How to Build a Data Strategy for Monetization in 7 Steps

7 Steps for Data Strategy for Monetization

How to Build a Data Strategy for Monetization in 7 Steps

The adoption of digital services across many industries is creating large volumes of data that can be valuable, particularly to financial services organizations.

This so-called ‘alternative data’, often produced as a byproduct of the core activities of corporations (and even of stock exchanges), can offer unique insights into market dynamics, customer behaviors and other factors that represent valuable analysis both to industry participants and analysts at financial services firms.

But although many potential data producers have valuable data that could generate considerable revenues, when it comes to monetizing these datasets they often don’t know where to start. The adoption of digital strategies may generate vast amounts of data, but its value lies in the end consumer’s ability to make use of it.

This requires consistency, completeness and governance to ensure it is collected, normalized, stored and distributed in a way that gives consumers confidence in its accuracy, integrity and timeliness.

As an essential first step to achieving this, prospective data producers need to put in place a coherent digital data strategy. Companies considering embarking on a program to monetize their datasets should take the following steps in order to put in place a strategic plan for launching new data products:

1. Make a data inventory.

Firms considering monetization of their data should first establish what data is held across the organization. The absence of a clear and harmonized view of data makes it impossible to complete subsequent tasks such as the formulation of an overarching data strategy, the assignment of ownership to particular data sets and identification of possible use-cases.

2. Assess your firm’s appetite for using data to help make better decisions – and promote strategic revenue generation.

For data to be useful for outside consumers it needs to be useful to your organization first. Does your company have the appetite and expertise to become a data-driven organization, one that perpetuates the use of accurate and relevant data in decision-making throughout the enterprise? For some firms, this requires a cultural change instituted from the most senior management that needs to happen before you can start thinking about how to translate datasets into strategic revenue streams.

3. Understand your target audience.

It’s imperative to understand who would find value in your data and to what purpose. This helps in assessing the opportunity at hand, but also informs the design of what will ultimately be your company’s data products. Alternative data is typically acquired by commercial data vendors like Refinitiv and Bloomberg; emerging alternative data vendors like Crux Informatics and Battlefin; large research shops; or directly by consumers themselves, including corporations and quants and analysts at investment firms and their service providers. It’s important to understand the dynamics of their interest and how they will put your data to work for business benefit.

4. Set a clear vision and scope.

The vision for the organization should be harmonized with the business’s objectives and overall strategy. For example, how can the firm’s use of emerging technologies like artificial intelligence (AI) be best deployed to streamline data operations in support of new data products. The vision and scope should prioritize the economic value of AI solutions, identify specific use cases and how it can be used within the organization.

5. Set up clear structures and relationships.

It’s essential to clearly communicate the strategic purposes of the data monetization program and understand the needs of various internal departments to provide relevant solutions.

6. Establish a governance framework.

The framework should provide oversight, monitor impact and consider the ethical and compliance issues that could arise from using data analytics and AI to make decisions.

7. Develop a go-to-market strategy.

As with any data product, it’s essential to develop a coherent plan to bring your data services to market in order to target corporate and financial clients seeking an information edge. Key to a successful campaign is getting the pricing right, taking into account data quality and comprehensiveness, delivery restrictions, accessibility and ease of use. It’s also essential to understand the costs involved in realizing the revenue opportunity from selling the data. 

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