Is data really that important to a business?
A quick survey of the top companies by market capitalisation readily reveals that data is key.
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Data is a vital asset for almost all businesses. In many cases it is fundamental to profitability and survival. Many businesses now have a mature understanding of the regulatory and commercial imperatives applicable to personal data, but may have not developed the same degree of data literacy in relation to industrial and non-personal data.
Managing, and extracting value from, such data can be a significant challenge. The increasing complexity of data capture, control, management, and analytics in the digital economy could well overwhelm businesses who are not sufficiently data literate. For example:
Accordingly there is potentially a large range of issues a business will need to consider when dealing with industrial and non-personal data. For example, how can businesses control and protect such data, use third party data, minimise risks in deploying data, value and monetise such data, and comply with rules and regulations applicable to (non-personal) data?
With the object of outlining the main issues a business should be mindful of in relation to protecting its data as an asset, working with partners using or monetising data and managing risks specific to data, we examine the following in more detail:
We discuss the legal position in a number of jurisdictions, using them as case studies, in order to illustrate principles of more general application in relation to industrial and non-personal data (given the breadth of laws potentially applicable, we do not attempt to state the law exhaustively in relation to such data for those jurisdictions). We do not address issues specific to personal data.
Local law advice should always be sought in relation to any data project.
A quick survey of the top companies by market capitalisation readily reveals that data is key.
The value that can be gained from data by businesses will inevitably lead to an increase in the use of data to improve daily operations and to develop new products, services and processes.
In many jurisdictions pure information, or data, is not considered to be property. This is because a claim to property in intangible information presents obvious definitional difficulties.
There is a patchwork of different rights, intellectual property rights and contract rights that may apply to data. Understanding the way in which these rights come into play enables a business to understand how its data assets can be protected.
Disruptive technologies, such as AI, IoT, AVs, distributed ledger technology (DLT), cryptocurrencies and smart contracts, generate many different forms of data. What are the particular characteristics of such data, and to what extent can intellectual property rights or other rights protect them?
In this section, we review the EU’s position with regards to industrial and non-personal data and look at whether other jurisdictions have similar initiatives.
Data location laws (in relation to industrial and non-personal data) can be restrictive (as in banking secrecy laws, which may require some types of data to remain onshore or to be “localised”) or liberalising (as in laws that ban the prohibition of export of data from a locality).
In furtherance of the objective of leveraging existing datasets paid for by public funds, a number of jurisdictions have sought to make public sector information (PSI) available to industry.
The exclusive possession or control of data can have antitrust / competition law considerations, giving rise to access disputes.
The uncertain nature of intellectual property rights in data means that “contract is king” in data transactions.
Data is an incredibly valuable resource for businesses, enabling organisations to effectively operate and to make business improvements. In order to exploit this value most effectively, businesses must invest in good data management.
Errors, incompleteness or biases within data may flow through, and be amplified by, data analytics process outputs upon which a business's strategic and investment decisions may depend, potentially causing business losses. In this section we deal with liability arising out of use of data / datasets that are in some respect sub-optimal.