Analytics

Analytics news, information, and how-to advice

artificial intelligence / machine learning

Dawn of intelligent applications

The future is intelligent applications. Learn how companies are shifting from big data to intelligent application approaches

analyzing performance of wireless mobile connectivity data statistics

An intro to Studio 3T, a MongoDB IDE

The growing third-party market is a key indication that MongoDB has moved from mere maturity to one of the dominant players in this market

Datacenter

Data democratization: finally living up to the name

Defining data democratized.

big data certification hand holding data

Dataops: agile infrastructure for data-driven organizations

While still emerging as an enterprise practice, dataops is increasingly driving teams to collaborate and organize in new ways to build, manage, deploy, and monitor data-intensive applications

elephant in the room5

AI: the challenge of data

While AI has been getting all the press, the elephant in the room is training data

iot data smartcity

System integrators in the driver’s seat for IoT and analytics

IoT requires specialized knowledge and diverse experience, which are exactly the qualities integrators bring to the table

data analytics chart money finance laptop computer

Harnessing the power of analytics to boost in-store sales

Strategically investing in analytics and data management can translate into improved customer experiences

cloud trends 2017

In 2018, can cloud, big data, and AI stand more turmoil?

We'll see several trends emerge in 2018 whose key focus will be on making new technology easy and consumable

Clash of fists in silhouette

Julia vs. Python: Julia language rises for data science

Python has turned into a data science and machine learning mainstay, while Julia was built from the ground up to do the job

artificial intelligence / machine learning / network

In a world of pervasive AI, testing will be a nightmare and metrics will be key

The sheer amount of data available today demands enterprises automate and make more decisions with artificial intelligence

Data analytics graph on an iPad Air tablet

Where are you in your analytical journey?

5 stages of the analytic process for data scientists and IT, plus a few best practices for tackling those stages.

haystacks

In the rush to big data, we forgot about search

In the cloud era, we need to look at search to be the glue that lets us find the data and analyze it together, no matter where it lives

big data vs cloud

The clash of big data and the cloud

Making good strategic design decisions about the locations of your data and processing is key

overflowing trash can with balled up paper

No, you shouldn’t keep all that data forever

Most of your old data is useless trash. So throw it away, rather than spend all the time and money hoping AI will figure something out about it

couple hug love

How in-memory computing drives digital transformation with HTAP

Meet in-memory computing (IMC) and hybrid transactional/analytical processing (HTAP), tech’s newest power couple

time clocks

Are you caught up with the real-time (r)evolution?

Real-time analytics have been around since well before computers. Here's where the evolution of real time is headed in 2018

raining data on keyboard programming developer code

Are you treating your data as an asset?

The best thing you can do is encourage a culture that is data-focused, one that realizes the importance of security and privacy, as well as understanding that data is crucial to your organization’s success

Data analytics graph on an iPad Air tablet

Modern IT and data science in an era of analytic deployment

How data scientists and IT build and deploy their analytics models

wireless network - industrial internet of things edge [IoT] - edge computing

Azure Databricks: Fast analytics in the cloud with Apache Spark

Microsoft’s partnership with Databricks adds new analytics tools to Azure’s data platform

data lake

Use the cloud to create open, connected data lakes for AI, not data swamps

There needs to be a material change in the way people think of solving complex data problems

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