Precisely

FEBRUARY 23, 2024

Insurance is an inherently data-driven industry. Even before the age of advanced analytics, experts in the industry were routinely using data to assess risk and price policies. Today, data analytics plays a more important role than ever. Innovators are in a race to see who can use it to their best advantage.

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Precisely

DECEMBER 21, 2022

In insurance, data is everything. Accurate, consistent, and contextualized data enables faster, more confident decisions when it comes to your underwriting, claims processing, risk assessments, and beyond. Let’s explore the impact of data in this industry as we count down the top 5 insurance blog posts of 2022. #5

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Precisely

DECEMBER 27, 2022

As 2022 wraps up, we would like to recap our top posts of the year in Data Integrity, Data Integration, Data Quality, Data Governance, Location Intelligence, SAP Automation, and how data affects specific industries. Read more > Best of Insurance In insurance, data is everything.

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Precisely

JULY 12, 2024

Consistent: Data is consistently represented in a standard way throughout the dataset. Quality data must meet all these criteria. If it is lacking in just one way, it could compromise any data-driven initiative. However, simply having high-quality data does not, of itself, ensure that an organization will find it useful.

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Precisely

SEPTEMBER 7, 2023

Advanced data analytics enable insurance carriers to evaluate risk at a far more granular level than ever before, but big data can only deliver real business value when carriers ensure data integrity. For P&C insurance, that starts with having accurate and precise information as to the location of an insured property.

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Precisely

OCTOBER 24, 2024

Maintaining data integrity during cloud migration is essential to ensure reliable and high-quality data for better decision-making and future use in advanced applications. Building this trust in your data is key to unlocking its full potential in the cloud.

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Monte Carlo

MAY 17, 2023

Many times this is by freeing them from having to manually implement and maintain hundreds of data tests as was the case with Contentsquare and Gitlab. “We We had too many manual data checks by operations and data analysts,” said Otávio Bastos, former global data governance lead, Contentsquare. “It

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