IBM PureData System

Meeting big data challenges with simplicity, speed and lower cost

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PureData System
for Hadoop
for Analytics
for Operational Analytics - selected tab,
for Transactions

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Gain a competitive advantage by handling complex analytics and a large volume of concurrent operational queries

When a credit provider tells a client it denied a transaction on her card today - on the other side of the world – she is not thinking about all the others checked in just that second… but the provider’s operational warehouse is

Businesses need actionable insight into their operations to gain a competitive advantage—and to do this, they must analyze large data sets comprising both historic and current (or operational) data. A high percentage of queries for operational analytics—often up to 80 percent—are interactive lookups focused on data about a specific customer, account or patient. These operational queries can also originate from call centers, mobile sales apps, real-time fraud detection systems or other applications that support real-time decision making.

To deliver the correct information as rapidly as possible, the data warehouse supporting these systems must be optimized for the right balance of analytics performance and operational query throughput. The PureData System for Operation Analytics is a data warehouse system for delivering insights to business operations for real-time decision making. It is optimized to handle complex analytics and designed to handle 1000+ concurrent operational queries¹.

Turn data into insight with in-database analytics
Using multidimensional cubing services, PureData System for Operational Analytics delivers rapid insight into high volumes of fast-moving data. Users can create, edit, import, export and deploy cube models over the relational warehouse schema to analyze multiple business variables. Cubing services help optimize performance for online analytical processing (OLAP) queries, providing more power for users to analyze data and generate business insight that can enhance both profitability and customer satisfaction.

¹Based on IBM internal tests of prior generation system, and on system design for normal operation under expected typical workload. Individual results may vary.