FineReport

MARCH 24, 2024

Data Analyst Job Description: Major Tasks and Duties Data analysts collaborate with management to prioritize information needs, collect and interpret business-critical data, and report findings. Python: Known for its text data handling capabilities and compatibility with various platforms and databases.

Statistics 52

Statistics Data mining Visualization Reporting 52

Sisense

SEPTEMBER 3, 2020

Data Scientist. Data scientists usually build models for data-driven decisions asking challenging questions that only complex calculations can try to answer and creating new solutions where necessary. While salaries for data analysts are often reasonably high, salaries for data scientists may be higher still.

Statistics 97

Statistics Metrics Visualization Finance 97

IBM Big Data Hub

AUGUST 28, 2023

Text mining —also called text data mining—is an advanced discipline within data science that uses natural language processing (NLP) , artificial intelligence (AI) and machine learning models, and data mining techniques to derive pertinent qualitative information from unstructured text data.

Data mining 53

Data mining Machine Learning Deep Learning Marketing 53

CIO Business Intelligence

MARCH 24, 2023

It encompasses the people, processes, and technologies required to manage and protect data assets. The Data Management Association (DAMA) International defines it as the “planning, oversight, and control over management of data and the use of data and data-related sources.”

Data Governance 142

Data Governance Management Metadata Data Quality 142

IBM Big Data Hub

FEBRUARY 8, 2023

Achieving Responsible AI As building and scaling AI models for your organization becomes more business critical, achieving Responsible AI (RAI) should be considered a highly relevant topic. This necessitates the detection of bias during data acquisition, building, training, deploying and monitoring models. billion by 2025.

Risk Management 52

Risk Management Risk Metadata Modeling 52

Ontotext

MAY 5, 2023

These two key data elements are used in approximately 80% of the use cases in the sector. It is reused in modeling the publication of entity data or regulatory-mandated data exchange, as seen in the example provided below. Integrating reporting to move to a more streamlined, efficient approach to data collection.

Data Collection 64

Data Collection Risk Data-driven Interactive 64

Smart Data Collective

FEBRUARY 28, 2023

Here at Smart Data Collective, we have blogged extensively about the changes brought on by AI technology. Diversification: Risk parity allows diversifying assets by spreading risk evenly across several asset classes, such as Equities Bonds Commodities Currencies It reduces the total portfolio risk.

Machine Learning 85

Machine Learning Risk Cost-Benefit Marketing 85

Cloudera

MARCH 18, 2021

A revised internal models approach (IMA) combined with an overhaul of the standardized approach (SA). From a data management point of view, FRTB’s requirements will require greatly increased quantities of historical data, along with an increased need for analysis and intensive computation against this data. .

Risk 55

Risk Machine Learning Risk Management Measurement 55

erwin

APRIL 21, 2020

As COVID-19 continues to spread, organizations are evaluating and adjusting their operations in terms of both risk management and business continuity. Data is critical to these decisions, such as how to ramp up and support remote employees, re-engineer processes, change entire business models, and adjust supply chains.

Measurement 97

Measurement Sales Data Governance Metadata 97

erwin

APRIL 21, 2020

As COVID-19 continues to spread, organizations are evaluating and adjusting their operations in terms of both risk management and business continuity. Data is critical to these decisions, such as how to ramp up and support remote employees, re-engineer processes, change entire business models, and adjust supply chains.

Measurement 96

Measurement Sales Data Governance Metadata 96

Domino Data Lab

FEBRUARY 4, 2019

Lately there’s been lots of dialog about bias in machine learning models, though considering the noise part of the equation is an important corollary. We’d love to hear your insights and perspectives about running data teams. The ability to measure results (risk-reducing evidence). Legal concerns, risk, compliance.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

O'Reilly on Data

OCTOBER 18, 2022

What if a named entity recognition (NER) system, based on a cutting-edge large language model (LLM), fails for Chinese, Cyrillic, or Arabic text? As AI technologies are adopted more broadly in security and other high-risk applications, we’ll all need to know more about AI audit and risk management. For our audits, BNH.AI

Risk Management 297

Risk Management Risk Testing Measurement 297

Alation

FEBRUARY 8, 2022

In this blog, we’ll do the same with data mesh, unpacking the four key pillars, with a few notes on the role of the data catalog. While data fabric takes a product-and-tech-centric approach, data mesh takes a completely different perspective. That model has failed to support rapid data growth.

Data Governance 52

Data Governance Data-driven Metadata Enterprise 52

datapine

AUGUST 29, 2022

Most people are aware that companies collect our GPS locale, text messages, credit card purchases, social media posts, Google search history, etc., and this book will give you an insight into their data collecting procedures and the reasons behind them. The subsequent chapters focus on predictive and descriptive analysis.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

Jet Global

JULY 28, 2023

The top responsibilities for finance teams throughout EMEA are: 65% Financial Planning and Analysis 54% Budget and Forecasting 48% Financial Modeling 48% Tax Management Nearly three-quarters (69%) of this year’s EMEA-based survey respondents feel pressure from inflation, economic disruption, and recession.

Finance 52

Finance Uncertainty Reporting Forecasting 52

Jet Global

MAY 9, 2024

Machine Learning Pipelines : These pipelines support the entire lifecycle of a machine learning model, including data ingestion , data preprocessing, model training, evaluation, and deployment. API Data Pipelines : These pipelines retrieve data from various APIs and load it into a database or application for further use.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52