Smart Data Collective

APRIL 11, 2019

Big data is playing a monumental role in economic development in 2019. It is being used in every industry from healthcare delivery to insurance modeling to political science. According to economic analysts, the market size for big data is currently estimated to be $203 billion. Here are some of the reasons.

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Big Data Insurance Statistics Technology 76

Smart Data Collective

SEPTEMBER 15, 2019

Big data is changing the financial industry in a truly astounding way. Hussain of Atos Spain published a white paper on the growing relevance of big data in the finance and insurance verticals. Financial professionals aren’t the only ones utilizing big data to make more informed financial decisions.

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Big Data Machine Learning Risk Insurance 86

CIO Business Intelligence

JUNE 7, 2022

It comprises the processes, tools and techniques of data analysis and management, including the collection, organization, and storage of data. The chief aim of data analytics is to apply statistical analysis and technologies on data to find trends and solve problems. It is frequently used for risk analysis.

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Data Analytics Diagnostic Analytics Management Analytics 135

Smart Data Collective

NOVEMBER 17, 2021

They have found that data analytics is a valuable component of marketing campaigns , financial planning objectives, human resource guidelines and much more. We have talked extensively about the types of industries that have been positively impacted by data analytics. Data Analytics is Helping Many Spotify Musicians Improve Their Reach.

Data Analytics 126

Data Analytics Analytics Advertising Data-driven 126

Domino Data Lab

JULY 28, 2021

Data science is an exciting, interdisciplinary field that is revolutionizing the way companies approach every facet of their business. This has led to rapid advancements, as the field’s interdisciplinary nature combines mathematics, statistics, computer science and business knowledge in new and novel ways. Computer Science Skills.

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Data Science Statistics Recreation/Entertainment Predictive Analytics 110

Smart Data Collective

SEPTEMBER 16, 2020

Predictive analytics, sometimes referred to as big data analytics, relies on aspects of data mining as well as algorithms to develop predictive models. These predictive models can be used by enterprise marketers to more effectively develop predictions of future user behaviors based on the sourced historical data.

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Predictive Analytics Analytics Predictive Modeling Data mining 121

AWS Big Data

JUNE 6, 2023

For our customers, setting up these data quality checks is manual, time consuming, and error prone. It takes days for data engineers to identify and implement data quality rules. They have to gather detailed data statistics, such as minimums, maximums, averages, and correlations.

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Data Quality Statistics Data Lake Visualization 92

IBM Big Data Hub

FEBRUARY 15, 2024

Surface temperature statistics paint a compelling picture of the changing climate: 2023, according to the European Union climate monitor Copernicus, was the warmest year on record—nearly 1.5 What do global climate change and global warming look like? degrees Celsius warmer than pre-industrial levels.

Insurance 70

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Smart Data Collective

MAY 27, 2021

Big data has led to a lot of major changes in the automotive sector. According to statistics from the Motor Cycle Industry Association in 2020 there were 78% more new motorbike registrations, so the word is starting to spread within the motorbike world, and fast. These changes don’t just apply to regular sedans.

Manufacturing 90

Manufacturing Insurance Marketing Statistics 90

IBM Big Data Hub

DECEMBER 8, 2023

A good practice might be setting up a weekly meeting time to discuss the risks or to use a statistics tool for tracking any changes in the risk profile. This strategy shifts the risk from the organization onto another party; in many cases, the risk shifts to an insurance company.

Risk 77

Risk Strategy Risk Management Insurance 77

Smart Data Collective

MAY 20, 2021

There are a lot of issues in big data that warrant discussion. It is important to be aware of the different online data analytics metrics and tools used to track people online, since they shape the direction of big data technology. General user’s statistics.

Advertising 85

Advertising Analytics Big Data Insurance 85

IBM Big Data Hub

FEBRUARY 13, 2024

Like diligent students, these generative models soak up information and identify patterns, structures and relationships between data points, which is how they learn the grammar of poetry, artistic brushstrokes and musical melodies. Imagine each data point as a glowing orb placed on a vast, multi-dimensional landscape.

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Enterprise Modeling Testing Data-driven 129

DataRobot Blog

APRIL 29, 2022

With the big data revolution of recent years, predictive models are being rapidly integrated into more and more business processes. when the Federal Deposit Insurance Commission (FDIC) announced its adoption of Supervisory Guidance on Model Risk Management , previously outlined by the FRB and OCC.

Risk 111

Risk Modeling IT Risk Management 111

CIO Business Intelligence

JULY 26, 2023

“We’ve been able to create some models that will analyze things like the listing comments and descriptions and tell you which properties are waterfront or not,” Wilhemy says, adding that such data gives its agents a competitive advantage by enabling them to reach out to a selective set of potential buyers first.

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Smart Data Collective

NOVEMBER 3, 2021

We have talked extensively about the role of AI in investment management and insurance. It refers to underwriting, customer onboarding, document management, analysis, and statistics. There is no denying the reality that artificial intelligence is setting new standards in the financial sector. The banking industry is among them.

Software 120

Software Data-driven Interactive Insurance 120

Peter James Thomas

AUGUST 18, 2018

From 2000 to 2015, I had some success [5] with designing and implementing Data Warehouse architectures much like the following: As a lot of my work then was in Insurance or related fields, the Analytical Repositories tended to be Actuarial Databases and / or Exposure Management Databases, developed in collaboration with such teams.

Data Lake 63

Data Lake Data Warehouse Data mining Statistics 63

Smart Data Collective

AUGUST 11, 2022

There are many great benefits of using data analytics to improve financial management strategies. Many investors are using data analytics to invest in stocks. Insurance companies are using data analytics to improve their actuarial processes. Data Analytics Helps Companies Create Better Cash Collection Strategies.

Data Analytics 115

Data Analytics Optimization Analytics Cost-Benefit 115

IBM Big Data Hub

SEPTEMBER 28, 2023

Financial tasks evaluated includes: providing sentiment scores for stock and earnings call transcripts, classifying news headlines, extracting credit risk assessments, summarizing financial long-form text and answering financial or insurance-related questions. The synthetic data generator service in watsonx.ai

Testing 97

Testing Finance Enterprise Modeling 97

Domino Data Lab

JULY 28, 2021

Data science is a field at the convergence of statistics, computer science and business. In this article, take a deep dive into data science and how Domino’s Enterprise MLOps platform allows you to scale data science in your business. What is Data Science and How is it Used? What are Data Scientists?

Data Science 45

Data Science Machine Learning Statistics Deep Learning 45

IBM Big Data Hub

APRIL 18, 2024

LLMs like ChatGPT are trained on massive amounts of text data, allowing them to recognize patterns and statistical relationships within language. Image (55%): Gen AI can simulate how a product might look in a customer’s home or reconstruct an accident scene to assess insurance claims and liability.

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Cost-Benefit Modeling Manufacturing Interactive 99

Smart Data Collective

NOVEMBER 29, 2019

Big data technology is essential to the field of robotics. Big data has made it easier than ever to automate certain processes with complicated robotic tools. These tools rely very heavily on big data technology. The Importance of Big Data in RPA. Big Data is Vitalizing the RPA Industry.

Big Data 54

Big Data Technology Cost-Benefit Insurance 54

Occam's Razor

MARCH 30, 2017

All this data, across tens of millions of identified signals to deliver a deeply unique message to humans that mattered the most – at scale. Sidebar: If you don’t know these three phrases, please watch my short talk: A Big Data Imperative: Driving Big Action.]. Students must learn how to turn data into knowledge.

Marketing Analytics 166

Marketing Analytics Marketing Analytics Deep Learning 166

Domino Data Lab

JULY 22, 2019

He was saying this doesn’t belong just in statistics. He also really informed a lot of the early thinking about data visualization. It involved a lot of interesting work on something new that was data management. To some extent, academia still struggles a lot with how to stick data science into some sort of discipline.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

Sisense

MAY 4, 2020

In The Wide World of Data , we dig into astonishing facts and trends about the size and growth of the datasphere, how data is being used, and how it truly makes up the entire world around us. Information about the size and growth of data is abundant. Organizations must adapt or die.

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Big Data Data-driven Snapshot Unstructured Data 78

Smart Data Collective

AUGUST 27, 2020

The UK Office of National Statistics shows that roughly 30% of all retail sales are conducted over the Internet. Amazon Prime feeds everything the average person needs with startling and polished ease, holding your credit card data so cleanly that it takes little more than a single click to make a purchase. All online.

Big Data 77

Big Data Finance Risk Insurance 77

Peter James Thomas

AUGUST 12, 2018

The above is a somewhat simple metric, in a section of Using historical data to justify BI investments – Part I , I cover some actual Insurance industry metrics that build on each other and are a little more convoluted. I explore some similar themes in a section of Data Visualisation – A Scientific Treatment. million ± £0.5

Metrics 49

Metrics Statistics Data Quality Measurement 49

O'Reilly on Data

MARCH 20, 2019

So, whatever the commercial application of your model is, the attacker could dependably benefit from your model’s predictions—for example, by altering labels so your model learns to award large loans, large discounts, or small insurance premiums to people like themselves. Sometimes also known as an “exploratory integrity” attack.)

Modeling 222

Modeling Machine Learning Predictive Modeling Consulting 222

AWS Big Data

SEPTEMBER 13, 2023

In the Table statistics section, you will see an output similar to the following screenshot. Here, the Full load rows and Total rows columns are important metrics whose counts should match with the record volumes of the 18 tables in the operational data source.

Data Lake 87

Data Lake Data Processing Metadata Snapshot 87

AWS Big Data

SEPTEMBER 6, 2023

Analytics Specialist Solutions Architect based out of Atlanta, specialized in building enterprise data platforms, data warehousing, and analytics solutions. He has over 17 years of experience in building data assets and leading complex data platform programs for banking and insurance clients across the globe.

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Data Warehouse Insurance Statistics Software 92

Sisense

DECEMBER 11, 2019

With Itzik’s wisdom fresh in everyone’s minds, Scott Castle, Sisense General Manager, Data Business, shared his view on the role of modern data teams. Scott whisked us through the history of business intelligence from its first definition in 1958 to the current rise of Big Data. A true unicorn.

Data Lake 80

Data Lake Big Data Sales Data-driven 80

Andrew White

JANUARY 11, 2021

As such banking, finance, insurance and media are good examples of information-based industries compared to manufacturing, retail, and so on. But we are seeing increasing data suggesting that broad and bland data literacy programs, for example statistics certifying all employees of a firm, do not actually lead to the desired change.

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