Data is all around us. In today’s globalized economy, some of the most basic things we do each day revolve around feeding us data about how we are living, the caloric index of the food we are eating, and the total distance or miles it takes to get somewhere. The clearest example of this is using Google maps on a regular basis. This technology has revolutionized travel. It has made drivers in the U.S., where, in most cities, car culture still dominates, reliant on Google maps, and the fastest travel routes to get to and from the office or to simply meet someone at a new restaurant.

We don’t think about it, but as consumers in the digital era, numbers and “data” are all around us, making us more susceptible to interpreting data to be of value, and to mean something, even if that’s not wholly the case.

The same can even be said with regard to online retailing during the Covid-19 pandemic. There has been a plethora of deals, exclusive offers, and so much more with regard to our favorite online retailers in the past few months. These offers make their way into clever email campaigns with time-sensitive deadlines and even the opportunity to open up new store credit cards online for quick savings. We might not realize it, but all of these numerical strategies represent new ways of handling data and an analytics team tinkering with what works and what doesn’t.

Mckinsey & Company reports that on the jobs side of things, companies are having a hard time finding the right talent trying to integrate data and analytics into their existing operations. “Approximately half of the executives across geographies and industries reported greater difficulty recruiting analytical talent than filling any other kind of role. Forty percent say retention is also an issue…”

Data Analytics for PhDs and Researchers

Data in this sense is as much a “disruptor” to the economy as it is a viable solution for numerically understanding the world and the people in it. It means that data, however large or small, needs to be de-coded, understood, and better yet, relative to other types of similar data. Comparing the performance data of a car salesman just doesn’t have much to do with the performance of a researcher's Citations metrics over the years.

In the case of Impactio, this is significant. One of the hallmark features of Impactio is the Metrics the service provides for academicians, and how they rank up to other academicians, whether in the hard or life sciences.

The data on Impactio reveals finer levels of distinctions for academics than ever before. In previous generations, PhDs were less common, and the way major academics were cited, or even heard of at the societal level was through grand accomplishments, or ones that were worthy of newspaper publications or more traditional media sources.

Today’s Ecosystem of Data in Higher Ed

However, today, as is the case with Impactio, that is not the case. With just a quick search on the laboratory, a new User can find the research of an academic from across the globe, working on social science research on a completely new sub-field of Earth Science. Accordingly, a researcher can see other researchers' Citation Metrics, including how many times they have been cited, and the major journals they have been published in. They can also, for example, go on different sites such as LinkedIn, and see that such Citations metrics don’t exist, and understand that if an academician publicly hosts their publication data, it might as well be worth advertising. That, to come back to a previous point hinted at, is where the competition and true value of data comes in, as it relates to whole new frontiers.

This ecosystem of data does a good job spreading when candidates start to understand, just like viewers, that a lack of Citation Metrics (of course for a very niche audience still) might work against them ultimately.