Page 4534 – SiliconANGLE

Where does today’s business draw the line with DIY IT? | #BigData

By R. Danes Sep 24, 2016

Open source software is free. So if companies can collect open-source software products, configure them, test them and learn to use them to run their businesses, what place do for-profit companies have in the…

Stacking the odds: Outsourcing lower-stack IT to focus on value-driving applications | #BigData

By R. Danes Sep 24, 2016

We frequently hear that companies want to focus their efforts up the stack, particularly on the customer-facing applications that differentiate them in the market. With all the innovation taking place in application development these…

Legacy companies can’t wait to start monetizing data — so where’s the application? | #BigData

By R. Danes Sep 24, 2016

Is it a good time to be a digital-only company or what? Some recent studies from MIT show that some of these assetless companies derive up to 84 percent of their S&P value just…

People puzzle: Combining social and CRM data to suss out customers | #BigData

By R. Danes Sep 24, 2016

Companies who use data to construct a picture of an individual customer have a daunting challenge in front of them. On the one hand, there is “hard” data on transactions, buying patterns and vital…

Does the modern application need 24/7 data replication? | #BigData

By R. Danes Sep 24, 2016

Replication of data is necessary for speed, function and consistency of data applications across environments. But a key question is how often a company’s data is replicating? For instance, if a business has a four-day…

Can Big Data level the playing field for digital-native and non-native companies? | #BigData

By R. Danes Sep 24, 2016

In the age of Amazon, Zappos and other digital-native giants, smaller businesses with traditional operations have had to face a hard reality: A company with a technological advantage could come along and eat your…

Data scientist or data janitor? Democratize data to get more from the data pros | #BigData

By R. Danes Sep 23, 2016

Data scientists are a rare and coveted breed of IT professional. Data science is a relatively young field; many computer science programs at universities still do not offer data science specializations. The data scientists…

Take it to the data: Working smarter, not harder on siloed data sets | #BigData

By R. Danes Sep 23, 2016

The main problem with getting Big Data to perform is, perhaps not surprisingly, its size. Massive data sets have to be stored somewhere, and this sometimes entails silos, which can be problematic for analytics.…

Wikibon

Wikibon trip report from OpenWorld 2016: Oracle stakes its cloud claim

By Peter Burris Sep 23, 2016

Do you want more Oracle? That is the fundamental question that executives at most of the Global 2000 should ask themselves as they make cloud decisions over the next few years. Here’s the trade-off:…

IoT express: Can the path to value from IoT be shortened? | #BigData

By R. Danes Sep 23, 2016

The Internet of Things (IoT) is a lot like Big Data in both the challenges it poses and the promises it holds. Like Big Data, it seeks to collect vast, scattered intelligence into one…

all stories

Where does today’s business draw the line with DIY IT? | #BigData

Stacking the odds: Outsourcing lower-stack IT to focus on value-driving applications | #BigData

Legacy companies can’t wait to start monetizing data — so where’s the application? | #BigData

People puzzle: Combining social and CRM data to suss out customers | #BigData

Does the modern application need 24/7 data replication? | #BigData

Can Big Data level the playing field for digital-native and non-native companies? | #BigData

Data scientist or data janitor? Democratize data to get more from the data pros | #BigData

Take it to the data: Working smarter, not harder on siloed data sets | #BigData

Wikibon trip report from OpenWorld 2016: Oracle stakes its cloud claim

IoT express: Can the path to value from IoT be shortened? | #BigData

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