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Big Data Predictive Analytics
for Proactive Decision Making

Shift Your Decision-Making from Reactive to Proactive


Past is prelude. Historical data provides signals that indicate what may happen in the future. By understanding signals coming from machines and sensors, server logs and other new data sources, organizations can use Hortonworks big data predictive analytics to predict future events and become more proactive.

Hadoop captures, stores and processes the large volumes of data streaming from connected devices and sensors that measure your business. A combination of Hadoop predictive analytics with a variety of data science and iterative machine-learning techniques can make confident real-time recommendations that reduce costs, improve safety, and inform investments.

Predictive Analytics Open New Paths to Value


Sensor data rapidly streams into companies and quickly grows to terabytes. Use HDP® Hadoop predictive analytics capabilities to predict when a piece of equipment may fail and then fix it before it breaks. A leading oilfield services company uses HDP to collect and analyze huge volumes of sensor data to anticipate equipment issues and perform maintenance before pumps break and jeopardize production.

Oil and Gas Does Hadoop


By analyzing resource utilization and adjusting based on changing conditions, companies can make the most of their finite resources. HDP brings together all the data and processing engines needed to model resource requirements and adjust in real time. One of the world’s largest electric utilities uses HDP to capture high volumes of smart meter data and apply predictive analytics to forecast consumption more accurately. This helps them match production with demand, avoiding unnecessary power generation while keeping the lights on.

Manufacturing Does Hadoop »


Historical pattern recognition with real-time data capture and analysis can show you where and how to intervene before it’s too late. Predict likely outcomes and take action that maximizes positive (or minimizes negative) results. UC Irvine Health uses HDP to predict the likelihood of hospital re-admittance for proactive intervention. Patients with congestive heart failure have a tendency to build up fluid, which causes them to gain weight. UCIH sends those heart patients home with a scale and instructions to weigh themselves once daily. The weight data is wirelessly transmitted to Hadoop where an algorithm determines which weight changes indicate risk of re-admittance, and the system notifies clinicians about those cases.

UCIH Case Study »

Predictive Analytics Use Cases

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Machines know things. Sensors stream low-cost, always-on data. Hadoop makes it easier for you to store and refine that data and identify meaningful patterns, providing you with the insight to make proactive business decisions using big data predictive analytics. See how Hadoop can be used to analyze heating, ventilation and air conditioning data to maintain ideal office temperatures and minimize expenses

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