Predictive analytics helps organizations use data in their daily decision-making to substantially improve outcomes.
Yet there are challenges facing current decision makers:
- Lack of Insight - into the true cost or value of their unstructured data
- Insufficient Access - lack of a data driven approach to problem solving
- Inability to Predict - to uncover data patterns and trends
How you can overcome these challenges:
- Smarter. Your predictive analytics solution must provide best practices out of the box. It also needs to be self-learning, capable of understanding both the context in which it's being used and how results will impact action.
- Automated. Your predictive analytics solutions must be able to handle a large number of decision points without needing to engage resources to manually develop and roll out statistical models.
- Embedded. In order to address the your need for a scalable decision-making engine, your predictive analytics solutions must be embedded into your processes. Solutions cannot be separate activities that churn out custom results.
Why is it important? Predictive analytics allows your organization to analyze patterns in both current and historical data sources to provide valuable insight and enable smarter outcomes. Interested in how you can gain insight from your big data.. Contact us.
Kinetek Sources:
Analytics for Everyone
IBM Next Generation Forecasting
IBM Seven Symptoms of Forecasting Illness
IBM Today's Dynamic Planning and Forecasting
