CIO Business Intelligence

JANUARY 9, 2024

ChatGPT caused quite a stir after it launched in late 2022, with people clamoring to put the new tech to the test. Our firm’s leaders] wanted to make sure there were guidelines in place to protect the company, its data, and its people.” 1 among the top three risks — followed by statistical validity and model accuracy.

IT 118

IT Risk Cost-Benefit Enterprise 118

O'Reilly on Data

JULY 28, 2020

Product Managers are responsible for the successful development, testing, release, and adoption of a product, and for leading the team that implements those milestones. 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.

Marketing 363

Marketing Experimentation Metrics Testing 363

IBM Big Data Hub

JANUARY 25, 2024

Over time, inventory managers have tested different approaches to determine the best fit for their organizations. Considering that IDC surveyed 37% of companies that manage spare parts inventory by using spreadsheets, email, shared folders or an uncertain approach, it becomes evident that this practice carries more risk than it might seem.

Optimization 73

Optimization Cost-Benefit Prescriptive Analytics Manufacturing 73

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. 6] Debugging may focus on a variety of failure modes (i.e.,

Machine Learning 198

Machine Learning Modeling Testing Risk Management 198

Alation

APRIL 18, 2023

For any data user in an enterprise today, data profiling is a key tool for resolving data quality issues and building new data solutions. In this blog, we’ll cover the definition of data profiling, top use cases, and share important techniques and best practices for data profiling today.

IT 52

IT Metadata Data Quality Data Governance 52

FineReport

MARCH 24, 2024

Data analysts contribute value to organizations by uncovering trends, patterns, and insights through data gathering, cleaning, and statistical analysis. They identify and interpret trends in complex datasets, optimize statistical results, and maintain databases while devising new data collection processes.

Statistics 52

Statistics Data mining Visualization Reporting 52

erwin

JANUARY 11, 2019

It’s only when companies take their first stab at manually cataloging and documenting operational systems, processes and the associated data, both at rest and in motion, that they realize how time-consuming the entire data prepping and mapping effort is, and why that work is sure to be compounded by human error and data quality issues.

Data Governance 40

Data Governance Risk Metadata Management 40

Ontotext

JUNE 22, 2023

Recent statistics shed light on the realities in the world of current drug development: out of about 10,000 compounds that undergo clinical research, only 1 emerges successfully as an approved drug. The current process involves costly wet lab experiments, which are often performed multiple times to achieve statistically significant results.

Metrics 52

Metrics Statistics Visualization Metadata 52

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. We’re not encouraging skepticism or fear, but companies should start AI products with a clear understanding of the risks, especially those risks that are specific to AI.

Enterprise 281

Enterprise Modeling Testing Reporting 281

O'Reilly on Data

MARCH 31, 2020

All you need to know for now is that machine learning uses statistical techniques to give computer systems the ability to “learn” by being trained on existing data. After training, the system can make predictions (or deliver other results) based on data it hasn’t seen before. Machine learning adds uncertainty.

Management 339

Management Machine Learning Experimentation Metrics 339

O'Reilly on Data

JULY 15, 2019

In recent posts, we described requisite foundational technologies needed to sustain machine learning practices within organizations, and specialized tools for model development, model governance, and model operations/testing/monitoring. Note that the emphasis of SR 11-7 is on risk management.). Sources of model risk.

Machine Learning 38

Machine Learning Management Enterprise Risk Management 38

Domino Data Lab

JUNE 23, 2019

As a result, Skomoroch advocates getting “designers and data scientists, machine learning folks together and using real data and prototyping and testing” as quickly as possible. As quickly as possible, you want to get designers and data scientists, machine learning folks together and using real data and prototyping and testing.

Management 110

Management Machine Learning Experimentation Metrics 110

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

CIO Business Intelligence

MARCH 14, 2024

Adopting and scaling AI Some of the NAO’s advice to government could also apply to enterprises, especially those with a similar aversion to risk and change. Testing and piloting AI is key to successful adoption, the study found, and the government bodies surveyed are doing well on this score, with 70% already doing so.

Testing 101

Testing Risk Digital Transformation Statistics 101

Domino Data Lab

MARCH 3, 2019

I’m here mostly to provide McLuhan quotes and test the patience of our copy editors with hella Californian colloquialisms. Companies are building data infrastructure in the cloud: 85% indicated they have data infrastructure in at least one cloud provider. Seriously, Ben gets credit for foresight on how to organize those surveys.

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

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. Risk Management (most likely within context of governance).

Data Analytics 52

Data Analytics Analytics Data-driven Finance 52

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

datapine

MAY 17, 2021

Your Chance: Want to test a professional data discovery tool for free? Benefit from modern data discovery today! What Is Data Discovery? Your Chance: Want to test a professional data discovery tool for free? Benefit from modern data discovery today! 5) Develop a data discovery model.

Visualization 161

Visualization Data-driven Business Intelligence Metrics 161