By Brigham Hyde, Relay Technoloy Management
At this year's annual World Economic Forum in Davos, Switzerland, the growing data deluge was a major hot topic among the foremost global community of business, political, intellectual and other leaders of society. Big Data has become a larger-than-life topic in the past year, but some don't fully understand what this means.
Gartner describes Big Data in terms of extreme information--the convergence, joining and presentation of enterprise information with high levels of:
-
Variety--The full complement of enterprise data sources and multiple data types as defined above: structured data, semi-structured data and unstructured content;
-
Velocity--Often time-sensitive, Big Data must be analyzed as it is streaming into the enterprise in order to maximize its value to the business;
-
Volume--Certain information sources can approach Big Data levels of volume;
-
Complexity of individual data types, including ensuring that all related information, regardless of format, can be easily joined and presented together in response to a service professional's query.
For example, banks use Big Data to predict how customers and competitors will behave in the market. It's important to have information readily available rather than located in multiple silos, to make decisions in real time. Retail chains and e-commerce sites use Big Data to get a better understanding of purchasing trends, in-store and online customer traffic patterns, and customer profiles. Without real-time insight or if information sits untouched in a database, it becomes stale and useless.
These challenges with information cut across every industry while finding solutions and methods to harness massive amounts of data into an easily digestible format to help make informed business decisions is imperative.
The highly competitive life sciences industry is no exception. With shortened product life cycles and shrinking opportunities to take advantage of unique intellectual property, it is imperative to have relevant information at your fingertips.
Unify information, quantify assets
In the past, the life sciences industry has suffered from a marketplace lacking informed decisions and a need for quantified information. With an overwhelming amount of information and complexity for early-stage drug development stakeholders from the biotech, pharmaceutical, technology transfer and investment communities, the life sciences industry has an urgent need to capture, access, compare and track comprehensive scientific, clinical, transactional and business information.
In order to find meaning in this information, the life sciences industry needs a way to integrate and present all information--structured, semi-structured and unstructured--in ways that enable the integration of seemingly unconnected, seemingly unconnectable information. Unified information access (UIA) is an emerging approach to effectively merging these disparate information silos.
UIA adds the "why" insights drawn from unstructured content through advanced text analytics and sentiment analysis. With a unified view of information, it's now possible to quantify assets.
Take a pharmaceutical compound for example. In order to quantify this asset, information must be unified to create and scale its value. A compound receives a score that is then correlated with the likelihood of a transactional event corresponding to value creation (M&A, licensing, investment, advancement of stage). The purpose of the score is to offer the ability to compare relative value between assets at a given time.
Value creation for a pharmaceutical asset is highly dependent on the stage of development (both clinical phase and experimental stage), thus more advanced assets are likely to have a higher score. Other factors that influence value include, but are not limited to, competitive environment, scientific evidence, research trends, investment, proof of mechanism, research investment, FDA trends and transactional trends.
Asset values can rise and fall and are deliberately weighted to be insulated from popular opinion. For example, a clinical failure in a given mechanism may have a negative effect initially, but the negative effect will decay over time if other evidence builds.
Avoid stale information
While it's important to unify information and quantify assets, the previous example also highlights why time is a key factor. With life sciences, the need to aggregate scientific and market data in real time to create informed business intelligence is essential.
In addition to the existing scientific publications, patents, diseases and clinical trials to keep track of, new information is created constantly.
Industry norms suggest that professionals spend 80% of their time locating relevant information and only 20% on actual research. Spending excessive amounts of time and effort identifying and analyzing drug assets and targets becomes inefficient and wasted as data quickly becomes stale.
For business and research professionals in the life sciences industry, a platform that provides decision support and data interaction makes the most sense.
Eliminate bias
Bias surrounds almost all decision-making processes, but in order to make an informed and objective decision, it must be removed from the process.
Take the now famous "Moneyball," for example. The Oakland A's used Big Data to their advantage by analyzing and identifying undervalued baseball players. By removing what they call "affirmation bias"--you've made up your mind, therefore you resist information that doesn't agree with the conclusion--and "appearance bias"--the idea that some players look like better players than others--from the system, the Oakland A's made data-driven decisions to build a winning team roster.
That's not to say that numbers are everything, but the bias that surrounds some assets can be so large that it makes it impossible to fairly judge one thing against something else.
For the life sciences industry to eliminate bias, avoiding the sole reliance on the qualitative perspectives of key opinion leaders and using a platform that can quantify all assets as described above are essential.
The future of Big Data
In a recent Fast Company article, Daniel Rasmus states, "The future of Big Data lies not in the stories of anecdotal triumph that report sophisticated, but limited accomplishments--no, the future of Big Data rather lies in the darkness of context, change, complexity and overconfidence."
So what does this mean? It's unclear what the future of Big Data holds for the life sciences industry. Many speculate but no one knows what's really in store in the years to come. One thing that's certain is that the life sciences industry is constantly changing and technology must continue to adapt to market needs.
The ability to unify information in real time, quantify assets and eliminate bias from the decision-making process is essential to success and will continue to be necessary moving forward so that the life sciences community can perform value-added analysis on their business development decisions.
Brigham Hyde is a co-founder and managing director of Relay Technology Management, a software analytics company focused on the life sciences industry, which aggregates scientific and market data in real time and provides competitive business intelligence to biotechnology and pharmaceutical companies and university technology transfer offices.