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What is data analytics? Analyzing and managing data for decisions

CIO

What is data analytics? Data analytics is a discipline focused on extracting insights from data. It comprises the processes, tools and techniques of data analysis and management, including the collection, organization, and storage of data. What are the four types of data analytics?

Analytics 336
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Cowbell raises $100M to build out its AI-based cyber insurance platform for SMBs

TechCrunch

Cybercrime is on the rise, and today an insurance startup that’s built an artificial intelligence-based platform to help manage the risks from that is announcing a big round of funding to meet the opportunity. “This is okay for any other kind of insurance but not for cyber risk.

Insurance 260
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Business intelligence and analytics firm Pyramid Analytics raises $100M

TechCrunch

Pervasive BI remains elusive, but statistics on the category reveal that about a third of employees use BI tools for analytics to inform strategy. The big data and business analytics market could be worth $684 billion by 2030, according to Valuates Reports, if such outrageously high estimates are to be believed.

Analytics 215
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Machine Learning: Unlocking the Next for Insurers

Coforge

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Space-Based AI Shows the Promise of Big Data

Cloudera

Although researchers can recruit “citizen scientists” to help look at images through crowdsourcing ventures such as Zooniverse , astronomy is turning to artificial intelligence (AI) to find the right data as quickly as possible. It’s a process that some companies call geospatial intelligence (GI). .

Big Data 103
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How the C-Suite Can Champion Sustainability Across the Enterprise

CIO

As a first step, companies can adopt data analytics to help reduce food or product waste. Circular economy: Re-use infrastructure for new technology initiatives instead of retiring equipment. As you can see, the list of ideas goes beyond just adding recycling bins in the data center.

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MLOps: Methods and Tools of DevOps for Machine Learning

Altexsoft

When speaking of machine learning, we typically discuss data preparation or model building. Living in the shadow, this stage, according to the recent study , eats up 25 percent of data scientists time. MLOps lies at the confluence of ML, data engineering, and DevOps. More time for development of new models.