Data quality, quantity, and velocity are becoming daily concerns for any data-driven firm. Data streams can point the way to new knowledge and strategies.
Today’s enterprise data management team knows the reality: data increases in quantity, quality, and velocity daily. Data sources are multiplying and varied. Cloud, mobile and social events—not just the transactions of the past —are invigorating enterprise data teams across many sectors, and this presents an enormous chance, and risk, to make businesses more agile, more efficient and more competitive—if they can act fast enough.
But these teams need to move beyond just managing data and turn towards taking action on continuous data streams. Today, many business-line decision-making tools require data to be stored and backed up before any analytic queries. Even advanced trickle-feed data warehousing updates can take between five minutes and two hours before that data is available to users.
IBM Streams closes this gap with data stream analytics tailored to each unique use case and industry. Using Streams, users can spot risks and opportunities in high-velocity data, and take action almost immediately. High-velocity flows of data from sources such as market data, IoT, mobile, sensors, and clickstreams remain largely un-navigated today, but it’s time to unlock this data to optimize decisions, improve business insights and accelerate responses to critical events.
Now, users can integrate and analyze data while in motion and understand the context of everything from people to machines. Organizations can leverage this insight to improve and create more sophisticated analytical models and provide fodder for cognitive systems.
IBM Streams promises a continuous, complete, and connected solution needed for the current competitive environment and the future:
Continuous – Analyzes data streams continuously and brings the analytics/algorithms to the data stream.
Complete – Users can build, deploy and score models on Streams, and integrate data streams into existing data and analytic environments.
Connected – Nearly any data source on the planet can connect to Streams, and can scale up and down as data streams change; it’s highly available and reliable.
Learn more about IBM Streams and the top use cases in the free white paper “Top Industry Use Cases for Stream Computing” here.
