Inaccurate perceptions of predictive analytics are common in the business world. In reality, predictive analytics is straightforward to understand, can leverage existing skillsets in business and IT organizations, and can deliver value in most industries and lines of business. Getting started with
What do transformational data leaders have in common? Each has found a way to do three things: (1) Make data a priority, (2) develop from within and (3) free data from silos within the organization. Such leaders face challenges common to many but often develop unique approaches driving
When customers or other key stakeholders expect to be able to connect with an organization instantaneously, extremely low latency, high throughput data and analytics flows and execution are absolutely essential. The advent of the Internet of Things is among several key drivers of the emergence of
Streaming analytics is becoming ubiquitous as data streams from a range of sources, including the Internet of Things, are now mainstream. Although streaming analytics is not a new technology, it is well suited for today’s real-time, low-latency business and consumer applications. And today’s data
Big data without context is pretty much useless, especially when that context can fluctuate so widely—which is why the role of Hadoop in creating accurate analytics is crucial for deriving value from big data.
Meeting today’s dynamic data requirements goes beyond technology that focuses on operational capture, decision-support-oriented consumption, and data governance. Enterprise architects need to take into account a wider array of data sources and establish a performance measurement plan that tracks