Debunking 8 Big Data and Analytics Myths - InformationWeek
IoT
IoT
Data Management
News
9/21/2017
07:00 AM
Connect Directly
Twitter
LinkedIn
Google+
RSS
E-Mail
100%
0%

Debunking 8 Big Data and Analytics Myths

As with other emerging tech concepts, big data and analytics are haunted by myths. Here are eight such myths that you will want to dispel as you advance your analytics strategy.
Previous
3 of 10
Next

Big data analytics are far too expensive

 

Image: Pixabay
Image: Pixabay


When you start discussing the topic of big data to those who aren’t fully informed, you often come away with the sense that many IT leaders feel that they can’t afford the cost of getting started. This likely came about because big data first became popular with the largest enterprise organizations. Story after story about big data being leveraged in companies such as Facebook, Microsoft and Wal-Mart led many to believe that this was a technology only attainable by the largest of organizations. While this may have in fact been true very early on, cloud-based big data and analytics solutions now allow companies to start small and scale their big data ambitions on an as-needed basis with relatively low start-up costs.

Andrew has well over a decade of enterprise networking under his belt through his consulting practice, which specializes in enterprise network architectures and datacenter build-outs and prior experience at organizations such as State Farm Insurance, United Airlines and the ... View Full Bio

We welcome your comments on this topic on our social media channels, or [contact us directly] with questions about the site.
Previous
3 of 10
Next
Comment  | 
Print  | 
More Insights
Comments
Newest First  |  Oldest First  |  Threaded View
ndeaa
50%
50%
ndeaa,
User Rank: Apprentice
10/4/2017 | 1:41:28 AM
Debunking 8 big data and Analytics myths
Very insightful information on Bigdata and anaytics!With an emphasis on predictive analytics, it is important to provide customers with the ability to move beyond simple reactive operations and into proactive activities that help plan for the future and identify new areas of business. Predictive models use known results to develop (or train) a model that can be used to predict values for different or new data. Modeling provides results in the form of predictions that represent a probability of the target variable (for example, revenue) based on estimated significance from a set of input variables.
News
Don't Collect Biometric Data Without Providing Notice
Lisa Morgan, Freelance Writer,  2/1/2019
Commentary
AI and the Next Recession
Guest Commentary, Guest Commentary,  1/24/2019
Commentary
The Title Machine Learning Engineer Will Start to Disappear
Guest Commentary, Guest Commentary,  2/7/2019
White Papers
Register for InformationWeek Newsletters
2018 State of the Cloud
2018 State of the Cloud
Cloud adoption is growing, but how are organizations taking advantage of it? Interop ITX and InformationWeek surveyed technology decision-makers to find out, read this report to discover what they had to say!
Video
Current Issue
Security and Privacy vs. Innovation: The Great Balancing Act
This InformationWeek IT Trend Report will help you better understand and address the growing challenge of balancing the need for innovation with the real-world threats and regulations.
Slideshows
Flash Poll