Is data really that important to a business?
A quick survey of the top companies by market capitalisation readily reveals that data is key.
Managing Data
Global | Publication | February 2021
Data is an incredibly valuable resource for businesses, enabling organizations to effectively operate and to make business improvements. In order to exploit this value most effectively, businesses must invest in good data management.
The aim of data management is to allow businesses to oversee and control their data resources, enabling organizations to optimize the use of their data in a manner which enhances the value of data while minimizing risk (e.g. legal and compliance risks) to the business.
What does data management include?
Data management aspects include the following
- Assigning responsibilities for data sets.
- Validating the quality of data.
- Identifying relationships within data sets.
- Recognizing and resolving faults within the data.
- Optimizing database systems (including database administration).
- Managing the backup and recovery of data, and its archiving and destruction.
It is a multi-disciplinary area, which requires structured input from a variety of teams across an organization such as IT, information security, records management, privacy, legal, compliance and business area representatives.
Data Governance
An important element of good data management is data governance. Data governance consists of the establishment of appropriate plans and policies at the “people level” to ensure effective data management within applicable legal and regulatory frameworks.
A key aspect of good data management, and a fundamental tenet of data governance, is accountability. Each major dataset should be formally managed by a named individual, who is responsible for the care and quality of the data which is assigned to them. These “data stewards” should ensure that such policies and procedures are followed in respect of their datasets, including around classification, validation and quality control of data.1
Data Ethics
Data ethics are linked to data governance. Data ethics moves focus from simply considering whether data use is legally compliant to whether data use is ethically defensible and what the wider societal consequences might be for the proposed data use. The use of data ethics by a business can be important in safeguarding business reputation, and can also have wider legal implications depending on the context (as ethical and legal issues are often inextricably linked).
Principles of data ethics, such as fairness (e.g. taking steps to reduce or eliminate inherent bias within data, especially in the context of AI machine learning) and transparency,2 are increasingly being built into data governance strategies. This can help establish defensible processes for the handling and sharing of data, particularly where the data use impacts individuals or society, or the data is the basis for businesses decision-making.
For more information on ethics and decision-making, see Artificial Intelligence and Ethics.
Footnotes
1
Office of the Deputy Prime Minister, London, Intra-governmental Group on Geographical Information, The Principles of Good Data Management, July 2005, https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/14867/Good_dataMan.pdf.
2
Government Digital Service, Data Ethics Framework, 16 September 2020, https://www.gov.uk/government/publications/data-ethics-framework/data-ethics-framework-2020.
The myriad ways in which data may be used
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.
Nature of data, ownership, and control
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.
Ways of protecting data
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 technology data: case studies
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?
The regulatory environment for data
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.
Free flow of data
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).
Data for re-use
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.
Antitrust / competition law data issues
The exclusive possession or control of data can have antitrust / competition law considerations, giving rise to access disputes.
Contractual considerations in sharing data
The uncertain nature of intellectual property rights in data means that “contract is king” in data transactions.
Managing Data
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.
Liability
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.