Debunking 8 Big Data and Analytics Myths - InformationWeek

InformationWeek is part of the Informa Tech Division of Informa PLC

This site is operated by a business or businesses owned by Informa PLC and all copyright resides with them.Informa PLC's registered office is 5 Howick Place, London SW1P 1WG. Registered in England and Wales. Number 8860726.

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
1 of 10
Next

There's little doubt that the concept of big data analytics has been dragged through the mud multiple times over the years. Early adopters struggled in many areas that ultimately led to higher than expected failure rates -- and ultimately -- a poor return on investment. Yet, many of the mistakes of the past have long since been overcome. What remains, however, are a number of myths surrounding concepts and implementation steps that some feel still reflect the truth.

Image: Pixabay
Image: Pixabay

Despite the less than stellar track record -- or perception -- big data remains a big deal. IDC released a forecast in the third quarter of 2016 that showed that the big data and analytics market hitting double digit year-on-year growth rates. If this is true, then many of those scary myths still floating around almost certainly must be wrong. Right?

The thing about the best and longest lasting myths, legends and lore is that there is always a nugget of truth that keeps the mistruth going. This is commonly the case with complex technologies that are often over hyped and ultimately become slower than expected to be adopted. Big data is one of those technologies, but it's not the only one. Other recent examples where negative myths have been formed around technology include software defined WANs (SD-WAN), IT security and even cloud computing. Yet, if the technology is ultimately the right fit for enterprise organizations, myths eventually are overcome and the truth is exposed.

Today, we're going to look at eight such myths that have come out of the big data and analytics movement. As you're flipping through the slides, try to figure out where the truth became skewed to the point where the fallacy was formed. This is the best way to tear down the myth and bring reality back into the picture. In most cases, a misconception surrounding some aspect of big data or analytics was due to an error in judgment made by a number of early adopters. In other situations, myths formed out of the enterprise IT department lacking the skills and tools required to run a big data project. Finally, a few fallacies came about based on simple misinformation and miscommunication regarding concepts and components of big data architectures.

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
1 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.
Commentary
Study Proposes 5 Primary Traits of Innovation Leaders
Joao-Pierre S. Ruth, Senior Writer,  11/8/2019
Slideshows
Top-Paying U.S. Cities for Data Scientists and Data Analysts
Cynthia Harvey, Freelance Journalist, InformationWeek,  11/5/2019
Slideshows
10 Strategic Technology Trends for 2020
Jessica Davis, Senior Editor, Enterprise Apps,  11/1/2019
White Papers
Register for InformationWeek Newsletters
State of the Cloud
State of the Cloud
Cloud has drastically changed how IT organizations consume and deploy services in the digital age. This research report will delve into public, private and hybrid cloud adoption trends, with a special focus on infrastructure as a service and its role in the enterprise. Find out the challenges organizations are experiencing, and the technologies and strategies they are using to manage and mitigate those challenges today.
Video
Current Issue
Getting Started With Emerging Technologies
Looking to help your enterprise IT team ease the stress of putting new/emerging technologies such as AI, machine learning and IoT to work for their organizations? There are a few ways to get off on the right foot. In this report we share some expert advice on how to approach some of these seemingly daunting tech challenges.
Slideshows
Flash Poll