Data analysis is a powerful skill that can help you gain insights, solve problems, and make decisions in your industry. However, data analysis also comes with some risks that you need to be aware of and manage. In this article, we will discuss some of the common risks of data analysis in different industries and how you can avoid or mitigate them.
One of the main risks of data analysis is working with poor quality data. Data quality refers to the accuracy, completeness, consistency, validity, and timeliness of the data you use for analysis. Poor quality data can lead to inaccurate results, misleading conclusions, and wasted resources. To ensure data quality, you need to follow best practices for data collection, cleaning, validation, and documentation. You also need to check the sources, methods, and assumptions of the data you obtain from external sources.
I prefer to divide this issue into two sections. First, let's discuss in-house data, when you have control over the data pipeline it's easier to gather trustworthy, valid, and clean data. But when data is coming from external sources, a more rigorous approach is required to ensure that the data is clean, reliable, relevant and can be trusted to drive insights out of it. No matter where the data is sourced from always inquire: How the data is collected? Why is it collected in the first place? Who had gathered or generated the data? What methods have been used to gather the data? When was the data gathered or generated? By asking such questions you will ensure that you know the quality of your data.
Working with CRM tools and event-based data, it's always a nausea to get the best data quality because it depends so much on user behaviors and 3rd party manipulations.
In my case, a lot of the data comes from Excel sheets. It is important to validate data at the earliest possible stage. If possible - at the stage of entering them by the user. - A correctly formatted date will not cause problems when converting to weeks or calculating time. - Ensure that data types (e.g., text, numbers, dates) are consistent and appropriate for the type of analysis you plan to perform. Incorrect data types can lead to errors during calculations. - If possible, implement drop-down lists or data entry controls in Excel to provide users with predefined choices. This reduces the risk of typos and ensures data consistency.
In healthcare, I most commonly see outside agencies and firms doing data builds and analysis, and I find it to be very problematic for a few reasons: 1. They typically don't understand the data very well (what's a conversion? Why doesn't it always align to payment dates? Etc.) - this can be trained for, but I have yet to see that prioritized in the SOW, usually because of budgetary constraints on time. 2. They don't understand how to get good data out of EMRs. This can be overcome if you get a true engineer who's willing to do the research, or if you've got a super user who is already doing some reporting that can train them. Beware promises around APIs from EMRs. Ran out of characters - DM me if you're curious about the rest!
Another risk of data analysis is compromising the security of the data you handle. Data security refers to the protection of the data from unauthorized access, use, modification, or disclosure. Data security is especially important for sensitive data, such as personal, financial, or health information. Data breaches can result in legal, financial, or reputational damages for you and your organization. To ensure data security, you need to follow the relevant laws, regulations, and policies for data protection. You also need to use secure tools, platforms, and protocols for data storage, transfer, and analysis.
Since I've worked in analytics, I have worked with financial and health data, managing critical data of banking customers and health patients. With this, comes a lot of responsibility to keep the data secure. Luckily, most of this responsibility falls on the company rather than the worker. As a worker, it's important to be cautious of any kinds of scams, not talk about private data outside of work, and always keep your work computer password protected in a secure location when you're not using it.
In medical Revenue Cycle Management, data analysis must rigorously comply with HIPAA, making data security paramount. As a Power BI developer, I prioritize safeguarding sensitive health information. This involves strict adherence to HIPAA, employing specific security measures like VPNs for secure remote access, and implementing two-factor authentication (2FA) with tools like Duo for added security. Regular security audits, employee training on data protection, and using encrypted platforms for data storage and transfer are also crucial. These measures ensure not just compliance but also the trust of patients and stakeholders in the integrity of their data.
Data quality serves as the cornerstone of any meaningful data analysis. The key takeaway is that data's provenance matters as much as its content. While technology has enabled us to gather vast amounts of data, discerning its authenticity remains a human-centric task. Just as Mohsen Kazemian points out, understanding the "how", "why", "who", "what", and "when" of data collection can make the difference between insightful conclusions and misguided decisions.
I am working in a digital platform that provide a virtual safe space for sexual survivors, everyday we handle sensitive and personal information. I would say the significant concern is protecting the data from unauthorized access, hacking, or other security breaches.
A third risk of data analysis is violating the ethics of the data you use or produce. Data ethics refers to the principles and values that guide the responsible and respectful use of data. Data ethics is closely related to data privacy, which refers to the rights and expectations of the data subjects or owners. Data ethics is also related to data fairness, which refers to the avoidance of bias, discrimination, or harm in data analysis. To ensure data ethics, you need to follow the ethical codes and standards of your industry and profession. You also need to respect the consent, preferences, and interests of the data subjects or owners.
Navigating the realm of data ethics requires us to don both a technologist's cap and a philosopher's robe. While data might be neutral, its collection, interpretation, and application are deeply human endeavors. We must continuously question and evaluate our actions, ensuring we're not just compliant, but also ethically sound.
A fourth risk of data analysis is misinterpreting the data or the results of the analysis. Data interpretation refers to the process of drawing meaning and implications from the data or the results. Data interpretation can be influenced by various factors, such as the context, the purpose, the audience, and the perspective of the analysis. Misinterpretation can result in erroneous or misleading claims, recommendations, or actions. To ensure data interpretation, you need to follow the logical and scientific methods and principles of data analysis. You also need to communicate the data or the results clearly, transparently, and objectively.
Having multiple sources of truth floating around an organization leads to a lot of wasted time and effort when departments are using different datasets. Establish a single source of truth that guides the organization in a given direction. Be sure the CEO all the way down to every individual contributor is using that single source of truth. This is a culture change and large investment for most organizations. So, it will take a long time to implement, but it will be worth it in the end.
From my experience in the music industry, we must be keenly aware of data literacy levels of various stakeholders when presenting data and platforms for a general audience. For less data-literate audiences like artists, use clear visualisations and focus on the most relevant insights. For executives, provide more detailed data with caveats on limitations. The key is to know your audience, assess their comfort with data, and shape your analysis and communication to optimize understanding and impact. With the right framing and transparency, data can enlighten all levels.
By far the most common mistake in interpreting data is to associate correlation with causality. In second place I’d place the tendency to extrapolate trends when there’s no reason for the trend to continue. The third most common error is to forget that statistical evidence is not always a reflection of an individual observation so there will be differences in the sample.
A fifth risk of data analysis is failing to adapt to the changing data environment. Data adaptation refers to the ability to adjust and update your data analysis according to the new or emerging data needs, opportunities, or challenges. Data adaptation is essential for staying relevant, competitive, and innovative in your industry. Data adaptation can be hindered by various factors, such as the lack of resources, skills, or vision. To ensure data adaptation, you need to follow the continuous and agile cycles of data analysis. You also need to monitor, evaluate, and learn from your data analysis outcomes and feedback.
The failure to adapt to the changing data environment can be likened to navigating a turbulent sea without adjusting the sails. It jeopardizes an organization's capacity to harness the full potential of data insights, hindering its ability to make informed decisions and remain agile in a dynamic market.
A sixth risk of data analysis is ignoring or underestimating the impact of your data analysis on your industry and society. Data impact refers to the effects and consequences of your data analysis on the stakeholders, processes, systems, or outcomes of your industry and society. Data impact can be positive or negative, intended or unintended, direct or indirect, short-term or long-term. To ensure data impact, you need to follow the strategic and sustainable goals and values of your industry and society. You also need to measure, report, and improve your data analysis performance and outcomes.
Every analysis we conduct sends ripples across our organizations and industries. Recognizing and respecting the broader implications of our findings is essential. It's not just about what the data reveals, but how those revelations can shape strategies, influence decisions, and drive meaningful change.
To ensure a positive and responsible approach to data impact, it is essential to align data analysis with the strategic and sustainable goals and values of the industry and society. This involves understanding the broader context in which data is being used and considering the potential ramifications of data-driven decisions. For example, data analytics should be used to promote inclusivity and fairness, rather than exacerbating existing inequalities.
For a digital platform that provide virtual safe space for sexual violence survivor, Analyzing data related to sexual survivors involves a deep understanding of the potential psychological impact on users. Ensuring that the analysis is conducted responsibly and ethically is crucial to avoid causing harm to individuals who have already experienced trauma.
In my experience it is important to understand what actual question you’re trying to answer with the data. Once you clearly know this you need to know what data sources there are for data collection. Additionally one would need to what processes have embedded quality to ensure accuracy. Qualifying the confidence of the data is also essential so as to not get blindsided by untrue assumptions. And finally it is important to understand the data in the context of what story the data is informing. Data does not lie. It is what it is. So bring honest about the accuracy or inaccuracy of data is vital one final thought is often were asked analyze data without knowing what we are truly trying to get to.