“Tim Sloane, Vice-president of Payments Innovation at Mercator Advisory Group: a lot of an enterprise data includes Personal Identifiable Information (PII), and the major challenge is to have that data analyzed by third parties without PII being leaked”.
According to Randy Koch, CEO of ARM Insight, for safe and secure data monetization (DM), every business should take to a DM road map: 1) be educated on compliance and regulatory requirements; 2) treat different types of data specifically; 3) keep the data prepared for machine learning (ML) usage; 4) use all the three DM channels while learning and leveraging other channels to maximize the profit and minimize the risks. According to Sloan, “data monetization is all about leveraging the data that you have through new channels”. Three types of data according to the levels of risk: 1) high risk - raw data with PII (customer’s name, their credit card number, address, contacts, date of birth, the purchase’s time and cost, etc.); 2) medium risk - anonymized data - a part of PII which does not identify the customer; 3) low risk - synthetic data - falsified data set created from the core data, i.g., slightly changed numbers that make the transaction irreversible. DM channels: a) internal – cleansed data may be analyzed for a new purpose, i.g., company’s customers and operations’ analysis; b) external – sell the data to third parties, i.g., to a restaurant chain that wants to know where its clients shop before and after visiting its restaurants; c) innovation – artificial intelligence (AI) products, i.g., chat bots, digital assistants, self-driving cars, or healthcare innovation cure. When being sold, “customers’ PII must be protected at all costs (…)”. According to Sloane and Koch, data must be accessible across the organization, so, demolish your data silos, keep your data cleansed, updated and prepared for ML analysis to be able to monetize it and better the company.