With 'black swan' events arriving more often and with greater impact, businesses must monitor the perishability of their data and the analytic models applied to it.
That means assuring the currency, accuracy and relevancy of their data and models. Knowing the lifecycle of these elements is critical for organizations seeking to anticipate and respond to unpredictable, severe changes more quickly and effectively than their competitors, perhaps even ensuring their salvation in the process.
Perishable data and analytic models make it much harder to anticipate and respond to sudden shifts in demand for products and services, the price or availability of raw materials, consumer sentiment, and whether customers and employees can access the business.
Businesses that keep their data and analytic models fresher can substantially increase not only their chances of survival, but of capturing a larger share of revenue and profits in the process. For example, we helped one global convenience store chain identify which products were selling most quickly at their stores during the pandemic. This allowed them to be sure they had enough of those goods in stock, and to place them near the checkouts so customers could spend as little time in the store as possible. This one insight drove about a 25% per customer increase in purchases of those products.
The deeper and more accurate insight a business can generate, the better it can provide more value internally and externally, such as providing disease-tracking data to public health agencies to help curb pandemics. The good they do can also make such companies more attractive to customers, employees and investors.
Understanding data and model perishability goes far beyond traditional measures such as age or recency. It requires continually assessing the three criteria that determine whether an organization is getting the real-time intelligence it needs to understand, adapt to, and even anticipate shifts in the business environment.