DataRobot

JULY 16, 2021

Transformative technologies such as autonomous vehicles will be possible only when there are clear methods and benchmarks to establish trust in AI systems. They all serve to answer the question, “How well can my model make predictions based on data?” The largest source of bias in an AI system is the data it was trained on.

Machine Learning 98

Machine Learning Uncertainty Modeling Measurement 98

O'Reilly on Data

DECEMBER 12, 2019

In addition to newer innovations, the practice borrows from model risk management, traditional model diagnostics, and software testing. There are at least four major ways for data scientists to find bugs in ML models: sensitivity analysis, residual analysis, benchmark models, and ML security audits. Sensitivity analysis.

Machine Learning 191

Machine Learning Modeling Testing Risk Management 191

FineReport

APRIL 9, 2024

Financial Services Optimization : In the financial services sector, a major institution leveraged a sophisticated BI platform to analyze market trends, customer behavior, and risk management strategies. This framework ensures that data remains accurate, consistent, and secure across all levels of the organization.

Business Intelligence 52

Business Intelligence Software Data-driven Predictive Modeling 52

Alation

MARCH 14, 2023

What is a data strategy? A data strategy is a long-term plan setting out the technologies, processes, people, and rules that manage data. It should make data available, maintain data consistency and accuracy, and support data security. Creating a clear process with documented steps will help.

Data Strategy 52

Data Strategy Strategy Metadata Data Quality 52

Ontotext

JULY 6, 2023

Transaction and pricing data (e.g., Changing the game with knowledge graphs So how does knowledge graph technology help enterprises in the Financial Services Industry become data-driven? Let’s consider an example about risk and opportunity event detection. investments, acquisitions, issued Instruments, etc.)

Cost-Benefit 52

Cost-Benefit Metadata Experimentation Risk 52

O'Reilly on Data

JULY 15, 2019

As companies use machine learning (ML) and AI technologies across a broader suite of products and services, it’s clear that new tools, best practices, and new organizational structures will be needed. Financial services firms have a rich tradition of being early adopters of many new technologies, and AI is no exception: Figure 1.

Machine Learning 38

Machine Learning Management Enterprise Risk Management 38

Sisense

SEPTEMBER 3, 2020

Besides strong technical skills (for instance, use of Hadoop, programming in R and Python , math, statistics), data scientists should also be able to tackle open-ended questions and undirected research in ways that bring measurable business benefits to their organization. See an example: Explore Dashboard.

Statistics 97

Statistics Metrics Visualization Finance 97

Cloudera

JUNE 29, 2023

To ensure the stability of the US financial system, the implementation of advanced liquidity risk models and stress testing using (MI/AI) could potentially serve as a protective measure. To improve the way they model and manage risk, institutions must modernize their data management and data governance practices.

Data Architecture 81

Data Architecture Risk Management Risk Management 81

CIO Business Intelligence

AUGUST 7, 2023

-based IT directors and vice presidents in companies with more than 1,000 employees to determine what keeps them up at night—and it comes as no surprise that one of their biggest nightmares is managing IT risk. Technology professionals are taking a more proactive and less reactionary approach to how they manage risk and cybersecurity.

Risk 98

Risk IT Management Risk Management 98

Cloudera

SEPTEMBER 7, 2023

It’s the most revolutionary technological development in at least a generation. But it’s also fraught with risk. This puts the onus on institutions to implement robust data encryption standards, process sensitive data locally, automate auditing, and negotiate clear ownership clauses in their service agreements.

Insurance 78

Insurance Risk Data-driven Finance 78

erwin

FEBRUARY 4, 2021

However, organizations still encounter a number of bottlenecks that may hold them back from fully realizing the value of their data in producing timely and relevant business insights. So what’s holding organizations back from fully using their data to make better, smarter business decisions? Data Governance Bottlenecks.

Data Governance 89

Data Governance Metadata Data Quality Risk Management 89

Octopai

JULY 12, 2020

An enormous amount of time was being wasted performing manual searches, as the BI team needed to frequently comb through the enterprise data warehouse’s fields to determine how each was calculated or to find their sources. Automated Data Lineage Helps BI & Analytics Understand Their Data.

Data Warehouse 58

Data Warehouse Data Quality Metadata Risk Management 58

datapine

AUGUST 29, 2022

And shows how big data and the advances in analytical technologies are shaping the way the world is perceived. 2) Designing Data-Intensive Applications by Martin Kleppman. 2) Designing Data-Intensive Applications by Martin Kleppman. Not only is it comprehensive and thorough, but also comprehensible.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

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

datapine

FEBRUARY 22, 2022

This includes defining the main stakeholders, assessing the situation, defining the goals, and finding the KPIs that will measure your efforts to achieve these goals. A planned BI strategy will point your business in the right direction to meet its goals by making strategic decisions based on real-time data. It’s that simple.

Business Intelligence 218

Business Intelligence Strategy Cost-Benefit Key Performance Indicator 218

AWS Big Data

AUGUST 14, 2023

In a similar vein, Bonsai artists must balance their vision for the shape of the tree with a respect for the natural characteristics and innate structure of the species they have chosen to work with—to ignore the biology of the tree would be disastrous to the longevity of the tree, as well as to the quality of the art itself.

Management 81

Management Risk Measurement Optimization 81

Andrew White

JANUARY 11, 2021

So the material is not designed for IT – but spans business and technology. The fill report is here: Leadership Vision for 2021: Data and Analytics. How do you think Technology Business Management plays into this strategy? Where does the Data Architect role fits in the Operational Model ? Governance.

Data Analytics 52

Data Analytics Analytics Data-driven Finance 52