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 198

Machine Learning Modeling Testing Risk Management 198

O'Reilly on Data

JULY 28, 2020

This isn’t always simple, since it doesn’t just take into account technical risk; it also has to account for social risk and reputational damage. A product needs to balance the investment of resources against the risks of moving forward without a full understanding of the data landscape. Data Quality and Standardization.

Marketing 363

Marketing Experimentation Metrics Testing 363

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. Machine learning developers are beginning to look at an even broader set of risk factors.

Machine Learning 38

Machine Learning Management Enterprise Risk Management 38

Domino Data Lab

JULY 21, 2019

how modern approaches can be used to obtain better quality data, and at a lower cost, to help support evidence-based policy decisions. unique challenges of sharing confidential government microdata and the importance of access in generating high quality inference. What am I talking about with the new types of data?

Metadata 65

Metadata Statistics Risk Machine Learning 65

O'Reilly on Data

NOVEMBER 28, 2023

Unexpected outcomes, security, safety, fairness and bias, and privacy are the biggest risks for which adopters are testing. And there are tools for archiving and indexing prompts for reuse, vector databases for retrieving documents that an AI can use to answer a question, and much more. Only 4% pointed to lower head counts.

Enterprise 281

Enterprise Modeling Testing Reporting 281

Grooper

AUGUST 19, 2019

Master data management. Data governance. Scoring – i.e. profitability or risk. Structured, semi-structured, and unstructured data. Data pipelines. Data science skills. Technology – i.e. data mining, predictive analytics, and statistics. Best practices for exploring collected data.

Analytics 54

Analytics Machine Learning Data Science Data-driven 54

CIO Business Intelligence

MARCH 14, 2024

AI has already found its way into many government departments, with the most common use of AI identified by the NAO being to support operational decision-making through automated document analysis, digital assistant functionality, and image recognition. Safety standards There is an element of caution to government departments’ approach.

Testing 101

Testing Risk Digital Transformation Statistics 101

IBM Big Data Hub

JANUARY 9, 2024

Companies still often accept the risk of using internal data when exploring large language models (LLMs) because this contextual data is what enables LLMs to change from general-purpose to domain-specific knowledge. This may also entail working with new data through methods like web scraping or uploading.

Enterprise 72

Enterprise Data Integration Data Quality Contextual Data 72

Andrew White

JANUARY 9, 2022

We found anecdotal data that suggested things such as a) CDO’s with a business, more than a technical, background tend to be more effective or successful, and b) CDOs most often came from a business background, and c) those that were successful had a good chance at becoming CEO or CEO or some other CXO (but not really CIO).

Analytics 64

Analytics Measurement Modeling Data-driven 64

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

NOVEMBER 27, 2019

Companies are no longer wondering if data visualizations improve analyses but what is the best way to tell each data-story. 2020 will be the year of data quality management and data discovery: clean and secure data combined with a simple and powerful presentation. 1) Data Quality Management (DQM).

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

O'Reilly on Data

MARCH 24, 2020

The assumed value of data is a myth leading to inflated valuations of start-ups capturing said data. 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.

Machine Learning 352

Machine Learning Statistics Data Quality Modeling 352