As a business intelligence (BI) professional, you use data to create solutions that provide insights and help organizations make better decisions. But to do that effectively, you need to handle data ethically, privately, and reliably. In this post, you will learn what these concepts mean, why they are important, and how to overcome the challenges and limitations related to them.
Data Ethics: Respect the Rights and Interests of Data Subjects
Data ethics is the application of well-founded standards of right and wrong to how data is collected, shared, and used. You have a responsibility to treat data ethically, especially when it involves personally identifiable information (PII), which can reveal a person's identity.
Treating data ethically means respecting the rights and interests of the data subjects, such as:
• Protecting their data from unauthorized access or inappropriate use
• Allowing them to inspect, update, or correct their data
• Obtaining their consent for data collection
• Giving them legal access to the data
It also means avoiding bias in data collection, analysis, and interpretation. Bias is any systematic error or deviation from the truth that affects the validity or reliability of data. Bias can lead to misleading or inaccurate results and unfair or harmful outcomes for individuals or groups.
Some of the common types of bias that you may encounter are:
• Confirmation bias: The tendency to seek or interpret data in a way that confirms your preexisting beliefs or expectations
• Selection bias: The distortion of data caused by using a sample that is not representative of the whole population
• Historical bias: The reflection of socio-cultural prejudices and beliefs in data collection or processing systems
• Outlier bias: The distortion of data caused by ignoring or hiding anomalies or extreme values that deviate from the norm
To avoid bias in data, you need to follow some best practices, such as:
• Recording your prior beliefs and assumptions before starting the analysis
• Using highly randomized and large datasets that are representative of the population
• Gathering more data and doing more research about the opposite side of your hypothesis
• Being cognizant of outliers and using appropriate measures of central tendency and dispersion
Data Privacy: Protect the Privacy and Security of Personal and Sensitive Data
Data privacy is the preservation of a data subject's information and activity any time a data transaction occurs. This is also called information privacy or data protection. Data privacy is concerned with the access, use, and collection of personal data. Data privacy is important because it protects the rights and interests of individuals, as well as their trust and confidence in organizations that handle their data.
One of the key strategies to maintain data privacy is data anonymization. Data anonymization is the process of protecting people's private or sensitive data by eliminating PII. Typically, data anonymization involves blanking, hashing, or masking personal information, often by using fixed-length codes to represent data columns, or hiding data with altered values.
Data anonymization is used in almost every industry. You probably won't personally be performing anonymization, but it's useful to understand what kinds of data are often anonymized before you start working with them. This data might include:
• Phone numbers
• Names
• License plates and license numbers
• Social security numbers
• IP addresses
• Medical records
• Email addresses
• Photographs
• Account numbers
Data anonymization helps keep data private and secure for analysis!
Data Availability: Ensure that Data is Accessible and Usable
Data availability is the degree or extent to which timely and relevant information is readily accessible and able to be put to use. Data availability is essential for creating reliable and impactful BI solutions that deliver value for organizations. However, there are many factors that can affect data availability and compromise the quality of BI solutions. Some of these factors are:
• Visibility: The degree or extent to which information can be identified, monitored, and integrated from disparate internal and external sources
• Update frequency: How often disparate data sources are being refreshed with new information
• Change: The process of altering data, either through internal processes or external influence
• Integrity: Data integrity issues include duplicates, missing values, inconsistent formats, or not following business rules. These issues can lead to inaccurate or incomplete results and damage the credibility of BI solutions. To ensure data integrity, you need to perform data quality checks, such as validating, cleaning, standardizing, deduplicating, and enriching data. You also need to document the data sources, processes, and rules that you use for your analysis.
• Visibility: Data visibility issues include lack of awareness or access to data stored in different departments or external sources. These issues can lead to missed opportunities or incomplete insights. To achieve data visibility, you need to work with your colleagues to create a list of data repositories for stakeholders. You can request a short interview with the data owners or ask them to complete a quick online survey about the data they collect and use. You also need to explore external data sources, such as free public datasets, that can contribute to your BI project.
• Update frequency: Data update frequency issues include mismatched or outdated data from different sources. These issues can lead to erroneous or misleading results and poor decision-making. To address data update frequency issues, you need to understand how the update frequency of different data sources can affect insights. You also need to align your data sources with your analysis goals and time frames, or use appropriate methods to account for the differences.
• Change: Data change issues include modifications or disruptions to data due to internal or external factors. These issues can lead to inconsistent or invalid results and reduced trust and confidence in BI solutions. To address data change issues, you need to have a plan for how you will keep stakeholders up-to-date on changes that might affect the project. You also need to encourage team members to think about what tools or methods they are using now, what could change, and how it may influence the data being tracked and how to fill any potential gaps.
Conclusion
Data ethics, privacy, and availability are important concepts that you need to consider as a BI professional. They help you respect the rights and interests of the people whose data you are working with, protect the privacy and security of personal and sensitive data, avoid bias in data collection, analysis, and interpretation, confirm that you are using the right data for the right stakeholders, ensure that your data is in the correct format and can be effectively used and shared, make sense of your results and explain them clearly, enhance your understanding and decision-making, and achieve better business outcomes.
By considering the ethical, privacy, and availability aspects of data, you can create more reliable and impactful BI solutions that deliver value for your organization.
