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DataOps Risk Insurance & Mission Control

DataKitchen

Chris Bergh shares how to manage data quality and pipeline risk through implementing a 'Mission Control' center for DataOps. The post DataOps Risk Insurance & Mission Control first appeared on DataKitchen.

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Solving the Insurance Industry’s Data Quality Problem

Corinium

Unfortunately for the insurance industry’s data leaders, many data sources are riddled with inaccuracies. Data is the lifeblood of the insurance industry.

Insurance 221
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The risks and limitations of AI in insurance

IBM Big Data Hub

In my previous post , I described the different capabilities of both discriminative and generative AI, and sketched a world of opportunities where AI changes the way that insurers and insured would interact. Technological riskdata confidentiality The chief technological risk is the matter of data confidentiality.

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As insurers look to be more agile, data mesh strategies take centerstage

CIO Business Intelligence

In this way, data may just be the ultimate disruptor – a fact that the insurance industry knows all too well. As data volumes continue to increase alongside a correlating number of business requests, modern insurance data leaders face a nuanced set of challenges. To learn more, visit us here.

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Optimizing Risk and Exposure Management – Roundtable Highlights

Cloudera

We recently hosted a roundtable focused on o ptimizing risk and exposure management with data insights. For financial institutions and insurers, risk and exposure management has always been a fundamental tenet of the business. Now, risk management has become exponentially complicated in multiple dimensions. .

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Snowcase Study: How Data Governance Gives Texas Mutual Insurance Company a Competitive Edge

Alation

Much of his work focuses on democratising data and breaking down data silos to drive better business outcomes. In this blog, Chris shows how Snowflake and Alation together accelerate data culture. He shows how Texas Mutual Insurance Company has embraced data governance to build trust in data.

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The Need For Personalized Data Journeys for Your Data Consumers

DataKitchen

While this is a technically demanding task, the advent of ‘Payload’ Data Journeys (DJs) offers a targeted approach to meet the increasingly specific demands of Data Consumers. Deploying a Data Journey Instance unique to each customer’s payload is vital to fill this gap.

Insurance 176