Streaming data analytics

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During an extended vacation, I was amused to watch so many of my family and friends, none of whom work in the data management field, integrate data into more of their daily lives. I watched my brother and father install a Wi-Fi-enabled thermostat in my parents’ house. My aunt and uncle showed me dashboard reports on their smartphone from the Bluetooth-enabled device they installed in my cousin’s college car to track his vehicle’s maintenance, travel history and provide emergency roadside assistance. My friend’s eight-year-old daughter happily narrated the GPS navigation directions to a Christmas-themed outdoor festival while also accurately explaining how the satellites and her mobile device stayed in sync with the car’s constantly changing position. Meanwhile her father was in the backseat with me periodically checking the package delivery status of a few last-minute presents. Another cousin and her group of friends explained to me how they used social media and other online resources to investigate a suspected catfisher they encountered using a popular mobile dating app.

Data, clearly, is changing the way my family and friends work and live.

Streaming data calls for nontraditional analytics

Similar to the way data has changed our day-to-day lives, streaming data has changed analytics in several different ways. For one thing, streaming has introduced a lot of new sources mostly external to the enterprise, including data flowing from sensors, RFID tags, smart meters, live social media, mobile devices and other internet-connected objects. Another important change, quite different from traditional analytics, is that streaming data often has to be analyzed before it’s stored – and in some cases it’s never stored.

In streaming analytics, more often it’s the data models and analytical algorithms that are stored, and streaming data is continuously queried as it passes through them. This is necessary because one of the key challenges of working with streaming data is acting on its potential insights quickly before the data, as it’s being generated or transmitted in real time, loses its value. Streaming analytics attempts to determine the data’s meaning and value, pinpoint relevance and generate instant alerts when there’s an urgency to take action. This analysis also enables enterprises to decide when streaming data should be stored, and therefore subjected to additional management and governance. And the insights gleaned from streaming data analytics may also be discovered to have value as a complement or supplement for other enterprise applications.

My previous post discussed data preparation for streaming data. Streaming data analytics, also referred to as event stream processing, is becoming an increasingly critical component of how enterprises make the best use of all available data to move quickly from insight to action, make sound data-driven decisions, and adapt to changing business conditions.

Read more in: Channeling Streaming Data for Competitive Advantage
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Jim Harris

Blogger-in-Chief at Obsessive-Compulsive Data Quality (OCDQ)

Jim Harris is a recognized data quality thought leader with 25 years of enterprise data management industry experience. Jim is an independent consultant, speaker, and freelance writer. Jim is the Blogger-in-Chief at Obsessive-Compulsive Data Quality, an independent blog offering a vendor-neutral perspective on data quality and its related disciplines, including data governance, master data management, and business intelligence.

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