Guest post written by Patrick Taylor
Patrick Taylor is CEO and founder of Oversight Systems, a provider of business analyticS software.
Moneyball, the 2011 film based on a bestselling book by Michael Lewis, reveals how the Oakland Athletics won almost two-thirds of its games in 2001, with a payroll just one-fourth of the mighty New York Yankees, and nearly beat the Yankees in the 2001 MLB playoffs.
The well-documented key to their success was making strategic decisions (assembling the team) based on deep data analysis. They ignored conventional wisdom about baseball metrics like batting averages, runs batted in and stolen bases in favor of a much more rigorous statistical analysis about player performance.
However, the wins started piling up only when Oakland applied their insights to day-to-day playing decisions like having batters take more pitches to tire the opposing pitcher. By using detailed data analysis in tactical decisions, they set league records for consecutive wins.
With the powerful combination of analytically driven strategic and day-today decisions, they created the most cost-effective competitive edge in all of major league baseball, leading some wags to start calling the A’s the Oakland Analytics.
Big Data, big potential
Just as the A's used data analytics to their advantage, companies can do the same by utilizing predictive business analytics. Mining enterprise data effectively to extract actionable business insights can provide executives not only the means to make better strategic decisions, but to also improve the critical, day-to-day decisions made at the front lines of business. Together, insightful strategic and tactical decision making creates for companies a clear competitive advantage, which can dramatically protect and accelerate their revenue and ultimately, their overall business success.
An example of the use of predictive business analytics is in conducting spend analysis to establish long-term contracts with vendors – a classic strategic use of analysis. Using analytics, procurement executives can also easily perform best price analysis on every purchase order line item as it is entered into the system, evaluating against previous purchases of similar quantities and delivery conditions at lower costs. Purchasing managers are alerted when there’s an opportunity to save money with a smarter purchasing decision. And for long-term contracts, every invoice is validated against contract terms, and checked for volume discounts before the invoice is paid.
Big Data, big puzzle?
Sifting through all the data that medium-to-large enterprises generate can be daunting. Transactions can produce from gigabytes to terabytes of detailed data in a single day, depending on a company’s size. IT’s buzzword for such enormous data stores is “Big Data.” These data sets are so large and complex that traditional tools like relational databases and visualization applications simply fall short of delivering useful insights, especially in real time of day-to-day decisions.
Large numbers of big companies around the world still employ traditional tools manually with such latency that their insights can be quite stale by the time decision-makers get them. Unfortunately in today’s ever-accelerating pace of commerce, business intelligence is quickly perishable. An operational “snapshot” taken days before may not suffice to help make decisions needed within a narrower window of time. It’s time for businesses to make a transformation – from utilizing their Big Data to make infrequent, strategic decisions to implementing a model of constant real-time adjustments, making everyday business operations more intelligent.
In the example of the Oakland A’s, real change came about not just by the strategic decision to bring in the right kind of players, but by using advanced analytics to make small, high-impact changes such as their habits of walking or getting a run.
Big Data, Big Data applications
Big Data, while ripe with potential to be mined for insights, needs a different, more dynamic approach to realizing its possibilities than the top-down, root-cause analysis of traditional business intelligence. With today’s decision-makers so busy, how do you give them clear, actionable insights? The answer is Big Data analysis applications.
Big Data analysis applications provide a highly scalable framework to deliver advisories that drive smarter day-to-day decisions. Big Data applications’ true power comes from the use of analytics to deliver actual analysis – actionable recommendations - directly to the front line of business. Big Data analysis applications are not another way for employees to ask questions, they are a means to provide employees with answers. It is the Moneyball equivalent of the instruction delivered to the batter in the dugout - it’s the 8th inning, take the walk.
These kinds of transaction-level insights in real-time provide a faster, more efficient way to improve operations. Big Data analysis applications allow businesses to ensure that they are using their resources intelligently - offering the perfect additional product to increase the average sale to a customer and supplying the negotiating leverage for a supply purchase. With smarter insights comes a healthier revenue stream, and improved overall business operations.
Big Data can provide protection for businesses too. In a recent incident, a Morgan Stanley employee was convicted of bribery. But their ongoing transaction analysis capabilities showed the DOJ and the SEC that the perpetrator was using extraordinary measures rather than exploiting flaws in the system. The firm was exonerated by the DOJ and SEC, saving millions of dollars in fines and a hit to their reputation.
Big Data, big results
Big Data analysis applications are highly scalable and therefore cost-effective for a wide range of enterprise applications. In one of its largest-scale deployments, a U.S. government shared services agency uses Big Data Applications to prevent over $1 billion in duplicate and improper payments in the course of processing trillions of transactions per year. It realized a full return on its investment in just 90 days.
In today’s highly competitive global economy, intelligent business decisions make the difference between success and failure. And while business intelligence is useful for strategic direction, to make smarter everyday decisions advisories must be delivered directly to the front line. Big Data analysis applications provide the means for the decision-makers to turn raw data into actionable, real-time insights, creating a competitive advantage. Clearly in the years to come, companies that “get” Big Data at both strategic and day-to-day levels will forge ahead of those that don’t.
