Ethical decisions are important for everyone at all levels of the organization but especially for management accounting and finance professionals; it’s essential that we make the right moral decisions that will put the business in the right place ethically and benefit humanity. Putting an ethics lens on data analytics helps professionals find the right answers to the questions raised by digging into the data. Analytics can also be used to identify ethical issues and uncover fraud very early.

Having ethics and data analytics top of mind during decision making can support an organization’s strategic planning, compliance, and risk management, as well as safeguard its reputation. Thus, the leadership team should make an effort to link ethics to its organization’s data and all its systems and processes.

Data analytics is a broad term that covers data governance, data science, data visualization, AI, and more. This bird’s-eye view of the topic will break down the key areas with which finance professionals must familiarize themselves.


It’s essential that data is credible and reliable. Are your data sources trustworthy? Who owns the data? Having clear data fields, definitions, and owners isn’t something to be taken lightly. Many data management programs can be used to ensure proper data governance that takes into account the European Union’s General Data Protection Regulation (GDPR) and other rules. As much as we all want data democracy in organizations, to be able to do the correct analysis, data security and access control need to be prioritized for confidential or sensitive data.


AI and machine learning are complicated topics, and there are many concerns about the ethics of AI uses, quality of data, unconscious bias inadvertently programmed into algorithms, and more.

Evaluating data quality is paramount. Let’s assume that you need to build a model but know the data isn’t 100% trusted. In cases when incorrect data goes into the model, you can expect a poor outcome or misleading results. If we know that some data isn’t correct and we’re creating a model on which we’ll be basing our decision making, then imagine how difficult it will be to make good decisions based on bad data. This can break a company. Quality of data is key, and it needs to be shared with stakeholders. Further, the quality of data and its impact need to be measured.

Unconscious bias is another thorny issue. The AI algorithm doesn’t technically think the way humans do and doesn’t necessary share our values. It isn’t like AI will act with unethical intentions; it will do what we tell it to do based on what we share with it via coding. We need to make sure that we tell AI what we want it to do, share the organization’s values with it, and clearly define how it needs to work. If we don’t vet algorithms for encoded biases, then the AI system might inherit unconscious bias programmed into the model, skewing the results.


Professionals must ensure that data visualizations are created ethically. A common pitfall is omitting data. If a visualization doesn’t include all the necessary data to make the correct decision, then this is an ethical concern. Make sure you aren’t cherry-picking and sharing only the data points for the story you want to share but rather sharing all the relevant data points and walking the audience through your analysis, explaining why you think your recommendation is the correct one.

The scale on a graph can be another unconscious ethical challenge. When trying to make the graph clear, we can scale the graph’s x-axis and y-axis in ways that can affect decision making through increasing that scale and showing the impact to be very big, though it could actually be a minor difference.


In data analytics, communication is key. It’s important to keep all stakeholders updated and ensure that there’s transparency in terms of anticipated timeline, data quality, and other important aspects of the project. If the project uses data of questionable quality, then that needs to be highlighted for all decision makers.

We also need to analyze the potential impact of the data quality—or lack thereof. If we’re working on a project that’s expected to be done in six months, but we hit a challenging bottleneck, then this needs to be communicated. All stakeholders have the right to understand the status of the project and what has impacted the timeline. A best practice is to manage data analytics projects using Agile methodology.


Data analytics can be a powerful tool to highlight ethical issues early on, ideally before they blow up into a compliance breach or scandal. Through predictive analytics, fraud and other types of unethical behavior can be flagged before a crisis happens. Individuals who engage in unethical behavior may be identified, potentially saving organizations from financial and reputational harm.

Data analytics needs ethical consideration for organizations to avoid potential pitfalls. It has the power to alert management to potential red flags that could indicate ethical challenges or compliance breaches. Interpreting data through an ethical lens is a powerful process that can add value to strategic planning and oversight. Finance professionals should speak up and share their thoughts and experience on ethical issues related to data analytics with their organization’s leaders to ensure that they’re up to speed on this vital subject.

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