While Elsevier has long been known as a leading publisher of scientific and medical information, the business is using cutting-edge technology to deliver that information in surprising ways. That transformation is the focus of a recent LinkedIn column by Bernard Marr. He writes:
The business has undergone thorough transformation in recent years to position itself as a provider of analytics tools and platforms for the medical, academic and scientific community. This has been achieved by building advanced analytics systems utilizing big data and machine learning on top of the huge amount of data collated and published by the company in its 140-year history.
Marr, a bestselling author who writes about technology and data in the business world, points out why that transformation has been so crucial; it comes down to tackling the universal challenge of information overload:
The problem these days is rarely a lack of information. It’s more likely to be a matter of where to find the right information at the right time, and accessing it in ways that fit in with the way researchers and scientists work on a day-to-day basis.
For a behind-the-scenes look at Elsevier’s work, Marr interviews Chief Technology Officer Dan Olley on the role of information analytics. In Olley’s words:
I believe we need to give our customers the knowledge that they need at the right time, rather than just a whole lot of content. This is how analytics comes into the picture for us – how do we give people the information they need to help them make the best decisions? To really help clinicians enhance lives, and help scientists make breakthroughs, rather than just give them stuff to read.
That information can take many forms, but be useful, it has to be easily discoverable, Marr explains:
Valuable as it is, if information is locked away in forms that only humans can read – journals, publication, documents, diagrams and photographs. This sort of information is known as unstructured data – because it doesn’t fit neatly into the rows and columns that traditional computer analytics software needs to be able to process data. As a result, it can only ever be processed as quickly as people can read it.
Marr delves into how Elsevier uses machine learning to extract insights from this data and turn it into information that can be used by people in a wide range of professions.