Our recent economy and business climate is no stranger to strife. In light of that fact, organizations are looking to mitigate losses and improve their bottom lines with deep investment into data analytics.
In fact, 64% of organizations are currently investing or planning an investment, according to a Gartner Inc. survey.
While we know there’s a lot of hype in big data, the “hype” is legit, according to Gartner’s advanced analytics research director Lisa Kart.
“Our survey underlines the fact that organizations across industries and geographies see ‘opportunity’ and real business value rather than the ‘smoke and mirrors’ with which hypes usually come,” Kart says.
The hype may actually do a lot of good, especially in the financial services industry as New York Times big data columnist Steve Lohr points out in a recent blog post.
“The idea is that the better measurement will inform better management of the economy,” he notes.
But that’s not necessarily true when you involve people, he says. Getting more measurement in a “crucial slice of the economy to guide policy and perhaps behavior” may be a better move.
Measurement of aggregate financial data could protect us by pointing to signs of trouble much sooner than in the past.
Lohr offers a good overview of what the Office of Financial Research (OFR) did to pinpoint areas of improvement in light of what happened to financial institutions prior to 2010. More “stringent reporting” became a norm, but it’s just a first step.
There is a need for more than data collection to warn of impending financial trouble, notes Richard Berner, director of the OFR. He suggests that organizations and the government can collect data on “money market funds, credit-default swaps, financial leverage and counterparty risk exposure” to show early warning signals of trouble.
But with that “data collection” comes great responsibility to protect the trade secrets of the individual financial institutions.
Berner points to an interesting concept that may predict problems for investors and regulators without compromising the “business” of the institutions being analyzed.
The “secret weapon” Lohr writes about in his blog boils down to collecting the data, encrypting it and aggregating it. By taking this approach, these privacy-preserving methods “solve the challenge of measuring aggregate risk among multiple financial institutions.”
So, what can individual financial institutions and every organization learn from this interesting research?
- Data collection and big data analytics can predict problems before they grow from a molehill to a mountain.
- A commitment to security can protect data, but also drive innovation and insight when looking for “warning signs.” Opportunities may even arise.
- Committing to big data analytics, particularly in financial services, is a good move despite the “hype” big data often receives.
