What industry takes risk management more seriously than finance? Corporate and personal investors want a gold-embossed sure thing when they sink their cash into a venture. So techies who come to them with an untested data analytics toy will likely find them tough customers. Some folks in the finance world are out to dispel anxieties by educating investors on why data sometimes picks a winner and why it may fail.
Jeffrey Bohn, chief science officer at State Street Global Exchange, said the confusion lay in the problem of data quality. He also believes that companies still do not have enough hands on deck to separate the wheat from the chaff.
“You still find 70 to 80 percent of the effort and resources focused on the data preparation step,” he told Jeff Frick (@JeffFrick), host of theCUBE*, from the SiliconANGLE Media team, during the recent the Chief Data Scientist, USA event.
Data scientists spread too thin
According to Bohn, more data stewards are need to select quality data and to free up analysts to innovate and find solutions.
“I’ve had problems where you have great models, but data quality produced some kind of strange answers,” he explained. “And then you have a senior executive who looks at a couple of anecdotal pieces of evidence that suggest there are data quality issues, and all of a sudden they want to trash the whole process and go back to more ad hoc, gut-based decision making.”
The best and the rest
Bohn argueds that to increase data quality, companies need to start culling from a greater number of sources.
“We’ve recently been very focused these days on trying to take unstructured data — so this would be text data, it might be in forms on PDFs or html document or text files — and marry that with some of the more standard structured or quantitative data,” he said.
Watch the complete video interview below: