Data Products: Pricing and Go-to-Market Strategy

Pricing Datasets: Realizing the Value of Your Data

Many corporations – from insurance companies to exchanges, retailers to energy firms – are awakening to the fact that the data they produce as part of their day-to-day operations may have substantial value. To capitalize on this untapped value, many are considering how to package their data into products that can form the basis of a sustainable and potentially lucrative data business.

But before they can establish data sales as a secondary, tertiary, or in some cases even primary revenue source, firms need to understand what makes their data valuable; only then can they set about putting in place a methodology for allocating the requisite resources for turning datasets into commercially viable data products and taking those products to market.

Quantifying the value of any given dataset so that it can be treated as a balance sheet asset is a challenge. For one thing, there is no unified methodology in the marketplace for assessing the value of a dataset. Moreover, issues such as incompleteness, inaccuracies or restrictions around usage may diminish the potential value of a dataset. In such cases, the ultimate value may depend on what can be done to mitigate these mitigate against these issues.

To ensure the optimal valuation of their data, originators need to take into account several factors. These include:

• Depth
• Coverage
• Breadth
• History
• Accuracy
• Consistency
• Completeness
• Timeliness
• Frequency

While the impact of these characteristics on the value of a dataset may be relatively self-apparent, a number of other, less obvious factors need to be taken into account.

Usage restrictions: Any restrictions on the use of a dataset will reduce its value. Restrictions could come in the form of regulatory requirements. For example, should a dataset be categorized as Personally Identifiable Information, it could be subject to privacy regulations such as the EU GDPR or the California Consumer Privacy Act (CCPA), substantially restricting the data owner’s ability to distribute or otherwise share the information. Data originators need to fully understand potential regulations or policies that may impact their plans for offering the data commercially to consumers, as this may affect its ultimate value or indeed the practicality of productizing the dataset.

Accessibility/interoperability: All things being equal, a consumer will choose the most accessible dataset. It’s essential to remove barriers to easy access to the dataset to make it consumable by potential clients. Similarly, interoperability of the data is often critical for the consumer to use the data easily and in a consistent manner. Data owners need to take steps – including adoption of data standards and mapping to recognized symbologies – to ensure their data can be used with other datasets and by applications in order to provide value to the consumer.

Liability and risk: Potential liability and risks associated with the data use will reduce its value. These may include risks arising from potential breaches of privacy regulations, copyright, intellectual property law and other rules and regulations.

Despite this lack of a consistent approach to valuing data, the same three approaches are typically used to value any asset – namely the Income, Market and Cost approaches – are still appropriate when it comes to data.

Income Approach: If the net cash flow benefits of the data can be reasonably quantified, the Income Approach provides a strong theoretical basis for valuing data.

Market Approach: If the value of the data is observable in an active market or transaction, the Market Approach may represent the most appropriate measure of value for the data.

Cost Approach: If the data can be reproduced or replaced, the Cost Approach can provide useful upper and lower bounds for valuation.

Armed with these methodologies – and evaluating their data catalogs and inventories – companies seeking to monetize their datasets can develop a rational and defensible pricing model for their data assets. But it’s also important to take into account external factors and understand the value eventual consumers may derive from the data before making determination of the viability of any potential data product.

A good starting point here is to assess which elements of your dataset may be of value to others. While these datasets may be of great value to your own business, it’s essential to establish that that value is not proprietary to you: Could others also derive value from the data?

Another measure of a dataset’s commercial viability relates to available opportunities to generate revenue from them. Here, the question is: Are the opportunities to generate revenue worth enough to allocate the resources to pursue them? Identifying potential consumers is key, but you need to set a plan for pursuing these opportunities and fully understand the management challenges and costs involved in reaching this target audience.

Indeed more broadly, before pulling the trigger on the initiative, you need to consider whether it’s feasible commercially, legally, operationally and ethically to license the data. When assessing feasibility, there are four principal considerations to take into account:

Client/Supplier Confidentiality: Are there elements of your datasets that contain information relating to existing clients or suppliers that may be sensitive? Items like transactions may be traceable to individuals or companies, possibly in breach of privacy laws or general confidentiality conventions. If this is the case, can the data be anonymized or pseudonymized?

Data That is Licensed and Not Owned: Does your dataset include information that you are ingesting from traditional (or alternative) suppliers’ data feeds? Licensing terms often preclude onward distribution of commercially operated data services or else impose fees. You need to understand how this could affect your datasets and data products.

Firm Confidentiality: Does the data in question relate to financial modelling or performance, strategic plans or R&D developments, which may be detrimental to your competitive advantage? This would be considered proprietary information and would not be advisable to sell.

Legal and Regulatory Restrictions: Does your dataset contain any personal identifiable information (PII) that cannot be shared under certain privacy regulations? Anonymization or pseudonymization can be a viable means of mitigating the risk associated with the management of personal data.

In summary, it is essential to have a solid understanding of the entire lifecycle of the data before you decide to proceed with plans to package and sell it. You need to review the terms and conditions of the contracts in place between your legal entities and your users, clients and suppliers to ensure that they are consistent with applicable data laws.

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You need to understand the data’s origins and sources, possible destinations and recipients, conditions of storage, and backup facilities. Even the legal jurisdiction in which the data is physically located comes into play since some major centers have restrictions on what types of data can leave the jurisdiction.

Stay tuned for our next blog post that will cover Data Governance, Privacy and Ethics in further detail. In the meantime, feel free to also download our white paper on Productizing and Monetizing Data.

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