Shifts in global and local economies due to the Covid-19 pandemic and the discrepant, ever-changing national and local responses have rendered the rudimentary analytic models of many organizations useless, or worse—detrimental to the business. Although forecasts crunching historical data may work fine in normal times when markets are relatively stable, in anomalous times like today these trend-based models falter. In times of turmoil, leading indicators prevail. Economists, business analysts and data scientists are finding that historical data from the last several recessions is of little use today. Trend-based models rely on the company’s own historical data, such as sales or production data, and sometimes macro-level industry data. These formulae expect trends to continue on a similar path and at a similar pace. Driver-based models, on the other hand, rely more on leading indicators of performance or business activity. They incorporate external data about situations or observations that highly correlate to and presage one’s own business outcomes. In this session, Mr. Laney will share findings from his groundbreaking global study of external data to explain: What kind of organizations are using external data sources and how Which external data sources are used for which kinds of purposes Which kinds of external data sources are being leveraged the most How the use of external data leads to improved competitiveness Differences in the reliance on external data by high-performing versus low-performing organizations.
Innovation Fellow, Data & Analytics Strategy, West Monroe