What determines whether something has an inherent, real value? In ancient times, a thing had value if it could be traded for something else of value. But data is intangible and constantly variable.
If a farmer in ancient Mesopotamia had excess grain but needed metal tools for farming, he could trade with a local blacksmith who had tools but needed grain. This is the backbone of supply and demand.
The same dynamic applies to data in our modern world of cloud-based ones and zeros. A company that generates data has created something of value to the originating company, to customers, and to other organizations that share the same customers.
As Big Data evolves and matures, the business world is going to need experts who have a masters in business analytics or a masters in data analytics. It is these trained individuals who truly understand the value of data and who know how to capitalize on that value.
Data Monetization: What It Means
In the past, companies wishing to track existing customers’ spending habits had to generate that data in-house. Marketing or inventory departments would then use the data to argue for new products, better ad campaigns, or adjustments in production.
For many modern companies, data has become a goal in and of itself, rather than just a means to a goal. But data is not inherently valuable by itself just because it’s data.
“Mere possession of data alone does not secure competitive success – that can only be achieved through engineering talent, quality of service, speed of innovation, and attention to consumer needs,” write legal experts D. Daniel Sokol and Roisin E. Comerford in their Cambridge University Press article, “Does Antitrust Have A Role To Play In Regulating Big Data?”
Data monetization means utilizing data to its utmost potential. To do so, companies need to devote as much time and effort to analyzing data and pursuing potential markets as they do to mining data. And even insightful analytics, lucrative data markets, and clean data mining techniques don’t account for all of the potential value associated with data.
More often than not, the value of data is seen in terms of its value to strategically aligned, partnered businesses only. Data can also be used to create value for customers.
“To build lasting advantage,” says marketing writer Niraj Dawar in his article, “Use Big Data To Create Value For Customers, Not Just Target Them” in Harvard Business Review, “marketing programs that leverage Big Data need to turn to more strategic questions about longer term customer stickiness, loyalty, and relationships. The questions that need to be asked of Big Data are not just what will trigger the next purchase, but what will get this customer to remain loyal.”
Complementary Data Alliances
Data used only to track customer spending and usage habits is data that is not being monetized properly. “The opportunities and potential of one organization using its data to both serve itself and also to help other organizations serve their customers better are limitless,” explains Mu Sigma Manager Kshira Saagar in his Wired Innovation Insights article, “Monetizing Data: Milking The New Cash Cow.”
Saagar offers several examples of how telecom companies (mostly cell service providers) maximize data monetization through strategic alliances with other companies:
- 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.
Strategic partnerships based on data monetization embody the law of supply and demand in that everyone involved benefits from the alliance.
Consumers benefit by receiving advertisements and offers that will prove much more useful and relevant to their lives than the one-size-fits-all advertising of yesteryear. Consumers also benefit from free apps and services that are subsidized by data mining entities.
At the same time, the partnered companies benefit from shared data while the telecom company benefits by enabling better mobile customer experiences.
Hurdles To Monetization
Although businesses are evolving steadily toward the goal of complete data monetization and flawless predictive models, a few problem areas have made themselves known.
Media business developer and consultant Greg Satell, in his Forbes article, “Has Our Faith In Data Been Misplaced?” sums up several obstacles to the ongoing data revolution:
- 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.
Maryville University’s MS In Business Data Analytics
The skyrocketing demand for business analytics experts lies at the heart of Maryville University’s online Master’s of Science in Business Data Analytics degree. At Maryville University, students learn how to monetize data and present competitive information to decision-makers. Graduates will have the training and knowledge to combine business operational data with the latest analytical tools, making them invaluable to employers.