I couldn’t help but notice that when Sharon Jones – the great soul singer – died of pancreatic cancer this past November 20th that it was just three days after Pancreatic Cancer Awareness Day. She was a unique talent, and only 60 years old. John Hurt was the latest notable to succumb to the disease a few weeks ago - 18 months after diagnosis.
Researchers are working hard to find a cure for pancreatic cancer, the fourth biggest cancer killer, which has taken the lives of Jones, Hurt, Steve Jobs and far too many others less well known. While there are several promising avenues of research and ambitious initiatives have been launched, finding a cure is still years away. That’s too long for most people with the disease, who don’t live more than a year past diagnosis. What’s needed is leverage: anything that will accelerate research will, quite simply, save lives.
The tool that we’re searching for is data analytics. I’ll admit that it may not sound like cancer’s magic bullet, but it could be that and it is much, much more. The type of data gathering and analysis which we can now achieve through the use of current technologies is not just potentially the means of helping to find cures to pernicious diseases, but it will be a transformative lever, one that will profoundly impact nearly every aspect of human life.
If you have been following media coverage of ‘big data’ over the last five years, you are well aware that businesses, governments and researchers are gathering trillions of bytes of information on nearly every transaction and movement we make. Through smartphones and tablets, wearable tech, sensors in appliances and industrial machinery, and web-enabled infrastructure – to name just a few of the tools used to collect information for analysis, a mountain of data has been amassed that’s growing with every tick of the clock.
This raw data needs to be refined in order to give it applicable value. That’s where artificial intelligence and machine learning are changing the landscape of data analysis.
No longer constrained by human brainpower, the simulations one can now run have a speed and depth unseen before. These processes are changing bioengineering, medicine, business and technological development with gathering momentum, and have the potential to impact the global economy. A 2014 McKinsey report suggests that $3-5 trillion in annual value could arise from the use of free or open data in applications across seven domains of the global economy, including education, transportation, oil and gas exploration, consumer products, electricity, healthcare, and consumer finance.
Even social fields like urban planning and public policy are being transformed by data, as the Internet of Things and cognitive computing influence how roads are built, where to situate a hospital and when new schools will be needed. Medical sciences are on the cutting edge of this transformation, as AI-powered data analysis revolutionizes diagnoses and development of cures. It’s exciting that data gathered through social media is having an impact, allowing researchers to predict fairly accurately which communities and regions in the country have tendencies towards behaviors that make them more susceptible to particular diseases.
To harness data’s power for the public good and in ways that create greater credibility and trust, those who analyze, share and sell data need to avoid the ‘black box’ approach. Presenting data and your analysis in a way that is transparent, and which allows the end-user to calculate the results themselves, is critical to building trust. In a world where data becomes ever more important, users will want to understand what’s behind it. In the increasingly data-driven economy and research climate, the black box model will become untenable.
An example of that transparency in play is seen in CiteScore, a set of metrics providing more comprehensive insights into scientific journal impact. Based on Scopus, a database of abstracts of scientific articles, and citations to and from scientific articles, CiteScore supports a deeper understanding of the performance of a broader set of journals over time using big-data technology to analyze the Scopus data though a straightforward calculation, with no secret algorithms or hidden details. This past month, altmetrics pioneer Plum Analytics – which reveals research interest and usage beyond traditional measures – is being added to the data available to Scopus. This open, aggregate approach has the advantage of giving researchers a quicker and deeper understanding of how their work is being used and communicated.
Approaches like these begin to provide researchers and those dependent on trusted data for decision-making with a more accurate picture of impact and what’s needed to achieve it. A basket of metrics – a single metric is not enough – coupled with transparency, and the speed and power of ever-improving technology, is revolutionizing how we make informed decisions in every field of endeavor, from medicine and tech research to social science and business.
With data analytics, we are on the way to creating an immense lever with transformative power. This is a trend that will impact every single one of us, and, when used effectively, with wisdom and transparency, it will measurably improve society. And, as it impacts the pace of new discoveries, it will save lives.