Common Data Monetization Strategies Among Major Tech Companies

The data monetization market is fragmented and highly competitive, with numerous companies vying for a piece of this lucrative pie through various growth strategies such as acquisitions, mergers, partnerships, and collaborations. One prominent example is the alliance between Accenture and Pitney Bowes Inc., which illustrates how large tech companies are leveraging each other's data assets to innovate and create new solutions, benefiting both parties and enhancing their competitive edge.

The Dynamics of the Data Monetization Market

Several key players operate within this market, each with a unique approach to data monetization. These include iConnectiva, Infosys Limited, Mahindra Comviva, 1010data, Accenture Plc, Adastra ALC, Cisco Systems Inc., Dawex Systems, Elevondata, Emu Analytics, Gemalto, Google LLC, IBM Corporation, and Monetize Solutions Inc. With each company adopting different strategies, the landscape is dynamic and continuously evolving.

Influencing Factors in Choosing Monetization Strategies

The decision on which data monetization strategy to adopt is heavily influenced by several key factors:

1. Rights and Ownership

The extent of control one has over the data dramatically impacts their ability to monetize it. Companies that own the data have more freedom to sell or license it. For instance, those who primarily handle non-owned data (like financial credit bureaus) often adopt data access models or sell aggregated data rather than raw data. Conversely, companies like Google and Facebook prefer advertising-driven models due to the vast user data they collect but do not own.

2. Depreciation Rate

Data is analogous to any asset, with a tendency to lose value over time. However, in certain scenarios, data can appreciate in value. Generally, the higher the depreciation rate, the easier it is to monetize raw data over aggregated or summarized data. Companies that generate dynamic, high-turnover data will typically focus on licensing or selling raw data, whereas those with static or slowly depreciating data will often use data access models or offer value-added services.

3. Examples of Monetization Models

Data Licensing

Data licensing involves selling the actual dataset to third parties. This approach is particularly effective when the data's value diminishes quickly, ensuring recurring usage and creating a more sticky product. Successful examples include financial data providers like Bloomberg and credit bureaus. More recently, Foursquare has also embraced this model, leveraging its geolocation data to offer insights to businesses.

Oracle/Expertise

This model is ideal for companies with slowly depreciating data assets that remain pivotal to their competitive edge. Rather than selling raw data, these companies work as service providers, offering data-based solutions without exposing the underlying data. Great examples include market research agencies and management consultancy firms. Companies like IBM and Accenture often use this strategy, providing analytical and consulting services based on their vast data assets.

Advertising

Arguably the most ubiquitous model among tech giants, advertising leverages massive user data without taking direct financial risk. This strategy is particularly viable for companies with large user bases but may not want to be perceived as engaging in data trading. Platforms like Google, Facebook, and Quora rely heavily on advertising to monetize user data.

Value-Added Services

This model is popular among companies that own user data but lack the necessary user engagement levels for a robust advertising platform. PayPal is a prime example of a company utilizing this strategy. By offering services like PayPal Credit, they leverage user data to create value-added offerings without directly selling the data.

Conclusion

The data monetization landscape is complex and multifaceted, with numerous strategies catering to different company needs and data types. Whether through data licensing, expertise-driven services, advertising, or value-added products, each approach offers unique benefits and challenges. As the market continues to evolve, tech companies must stay agile and strategic in their data monetization efforts to remain competitive and innovate.