Remove 2016 Remove Agile Remove Analytics Remove Big Data
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Telecom Network Analytics: Transformation, Innovation, Automation

Cloudera

One of the most substantial big data workloads over the past fifteen years has been in the domain of telecom network analytics. The Dawn of Telco Big Data: 2007-2012. Suddenly, it was possible to build a data model of the network and create both a historical and predictive view of its behaviour.

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4 Megatrends in Big Data in 2019 and Beyond

Agile Engine

2018 was the second consecutive year when Gartner published an obituary of Big Data. No one, including Gartner, thinks Big Data is dead. Au contraire, Big Data has grown so ubiquitous it became “just data”, argue the authors of the obituaries. Trend 1: From Big Data to “Just Data”.

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Regarding Big Data, One Manager’s “Resistance” Is Another Manager’s “Caution”

CTOvision

M att Turck has some interesting things to say about “big data” in “ Is Big Data Still a Thing? The 2016 Big Data Landscape).” Add in sensor based and unstructured data and you have situation where traditional data management thinking may prove inadequate. How much time will it take?

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Closing the Gap Between the Digital Haves and Have-Nots

Cloudera

Savvy medium-sized businesses have opportunities to implement data tools as they become more widespread and affordable. . 86% of the companies adopting big data and data analytics state that adopting the technology has had a positive impact. . The returns are tangible. Challenges.

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Digital Risk Management Leader RiskIQ Raises New Funding To Expand Platform Ecosystem, Sales and Digital Risk Applications

CTOvision

SAN FRANCISCO – November 10, 2016 -- RiskIQ, the leader in digital risk management, today announced that it closed $30.5 Similar to Google, RiskIQ applies machine learning and data science to continuously improve platform intelligence and broaden functionality by leveraging big data, customer usage and attack activity.

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Five Trends for 2019

Hu's Place - HitachiVantara

Public cloud, agile methodologies and devops, RESTful APIs, containers, analytics and machine learning are being adopted. ” Deployments of large data hubs have only resulted in more data silos that are not easily understood, related, or shared. Happy New Year and welcome to 2019, a year full of possibilities.

Trends 86
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AI Applications in Cybersecurity with Real-Life Examples

Altexsoft

Additionally, tools like user and event behavior analytics (UEBA), when powered by AI, can analyze user behavior on servers and endpoints, and then detect anomalies that might indicate an unknown attack. Machine learning can quickly scan large amounts of data and analyze it using statistics. Want to write an article for our blog?