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A Brave New Data World

  Article published in DM Direct Newsletter
November 17, 2006 Issue
 
  By Sid Banerjee

After decades of prosperity, pharmaceutical and biotech firms are rethinking the way they do business to drive growth, boost productivity and exploit new technology. A technological revolution is sweeping through the industry. The result will be better medicines, more targeted research and development, and a new perspective on drug companies by consumers.

Increased access to customer information is resulting in greater quantities of drugs targeting more and more specific disease states. Progress in technological advancements within the industry will mean cheaper, faster and more effective clinical trials and more targeted marketing of new drugs. Alliances and joint ventures between biotech, contract research organizations and pharmaceutical companies are changing the face of the industry as we know it.

In such an environment of rapid change and increasing types and amounts of information, effectively utilizing marketing and sales resources has become a top priority for pharmaceutical and life sciences organizations. With costs escalating to over $802 million to bring a single drug to market, the industry strives to understand and react to customers' evolving desires, as well as utilize detailed customer behavior and transaction information to drive customer acquisition, loyalty, satisfaction and profitability. This is especially important given the saturation of sales forces, with 100,000 reps chasing 120,000 physicians in the U.S. alone. It has never been more important to find creative ways to leverage all available data to optimize existing resources.

Harnessing the Power of Consumer Sentiment

The pharmaceutical and life science industry historically collects and manages large amounts of customer data to strategically manage their business. Although business intelligence (BI) tools are used to access structured data, an exponentially growing universe of information about customers is locked in unstructured content such as call center notes, email exchanges and clinical data. This problem is exacerbated by the emergence of consumer-generated media, such as blogs, which are becoming increasingly influential to customers and prospects, and are causing a dramatic reduction in the time between the launch of a campaign and the measurable public response to that campaign. A true measure of customer loyalty, sentiment and interest will increasingly need to incorporate these unstructured data elements.

Providing a Little Bit of Structure Goes a Long Way

The growing sea of information available online and within organizations makes it increasingly difficult to understand, connect and analyze vital pieces of customer information. This type of data is typically utilized using subjective approaches that are unreliable for decision-making and difficult to merge with existing analytical applications. To make matters worse, the process of analyzing unstructured content is tedious, manually intensive and time-consuming. Fortunately for industry, text-processing technologies are rapidly maturing to enable concept/entity extraction, relationship tagging and other paradigms to allow more structure to be applied to unstructured data. However, even more advanced implementations of text-processing technologies require complex programming work, which to date is totally disconnected from time-tested analysis approaches used in the BI world.

So how can enterprises better enable users to spend their days making informed decisions versus gathering data? Fortunately, there is much to be learned from two decades of struggling with similar problems in the structured data world. We now know that as needs change and evolve, organizations will require the flexibility to integrate the most appropriate text-processing technologies to extract desired information. They must enable users to apply time-tested analytical approaches that can be modified or expanded upon as understanding of issues and opportunities emerges from the data itself. For example, a pharmaceutical company should be able to apply a multidimensional analysis (i.e., "slice and dice") to blogs, doctors' notes and email text for assessing trends, root causes and relationships between issues, people, time to resolution, etc. Organizations should have the infrastructure, storage and user interfaces to process and efficiently explore large volumes of data. And they need to easily leverage their existing BI and data warehousing (DW) tools presently used only for structured data analyses to analyze unstructured data alongside structured data.

Why Work for Your Data?

Theodore Roosevelt once said that nothing in this world is worth having unless it is difficult to obtain. In the business world, if it is difficult, chances are it costs more money to do. Fortunately, the many challenges traditionally associated with unstructured data analytics can now be easily overcome by applying the lessons learned over the past ten years in the BI and data warehousing space.

What have we learned? Essentially, to be successful in the unstructured world, businesses need to apply a platform to leverage their existing BI investments and also efficiently and effectively store, transform and analyze unstructured data - and do so in a way that is easy to manage and scale.

Thankfully, technology has evolved to enable users to directly mine text alongside existing structured data, using standard BI tools and analysis techniques to address a host of real-world business needs.

The benefits for the pharmaceutical and biotech industries are enormous and include:

  • Attitude tracking and perception analysis. The ability to extract information from the various types of textual data you have gathered from customers - or even data that customers have generated themselves on blogs or discussion groups - enables a much deeper understanding of customer attitudes and sentiment towards various brands, categories or companies. This information can be used to pursue or modify a sales or marketing campaign to targeted customer segments.
  • Issue categorization and analysis. A categorization engine that automatically organizes incoming feedback and issues eliminates manual categorization work by employees. Additionally, the application of traditional analytical techniques, such as a multidimensional analysis to text data allows for easy assessment of trends, root causes and relationships between issues, people and time to resolution, etc.
  • Opinion leader analysis. Pharmaceutical organizations must ensure that individuals in the research community or other news-generated sources are consistently monitored over time to maintain an accurate view of perception within the marketplace. Much of the data to evaluate the positive and negative impact, however, is locked within online media. For example, a health report may indicate a negative perception with a medication, but mining the interaction data reveals that the same issue has been discussed by opinion leaders in the media, on blogs, etc. Accessing all data, despite structure, allows for the ability to quantify opinion leader interactions and correlate that information with sales data for a faster and more targeted response.
  • Brand management. By mining internal and external customer feedback, brand teams are able to better quantify the impact of various marketing campaigns. For example, a sentiment indicator can be measured at key points in time to measure changes in customer attitudes towards a brand, following a specific television advertising campaign. Also, gaps between how brand managers want their customers to experience the brand, products, and services versus what customers are saying about those experiences can be readily analyzed and understood.
  • Medical research and development. Clinical notes, discharge summaries, pathology notes and medical journals all contain valuable text information that can be analyzed and integrated with structured patient record information. Extracted information can be used to securely and efficiently identify adverse events, drug discovery and opportunities for quality improvements. For example, all patients suffering from migraine headaches could be analyzed to find trends, anomalies or patterns associated with various treatment approaches.

It's a Brave New World

Breaking down the wall between unstructured and structured information has opened up a whole new world of possibilities for pharmaceutical and biotechnology companies. Harnessing the power of consumer insight will provide executives at every level of an organization with information they need to manage the changing landscape of the industry and the growing access to all types of information. All they have to do is reach out and grab it.

...............................................................................

For more information on related topics visit the following related portals...
Analytics, Data Management and Unstructured Data.

Sid Banerjee is chairman and CEO of Clarabridge, an emerging leader in the text-mining space. He is a notable entrepreneur in the industry, a co-founder of Clarabridge and Claraview, with over 15 years of business intelligence consulting experience. He currently resides in Washington, DC.



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