One of the greatest benefits of using analytics is that it can help decision makers and data scientists spot new trends or anomalies in data that, in turn, can generate new
One of the greatest benefits of using analytics is that it can help decision makers and data scientists spot new trends or anomalies in data that, in turn, can generate new business opportunities or help mitigate risk. Or, stated another way, using analytics to spot the “unknowns” in data.
This is something that the healthcare industry has been doing for years. For instance, researchers at Children’s Hospital Boston recently created a new method for assessing and monitoring drug safety that combines multiple forms of widely-available data to help predict adverse drug reactions.
Unlike existing approaches that rely on detecting evidence of drug safety issues as they accumulate over time in clinical databases, this new method developed by researchers at Children’s Hospital Boston offers the potential to identify harmful drug reactions much more rapidly.
In fact, the mathematical model helped the researchers successfully predict 42% of the adverse drug relationships that were unknown in 2005 but were identified over the next five years, according to iHealthBeat.
Data and analytics can also help decision makers anticipate and even predict risk, as noted in a recent white paper on the topic by Maritz Research. This can apply to potential risks that can crop up with an investment, unforeseen issues that can interrupt a supply chain, as well as business strategy or operational risks (e.g. the potential infrastructure vulnerabilities in different geographies where companies conduct business or receive outsourced services).
The Maritz white paper brings up several salient points about the benefits of uncovering unknowns, including the importance of monitoring information related to an organization or the potential risks relative to a company’s business partnerships or investments on a near real-time basis, particularly given how global business now occurs on a real-time basis.
This also underscores the importance of analyzing big data to rapidly spot unknowns that can quickly surface and undermine a company’s credibility.
For example, the use of big data might have enabled Nestle to anticipate the maelstrom of social media venom that was unleashed by customers and activists after Greenpeace UK posted a video denouncing the environmental impact that Nestle’s use of palm oil was having on orangutans. Of course, the negative comments posted by customers and activists on Facebook and Twitter only escalated after Nestle requested that the Greenpeace video be removed from YouTube citing a copyright complaint.
These examples of unknowns that can be identified and acted on merely scratch at the surface of what’s available for data scientists and decision makers. What have your experiences been and how has the unearthing of unknowns helped your organization?