A lot can be accomplished with predictive analytics, the catch-all term for data that is gathered from various devices and sensors and then fed to an algorithm designed to predict behavior. Even if they've never heard the technical terminology, many consumers come into contact with predictive analytics each day, such as when they see "You Might Also Like" widgets around the Web (based on browsing or buying history), or a sea of personalized suggestions in their Netflix queues.
Predictive analytics for enterprises: What are the advantages?
For enterprises, predictive analytics provide prime opportunities to engage more directly with customers while fine-tuning their products and services. Examples of predictive analytics at the organizational level include:
- Combing through social media channels to discover popular topics of conversation and include them in a future campaign
- Synthesizing medical patient data to learn what given individuals may be predisposed to
- Using cyber forensics tools to discern patterns in crime and create highly targeted prevention efforts
These possibilities just scratch the surface. A May 2014 Research on Global Markets report predicted that the big data services market would have a 32 percent compound annual growth rate from 2013 to 2018. Predictive analytics may come to the forefront, especially as more endpoints are connected to the Internet and become feeders for predictive algorithms.
"By using current and historical data, we can predict trends that can improve program performance and operational efficiency," wrote Thom Rubel for FCW. "[P]rograms that are collectively designed to ensure the smooth flow of people and commerce are typically informed by multiple data sources generated by people or things. Predictive decision-making ensures that the right combinations of information come together based on business rules that optimize desired outcomes - think smooth traffic flows."
How can organizations make the most of predictive analytics? Technical and procedural changes are required for adequately adjusting to environments in which information accumulates rapidly and must be acted upon in short order. Tools such as enterprise job schedulers facilitate the development of efficient predictive analytics.
Shoring up infrastructure and processes for predictive analytics
Predictive analytics have come a long way since the early days of the Web, but there's still a lot of untapped potential. MediaPost's Laurie Sullivan looked at the some of the technical challenges as they relate to retail and entertainment, such as better accounting for time- and income-based needs. She also pointed out that it took years for Netflix to improve its movie suggestions, using hundreds of algorithms.
IT personnel need the time and resources to optimize predictive analytics. Enterprise job scheduling software removes the burden of having to manually run tons of routine operations for handling data storage and analysis. Workers may now have to deal with both structured and unstructured data, as well as damaged assets, all while writing and updating code to ensure data integration across different platforms, but these tasks need not be overwhelming.
A job scheduler ensures better workload management, more exceptions for scheduled tasks and centralized management. In short, it is a key tool for automating and streamlining the complex processes that support high-quality predictive analytics.
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