Organizations make thousands or even millions of decisions each day that can impact business operations, competitiveness and growth. IBM recently completely a survey where 3 out of 4 business leaders say that more predictive information would drive better decisions, such as:
Predictive analytics helps organizations use their data to make better decisions by allowing them to draw reliable, data-driven conclusions about current conditions and future events. By deploying predictive analytics, organizations are addressing their business issues proactively to get the best outcomes.
There are three pillars of predictive analytics: customer analytics, operational analytics, and threat and fraud analytics.
Customer analytics enables organizations to better understand their customers and predict what they are likely to do. By acquiring customers more efficiently, growing the value of existing customers, and retaining profitable customers for longer. All these goals are assisted by prediction – for example whether an individual from a mailing list is likely to be a profitable customer, or whether they are likely to be interested in a particular product, or whether someone’s behavior indicates that they may be thinking of switching to a different supplier.
Operational analytics generally revolves around assets, whether it is helping to manage physical or virtual assets -- from identifying the right physical inventory to stock in your supply chain, to assessing how many components to purchase to support your production facilities. It enables organizations to maintain physical infrastructure and capital equipment. And by ensuring the allocation of people and cash in the most efficient manner, maximize capital. For example, preventing unscheduled downtime of a truck, by analyzing the service history to identify when certain parts are likely to fail based on the conditions or environment it’s working in, rather than just on time.
Threat and fraud analytics is used to detect suspicious or anomalous transactions, like potentially fraudulent insurance claims, or detect money laundering activities. In this case it is about monitoring your environment by including a wide variety of data across multiple sources, detecting suspicious behavior to identify threats, information breaches, crime or fraud, and then control the outcomes to deliver the best response to reduce exposure or loss and maximize the impact of any action taken.
Decision Management is a business discipline that combines a variety of techniques to enable optimizing actions with resource constraints and aligning execution with strategy. It empowers real-time and adaptive decisions accommodating changing conditions to provide front-line employees and systems with recommended actions.
AIX Solution Edition for SPSS is a starting point for predictive analytics that is easy to deploy on Power servers. Ad Hoc Analytics configurations provide client and server side analytics for data mining and statistics on structured and unstructured data. It is designed for professional analysts. An Advanced Analytics approach enables collaboration between professionals and business analysts, efficiently driving predictive analytics into the heart of the enterprise. It includes powerful data mining and statistics capabilities. The recommended solution includes the AIX operating system, PowerVM virtualization, WebSphere Application Server and one or more of the following SPSS products:
The POWER7+ architecture is built for analyzing vast amounts of data, and delivering results to users across the organization quickly, securely and cost effectively. A recent study shows that Power Systems clients could potentially deploy new analytics solutions with an 8.8 month payback period, and an additional 64% return on their investment1.
SPSS Modeler is optimized for the Power server architecture providing consistently high levels of performance even when reading tens of millions of records. The Power server infrastructure is also highly efficient at handling the scoring transaction load of Collaboration and Deployment Services with large data volumes. This means it is more likely to be able to execute scoring with real-time operational transactions, and keep up with the operational throughput than a comparable x86 server scale out configurations. This is especially important for high data volumes associated with threat/fraud detection, or anytime that critical decisions must be made in real-time.
With the scalability of PowerVM, predictive analytics and decision management functions can dynamically share processor, memory and I/O resources to maximize server utilization and ROI. This ensures that processing power is available for scoring and deployment of results such as fraud detection or customer service offers is delivered to front line workers in real time - without the performance penalty of VMware2.
1 Forrester Consulting on the Total Economic Impact of Analytics on IBM Power Systems
2 Edison Group: IBM PowerVM Virtualization Technology on IBM POWER7 Systems. A Comparison of PowerVM and VMware vSphere (4.1 & 5.0) Virtualization Performance