FineReport

MARCH 24, 2024

Their role extends to managing information for corporate decision-making, improving reporting systems , and performing complex analyses. Additionally, they facilitate organizational risk assessments, provide consulting services to leadership, and mentor junior analysts.

Statistics 52

Statistics Data mining Visualization Reporting 52

Ontotext

JULY 6, 2023

Let’s consider an example about risk and opportunity event detection. Case studies The risk and opportunity event detection use case discussed above combines all of Ontotext’s capabilities: storing and managing large amounts of data adding meaning to it (e.g.,, The solution brings many business benefits.

Cost-Benefit 52

Cost-Benefit Metadata Experimentation Risk 52

CIO Business Intelligence

SEPTEMBER 2, 2022

Finance companies collect massive amounts of data, and data engineers are vital in ensuring that data is maintained and that there’s a high level of data quality, efficiency, and reliability around data collection.

Finance 98

Finance IT Software Reporting 98

CIO Business Intelligence

AUGUST 31, 2022

Finance companies collect massive amounts of data, and data engineers are vital in ensuring that data is maintained and that there’s a high level of data quality, efficiency, and reliability around data collection.

Finance 98

Finance IT Software Reporting 98

DataRobot

JULY 16, 2021

They all serve to answer the question, “How well can my model make predictions based on data?” In performance, the trust dimensions are the following: Data quality — the performance of any machine learning model is intimately tied to the data it was trained on and validated against.

Machine Learning 98

Machine Learning Uncertainty Modeling Measurement 98

Sisense

SEPTEMBER 3, 2020

Programming and statistics are two fundamental technical skills for data analysts, as well as data wrangling and data visualization. ROI (return on investment) is also a key concern, as business analysts apply their data-related activities to finance, marketing, and risk management, for instance.

Statistics 97

Statistics Metrics Visualization Finance 97

DataRobot Blog

JULY 21, 2022

If there is no forward-looking predictive component to the use case, it can probably be addressed with analytics and visualizations applied to historical data. Inquire whether there is sufficient data to support machine learning. Document assumptions and risks to develop a risk management strategy.

Machine Learning 98

Machine Learning Metrics Modeling Testing 98

IBM Big Data Hub

DECEMBER 4, 2023

Migrating to Oracle requires thorough planning whether a business intends to adopt the platform for the management of a single process—such as finance or human resources—or migrate the entire organization’s operations into the cloud. Data quality: Ensure migrated data is clean, correct and current.

Testing 81

Testing Consulting Digital Transformation Cost-Benefit 81

Domino Data Lab

FEBRUARY 4, 2019

Eric’s article describes an approach to process for data science teams in a stark contrast to the risk management practices of Agile process, such as timeboxing. As the article explains, data science is set apart from other business functions by two fundamental aspects: Relatively low costs for exploration.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

DataRobot Blog

DECEMBER 20, 2022

The use of artificial intelligence (AI) in the investment sector is proving to be a significant disruptor, catalyzing the connection between the different players and delivering a more vivid picture of the future risk and opportunities across all different market segments. You can understand the data and model’s behavior at any time.

Management 59

Management Machine Learning Optimization Modeling 59

datapine

AUGUST 29, 2022

The book has three main ideas: The biggest risk your company faces is investing a lot of time and resources into building something that the market doesn’t want. and this book will give you an insight into their data collecting procedures and the reasons behind them. Product/market fit is THE most important factor to get right.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

datapine

FEBRUARY 22, 2022

Improved risk management: Another great benefit from implementing a strategy for BI is risk management. Clean data in, clean analytics out. Cleaning your data may not be quite as simple, but it will ensure the success of your BI. Indeed, every year low-quality data is estimated to cost over $9.7

Business Intelligence 218

Business Intelligence Strategy Cost-Benefit Key Performance Indicator 218

Andrew White

JANUARY 11, 2021

But we also know not all data is equal, and not all data is equally valuable. Some data is more a risk than valuable. Additionally, the value of data may change, and our own personal judgement of the the same data and its value may differ. Value Management or monetization. Product Management.

Data Analytics 52

Data Analytics Analytics Data-driven Finance 52

Jet Global

MAY 9, 2024

Real-Time Analytics Pipelines : These pipelines process and analyze data in real-time or near-real-time to support decision-making in applications such as fraud detection, monitoring IoT devices, and providing personalized recommendations. For example, migrating customer data from an on-premises database to a cloud-based CRM system.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52