorded data pertaining to administrative matters on a knotted-string device, known as the khipu (or quipu). Most such records pertained to imperial statistics, especially those concerning such matters as censuses, tribute records, goods stored in state storehouses, and so on. This was a tradition of data, statistics and analytics that was completely alien to Europeans at the time of their first encounter with the Inkas, in the early 16th century C.E. – as well as to most of us today.

This presentation will challenge us to consider an alternative tradition of synthesizing and recording information which, it is suggested, offers unique insights into data, analytics, and the nature of “business intelligence.”

During this session Dr. Borne will begin with his view of the disciplines and practices of data science and advanced analytics. He will discuss major challenges and myths that impede the implementation of data analytics in organizations, and then present solutions and strategic responses to those.

Strategies include: (1) Fail fast to Learn fast with DataOps; (2) Adopt a Culture of Experimentation; and (3) Think Analytics-First by focusing on purpose, products, and outcomes.

He will introduce some novel approaches to predictive analytics that are different from standard time series forecasting. These methods are appropriate in the big data and data science era in which data sources and data formats from diverse sensors extend far beyond traditional time series. Several examples and their related stories will be presented to illustrate the concept of Forecasting 2.0. In all of these cases, our ultimate goal is to generate business value from our rich data assets.