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

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 190

Machine Learning Modeling Testing Risk Management 190

Ontotext

JULY 6, 2023

By promptly identifying and addressing risks, it enhances operational resiliency and enables proactive risk management. The solution also reduces incident response times, optimizes processes and streamlines asset management. First of all, it helps bridge the gap between business abstracts and technical realities.

Cost-Benefit 52

Cost-Benefit Metadata Experimentation Risk 52

Alation

MARCH 14, 2023

All critical data elements (CDEs) should be collated and inventoried with relevant metadata, then classified into relevant categories and curated as we further define below. Some business processes may need reviewing to include data analysis — even going as far as requiring specific data to make a business decision.

Data Strategy 52

Data Strategy Strategy Metadata Data Quality 52

CIO Business Intelligence

APRIL 29, 2022

From a policy perspective, the organization needs to mature beyond a basic awareness and definition of data compliance requirements (which typically holds that local operations make data “sovereign” by default) to a more refined, data-first model that incorporates corporate risk management, regulatory and reporting issues, and compliance frameworks.

Digital Transformation 114

Digital Transformation Strategy Data-driven Data Strategy 114

CIO Business Intelligence

SEPTEMBER 2, 2022

Back-end software engineers are responsible for maintaining the structure of server-side information by optimizing servers, implementing security measures, and developing data storage solutions. Back-end software engineer.

Finance 98

Finance IT Software Reporting 98

CIO Business Intelligence

AUGUST 31, 2022

Back-end software engineers are responsible for maintaining the structure of server-side information by optimizing servers, implementing security measures, and developing data storage solutions. Back-end software engineer.

Finance 98

Finance IT Software Reporting 98

DataRobot Blog

MAY 26, 2022

To start with, SR 11-7 lays out the criticality of model validation in an effective model risk management practice: Model validation is the set of processes and activities intended to verify that models are performing as expected, in line with their design objectives and business uses. Conclusion.

Risk 52

Risk Modeling Metrics Business Objectives 52

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. We now get the benefit of proliferated data quality.” – Andrew Stewardson, Data Risk Manager, FCSA.

Data Warehouse 58

Data Warehouse Data Quality Metadata Risk Management 58

AWS Big Data

AUGUST 14, 2023

When are data products deprecated, and who is accountable for the consequences to their consumers? How do we define “risk” and “value” in the context of data products, and how can we measure this? Whose responsibility is it to justify the existence of a given data product?

Management 77

Management Risk Measurement Optimization 77

O'Reilly on Data

JULY 15, 2019

After the 2008 financial crisis, the Federal Reserve issued a new set of guidelines governing models— SR 11-7 : Guidance on Model Risk Management. Note that the emphasis of SR 11-7 is on risk management.). Sources of model risk. Model risk management. AI projects in financial services and health care.

Machine Learning 37

Machine Learning Management Enterprise Risk Management 37

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. However, because most institutions lack a modern data architecture , they struggle to manage, integrate and analyze financial data at pace.

Data Architecture 83

Data Architecture Risk Management Risk Management 83

CIO Business Intelligence

AUGUST 7, 2023

According to Rocket Software’s survey, nearly half of respondents (42%) noted that they think about IT risk daily (31%) and even multiple times a day (11%). Sixty-three percent of IT leaders even measure success within their IT organization by their ability to reduce risk.

Risk 89

Risk IT Management Risk Management 89

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. Overcoming Data Governance Bottlenecks. Put data quality first : Users must have confidence in the data they use for analytics.

Data Governance 89

Data Governance Metadata Data Quality Risk Management 89

Cloudera

SEPTEMBER 7, 2023

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. But these measures alone may not be sufficient to protect proprietary information. AI-ify risk management.

Insurance 79

Insurance Risk Data-driven Modeling 79

DataRobot Blog

JULY 21, 2022

Inquire whether there is sufficient data to support machine learning. Outline clear metrics to measure success. Document assumptions and risks to develop a risk management strategy. Data aggregation such as from hourly to daily or from daily to weekly time steps may also be required. Define project scope.

Machine Learning 98

Machine Learning Metrics Modeling Testing 98

Andrew White

JANUARY 11, 2021

What are you seeing as the differences between a Chief Analytics Officer and the Chief Data Officer? Value Management or monetization. Risk Management (most likely within context of governance). Product Management. measuring value, prioritizing (where to start), and data literacy? Governance.

Data Analytics 52

Data Analytics Analytics Data-driven Finance 52

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

datapine

AUGUST 29, 2022

are using big data to their advantage, including: Progressive Insurance’s use of GPS trackers/accelerometers which determine customer safety ratings Google’s ability to predict local flu outbreaks by measuring spikes in flu-related local searches The government of Boston fixing potholes using data that residents enter into their smartphones.

Big Data 263

Big Data Data Analytics Analytics Data mining 263