O'Reilly on Data

NOVEMBER 29, 2022

While it’s unlikely that the bill will become law, merely raising the possibility of criminal prosecution and jail time has upped the ante for “ commercial entities that operate high-risk information systems or automated-decision systems, such as those that use artificial intelligence or machine learning.”. Who will watch the watchmen?

Machine Learning 278

Machine Learning Recreation/Entertainment Risk Testing 278

CIO Business Intelligence

FEBRUARY 3, 2023

Optical character recognition, for example, might extract a purchase order from an uploaded document image and trigger accounting software to deal with it. The ability to suck words and numbers from images are a big help for document-heavy businesses such as insurance or banking.

Data-driven 145

Data-driven Interactive Enterprise Statistics 145

IBM Big Data Hub

APRIL 9, 2024

” The historical sources watsonx.data accesses comprise relational, object and document databases, including IBM® Db2® , IBM® Cloudant , IBM Cloud® Object Storage and PostgreSQL. “Rather than fine tuning the models through the tournament, we fine modify the input statistics that go into the models.

Management 87

Management IT Machine Learning Metrics 87

CIO Business Intelligence

AUGUST 14, 2023

Major finance and business information, along with sales and subcontracting documents, were processed manually and offline. Up until 2021, it often fell short – and for good reason. Even as the Huabao Group expanded, its digitization effort lagged. Nor was there a comprehensive understanding of changing consumer trends.

Key Performance Indicator 97

Key Performance Indicator Sales Manufacturing KPI 97

IBM Big Data Hub

DECEMBER 4, 2023

The most common insurance use cases include optimizing processes that require processing large documents and large blocks of text or images. Customer engagement Providing insurance coverage involves working with numerous documents. IBM works with several insurance companies to identify high-value opportunities for using generative AI.

Insurance 56

Insurance Digital Transformation Risk Management Risk 56

Smart Data Collective

NOVEMBER 3, 2021

It refers to underwriting, customer onboarding, document management, analysis, and statistics. Loan approval, as one of the biggest bottlenecks due to inconsistency of information between teams, may increase business risks. End-to-end loan management with AI. Integrated lending module.

Software 120

Software Data-driven Interactive Insurance 120

BizAcuity

APRIL 1, 2023

Anti-Money Laundering (AML) is increasingly becoming a crucial branch of risk management and fraud prevention. In fact, online casinos as an industry carries the biggest risk of money laundering. OCR is widely used to digitize all kinds of physical documentation. Predictive Analytics can help businesses in reducing risk (eg.

Machine Learning 52

Machine Learning Predictive Analytics Predictive Modeling Risk 52

AWS Big Data

MARCH 13, 2024

RAG is a machine learning (ML) architecture that uses external documents (like Wikipedia) to augment its knowledge and achieve state-of-the-art results on knowledge-intensive tasks. We introduce the integration of Ray into the RAG contextual document retrieval mechanism. Open the CreateRayCluster document. json| jq '.data[].paragraphs[].qas[].question'

Data Processing 116

Data Processing Dashboards Machine Learning Management 116

CIO Business Intelligence

JULY 6, 2023

Classic examples are the use of AI to capture and convert semi-structured documents such as purchase orders and invoices, Fleming says. We’re equipping this tool with a private ‘knowledge base’ of AT&T-specific data, with chat enabled to get answers directly from these internal AT&T documents and materials.”

Cost-Benefit 119

Cost-Benefit Technology Unstructured Data Testing 119

BizAcuity

JANUARY 18, 2023

Anti-Money Laundering (AML) is increasingly becoming a crucial branch of risk management and fraud prevention. In fact, online casinos as an industry carries the biggest risk of money laundering. OCR is widely used to digitize all kinds of physical documentation. Predictive Analytics can help businesses in reducing risk (eg.

Machine Learning 52

Machine Learning Predictive Analytics Predictive Modeling Risk 52

CIO Business Intelligence

NOVEMBER 7, 2023

This analysis isn’t just a statistical exercise but a crucial necessity in our interconnected world, where the security of our digital infrastructure is intertwined with our physical safety and economic stability. PAM can help prevent insider threat risks and reduce the potential of credentials becoming compromised.

Measurement 127

Measurement Risk Strategy Statistics 127

CIO Business Intelligence

JANUARY 9, 2024

He and his team have created information decks, documents, and presentations that describe the various types of AI and how they can be used and explain how and where AI and machine learning may be useful — and why it’s not the solution to all the problems they have. What is our appetite for risk and how do we address it?

IT 131

IT Risk Cost-Benefit Enterprise 131

Smart Data Collective

MAY 25, 2023

Recent statistics indicate that 43% of cyberattacks target small businesses, and 60% of the attacked enterprises go out of business in six months. Develop a cybersecurity plan A cybersecurity plan, a written document, comprises details regarding your business’s security procedures, policies, and countermeasure-remediation plan.

Internet of Things 89

Internet of Things Cost-Benefit Risk Software 89

erwin

JANUARY 11, 2019

A company can’t effectively implement data governance – documenting and applying business rules and processes, analyzing the impact of changes and conducting audits – if it fails at data management. And there’s control of that landscape to facilitate insight and collaboration and limit risk.

Data Governance 40

Data Governance Risk Metadata Management 40

datapine

JANUARY 24, 2021

Additionally, incorporating a decision support system software can save a lot of company’s time – combining information from raw data, documents, personal knowledge, and business models will provide a solid foundation for solving business problems. There are basically 4 types of scales: *Statistics Level Measurement Table*.

IT 317

IT Statistics KPI Data-driven 317

erwin

APRIL 29, 2020

The CEO also makes decisions based on performance and growth statistics. Business terms and data policies should be implemented through standardized and documented business rules. An automated data lineage solution stitches together metadata for understanding and validating data usage, as well as mitigating the associated risks.

Metadata 111

Metadata Data Governance Key Performance Indicator Data Quality 111

Smart Data Collective

NOVEMBER 29, 2021

AI helps businesses to minimize risks across every line of business. When considering investment income opportunities, banks need to compare multiple options and pick those that provide the best return at an acceptable risk. The above-mentioned regulatory risks—as well as reputational ones—often happen because of human error.

Cost-Benefit 109

Cost-Benefit Risk Finance Statistics 109

DataRobot Blog

SEPTEMBER 1, 2022

Imagine yourself as a pilot operating aircraft through a thunderstorm; you have all the dashboards and automated systems that inform you about any risks. New DataRobot Large Scale Monitoring allows you to access aggregated prediction statistics. You use this information to make decisions to navigate and land safely. Request a Demo.

Metrics 145

Metrics Statistics Modeling Data Science 145

O'Reilly on Data

MARCH 24, 2020

John Myles White , data scientist and engineering manager at Facebook, wrote: “The biggest risk I see with data science projects is that analyzing data per se is generally a bad thing. So when you’re missing data or have “low-quality data,” you use assumptions, statistics, and inference to repair your data.

Machine Learning 346

Machine Learning Statistics Data Quality Modeling 346

Corinium

APRIL 25, 2019

For example, P&C insurance strives to understand its customers and households better through data, to provide better customer service and anticipate insurance needs, as well as accurately measure risks. Life insurance needs accurate data on consumer health, age and other metrics of risk. Now, there is a data risk here.

Insurance 150

Insurance Risk IoT Cost-Benefit 150

The Unofficial Google Data Science Blog

DECEMBER 4, 2019

With those stakes and the long forecast horizon, we do not rely on a single statistical model based on historical trends. It provides the occasion for deeper exploration of which inputs that can be influenced and which risks can be proactively managed. The alternative we use is the forecast triangulation framework described above.

Forecasting 113

Forecasting Data Science Statistics Uncertainty 113

CIO Business Intelligence

FEBRUARY 13, 2023

The name references the Greek letter sigma, which is a statistical symbol that represents a standard deviation. The process aims to bring data and statistics into the mesh to help objectively identify errors and defects that will impact quality. Six Sigma was trademarked by Motorola in 1993.

Management 84

Management Statistics Manufacturing Measurement 84

CIO Business Intelligence

OCTOBER 13, 2023

It’s a role that requires experience with natural language processing , coding languages, statistical models, and large language and generative AI models. The role of algorithm engineer requires knowledge of programming languages, testing and debugging, documentation, and of course algorithm design.

Deep Learning 143

Deep Learning Machine Learning Modeling Software 143

IBM Big Data Hub

MAY 1, 2023

The insurance business model itself is predicated on the use of mathematical and statistical methods to process personal and non-personal data to underwrite risks and price insurance policies, to quantify losses, to pay customers’ claims, and to identify and prevent insurance fraud.

Insurance 83

Insurance Predictive Modeling Cost-Benefit Statistics 83

O'Reilly on Data

MARCH 20, 2019

Like many others, I’ve known for some time that machine learning models themselves could pose security risks. An attacker could use an adversarial example attack to grant themselves a large loan or a low insurance premium or to avoid denial of parole based on a high criminal risk score. Newer types of fair and private models (e.g.,

Modeling 222

Modeling Machine Learning Predictive Modeling Consulting 222

CIO Business Intelligence

NOVEMBER 27, 2023

Cybersecurity risks This one is no surprise, given the scary statistics on the growing number of cyberattacks, the rate of successful attacks, and the increasingly high consequences of being breached. They’re wondering how AI technologies, such as ChatGPT and generative AI in general, will increase risks.

IT 124

IT Digital Transformation Reporting Finance 124

Ontotext

JUNE 22, 2023

Recent statistics shed light on the realities in the world of current drug development: out of about 10,000 compounds that undergo clinical research, only 1 emerges successfully as an approved drug. The current process involves costly wet lab experiments, which are often performed multiple times to achieve statistically significant results.

Metrics 52

Metrics Statistics Visualization Metadata 52

Cloudera

OCTOBER 8, 2021

When an Impala coordinator receives a query from the client, it parses the query, aligns table and column references in the query with data statistics contained in the schema catalog managed by the Impala Catalog server, and type checks and validates the query. . Impala Admission Control in Detail. Admission control is largely defined by .

Data Processing 79

Data Processing Statistics Risk Optimization 79

bridgei2i

OCTOBER 7, 2020

The unprecedented circumstances surrounding pandemic have increased awareness around insurable risks across different financial losses that can be covered as everyone in the world have faced financial losses like unemployment, travel cancellation, health issues, business interruptions, etc. Underwriting.

Insurance 52

Insurance Sales Strategy Digital Transformation 52

O'Reilly on Data

DECEMBER 12, 2019

1] This includes C-suite executives, front-line data scientists, and risk, legal, and compliance personnel. These recommendations are based on our experience, both as a data scientist and as a lawyer, focused on managing the risks of deploying ML. That’s where model debugging comes in. Sensitivity analysis.

Machine Learning 191

Machine Learning Modeling Testing Risk Management 191

datapine

JANUARY 6, 2022

Secondary Research: much like how patterns of behavior can be observed, different types of documentation resources can be coded and divided based on the type of material they contain. More often than not, it involves the use of statistical modeling such as standard deviation, mean and median.

Visualization 246

Visualization Dashboards Cost-Benefit Measurement 246

CIO Business Intelligence

APRIL 7, 2022

KPMG calculates a score for an employee’s risk of attrition, tries to identify a reason for that, and then suggests a remediation. By analyzing messaging metadata, “not the messages themselves,” he says, “we can now statistically prove that certain types of communication behavior directly correlate to business performance.”.

Consulting 144

Consulting Management Interactive Metadata 144

Domino Data Lab

JULY 19, 2021

They document this work for the benefit of future data scientists and auditors alike. They typically specialize in different types of tasks, such as marketing analysis, financial analysis, or risk analysis. Data Engineer. ML Architect. Throughout the project life cycle, they oversee MLOps processes. Domain Expert/Business Translator.

Enterprise 85

Enterprise Data Science Testing Modeling 85

Smart Data Collective

SEPTEMBER 21, 2022

As far as Data Analysis is concerned, potential employees should have an extensive knowledge of quantitative research, quantitative reporting, compiling statistics, statistical analysis, data mining, and big data. The old adage that you can build a better mousetrap and the world will beat a path to your door doesn’t hold up.

Statistics 84

Statistics Software Data mining Marketing 84

CDW Research Hub

NOVEMBER 6, 2020

Compile and document this information. As a best practice, you want to limit who has access to secure information to minimize risk and liability. Know your security risks. Most importantly, don’t forget to factor in human risk. Are there security policies or regulatory controls in place?

Scorecard 40

Scorecard Risk Business Driver Strategy 40

Alation

APRIL 18, 2023

DAMA defines data profiling as: An approach to data quality analysis, using statistics to show patterns of usage, and patterns of contents in an automated manner. The tools provide data statistics, such as degree of duplication and ratios of attribute values, both in tabular and graphical formats.

IT 52

IT Metadata Data Quality Data Governance 52

Sisense

SEPTEMBER 25, 2020

A data scientist must be skilled in many arts: math and statistics, computer science, and domain knowledge. Statistics and programming go hand in hand. Mastering statistical techniques and knowing how to implement them via a programming language are essential building blocks for advanced analytics. Linear regression.

Statistics 98

Statistics Predictive Modeling Modeling Machine Learning 98