Vertex has now revamped its figures, claiming that approximately 35 percent of patients experienced an improvement of 5 percentage points or more, while 19 percent experienced an improvement of 10 percentage points or more. Moreover, those percentages are absolute. The company posted more data in a document on its Website. Vertex told The Wall Street Journal and other publications that the discrepancy in Phase II testing was due to a “misinterpretation” between it and an outside vendor responsible for results analysis. Whatever the ultimate reason, and even though the study’s percentages were still in positive (albeit reduced) territory, Wall Street pummeled Vertex’s stock price May 29. Pitfalls Biotech companies are frequently cited as a prime consumer of data analytics and business-intelligence products, and with good reason: medical research involves mountains of data that need to be carved in multiple ways. While the tools and techniques utilized by Vertex’s unnamed vendor remain unclear, it’s true that accurate analysis of a dataset can mean the difference between insights that yield a billion-dollar product, and a mess of numbers that lead researchers astray. Despite a widespread recognition of the importance of data-analytics tools, many companies are still struggling to adopt platforms and procedures that work. A recent survey by the Association for Information and Image Management (AIIM) found that 26 percent of reporting organizations struggled to organize content, with another 30 percent claiming poor B.I. capabilities and reporting. At the same time, 70 percent could picture a “killer application” capable of analyzing data in a way that improved the company’s outlook. And it’s no wonder they want that killer application. At least in theory, good data analysis can translate into more efficiencies and revenue. But data mistakes cause pain, as well. Image: keellla/Shutterstock.com Vertex Illustrates Need to Avoid Data Mistakes
For anyone seeking an example of how a misread data report can translate into a crisis, look no further than drug-maker Vertex Pharmaceuticals. Vertex has been testing how two drugs, VX-809 and Kalydeco (ivacaftor), affect lung function in adult patients affected with cystic fibrosis (and who had two copies of a common mutation to a particular gene, F508del). Earlier in May, the company reported that approximately 46 percent of patients taking both drugs experienced an improvement in lung function of 5 percentage points or more, while 30 percent experienced an improvement of 10 percentage points or more. Those percentages are relative, not absolute (as originally reported). Vertex has now revamped its figures, claiming that approximately 35 percent of patients experienced an improvement of 5 percentage points or more, while 19 percent experienced an improvement of 10 percentage points or more. Moreover, those percentages are absolute. The company posted more data in a document on its Website. Vertex told The Wall Street Journal and other publications that the discrepancy in Phase II testing was due to a “misinterpretation” between it and an outside vendor responsible for results analysis. Whatever the ultimate reason, and even though the study’s percentages were still in positive (albeit reduced) territory, Wall Street pummeled Vertex’s stock price May 29. Pitfalls Biotech companies are frequently cited as a prime consumer of data analytics and business-intelligence products, and with good reason: medical research involves mountains of data that need to be carved in multiple ways. While the tools and techniques utilized by Vertex’s unnamed vendor remain unclear, it’s true that accurate analysis of a dataset can mean the difference between insights that yield a billion-dollar product, and a mess of numbers that lead researchers astray. Despite a widespread recognition of the importance of data-analytics tools, many companies are still struggling to adopt platforms and procedures that work. A recent survey by the Association for Information and Image Management (AIIM) found that 26 percent of reporting organizations struggled to organize content, with another 30 percent claiming poor B.I. capabilities and reporting. At the same time, 70 percent could picture a “killer application” capable of analyzing data in a way that improved the company’s outlook. And it’s no wonder they want that killer application. At least in theory, good data analysis can translate into more efficiencies and revenue. But data mistakes cause pain, as well. Image: keellla/Shutterstock.com
Vertex has now revamped its figures, claiming that approximately 35 percent of patients experienced an improvement of 5 percentage points or more, while 19 percent experienced an improvement of 10 percentage points or more. Moreover, those percentages are absolute. The company posted more data in a document on its Website. Vertex told The Wall Street Journal and other publications that the discrepancy in Phase II testing was due to a “misinterpretation” between it and an outside vendor responsible for results analysis. Whatever the ultimate reason, and even though the study’s percentages were still in positive (albeit reduced) territory, Wall Street pummeled Vertex’s stock price May 29. Pitfalls Biotech companies are frequently cited as a prime consumer of data analytics and business-intelligence products, and with good reason: medical research involves mountains of data that need to be carved in multiple ways. While the tools and techniques utilized by Vertex’s unnamed vendor remain unclear, it’s true that accurate analysis of a dataset can mean the difference between insights that yield a billion-dollar product, and a mess of numbers that lead researchers astray. Despite a widespread recognition of the importance of data-analytics tools, many companies are still struggling to adopt platforms and procedures that work. A recent survey by the Association for Information and Image Management (AIIM) found that 26 percent of reporting organizations struggled to organize content, with another 30 percent claiming poor B.I. capabilities and reporting. At the same time, 70 percent could picture a “killer application” capable of analyzing data in a way that improved the company’s outlook. And it’s no wonder they want that killer application. At least in theory, good data analysis can translate into more efficiencies and revenue. But data mistakes cause pain, as well. Image: keellla/Shutterstock.com 