Future of BI: Opportunities, Pitfalls, and Threats

Opportunities

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.

Pitfalls

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.

Threats

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?

References

Graham, Mackenzie. (2018). Facebook, Big Data, and the Trust of the Public. Retrieved from http://blog.practicalethics.ox.ac.uk/2018/04/facebook-big-data-and-the-trust-of-the-public/

Jürgensen, K. (2016). Master Data Management (MDM): Help or Hindrance? Retrieved from https://www.red-gate.com/simple-talk/sql/database-delivery/master-data-management-mdm-help-or-hindrance/

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 https://www.pyramidanalytics.com/blog/details/blog-guest-bi-trends-of-2019-discussed

Rees, G., & Colqhuon, L. (2017). Predict future trends with business intelligence. Retrieved from https://www.intheblack.com/articles/2017/12/01/future-trends-business-intelligence