Predictive analytics is a subset of data mining — it helps companies extract information from data sets and predict future trends. In the shadow of a recent financial crisis, it is only natural to explore whether predictive analytics could help to prevent similar events in the future. According to the founder of Predictive Analytics World, Eric Siegel, the power of predictive analytics is to “predict who will buy, click, lie or die.” In this article, Research Optimus discusses how more effective strategic planning and management — facilitated by predictive analytics — can minimize economic crises that were previously “unpredictable.”
Thinking about Global Recession
While economists are frequently thinking about global recession causes and effects, business owners and corporate managers should be equally interested. However, this often leads to mistakes and errors. For example, two recent heads of the Federal Reserve — Alan Greenspan and Ben Bernanke — have publicly admitted that they “misjudged the risks” of the Great Recession.
At the same time, Sheila Bair (head of the Federal Deposit Insurance Corporation from 2006 to 2011) and well-known investors such as John Paulson saw something that leading economists missed. Mr. Paulson “saw” the housing bust and subprime mortgage crisis coming and profited to the tune of several billion dollars.
What does this mean for both Wall Street and your business? Corporate executives should always be looking at future trends, and predictive analytics is an effective tool for providing needed analytical insights for both research companies and other businesses.
Predictions about Recessions
Even though the stock market is enjoying record highs on a daily basis, research and business managers cannot afford to overlook the next recession prediction. What would your company have done differently with a better “look ahead” prior to 2008? Almost a decade ago, experts like Sheila Bair and David Levy of Levy Forecasts were forecasting a financial crisis due to the housing mortgage bubble.
Levy’s current forecast is for a U.S. recession next year (2016) — instead of the housing bubble, he says that downturns in other countries such as China and Russia will shape the next U.S. financial downturn. For those hoping that the growth of the U.S. economy will help restore foreign economies and prevent such a recession, Cornell University economist Eswar Prasad is not optimistic: “… the U.S. consumer isn’t in a position to take on the burden.”
The Emergence of Predictive Analytics Post-Recession
After any financial downturn, companies are always looking for accurate signals that will help them to understand current spending habits and increase production. Even though predictive analytics is not a brand new management tool, it had not been thoroughly “tested” in a post-recession environment until recent years.
One of the biggest challenges for companies operating in a “Big Data” environment is to help them facilitate intelligent planning and analysis after sifting through the massive amounts of data. Recent applications of predictive analysis have already helped businesses to plan ahead for appropriate production levels by evaluating a combination of often contradictory factors — for example, consumer debt levels, motor vehicle sales, personal savings rates, capital spending plans and export of manufactured goods. Predictive analytics have proven especially effective in helping with product recommendations for corporate managers.
How Predictive Analytics Is Helping Wall Street
Using Wall Street and related businesses as a reference point, there are ongoing examples of helpful analytical insights for companies — all powered by predictive analytics. Here are a few illustrations:
- Investors — While early uses of quantitative financial analysis by high frequency traders often seemed to leave smaller investors in the dust, today’s powerful data analytic tools are now equally available to all investors.
- Citrix — This company was featured in a Wall Street Journal story about how they have made data centers more efficient with predictive analytics.
- Hospitals — As both the U.S. government and consumers search for a better way to manage and control healthcare costs, hospitals are using predictive analytics in new ways such as slashing post-surgical infections.
- Credit Card Companies — MasterCard paid $600,000 for predictive technologies that help measure trends involving data for merchandising and pricing in an effort to provide more effective services to merchants.
Financial Research and Predictive Analytics
It is of critical importance for businesses to have realistic expectations when assessing the potential value of predictive analytics. For example, do not expect that this valuable tool will truly predict the future of financial markets. However, here are four tangible areas in which predictive analytics can “make a positive difference” in financial research and your company’s bottom line:
- Improve Customer Satisfaction and Loyalty
- Acquire Operational and Business Intelligence
- Facilitate Regulatory Compliance
- Optimize Invested Capital
To make the best uses of data, both Wall Street and non-financial companies must increasingly make faster decisions. This often involves correlation analysis of many different variables — identifying classifications and uncovering hidden patterns requires sophisticated mathematical models. Predictive analytics is “up to the challenge” of doing all of this with both speed and scale.
New Methods for Intelligent Planning and Analysis
Of course, predictive analytics does have some practical limitations for research companies as well as other business users — there are also frequent changes impacting new predictive analytics technologies. One of the best strategies for coping with both the limitations and changes is to outsource predictive analytics needs to data management experts like Research Optimus.
Please join in the predictive analytics conversation by leaving your comments below and sharing your thoughts by using social media buttons.
– Research Optimus
-Research Optimus
Prev
