Globally, we’re producing more data than ever before. But the events that catalyzed and accelerated that historic increase caught us by surprise, so the methods that we use to handle it are still catching up. None of this is news to anybody, but it’s less obvious why this is a problem.
After all, data is the lifeblood of innovation, and the better informed we are, the higher the quality of our decision making. But if you don’t have the right tools to organize that data and extract insights from it, it’s very much a case of “water, water, everywhere, but not a drop to drink.”
Data overload: too much of a good thing?
What we’ve described is the unfortunate situation that many life sciences businesses find themselves in, and now it has a name: data overload. Companies that rely on old, manual methods, may find themselves overburdened by the volume and velocity of data. So how should companies be leveraging big data, without triggering data overload?
The answer lies in AI technologies like Natural Language Processing (NLP), that enable teams to digest huge amounts of information, quickly, and distill actionable insights from the data stream.
In this article, we’re going to explore the key problems that data overload causes, and how solutions like Similari leverage AI and NLP to address these challenges.
Data overload woes for R&D and innovation teams
Data overload happens when there is too much data to effectively process, analyze, and make decisions from. In the age of “Big Data”, companies urgently need a way to sift this information before they can use it. Ultimately, it’s not about how much data you have, but how you use it to achieve your goals.
Back in 2001, Doug Laney defined Big Data using three essential characteristics:
Volume: the amount of data that needs to be processed and stored
Velocity: the rate of real-time data creation
Variety: this data can be structured, semi-structured or unstructured
And with Big Data now bigger, faster and more varied than ever before, businesses need to guard against risk factors like these:
More clinical trials, publications and drug patents are good news in a sense: they increase the amount of information available. But the sheer volume of available data makes it difficult for businesses to process it, let alone extract insights by selecting the most relevant and actionable information.
This inefficiency arises because traditional search methods are manual and reactive (or historical). They involve sifting through the results of events that have already taken place. But in a dynamic environment (remember: big data is generated in real time), this isn’t enough. What do you do when one set of scientific results supersedes a previous one? AI resolves this problem neatly – we’ll get to that a little later.
In the face of increased volume and velocity of data, companies that rely on manual or database search methods inevitably have to choose between limiting the scope or the depth of their research. When your focus is too narrow, or too shallow, you risk missing out on key market trends and opportunities. But the opposite problem is equally dangerous: choice paralysis is a very real problem, as articulated in the so-called Hick’s Law: an excess of options can actually slow down decision-making. This is crippling for humans, but not for AI (more on this later).
Perhaps worse than missed opportunities and decision paralysis, data overload can lead companies to take decisive steps – but in the wrong direction. Fixating on a single data point, or misunderstanding facts in their particular context, can lead to inaccurate conclusions.
Addressing data overload with Natural Language Processing
NLP is an interdisciplinary field that brings together artificial intelligence, linguistics and computer science. It focuses on the interaction between computers and human language. NLP techniques are used to analyze, understand, and generate human language in a way that AI can understand and process. If it’s sophisticated enough, NLP can read and comprehend written texts, extracting the most salient points and distilling insights for the human decision-maker using the system.
NLP alleviates the burden of data overload by automating away mundane, time consuming workflows, allowing data intelligence professionals to focus on making sound decisions. Crucially, it also replaces reactive search with proactive insight: when facts change, an AI-powered system can register the change almost instantaneously. As we discussed earlier, that overcomes one of the most profound weaknesses of traditional methods.
Key use cases for NLP in the life sciences
Literature mining: R&D teams can use NLP techniques to extract information from large volumes of scientific literature, such as research papers and patents. This can be used to identify new drug targets, understand disease mechanisms, and track the progress of scientific research.
Clinical trial management: NLP can be used to extract information from clinical trial protocols and reports, such as inclusion and exclusion criteria, adverse event reports, and treatment efficacy. This information can be used to select appropriate sites and improve the design of clinical trials. It can also help companies avoid costly and redundant dead ends.
Drug discovery: NLP can be used to extract information about chemical compounds, proteins and genes to identify new drug targets, and better understand the mechanism of action of existing drugs. And it can get this all done fast – and more accurately – than any human could, by automating key components of the process:
- Surveying biomedical literature for specific genes related to therapeutic outcomes
- Identifying white spaces for specific disease targets
- Searching patent data concerning specific technologies
Further applications for NLP in life sciences research and innovation
Executive buy-in: R&D and innovation can cut through the noise of data overload and find the precise insights that bolster the business case for each new initiative. Using this information, they can motivate resource allocation to the C Suite based on sound, up-to-date insights.
Build partnerships: Using NLP techniques, business development and partnership teams can parse scientific literature and extract key information from these documents, such as the names of researchers, institutions, and technologies. All of this data can be used to identify potential partnerships and opportunities for collaboration.
From data overload to data motherlode: how Similari harmonizes data to supercharge innovation
The proliferation of data isn’t going to slow down – in fact, we can expect to see over 180 zetabytes in 2025, as the curve gets steeper. Next generation AI technologies make it possible to keep up, by translating huge swathes of data into graphs and presentations that make sense for the humans who use them.
Similari empowers scientists, business leaders and data professionals to harmonize data, reveal insights and make the sound, innovative decisions that will shape the future of life sciences.
Get in touch with our team to learn more, or schedule a demo to see exactly what Similiari can do.