The Real Value Of Data
Data Monetization: What It Means
Complementary Data Alliances
- Banks – Shared data between banks and cell service providers allows for more accurate fraud detection services through location data and purchase records.
- Retail Outlets – Personalized offers can accurately target consumers through mobile channels based on social activity and browsing history.
- Advertising Agencies – Trading data with advertising firms allows for better targeting, which in turn results in more successful ad campaigns.
- App Developers – The data collected through smartphone usage can better equip mobile app developers to design with unmet consumer needs in mind.
- Original Equipment Manufacturers (OEMs) – Better equipment features can be developed through data analytics of mobile device usage, social activities, location data, and purchase habits.
Hurdles To Monetization
- The Replication Crisis – New analytics insights are prized over the testing and replication of current insights to ensure veracity. The race to deliver new insights can result in analyses that contain simple errors, and those simple errors, if they remain unchecked, can blossom into large problems and bad business decisions.
- Bad Context – Simply analyzing data without applying it to the context from which it came can result in false or misleading conclusions. For example, data analytics might notice a surge in water bottle purchasing in Florida, but fail to take into consideration that the surge was precipitated by a hurricane.
- Inaccurate Inventory Data – Employees at local branches often make inventory mistakes. “With such sophisticated systems, it seems unbelievable that these kinds of errors would be so pervasive,” says Satell. “However, when you treat people as mere data points, pay them poorly, and cut back on training to save money, data quality suffers. Is it any wonder that overworked, ill-trained employees make mistakes?”
- Leaving Out The Human Brain – Big Data allows us to make decisions with the backing of massive computer processing power. The process should be extending the reach of the human brain, not replacing it. Data is nothing without competent, imaginative human analysis.