The fourth industrial revolution was heralded by companies collecting data on all aspects of their operations: from the heat and moisture levels in their factories, to the fuel expended in delivering finished goods. Over time, more companies and industries starting using their operational data to gain incremental efficiencies, improve yields, predict demand, reduce waste, and improve profits. As it proved its worth time and time again, data analytics became less of a frill and more of a fundamental business process.
Following the advent of social media and other digital services, insights based on user-generated data opened up new and exciting possibilities for organizations to learn more about their customers to improve their services and target their markets and individuals more effectively. High street retailers discovered new data-based services to help them compete with online rivals. Healthcare providers used data to predict demand for treatments and medical services. Insurers could more accurately predict risk and price policies more fairly and competitively. Universities found that they could analyze data to predict when students were at risk of dropping out, so that they could offer the required support.
The importance of solid data ethics is becoming more apparent on a daily basis. Brand reputation is at stake with consumer trust being harder to gain and easier to lose than ever before.
However, the fines levied by a growing list of regulators remind organizations that, where people’s data is being used as a source of insights, it is fundamentally users’ data and, even after consent is given to collect it, that data must be used ethically.
Last week, the French data protection regulator, CNIL, fined Google 50 million Euros, for contravening the EU General Data Protection Regulation. The record fine was imposed after the regulator upheld a complaint by two pressure groups: La Quadrature du Net (LQDN) and None of Your Business (nyob), which put the case that Google’s terms and conditions effectively robbed users of the right to opt out of their data being processed for the personalization of adverts.
CNIL’s fine comes just one month after the Italian Competition Authority fined Facebook 10 million Euros for emphasizing the fact that the social media platform was a free service, while not making it clear to Italian users how their data would subsequently be used for commercial purposes In September 2018, the UK Information Commissioner’s Office fined Facebook £500,000, the maximum amount possible, for sharing UK users’ data with Cambridge Analytica. Two years earlier, Germany’s data protection commissioner banned Facebook from collecting data from 35 million German WhatsApp users.
Beyond the obvious demand for data to be handled and stored securely, is the question of how data is actually used by the businesses that collect it. Consumers today are more aware of malpractice and want to know ‘Is our data solely being used to deliver better services, and more relevant product ads, or is it being sold to a third-party, without our knowledge or consent?’
The importance of solid data ethics is becoming more apparent on a daily basis. Brand reputation is at stake with consumer trust being harder to gain and easier to lose than ever before. So how do you ensure that your organization engages with your customers and prospects in the right way?
The three pillars to managing the use of data are data access, data quality and data insights.
To determine the ethical use of data organizations must first understand what data they hold, whether clear consent was given to collect it, and what it relates to.
Start by asking yourself the following questions: what data do we have? Where does it come from? Which processes use what data?
Having solid access to all this data is only the first part. Assuming you can access all this information, ask yourself the following questions of it: is the data correct at the point of collection? How is data moved throughout the organization and how is it changed along the way? What rules and changes have been applied to the data? Do different pieces of data represent the same thing? What personal data exists? What should we anonymize?
Only when we have access to all the data and we can verify its integrity can we ask ourselves what insights can we derive from this data. How can we combine and visualize different data sets? What does the underlying data look like? Who has access to this data? Where are the results being used? What conclusions can we draw from the data?
8 Steps to Ethical Use of Data
So how do you ensure your use of data is ethical? How can you harness the power of automated data management to help your organization to work with data in the correct way?
- The highest priority is to respect the persons behind the data. The potential harm to individuals and communities should be the paramount consideration when deciding whether or not to use particular datasets to derive fresh insights.
- Attend to the downstream use of datasets. Data professionals should strive to use data in ways that are consistent with the intentions and understanding of the disclosing party. Data has the tendency to spread within an organization. Consent may be given for one scenario, but be aware how your results might be used downstream for different purposes.
- Remember there is no such thing as raw data, all datasets and accompanying analytical tools carry the history of human decision-making. As much as possible that history should be auditable, including mechanisms for tracking the context of collection, methods of consent, the chain of responsibility, and assessments of quality and accuracy of the data.
- Laws and regulations have largely failed to keep up with the pace of digital innovation and existing regulations are often miscalibrated to prevent risks. You should view compliance with current laws as the bare minimum. In this context, compliance means complacency. To excel in data ethics, leaders must create their own compliance frameworks that outperform legislated requirements.
- Be wary of collecting data just for the sake of more data. Many organizations today are collecting data because it might be useful for future analytical purposes. However in many cases less data might actually lead to better analysis and certainly carries less risk.
- Make sure you are transparent about what data you were capturing, how data is being used, and why. Maximizing transparency at the point of data collection can minimize more significant risks as data travels through the organization.
- Designing for transparency means we should aspire to design practices that incorporate transparency, accountability and auditability. Data ethics is an engineering challenge worthy of the best minds in your organization.
- Data ethics poses organizational challenges that cannot be resolved by simply adhering to familiar Data governance policies. Be sure to review these policies regularly, they should be robust and known to all team members. Only if we all play by the same rules can we ensure we comply with the corporate vision and create the best value add for our customers.
Be the Strongest Link in the Data Chain
It is our collective job as data professionals to adhere to professional standards. Be sure to know the true origins of the data you’re working from and what its intended use is.
A good first step for organizations embarking on digital transformation and striving to become truly data driven is to limit the data available and the number of processes that data goes through, so that only the data that is required for a given task is processed. This will allow you to minimize risks and responsibly and ethically govern your data-oriented propositions. Solid data management is the foundation on which ethical data processing is built. All of these challenges can be addressed using an end-to-end data management platform.