Optimal data commercialisation: transforming raw data into revenue-generating insights


Financier Worldwide Magazine

August 2018 Issue

It is no secret that businesses have started to amass a wealth of data. And as innovative technologies offer faster communication, greater storage capabilities and more robust analytics, businesses now have the tools to turn their raw data into extremely valuable company assets. But to capitalise on the disruptive potential of data commercialisation, companies will need to develop a strategy that not only focuses on harnessing the power of new technology, but also captures the value that legal solutions provide.

What is data commercialisation?

Companies like Alphabet, Amazon, Apple, Facebook and Microsoft have long understood the value of enhancing customer data to provide new insights and improve their products and services. With hundreds of billions of dollars of combined annual revenue, these companies have demonstrated that with the right technologies and the right data, they can process ordinary information about their customers and transform it into powerful insights into their shopping habits or use predictive algorithms to anticipate their customers’ product preferences. But as these technologies develop, more businesses can realise the benefits of data commercialisation.

A modern data commercialisation strategy will consider the value of company data, the technology solutions required to enhance the value of data and compliance with regulatory regimes, and privacy and security standards.

The unseen value of your company data

Businesses might be prone to underestimate the ways in which their data can bring value, both in improving performance internally, and through commercialisation as a new revenue source. For example, Microsoft streamlined its sales operations to decrease employee time devoted to each potential sale by 10 to 15 minutes by centralising data on each sales opportunity. The data was then enhanced using predictive analytics software to allow sales employees to gauge the likelihood that an opportunity would lead to a sale while also providing them with critical information needed to make a successful sale. Here, it was simply a matter of collecting information from internal sources to improve the efficacy of a sales operation.

The emerging arena of data commercialisation focuses on taking raw data obtained from business operations and converting it into a new source of revenue. Industries such as financial services have already taken the lead in providing new products and services with insights gained from customer data. Revenue generated from data commercialisation in the banking and finance sector alone may already be as high as $300bn per year. Virtually all other industry sectors can benefit from commercialising their data as well.

Effective data commercialisation includes challenges related to talent and technology. Leveraging company data to turn, for example, a customer’s credit card statements into a customised platter of rewards programmes and discounts would require at least a small team of data scientists and programmers to develop software to process that data. And for companies taking the leap into processing data from automotive sensor technology, which, according to one company’s estimate, might require data processing capabilities of 25 gigabytes per hour, the demands for technical expertise and investment in technological solutions increase dramatically.

Technological considerations

Three major technological developments are expected to drive significant growth in data commercialisation: (i) the Internet of Things (IoT); (ii) blockchain and smart contracts; and (iii) machine learning and artificial intelligence (AI). Each one of these innovations has already changed how companies manage their data sets, but the best strategies will consider using a combination of the three to get the most out of their data.

IoT devices

According to one industry analyst, by 2020, the data received from the IoT will be more valuable to businesses than the profits gained from the sale of an IoT device. Automotive companies will be in the business of selling the massive amount of data collected from sensors located around the car to other companies which might be interested in that data in order to provide real-time updates to highway conditions, parking space availability or as test driving models for use in self-driving vehicles. Biometric data, already being collected via ‘wearable’ technology, will provide companies in the healthcare and life sciences sectors with real-time information about an individual’s heart rate, fitness levels, temperature and blood sugar levels.

In order to manage all of that data effectively, companies will need to establish partnerships with cloud service providers and analytics companies. In entering into these partnerships, businesses will want to ensure that their partners protect the data that they are sharing and maximise their commercialisation opportunities. This will include protections from theft by a cyber attack or data breach, business continuity and service level commitments, intellectual property (IP) rights protections that prevent the transfer of data to an unauthorised user, guarantees that anonymised data will remain confidential, limitations on further sharing of data and restrictions on the ability to reverse engineer data to derive valuable source algorithms. Thoughtful formation of these agreements is essential to increasing the ROI for a company’s data commercialisation strategy and lowering the risks of costly litigation.

Blockchain ledgers and smart contracts

As a company’s data commercialisation strategy begins to develop new revenue streams, the worst-case scenario would be for that data to be used by an unauthorised company to siphon off that new revenue stream. Thus, strong IP protection of newly enhanced data will be critical to protecting investments. Even though the technology is still nascent, blockchain technology shows promise for providing more efficient means for a company to prove ownership of its IP.

Blockchain technology provides an ‘open, distributed ledger’ that facilitates the secure transfer and record-keeping of information. Once that data is recorded on the public ledger, it is difficult to alter the record without verification from the entire distributed network. For IP purposes, this could mean that business may be able to record a large number of verified entries on the ledger, in turn creating a blockchain time-stamp of ownership at a fraction of the cost. For large amounts of data, this can prevent unauthorised use by parties not bound to an agreement.

Smart contracts, another emerging technology operating on the blockchain, can provide added functionality and enforcement mechanisms to the terms of any agreement your business may have with another party using your data. Smart contracts, which add pre-programmed functionality and self-executing code to the blockchain verification processes, allow certain conditions to trigger certain corresponding actions. For example, a smart contract can be programmed with payment processing capabilities to automatically compensate IP owners when certain conditions are met. This could provide businesses which licence their data sets to other parties with an immediate and self-enforcing revenue stream.

AI and machine learning

Over recent years, the technology industry has devoted billions of dollars to developing AI and machine learning in an effort to mature these technologies’ commercial viability. Amazon’s ubiquitous first-wave commercial IoT product, Echo, relies heavily on AI and machine learning to adapt to its user’s voice commands and preferences. Through its use in the home, this IoT device gathers data directly from the consumer’s commands and processes the data to recommend consumers better products and services. Here, the AI supplements the data-gathering capabilities of the IoT device, and in turn, eliminates the need to work with partners or vendors which enhance the data for a price.

The full analytical power of AI may still be too costly for some businesses to implement in their data commercialisation strategy, but as this technology becomes more affordable, more companies can leverage its data-enhancing insights. In the meantime, companies might want to focus on building a strong talent pool of data experts and perfect their data management operations.

The changing legal and regulatory landscape

The General Data Protection Regulation (GDPR) will have wide-ranging implications for many companies’ data commercialisation strategies. The GDPR defines personal data broadly enough to potentially implicate data processed by automated systems like AI, and requires data controllers to provide individuals with privacy notices. For example, where the data processing involves “automated decision-making, including profiling”, the privacy notice must include “meaningful information about the logic involved”. The GDPR also requires data controllers to provide individuals with rights of access, rectification, erasure, restriction of processing, data portability and objection to certain types of processing. Companies will have to design or employ data commercialisation tools with these rights in mind and provide mechanisms for individuals to exercise such rights where AI outputs may include personal data.

Those seeing patents and copyrights to protect IP rights as creators of novel works in data commercialisation tools and outputs can face certain challenges. Evolving data commercialisation strategies implicate complex questions of inventorship and ownership. For example, when an AI system creates visual, audio or textual compositions, a question yet to be clearly resolved is whether a non-human can be considered an author for copyright ownership purposes. Similar questions arise when AI algorithms develop new algorithms or other materials, including improvements in the machine learning context. Moreover, if technical developments are created through algorithms of an automated system, it is debatable whether human developers of the underlying system should be deemed the inventors of the automated output.

Working toward a complete and revolutionary data commercialisation strategy

The potential for businesses to capitalise on new revenue streams from data commercialisation is becoming increasingly important to maintaining a competitive edge. In today’s fast-moving business environment, it is critical to keep pace with these evolving technologies, while being mindful of existing legal and regulatory ambiguities. However, navigating these waters can prove to be a lucrative pursuit for those able to develop a winning data commercialisation legal strategy.


Zenas J. Choi and Cullen G. Taylor are partners at Hogan Lovells. Mr Choi can be contacted on +1 (703) 610 6175 or by email: Mr Taylor can be contacted on +1 (703) 610 6177 or by email:

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