Data analytics is a common fixture in most modern enterprises. Along with a deep understanding of which data to collect, how to store it, and how to use it, it’s equally important for them to do all of that without invading or harming the privacy of the data sources.
Enter data ethics. Data analytics professionals must be aware of the requirements of data ethics and the implications of non-compliance so they can be responsible for data usage and analysis. Enterprises are looking for individuals who can help monitor and manage their compliance with data regulations.
This article will explore the concept, examples, best practices, and benefits of data ethics. You will also learn how an industry-ready data analytics bootcamp will help you dip your toes in this dynamic field.
What is Data Ethics, and What Does It Mean?
Data ethics is the field of ethics that assesses an organization’s generation, collection, protection, and use of data. It is a group of principles that defines the proper manner of data gathering and dissemination and flags dubious instances where data integrity may be compromised. It consists of guidelines that enterprises can follow to frame their data policies. In short, it tells organizations if they are using their data correctly and ethically.
Examples of Data Ethics
With the advent of AI, data ethics has become even more challenging to understand. Let’s discuss some real-world examples demonstrating its pivotal role in organizations.
- IBM has gone above and beyond to integrate data ethics into its AI systems. They work towards eliminating bias from AI systems by stressing transparency and explainability.
- Apple has formulated a privacy policy focusing on on-device processing and data minimization. User privacy, transparency, and control rank high on their data privacy principles. The policy aims to collect as little data as possible and process it on the device itself rather than on the cloud, thus eliminating the chances of data breaches via the cloud.
- Maersk has adopted data ethics policies that ensure the data is transparent and secure. They use the data to innovate and create value without compromising data integrity and privacy.
- The European Union created the General Data Protection Regulation (GDPR) to ensure that all companies treat their data with integrity and respect. Enterprises must demonstrate their compliance with the GDPR to ensure the credibility and trustworthiness of their data protection systems.
- Microsoft gives its users complete control over their data. They can decide what data to provide, its storage, access, and deletion. Microsoft has created a robust system that works with the data available without forcing users to reveal personal details.
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Why Data Ethics is Important
In 2022, almost 74 percent of the respondents from the UK and USA expressed concerns about their data being used unethically to track and target their online behavior. An increase in digitalization gives these concerns more credence than ever before.
This highlights the critical need for data ethics to build customer trust that their data will be used only for the purpose stated in the company’s data policies. Implementing data ethics proves to customers that their data is secure and stored appropriately in the organization’s systems.
Further, data ethics provides guidelines for using and analyzing the data. This ensures that no inherent bias exists in the models used for the evaluation and prevents deliberate attempts to ignore selective data. Equal treatment of the available data ensures an appropriate decision-making process.
Several governments worldwide have established regulations that companies must follow. An effective data ethics policy must ensure the organization complies with the regulations. It gives further credence to the policy and increases the brand image.
Hence, almost 70 percent of organizations employ an internal manager who leads and implements data ethics policies. Data ethics has thus emerged as a vibrant field for aspiring data analytics professionals.
Key Principles of Data Ethics
Data ethics comprises certain principles that are central to formulating a comprehensive data ethics policy. Here are the key principles that you must remember.
Transparency
Transparency entails lucidity and clarity in data generation and gathering, data type, storage practices, and data deletion and sharing. You must clearly communicate to users how you will be using their data. For example, you must have a data ethics policy on your website, and users must be able to understand your actions.
Accountability
Accountability implies that the organization is responsible for the data after it is collected. If there are any breaches, deletions, or leaks, the organization is held accountable for the violation and its consequences for users. The Equifax data breach is a great example. The social security numbers and passwords of almost 147 million people were stolen. Due to its poor data security policies, Equifax was deemed accountable for this data breach.
Individual Agency
Individual agency refers to the control users have over managing their data. They must be able to access, remove, change, or modify their data. Their explicit consent is required to gather and use the data. You must also provide the option for users to decide if they want to synchronize their data across all the devices, share it among different websites, or restrict it to a particular system. This is especially true for a website like Google, where people can use their Google ID to register and log in to multiple websites.
Privacy
Privacy involves protecting data from public exposure. You must ensure that the data entered is accessible only by the users or credible parties. For example, supermarkets sometimes ask for phone numbers and create a profile in their system to improve customer outreach. However, they must explain that they will not use or share the phone numbers illegally.
Fairness and Impartiality
Organizations must ensure that data practices, algorithms, and AI systems are bias-free and do not affect a certain group. This is especially important when data is extensively treated by automated systems such as ML and AI. For example, a hiring AI algorithm at Amazon used historical data and developed an implicit bias against women, skewing the resume screening.
Data Minimization
Data minimization refers to collecting only selective data that is crucial and discarding or not asking for any other information. This will minimize the efforts required in data collection, analysis, and storage. This also reduces the pressure on the users to complete all the information asked for in the data forms. For example, an email address is typically a norm, but a phone number or address may not always be required. Age and gender are other data types that are not always necessary for the application. Hence, you must keep these questions at the users’ discretion.
Limited Storage
It refers to deciding which data you no longer need and limiting how long it will be stored. For example, in certain universities, research data is stored for seven years after a student’s graduation. This prevents data from gathering dust in storage and restricts its access.
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Benefits of Data Ethics
Data ethics ensures that the organization pays sufficient attention to the collection and usage of data rather than ignoring the information and letting it fall through the cracks. Here are some of the benefits that these enterprises reap by implementing data ethics:
- Greater goodwill in the market
- Higher customer loyalty and low customer churn.
- Improved brand value and reputation.
- Better credibility in the market due to good regulatory compliance
- Excellent demonstration of fair decision-making
- Controlled and ethical AI practices
- Increased sales and market value
- Higher awareness among employees regarding the organization’s policies
- Enhanced data analysis due to verified and traceable data sources
- Development of an attitude toward continuous improvement in algorithms and AI
- Demonstration of accountability of the enterprise toward addressing issues satisfactorily
However, there are instances where the data policies may be ignored or misinterpreted. The Cambridge Analytics scandal is a glaring example of where Facebook landed in a scandal for gathering user data without consent.
In addition to a public relations fiasco, such instances can lead to customer loss and a dent in the financial bottom line. In today’s world of social media, such negative news can spread far and wide and affect the company’s image worldwide in no time. Further, the data leaked is vulnerable to malicious usage that may damage the individuals involved and snowball into greater issues.
Data Ethics Best Practices for Data and Software Professionals
Now that we know the advantages and pitfalls of data ethics let’s review some best practices you must follow to ensure the correct implementation of data ethics in your processes.
- Ensure that the terms and conditions of data usage are conveyed
- Explain in simple language which data is collected, how it is stored, and why it is used
- Provide options for users to select which data they want to give
- Include the option to refuse certain questions if they are not crucial to your process
- Ensure that the users can access and delete their data from certain applications or completely if they wish to do so
- Prepare and anticipate potential problems with the data collected and implement responses to failures in data security or usage
- In case of an incident, communicate it to the customers immediately and assure them of a quick response with minimal damage
Also Read: SQL for Data Analysis: Unlocking Insights from Data
Learn Essential Data Skills and Kickstart a Lucrative Career
Data ethics are here to stay and will only get more stringent as more companies adopt data analytics and AI as part of their processes. As data ethics regulations become stricter, industries need experts who can use data analytics to navigate those complexities. If that’s the career you’re aiming for, you need to acquire sound knowledge of the intricacies of data analytics.
This data analytics program is specially curated to build data analytics knowledge from the bottom up. You will learn critical skills such as building data pipelines, clustering using k-means, data analytics using R and Python, data mining, and data ethics.
Industry leaders will also train you in Power BI and Tableau. This course provides a great opportunity to explore generative AI and get hands-on with how data analytics works with AI.
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