However, this is only the tip of the iceberg. For many enterprises, an additional challenge will be using the right data to answer the most pressing questions.
As Professor Daniel Guetta, Director of the Business Analytics Initiative at Columbia Business School and Columbia Engineering, says, "Collating the data you already have across existing systems and ensuring your systems can cope with its volume and complexity is the priority. Then, you need to make sure you're clear on why you're bringing in external data." To find relevant external data sources, you need a strong understanding of what factors can influence your business.
As he explains, though weather forecasts are often cited as a useful external data source for many enterprises – in predicting demand, for example – they're not useful to everyone. "Consider how the data will help you. How does it relate to your business? This is particularly important in times of constant change."
Professor R.A. Farrokhnia, Executive Director of Advanced Projects and Applied Research in Fintech at Columbia Business School agrees. He cites the renowned British economist Ronald H. Coase, who said: “If you torture the data long enough, it will confess to anything." Instead, enterprises need to be clear on the questions they need to ask where AI and machine learning could be used to find the answers.
“Companies that understand their own limitations – in terms of their access to data and its quality, as well as more intangible business attributes, such as innovation culture – achieve better results with forecasting. This is because they understand what they can and should forecast with a laser-like focus instead of trying to forecast everything," says Farrokhnia.