Big Value in Big Data
Underlying the interest in Big Data is the desire to gain a competitive advantage via speed to insight and faster, more effective
decision-making. Companies that lag behind their peers and other stakeholders in making use of Big Data risk being at a disadvantage
at the research bench, in the market, and at the negotiation table. It becomes obvious even through the following broad list
of potential applications that no company could afford to be left behind in using Big Data, which can be used to:
» Uncover unmet needs
» Assess the feasibility of clinical trial designs and recruit trial subjects
» Demonstrate product value
» Conduct pharmacovigilance
» React more quickly to market changes via real-time market measurement and sophisticated KPIs
» Enhance commercial activities and enable more personalized messaging
» Deploy predictive capabilities rather than retrospective analytics
Ready or Not, Here It Comes
Like a kid in a candy store, the industry is looking at the new data sources and the insights they can offer as a tempting
array of choices. But on what should they spend their dime? What is the best investment strategy? Which new sources will yield
the greatest return? What might they miss if they head down one path too early?
This speaks to the need to not only understand what can be gained from each new source of information, but to also work backwards
from an understanding of what it is a company wants to achieve. Just as "form follows function" in design, "data follows strategy"
Questions also inevitably arise around a company's ability—from a technical, organizational, and intellectual standpoint—to
extract the value from Big Data. But before they begin, organizations must consider collection, aggregation, integration,
and presentation of the data. What technology investment is required to store and mine the data? What should be done in-house,
versus by business partners? What skills and IP should a company safeguard as a competitive advantage?
Are We There Yet?
We contend that the necessary technology to deal with Big Data either exists today or is currently in development. And, since
technology is always advancing and changing, it could be argued that a company's plans should be "technology agnostic." Consider
the various layers of technology involved:
Collection, Aggregation, and Storage. The technology exists to collect, aggregate, and store massive amounts of structured and unstructured data. It requires a
lot of "heavy lifting," but it can be—and is—done. IMS's technology infrastructure is a case in point: We routinely process
38 billion transactions each year, which are collected from nearly 700,000 reporting sites worldwide. Few pharmaceutical companies
are likely able to process data on this scale cost-effectively on their own, simply because of the upfront investment required.
Even so, we are seeing advances today and on the five-year horizon that will short-circuit some of the work and time required
to code and extract data. In the near-term, there is a methodology being tested by Internet search companies that may be applicable
to industry databases. Essentially, it uses algorithms to reduce the amount of data that needs to be restructured when it
is loaded by spotting what has changed. Further into the future, we may be able to do away with having to retrieve data from
storage when we need it; everything could be immediately accessible in memory on solid-state disks.
Analytics. The tools that can support analytics across Big Data are becoming available. Software manufacturers and service providers
are investing billions in their development, although we certainly are not there yet when it comes to being able to analyze
every aspect of Big Data simultaneously. One scientist captured it well. While referring to his computer, he lamented, "The
cure for cancer is in that box, but we can't get it out."
One exciting area of progress is in predictive analytics (as opposed to retrospective analytics). This will be invaluable
in marketing (being able to predict customer behavior), outcomes research, and potentially for payment and reimbursement schemas
based on historical behaviors.
Reporting. Great strides are being made in data visualizations, and mobile business intelligence is quickly emerging as a capability.
Data visualization tools can be utilized to enable non-data programmers to analyze the data in real-time. More comprehensive
mobile reporting is on the way, and companies ought to be establishing their mobile strategy now.
So, the necessary technology is coming along, and, in fact, is already ahead of most firms' ability to adopt it. The limiting
issues and most daunting challenges around Big Data are related more to a scarcity of resources (talent, time, and budget)
than they are to technological capabilities.