Highly competitive organizations and innovative companies are seeking new competitive fields and exploring new avenues to success. Technological advancements continue to pave the way for a new era of competitiveness where data is viewed as a strategically evolving asset capable of unleashing new opportunities for monetization.
Data monetization is not about information technology or business intelligence but rather an effective and timely exploitation of a new asset class and converting it into profits via improved revenues or reduced costs. However, it must be distinct from the tactical management of the enterprise. As an analogy, if BI tools and databases are the pipelines, data is the electricity that flows through. Thus, the electricity must be monetized rather than the pipeline network, however effective.
Thus, data monetization must be closely knit with the technological capabilities of the enterprise, and a data strategy must first define its value to the enterprise, value to clients and potential value to third parties.
3 Common Approaches
A comprehensive data monetization strategy must entail deploying business models that reflect internal goals or external customers. A few of these models are:
1. Return on Advantage Model –
Many enterprises are defining the application of data analytics through a return on advantage model wherein an organization utilizes its internal performance data to triangulate it with information on external demographics to create a competitive edge. This monetization model centers around initiatives including:
Customer Targeting – when purchasing patterns are used to identify patterns in purchasing and buyer clusters and possibilities to cross-sell and up-sell. In this case, monetization is to sell more compatible products more effectively with a return on advantage realized when revenues are enhanced.
Risk Mitigation – based on system access patterns and purchases matched against external credit data, organizations can identify risk-prone accounts, potential customers or fraud opportunities. In this manner, data is monetized when losses and operational costs are reduced, and businesses gain a competitive advantage and generate a return on data assets.
2. Premium Service Model –
Most often, a premium service model includes delivering data to third parties or end users via a software as a service (SaaS) where customers extract / access data via a portal for a subscription fee. Data monetization of the ratios model occurs when the premium service model, though fee-based, offers a level above average and can only be advantageous when returns are linked to incremental revenue generation.
3. Syndication Model –
Where data is transformed (not raw data) and delivered to third-party customers or end users is when the syndication model makes the most sense. An apt use includes entities that utilize this data for their product/development efforts after receiving the syndicated data feed via pre assembled reports. This is when the data owner sells the same data set via reports or on an ad-hoc basis.
A data monetization strategy must be balanced with an expanded view of opportunities and tactical value delivery. For this, an inquisitive mind and a disciplined approach are key. Organizations must consider 4 Critical Discovery Concepts as they explore their strategy and approach.
When data is aggregated via several dimensions and analyzed to identify patterns, shifts and anomalies.
When data is validated and verified for insights through cross-verification, it gives rise to newer data when it is correlated in unique ways.
FRAME OF REFERENCE
When data is viewed from different perspectives to compare, explore and the enhanced insights it can produce.
PRESERVATION OF PRIVACY
When data is viewed beyond its “transactional” value, it is utilized to discover high-value opportunities.
Data monetization must be about enhancing competitiveness but also help leadership achieve clear differentiation, including ” What’s our business model?” or “How do we plan to make money?” Data monetization strategy begins to take shape when this fundamental question is answered. The best way to begin is to analyze the current state and the organization’s resolve.
Lastly, factors like data’s ever-evolving and changing characteristics, market dynamics, and existing value delivery processes must all be considered while challenging the entire organization to brainstorm on questions that may amplify or hinder its growth, both in the short and the long term.