How Mastercard’s NuData uses the power of the cloud and machine learning to improve fraud detection
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How Mastercard’s NuData uses the power of the cloud and machine learning to improve fraud detection

Cyberattacks continue to dominate the headlines. Attempts at digital fraud shot up during the first four months of 2021, especially in the financial services industry, where they ballooned 109% in the U.S. and 149% globally...

Building a modern data strategy without compromises
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Building a modern data strategy without compromises

Legacy data infrastructures can no longer support the massive amounts of data that companies now create and collect. A modern data strategy, created from data lakes, purpose-built data stores, machine learning, and other...

Modernizing the data warehouse
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Modernizing the data warehouse

Years of exponential data growth, evolving business needs, and rising maintenance costs have put a strain on existing data infrastructure. The traditional data warehouse, with its inability to handle data from new sources or...

A unified approach to data governance
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A unified approach to data governance

As more data migrates to the cloud, driven by the cloud’s near-infinite scale and horsepower, it’s imperative that enterprise data governance models evolve in step. IT and business leaders need up-to-date policies to protect data...

Making BI accessible to everyone
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Making BI accessible to everyone

While business intelligence (BI) was once reserved for the likes of highly skilled data scientists and IT professionals, advances in cloud and machine learning technologies are putting powerful BI capabilities into the hands of...

Why a modern data infrastructure is vital for cost-effective digital transformation
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Why a modern data infrastructure is vital for cost-effective digital transformation

If your aging on-premises data systems are unable to meet current demands for business agility and are time-consuming and expensive to manage, why continue to invest in them? Organizations looking to become more data-driven should...

The case for using purpose-built databases for modern applications
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The case for using purpose-built databases for modern applications

For almost three decades, people typically built applications against a single database. In the 1980s, client-server was introduced and apps started to become more distributed in nature, but the underlying data model was...

Machine learning is demonstrating its mettle across industries
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Machine learning is demonstrating its mettle across industries

The use cases and applications for machine learning offer benefits for most organizations, according to experts.

Experts agree: A data-driven culture starts at the top
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Experts agree: A data-driven culture starts at the top

No matter the analogy about data — that it’s the lifeblood, fuel, engine, or new oil — business leaders have little doubt about its importance to driving decision making and delivering business value. The challenge for many...

Cultivating a data-driven corporate culture
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Cultivating a data-driven corporate culture

Limitless cloud resources, cloud-based analytics, and machine learning are revolutionizing decision-making and automation in enterprises. Taking advantage of these capabilities, however, can be a struggle for companies steeped in...

Reinventing your business with data
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Reinventing your business with data

Building a business that is sustainable for a long period of time isn't easy. It requires reinvention—likely multiple times over. Only 50% of businesses that were on the Fortune 500 list in 2000 are still there today. The...

How machine learning helps improve your powers of prediction
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How machine learning helps improve your powers of prediction

Machine learning models that analyze enormous amounts of data with seemingly impossible speed and scale enable organizations to make a broad variety of predictions, ranging from when someone might suffer a heart attack, who might...

Why machine learning needs to get closer to your data
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Why machine learning needs to get closer to your data

For machine learning to be used more widely, it needs to be brought closer to the data that fuels ML modeling and insights. For database developers and data analysts that are still getting up to speed on ML modeling, the ideal...

Why machine learning needs to get closer to your data
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Why machine learning needs to get closer to your data

Integrating ML models directly into database tools makes it easier for developers to extract meaningful insights from their data.

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How to jump-start machine learning production

Clean, well-structured data and executive buy-in are critical factors in scaling ML efforts

Disrupting the status quo with machine learning
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Disrupting the status quo with machine learning

Big waves of business process reengineering have historically been few and far between. One reason has been the length of time it takes humans to analyze trends, conceive new ways of operating, and then integrate the process...

How to jump-start machine learning production
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How to jump-start machine learning production

A recent Gartner report found that companies with AI experience were only able to move 53% of their proof-of-concept projects into production over the previous two years. Factors for the stalled projects or outright failures...

Extracting value from data: How the cloud can help
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Extracting value from data: How the cloud can help

Businesses are collecting massive amounts of data annually, but do they really know how to make good use of it? A recent study from NewVantage Partners finds the answer is: not really. Nearly three-quarters of organizations in the...

The future of databases and application development
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The future of databases and application development

Microservices have emerged as the favored development approach for cloud computing platforms. Importantly, microservices architectures have finally reached the database, unlocking new opportunities for innovation and business...

From POC to production: Keys to scaling machine learning
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From POC to production: Keys to scaling machine learning

Many machine learning (ML) projects stall between proof-of-concept (POC) and full-scale production. Successfully scaling pilots across the organization requires a combination of business leadership buy-in, the right skills and...

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