IBM Big Data Hub

APRIL 9, 2024

” The historical sources watsonx.data accesses comprise relational, object and document databases, including IBM® Db2® , IBM® Cloudant , IBM Cloud® Object Storage and PostgreSQL. ” Training and testing models The Masters digital team used watsonx.ai Lastly, watsonx.data pulls from live feeds.

Management 84

Management IT Machine Learning Metrics 84

IBM Big Data Hub

DECEMBER 4, 2023

The most common insurance use cases include optimizing processes that require processing large documents and large blocks of text or images. Customer engagement Providing insurance coverage involves working with numerous documents. IBM works with several insurance companies to identify high-value opportunities for using generative AI.

Insurance 56

Insurance Digital Transformation Risk Management Risk 56

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. To get past those points, Colisto worked to educate leaders about the capabilities and risks of AI, seeking to move the company from “no to know.” What is our appetite for risk and how do we address it?

IT 129

IT Risk Cost-Benefit Enterprise 129

Smart Data Collective

NOVEMBER 3, 2021

It refers to underwriting, customer onboarding, document management, analysis, and statistics. Loan approval, as one of the biggest bottlenecks due to inconsistency of information between teams, may increase business risks. End-to-end loan management with AI. Integrated lending module.

Software 121

Software Data-driven Interactive Insurance 121

CIO Business Intelligence

NOVEMBER 7, 2023

This analysis isn’t just a statistical exercise but a crucial necessity in our interconnected world, where the security of our digital infrastructure is intertwined with our physical safety and economic stability. PAM can help prevent insider threat risks and reduce the potential of credentials becoming compromised.

Measurement 124

Measurement Risk Strategy Statistics 124

O'Reilly on Data

MARCH 24, 2020

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. So when you’re missing data or have “low-quality data,” you use assumptions, statistics, and inference to repair your data.

Machine Learning 346

Machine Learning Statistics Data Quality Modeling 346

BizAcuity

APRIL 1, 2023

Anti-Money Laundering (AML) is increasingly becoming a crucial branch of risk management and fraud prevention. In fact, online casinos as an industry carries the biggest risk of money laundering. OCR is widely used to digitize all kinds of physical documentation. Predictive Analytics can help businesses in reducing risk (eg.

Machine Learning 52

Machine Learning Predictive Analytics Predictive Modeling Risk 52

AWS Big Data

MARCH 13, 2024

RAG is a machine learning (ML) architecture that uses external documents (like Wikipedia) to augment its knowledge and achieve state-of-the-art results on knowledge-intensive tasks. We introduce the integration of Ray into the RAG contextual document retrieval mechanism. Open the CreateRayCluster document. json| jq '.data[].paragraphs[].qas[].question'

Data Processing 107

Data Processing Dashboards Machine Learning Management 107

BizAcuity

JANUARY 18, 2023

Anti-Money Laundering (AML) is increasingly becoming a crucial branch of risk management and fraud prevention. In fact, online casinos as an industry carries the biggest risk of money laundering. OCR is widely used to digitize all kinds of physical documentation. Predictive Analytics can help businesses in reducing risk (eg.

Machine Learning 52

Machine Learning Predictive Analytics Predictive Modeling Risk 52

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., Sensitivity analysis.

Machine Learning 191

Machine Learning Modeling Testing Risk Management 191

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 362

Marketing Experimentation Metrics Testing 362

datapine

JANUARY 6, 2022

Secondary Research: much like how patterns of behavior can be observed, different types of documentation resources can be coded and divided based on the type of material they contain. More often than not, it involves the use of statistical modeling such as standard deviation, mean and median.

Visualization 246

Visualization Dashboards Cost-Benefit Measurement 246

CIO Business Intelligence

FEBRUARY 13, 2023

The name references the Greek letter sigma, which is a statistical symbol that represents a standard deviation. Larger is Better involves a “lower specification limit,” such as test scores — where the target is 100%. During the improve phase, the focus is on testing the best potential solutions identified in the earlier phases.

Management 81

Management Statistics Manufacturing Measurement 81

datapine

JANUARY 24, 2021

Additionally, incorporating a decision support system software can save a lot of company’s time – combining information from raw data, documents, personal knowledge, and business models will provide a solid foundation for solving business problems. There are basically 4 types of scales: *Statistics Level Measurement Table*.

IT 317

IT Statistics KPI Data-driven 317

erwin

JANUARY 11, 2019

A company can’t effectively implement data governance – documenting and applying business rules and processes, analyzing the impact of changes and conducting audits – if it fails at data management. And there’s control of that landscape to facilitate insight and collaboration and limit risk.

Data Governance 40

Data Governance Risk Metadata Management 40

Domino Data Lab

JULY 19, 2021

They document this work for the benefit of future data scientists and auditors alike. Data scientists create a model and test it with algorithms and data. The tested model is transitioned from the data scientists to DevOps and infrastructure engineers in a production environment. Data Scientist. Data Engineer. ML Architect.

Enterprise 85

Enterprise Data Science Testing Modeling 85

CIO Business Intelligence

OCTOBER 13, 2023

It’s a role that requires experience with natural language processing , coding languages, statistical models, and large language and generative AI models. The role of algorithm engineer requires knowledge of programming languages, testing and debugging, documentation, and of course algorithm design.

Deep Learning 143

Deep Learning Machine Learning Modeling Software 143

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

The Unofficial Google Data Science Blog

DECEMBER 4, 2019

With those stakes and the long forecast horizon, we do not rely on a single statistical model based on historical trends. For example, we may prefer one model to generate a range, but use a second scenario-based model to “stress test” the range. A single model may also not shed light on the uncertainty range we actually face.

Forecasting 113

Forecasting Data Science Statistics Uncertainty 113

IBM Big Data Hub

APRIL 18, 2024

LLMs like ChatGPT are trained on massive amounts of text data, allowing them to recognize patterns and statistical relationships within language. Here are some areas where organizations are seeing a ROI: Text (83%) : Gen AI assists with automating tasks like report writing, document summarization and marketing copy generation.

Cost-Benefit 88

Cost-Benefit Modeling Manufacturing Interactive 88

datapine

SEPTEMBER 29, 2022

It involves: Reviewing data in detail Comparing and contrasting the data to its own metadata Running statistical models Data quality reports. Data processes that depended upon the previously defective data will likely need to be re-initiated, especially if their functioning was at risk or compromised by the defected data.

Data Quality 241

Data Quality Metrics Data-driven Management 241

Smart Data Collective

SEPTEMBER 21, 2022

As far as Data Analysis is concerned, potential employees should have an extensive knowledge of quantitative research, quantitative reporting, compiling statistics, statistical analysis, data mining, and big data. The old adage that you can build a better mousetrap and the world will beat a path to your door doesn’t hold up.

Statistics 85

Statistics Software Data mining Marketing 85

CDW Research Hub

NOVEMBER 6, 2020

Compile and document this information. As a best practice, you want to limit who has access to secure information to minimize risk and liability. Know your security risks. Most importantly, don’t forget to factor in human risk. Are there security policies or regulatory controls in place?

Scorecard 40

Scorecard Risk Business Driver Strategy 40

Alation

APRIL 18, 2023

DAMA defines data profiling as: An approach to data quality analysis, using statistics to show patterns of usage, and patterns of contents in an automated manner. The tools provide data statistics, such as degree of duplication and ratios of attribute values, both in tabular and graphical formats.

IT 52

IT Data Quality Metadata Data Governance 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. 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 271

Enterprise Modeling Testing Reporting 271

Domino Data Lab

JULY 21, 2019

pragmatic evaluation of risk-utility tradeoffs, or the tradeoff in risk resulting from greater data usage versus the risk of disclosure or reidentification. A statistical agency collected it, curated it, documented it, sent it out. A few highlights from the session include. Let me tell you the story.

Metadata 65

Metadata Statistics Risk Machine Learning 65

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

The Unofficial Google Data Science Blog

OCTOBER 31, 2016

To answer those questions, I put together a document shared Google-wide which I optimistically and simply titled “Good Data Analysis.” To my surprise, this document has been read more than anything else I’ve done at Google over the last eleven years. Why has this document resonated with so many people over time?

Metrics 107

Metrics Measurement Statistics Data Collection 107

Domino Data Lab

AUGUST 26, 2019

This control prevents overfitting and reduction in predictive accuracy on new test data. If we don’t like the result of testing after training, we can also select an entirely different learning machine with different internals. If you look at the documentation, you’ll see that n_neighbors and p are simply integers.

Testing 79

Testing Modeling Machine Learning Metrics 79

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 108

Testing Risk Digital Transformation Statistics 108

Domino Data Lab

AUGUST 8, 2019

The second rule of Foo Camp concerns a practice called “FrieNDA” where people speak candidly and others respect that without either (1) “Oh gosh I must live tweet stream this amazing insight” or (2) anybody having to sign legal documents. The probabilistic nature changes the risks and process required. Sci Foo 2019. machine learning?

Data Science 71

Data Science Machine Learning Data Governance Statistics 71

Domino Data Lab

JULY 9, 2019

Because I’m an academic I like to look at the original founding documents. Here is an ancient document about data science, specifically a piece by Jeff Hammerbacher. The other ancient and canonical document for trying to explain to people above me what we do in the data science group is this one. Good, okay.

Data Science 112

Data Science Machine Learning Advertising Modeling 112

The Unofficial Google Data Science Blog

JUNE 30, 2016

A naïve way to solve this problem would be to compare the proportion of buyers between the exposed and unexposed groups, using a simple test for equality of means. Identification We now discuss formally the statistical problem of causal inference. We start by describing the problem using standard statistical notation.

Statistics 44

Statistics Optimization Modeling Experimentation 44

O'Reilly on Data

MARCH 23, 2023

What is it, how does it work, what can it do, and what are the risks of using it? It’s by far the most convincing example of a conversation with a machine; it has certainly passed the Turing test. And it can look up an author and make statistical observations about their interests. But it is an amazing analytic engine.”

IT 262

IT Modeling Testing Risk 262

CIO Business Intelligence

APRIL 7, 2022

KPMG calculates a score for an employee’s risk of attrition, tries to identify a reason for that, and then suggests a remediation. With back-testing and cross-validation, we found that we’ve consistently been able to predict two-thirds of people about to resign, and save 10% to 20% of the ones we identified,” he says.

Consulting 143

Consulting Management Interactive Metadata 143

Jet Global

MAY 15, 2020

The balance sheet and the income statement are the two other financial reporting documents that provide a substantial amount of information pertaining to financial KPIs and metrics. Quick Ratio – This financial metric is commonly referred to as the “Acid Test Ratio” (acid was historically used to determine if gold was genuine or not).

Metrics 98

Metrics Finance Reporting KPI 98

Jet Global

MAY 1, 2023

CEO Priorities Grow revenue and “hit the number” Manage costs and meet profitability goals Attract and retain talent Innovate and out-perform the competition Manage risk Connect the Dots Present embedded analytics as a way to differentiate from the competition and increase revenue. Present your business case.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52