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The Ethics of Data Mining

Collecting and storing data for analysis has various business applications, from operations and strategic partnerships to sales and marketing. Due to improvements in data collection and warehousing technologies, businesses are amassing ever-increasing volumes of customer data. However, as this information is collected, privacy concerns, the transformation of raw data into useful information and misuse of data are also increasing.

Today’s data leaders face ethical challenges as they navigate a contentious legal and financial environment. Data mining is quickly becoming a synonymous with exploiting customers for profit. With rising consumer awareness, new laws have emerged worldwide, including sector-specific laws in the U.S. and the General Data Protection Regulation (GDPR) in Europe, currently the world’s toughest privacy and security law. However, businesses may follow existing laws and cross ethical boundaries nonetheless. Laws alone are not sufficient to establish a consensus set of ethical practices. That is incumbent upon business schools and industry leaders.

A Master of Science (M.S.) in Information Science can equip professionals with an ethical, appropriate understanding of data mining and its implications.

Ethical Concerns in Data Mining

There are three chief concerns in data mining and use:

  1. Transparency: Customers should have a certain amount of visibility into and control over how their data is collected and used. Companies should be forthcoming with their data collection and use practices and ask permission before acting rather than asking for forgiveness after the fact. However, transparency with opt-in or opt-out procedures is not sufficient. Customers should be presented with and asked to explicitly consent to specific language around data access and usage in order to make informed choices. Mass broadcasts of fine print opt-in messages are not solving today’s data collection and usage transparency concerns.
  2. Personal data: Currently, there is no industry or political standard in the U.S. regarding the legal parameters or definition of personal data. Today, businesses operate largely with sector-specific regulations and their own beliefs about what constitutes personal data. Often, these ideas center around legal consent, rather than types of data and how companies can and cannot use them. This latitude presents risks to customers.
  3. Governance: Even in the EU, where the GDPR offers a more comprehensive legal framework for data practices, control within companies is just as essential to protecting consumer data. There must be leaders assigned to policy development, supervision and enforcement. Without proper governance, ethical lapses and legal troubles are inevitable.

Best Practices in Ethical Data Mining

Having and enforcing a strong data policy is a competitive advantage in the marketplace and the community. Conversely, data negligence can adversely affect customers’ reputations, which can eventually doom a business. Business leaders of reputable companies know this, and most are doing all they can to develop formalized policies and standards. Many are working with leading academic institutions to formalize standards in training and education.

Here are a few best practices that are common among successful corporations today:

  • Develop a culture of data transparency. Company leaders from the top down should consistently communicate — internally and externally — the importance of a coherent data policy, what it includes and how it evolves. Everyone in the organization is responsible for upholding the company’s data values.
  • Establish a companywide vision for the data policy. The standards should reflect the industry’s context, the organization’s values and provide examples through use-specific cases.
  • Set up a data ethics board. Data policies have implications in every business department, so having a cross-functional ethics board in charge of data policies enables representation from each. Representatives from the C-suite, operations, legal, finance, IT and other departments can collectively determine the implications of each standard across the company and what would be involved in creating customer transparency and buy-in, as well as enforcement on the company side.
  • Define ownership and accountability of different programs. When an algorithm needs to be changed or a system’s access to data adjusted, who is responsible? What is the communication chain? For every data collection, processing, storage and analysis practice, there should be responsible individuals aligned with it.
  • Become more customer-centric. Prioritize the customer impact over financial impact as you consider data policies and decisions. Protect customers with a framework of policies designed to protect their valuable data, and limit access to only those who are trusted to use data in accordance with policy.

The Curriculum

The program’s course on Data Mining and Business Analytics, as well as the broader program curriculum, has a strong business and data ethical component. The course explains techniques and tools for transforming an organization’s raw data into meaningful and useful information for business decision-making. Major topics include data warehousing, data mining, data analytics and statistical modeling.

If you are interested in positively impacting one of the defining business and cultural issues of our time, a Master of Science in Information Systems may open doors to boardrooms where your values and influence are needed.

Learn more about Texas A&M International’s online Master of Science in Information Systems program.

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