The Privacy Divide: Social Media and Personal Genomic Testing

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With every advance in technology comes a trade-off of some kind. Where the use of personally-identifiable information is concerned, the trade-offs typically involve the exchange of privacy and confidentiality for a non-monetary benefit. In the early days of social media, conventional wisdom said the product was the service. However, we have seen over the last decade that the users of such platforms are the products, the perceived benefits merely carrots on sticks to keep the products (users) engaged in the cycle. We willfully pour details of ourselves into various social media outlets, despite the documented bad behaviors by giants like Facebook, and mostly remain complacent in having our personal data packaged and leveraged against us by various business interests.

However, in the conversation I’ve had around personal genomic testing (PGT), I’ve noticed that many are quick to cite data privacy and risk as a key reason not to participate. Think about this. On one hand, we have evidence to prove Facebook has been using our data in dubious ways, yet we keep pouring ourselves into it (McNamee, 2019). On the other hand, the potential benefits of PGT are outweighed by a fear of that data potentially being misused.

My purpose is not to minimize the potential hazards around PGT. Consider the following risks: (a) hacking; (b) profit or misuse by the company or partners; (c) limited protection from a narrow scope of laws; (d) requests from state and federal authorities; and (e) changing privacy policies or company use due to mergers, acquisitions, bankruptcies, et cetera (Rosenbaum, 2018). In the face of potential benefits from PGT, these are serious caveats. But read that list outside of this context, and it is equally applicable to the data we generate and provide to social media outlets on a daily basis.

As of yet the privacy regulations around social media use only exist within the context of the company itself—that is, there are no substantial federal regulations in the US on the matter, only the GDPR in the EU (St. Vincent, 2018). Where health information is concerned, the US does have slightly more mature federal regulation. The Health Insurance Portability and Accountability Act (HIPAA) requires confidentiality in all individually-identifiable health information; in 2013, this law was extended to genetic information by way of the Genetic Information Nondiscrimination Act (GINA). While the rules prohibit use of genetic information for underwriting purposes, there is no restriction on the sharing or use of genetic information that has been de-identified (National Human Genome Research Institute, 2015). De-identification is not entirely foolproof. There are cases in which the data can be re-identified (Rosenbaum, 2018).

The incongruence is puzzling. In the case of social media, users willfully provide a wealth of data points on a regular basis to companies that repackage and monetize that data for dubious purposes, in the absence of meaningful US legislation to protect it. In the case of PGT, where at least HIPAA and GINA have a rudimentary level of codified protection, users’ hesitance appears to be much more pronounced.


McNamee, R. (2019). Zucked: Waking up to the Facebook catastrophe. New York: Penguin.

National Human Genome Research Institute. (2015). Privacy in genomics. Retrieved from

Rosenbaum, E. (2018). Five biggest risks of sharing your DNA with consumer genetic-testing companies. Retrieved from

St. Vincent, S. (2018). US should create laws to protect social media users’ data. Retrieved from

Future of BI: Opportunities, Pitfalls, and Threats


Master data management (MDM). A few years ago this was thought to be a dead concept and I wonder how much of that sentiment was driven by the advent of data lakes, unstructured processing, artificial intelligence, et cetera. We have come far enough now to know that (a) the two do not have to be mutually exclusive, and (b) MDM is seeing a resurgence as the importance of data governance and quality management grows. Regardless of how the data is used, it must be clean and relevant.

Ethics. Cambridge Analytica should not have been the first watershed moment in the ethics of big data and business intelligence. While a number of industries have established sub-disciplines in ethics, data science and business intelligence are young, and this will continue to grow. That particular scandal did peel the layer of collective public naivete back. We are more attuned now to the potential pitfalls of big data in the hands of companies with less-than-best intentions. However, willful ignorance does remain and this is a major opportunity for growth.

Data-driven cultures and citizen data scientists. Business intelligence has expanded from a small cadre of statisticians and developers to include more subject-area experts and regular business users. This democratization of data science is largely due to the ease of use of popular analytics packages such as Tableau and Qlik. As the black box of analytics is demystified and the power is put in the hands of more users, data-driven cultures will become easier to create in organizations.


Over-reliance on the next-best-thing. Let’s admit it: there are some impressive analytics packages on the market right now. The innovations in data science are exciting. But without a focus on less-flashy elements such as data governance and the right people-processes, whatever the next best thing might be will fail. It is tempting to get caught up in the continuous cycle of innovation and forget about these critical elements.

De-valuing BI talent. The release of analytics packages that an average business user can pilot without the need of a dedicated statistician or business intelligence developer has done many good things for the discipline, but going too far in this direction is a potential pitfall. Socially, we are in the era of experts and scientists being ignored in favor of what people believe they know (cite). Between this predisposition and more functions being in the reach of regular business users, there is a potential for BI experts to be brushed aside and their talent de-valued.

Checking our brains at the door. As useful and amazing as business intelligence has become in organizations, it may be tempting to put more and more decision-making power on artificial intelligence at the expense of human intelligence. Plenty of films have used this premise as fodder for apocalyptic computers-take-over-the-world stories. But on a more practical level, business intelligence is all about serving up the right information so decision-makers can make the right calls—not making all the decisions for them.


Inflexible organizations. Organizational culture can be a great asset or opportunity, but it can also be an incredible hindrance. Even the best deployments with the best intentions can be rendered useless if an organization is not willing to embrace whatever change is necessary to take advantage of it all. This is not a new threat, per se, but one that will always be around.

Bad actors. We like to believe that big data and the algorithms that drive how we interact with it are neutral at best. However, as McNamee (2019) notes, it is possible for bad actors to utilize otherwise benign data and algorithms for nefarious purposes. As collections of data grow and algorithms to drive outcomes or profit grow, the chances of these bad actors to utilize them become more and more likely.

Lack of transparency. This may be considered a threat more in the big data realm in general more so than in business intelligence, but it does bear highlighting within this context. Businesses use proprietary algorithms and logic that turn troves of data into consequential decisions about our lives. These also shape the world that we see through our consumption of news and social media websites. Do we remain in willful ignorance of how those are served up to us, or do we push for more transparency there?


Graham, Mackenzie. (2018). Facebook, Big Data, and the Trust of the Public. Retrieved from

Jürgensen, K. (2016). Master Data Management (MDM): Help or Hindrance? Retrieved from

McNamee, Roger. (2019). Zucked: Waking up to the Facebook catastrophe. New York: Penguin.

Nichols, T. (2017). The death of expertise: The campaign against established knowledge and why it matters. New York: Oxford UP.

Pyramid Analytics. The Business Intelligence Trends of 2019 Discussed. Retrieved from

Rees, G., & Colqhuon, L. (2017). Predict future trends with business intelligence. Retrieved from

Online Profiling and BI

First, we must define online profiling, also known as contextual marketing in advertising circles. It is a marketing and data-gathering process which “takes into account the users’ needs, habits, and goals to create a personalised web experience” (Elhawli, 2017). While it may not appear to be what conventional wisdom defines as business intelligence, the same principles are there: data is gathered, made sense of, and served to an information consumer in order to increase the company performance. The information consumer, in this case, may be considered either the actual user of the website or the layer of web platform between the data (recommendations) and website user.

The steps necessary for making the items actionable follow typical business intelligence processes. First, the “sheer volume of data now available to marketers” requires focusing on what data is relevant to the desired outcomes (Pophal, 2014). These outcomes and relevant data vary by market and platform. In many cases, the data itself yields important clues on what exactly is important—akin to an exploratory data analysis in traditional business intelligence implementations.

Another similarity is the platforms upon which the data must be presented. This may be considered a parallel to the various visualization and reporting platforms available to an information consumer. There are multiple data points involving user device and platform (i.e., Mozilla on Mac OS or Chrome on Android), which must then be utilized to determine how the information is best served up.

These data points also influence the intake process, parallel to the ETL stage in traditional business intelligence. Imagine the “multiple inputs around consumers and the devices they’re carrying-or wearing-that receive those inputs” as systems of record contributing to a master data aggregator (Pophal, 2014). These must work in near real-time, driving outputs and giving context to other inputs.

Moving from descriptive to predictive analytics is another similarity between online profiling and conventional business intelligence. Current online profiling “is largely driven by what consumers have done, the future will focus on what they will do” (Pophal, 2014). The transition from descriptive to predictive is a milestone in business intelligence maturity (LaValle, et al., 2011) and this is no different in online profiling. Serving up mountains of What Happened? insights can only go so far. Transitioning from that to So What? and Now What? crosses a hurdle into the next realm of usefulness. This cannot be at the expense of best practices, though—as the celebrated rise and embarrassing fall of Google Flu Trends reminds us (Lazer & Kennedy, 2015).


Elhawli, A. (2017, October 12). Why content & contextual marketing are key to your success. Retrieved February 2, 2019, from

LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2011). Big data, analytics and the path from insights to value. MIT Sloan Management Review, 52(2), 21–31.

Lazer, D., & Kennedy, R. (2015, October 1). What we can learn from the epic failure of Google Flu Trends. Retrieved February 2, 2019, from

Pophal, L. (2014). The technology of contextualized content: What’s next on the horizon? Retrieved from