Lessons from the Industrial Revolution: Four principles of big data ethics

The industrial revolution saw a transition from manual to mechanical manufacturing processes. In what was a major turning point in history, almost every aspect of daily life was influenced in some way as machines and energy were harnessed to do things instead in place of manpower. 200 years later we find ourselves in the midst of an information revolution, where large datasets are increasingly being mined for actionable insights and being used to make predictions. In many ways, this digital era is comparable to the industrial revolution, as big data predictions begin to influence all kinds of human activities and decisions, from dating to terrorism prevention.

Our latest Global CIO Survey found that big data and analytics are top priorities for IT leaders this year, with most respondents citing them as integral to business innovation. This was supported by a local Gartner study, which found that CIOs in Australia are prioritising business intelligence and analytics investments as they look to monetise big data. This reflects an ongoing trend towards real time business in which anywhere, anytime access to applications and insight is becoming ever more important.

But just as technological developments required the establishment of workplace and environmental rules during the industrial revolution, this big data phase of the information revolution calls for a set of legal and ethical norms. In last week’s blog, we discussed the three paradoxes of big data, identified by Jonathan King and Neil Richards. This week, we outline what King and Richards call ‘big data ethics’ – four high level principles that they propose inform the establishment of big data rules.

  1. Privacy

Privacy in an age of surveillance and big data is not dead, but notions of privacy are changing with society as they always have. In this information revolution, the collection, use, and analysis of personal data is inevitable. Ensuring privacy of this data is a matter of defining and enforcing information rules – not just rules about data collection, but also data use and retention. Individuals should have the ability to manage the flow of their private information across third party analytical systems.

  1. Confidentiality

Shared private information can still remain confidential. Individuals generate data with trusted services by design, such as GPS devices, address books, and banking apps. However, just because users are willing to share this information doesn’t mean data collectors can adopt an ‘anything goes’ attitude. Just as an individuals expect information divulged to a doctor or lawyer to remain confidential, principles in time should apply to relationships consumers have with service providers and others who are gaining information in trust.

  1. Transparency

Individuals should know the ground rules upon which their information is gathered, shared, stored and processed. Big data becomes a business when information is shared with third parties without the data generator’s knowledge or consent. Individuals should be given a transparent view of how their data is being used – or sold ­– for secondary uses.

  1. Identity

Boundaries need to be established around what types of things should and shouldn’t be done with algorithms. Big data analytics gives organisations the power to moderate and even make decisions for individuals. Moreover, it enables things to be done in the digital world that are forbidden in the analogue world, such as the ability to racially profile people. A line needs to be drawn between big data predictions and inferences that are ethically acceptable, and which aren’t.

Just as was the case in the industrial revolution, society as we know it is being radically changed by technological innovations. The latest developments in big data call for a societal discussion around what kinds of rules, norms, and best practices should be established to strengthen transparency, be mindful of identity and ensure that the power that comes with big data is not abused. While the amount of personal information that is being recorded is increasing, so too is the need for rules to govern this social transformation. With their four principles of big data ethics, King and Richards have started to educate the debate.

McKinsey & Co have said that “Radical customisation, constant experimentation, and novel business models will be new hallmarks of competition as companies capture and analyse huge volumes of data.” Download our complimentary whitepaper to learn how your organisation needs to change to suit the evolving digital landscape: Why every CEO wants to lead a service defined enterprise – and why the CIO needs to make it happen.


Tags big data, big data analytics, CIO, Digital Transformation, analytics, Big data ethics, Business intelligence, Gartner, information age, IT leadership, Digital age, Digital era, Global CIO Survey, McKinsey & Co


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