CIO Business Intelligence

OCTOBER 5, 2023

As chief digital and technology officer at CBRE, Davé recognized early that the commercial real estate industry was ripe for AI and machine learning enhancements, and he and his team have tested countless use cases across the enterprise ever since. For AI, the high-value quadrant is where you’ll find most predictive modeling.

Experimentation 130

Experimentation Strategy Machine Learning Interactive 130

IBM Big Data Hub

JUNE 29, 2023

Enterprises are constantly looking for new ways to optimize performance, increase reliability and extend asset lifespans—all without adding unnecessary costs. This information is then used to build predictive models of an asset’s performance over time and help spot potential problems before they arise.

Cost-Benefit 85

Cost-Benefit Measurement Management Predictive Analytics 85

Domino Data Lab

JULY 28, 2021

There are many software packages that allow anyone to build a predictive model, but without expertise in math and statistics, a practitioner runs the risk of creating a faulty, unethical, and even possibly illegal data science application. All models are not made equal.

Data Science 110

Data Science Statistics Recreation/Entertainment Predictive Analytics 110

IBM Big Data Hub

FEBRUARY 14, 2024

By infusing AI into IT operations , companies can harness the considerable power of NLP, big data, and ML models to automate and streamline operational workflows, and monitor event correlation and causality determination. Banks and other lenders can use ML classification algorithms and predictive models to suggest loan decisions.

Cost-Benefit 87

Cost-Benefit Insurance Unstructured Data Machine Learning 87

Smarten

AUGUST 21, 2019

Assisted Predictive Modeling – These tools enable the average business user to leverage sophisticated predictive algorithms without requiring statistical or data science skills. Users can highlight trends and patterns, test hypotheses and theories to reduce business risk, and easily predict and forecast results.

Analytics 51

Analytics Predictive Modeling Visualization Statistics 51

Business Over Broadway

SEPTEMBER 27, 2018

pharmacogenomics) and risk assessment of genetic disorders (e.g., genetic counseling, genetic testing). Analytics applied to these types of data help you generate better predictive models because your integrated data contain all the key variables that are useful in predicting customer churn.

Machine Learning 76

Machine Learning Data Integration Marketing Predictive Modeling 76

Smarten

JANUARY 8, 2019

Assisted predictive modeling suggests techniques to analyze data that will result in the right outcome for the goals of the analysis. Business users get support for day-to-day decisions and can quickly and easily test theories and hypotheses in a risk-free environment.

Analytics 40

Analytics Predictive Modeling Visualization Business Analysis 40

Smarten

JANUARY 31, 2019

Put simply, business Intelligence uses historical data to reveal where the business has been, and managers can use this data to predict competitive response and discover what is changing in customer buying behavior and in sales. Advanced Analytics is the logical tool to help a business optimize its investments and achieve its goals.

Business Intelligence 40

Business Intelligence Analytics Predictive Modeling Forecasting 40

DataRobot Blog

JUNE 23, 2022

Your artificial intelligence (AI) system has given you this “predicted feature” in addition to what you already know about the applicant. Business rules set by the credit committee to control business risk and the loan portfolio also constrain accepting this application and advancing the loan. Automate and Scale Your Decisions.

Predictive Modeling 59

Predictive Modeling Modeling Data Science Measurement 59

IBM Big Data Hub

JULY 6, 2023

Some examples of data science use cases include: An international bank uses ML-powered credit risk models to deliver faster loans over a mobile app. An AI-based medical assessment platform analyzes medical records to determine a patient’s risk of stroke and predict treatment plan success rates.

Machine Learning 58

Machine Learning Data Science Statistics Deep Learning 58

IBM Big Data Hub

OCTOBER 16, 2023

Marketers use ML for lead generation, data analytics, online searches and search engine optimization (SEO). Banks and other financial institutions train ML models to recognize suspicious online transactions and other atypical transactions that require further investigation. Many stock market transactions use ML.

Machine Learning 51

Machine Learning Marketing Forecasting Modeling 51

Smarten

JUNE 25, 2018

Loan applicants in a bank might be grouped as low, medium, and high risk applicants based on applicant age, annual income, employment tenure, loan amount, the number of times a payment is delinquent etc. About Smarten.

Enterprise 40

Enterprise IT Business Intelligence Predictive Modeling 40

DataKitchen

SEPTEMBER 21, 2023

L1 is usually the raw, unprocessed data ingested directly from various sources; L2 is an intermediate layer featuring data that has undergone some form of transformation or cleaning; and L3 contains highly processed, optimized, and typically ready for analytics and decision-making processes. What is Data in Use?

Testing 169

Testing Data Quality Predictive Modeling Metrics 169

IBM Big Data Hub

APRIL 5, 2024

No single system can manage all the required information views to enable end-to-end optimization. The maintenance, repair and overhaul (MRO) system provides necessary spare parts to carry out work and maintains an optimum stock level with a balance of stock out risk and part holding costs. Asset data is the basis for the network.

Management 70

Management Risk Optimization Predictive Modeling 70

O'Reilly on Data

SEPTEMBER 19, 2023

While data scientists were no longer handling Hadoop-sized workloads, they were trying to build predictive models on a different kind of “large” dataset: so-called “unstructured data.” You can see a simulation as a temporary, synthetic environment in which to test an idea. And it was good.

Machine Learning 247

Machine Learning Testing Modeling Cost-Benefit 247

CIO Business Intelligence

MARCH 5, 2024

CIOs must also partner with CISOs, legal, human resources, and business leaders to build awareness of policies and develop a generative AI risk management strategy. CIOs and IT leaders are at the center and must decide what copilots to test, who should receive access, and whether experiments are delivering business value.

Cost-Benefit 124

Cost-Benefit Advertising Experimentation Software 124

O'Reilly on Data

SEPTEMBER 15, 2020

Technical competence results in reduced risk and uncertainty. AI initiatives may also require significant considerations for governance, compliance, ethics, cost, and risk. Likewise, AI doesn’t inherently optimize supply chains, detect diseases, drive cars, augment human intelligence, or tailor promotions to different market segments.

Cost-Benefit 293

Cost-Benefit Advertising ROI Machine Learning 293

Cloudera

SEPTEMBER 7, 2023

But it’s also fraught with risk. This June, for example, the European Union (EU) passed the world’s first regulatory framework for AI, the AI Act , which categorizes AI applications into “banned practices,” “high-risk systems,” and “other AI systems,” with stringent assessment requirements for “high-risk” AI systems.

Insurance 79

Insurance Risk Data-driven Finance 79

Occam's Razor

NOVEMBER 17, 2008

There is a tendency to think experimentation and testing is optional. You can start for free with a superb tool: Google's Website Optimizer. For example I am quite fond of the fact that with Offermatica you can "trigger" tests based on behavior. I cannot recommend enough the wisdom of starting with a A/B test.

Testing 112

Testing Experimentation Risk Optimization 112

IBM Big Data Hub

JULY 5, 2023

It allows the company to run tests and predict performance based on simulations. With a good digital twin, it’s possible to predict how an asset will perform under certain conditions and what it’s projected lifespan will be. A digital twin is a virtual representation of an asset that a company intends to purchase.

Strategy 56

Strategy Management IoT Cost-Benefit 56

CIO Business Intelligence

AUGUST 15, 2023

In partnership with OpenAI and Microsoft, CarMax worked to develop, test, and iterate GPT-3 natural language models aimed at achieving those results. The CarMax team also gathered, scrubbed and formatted data from thousands of vehicles to feed into the models, fine-tuning them as the project advanced.

IT 98

IT Manufacturing IoT Cost-Benefit 98

DataRobot Blog

DECEMBER 1, 2022

As you upload your data, DataRobot will do some initial exploratory data analysis to get a deeper understanding of the dataset prior to model training. Next, choose your target variable—in this instance it is automatically detected as a classification problem and an optimization metric is recommended. Automate Feature Engineering.

Machine Learning 52

Machine Learning Marketing Modeling Statistics 52

CIO Business Intelligence

MARCH 22, 2023

Additionally, federated learning does not address the inference stage, which still exposes data to the ML model during cloud or edge device deployment. Differential privacy Differential privacy provides margins on how much a single data record from a training dataset contributes to a machine-learning model.

Machine Learning 105

Machine Learning Cost-Benefit Modeling Risk 105

Alation

APRIL 14, 2022

Automatic sampling to test transformation. They strove to ramp up skills in all manner of predictive modeling, machine learning, AI, or even deep learning. These factors risk data originating in far-flung environments, where the data structures and semantics are not well understood or documented. Visual Profiling.

Metadata 59

Metadata Data Governance Modeling Data-driven 59

Domino Data Lab

APRIL 21, 2021

Rules-based fraud detection (top) vs. classification decision tree-based detection (bottom): The risk scoring in the former model is calculated using policy-based, manually crafted rules and their corresponding weights. This is to prevent any information leakage into our test set. 2f%% of the test set."

Statistics 69

Statistics Machine Learning Modeling Metrics 69

DataRobot Blog

JANUARY 10, 2023

Most, if not all, machine learning (ML) models in production today were born in notebooks before they were put into production. Clinics and hospitals like Phoenix Children’s use AI to predict which patients are at risk of contracting an illness so that they can then prescribe medication and treatment accordingly.

Experimentation 52

Experimentation Machine Learning Data Science Modeling 52

The Unofficial Google Data Science Blog

DECEMBER 4, 2019

by THOMAS OLAVSON Thomas leads a team at Google called "Operations Data Science" that helps Google scale its infrastructure capacity optimally. A single model may also not shed light on the uncertainty range we actually face. It is therefore good discipline to provide forecast release notes explaining key risks and drivers of change.

Forecasting 113

Forecasting Data Science Statistics Uncertainty 113

Domino Data Lab

JULY 9, 2019

Diving into examples of building and deploying ML models at The New York Times including the descriptive topic modeling-oriented Readerscope (audience insights engine), a prediction model regarding who was likely to subscribe/cancel their subscription, as well as prescriptive example via recommendations of highly curated editorial content.

Data Science 112

Data Science Machine Learning Advertising Modeling 112

DataRobot

JANUARY 20, 2022

A financial squeeze on healthcare, post-pandemic, is widely regarded as inevitable, and as the NHS recovers, it will need to achieve increased efficiency savings through clinical and operational process redesign and optimization. ICSs can reduce the time taken to build population health registries and predictive models by up to 90 percent.

Data-driven 145

Data-driven Experimentation Modeling Predictive Modeling 145

DataRobot Blog

JUNE 24, 2022

Thanks to the increasingly rapid evolution of AI and advances in machine learning, the real estate industry has a more vivid picture of future risk and opportunities across all different market segments: offices, residential, retail, logistics, hotels, OPRE and data centers.

Forecasting 52

Forecasting Machine Learning Marketing Data-driven 52

Domino Data Lab

AUGUST 1, 2021

This dataset classifies customers based on a set of attributes into two credit risk groups – good or bad. This is because in this post we are not after a state-of-the-art classifier, but want to rather focus on the interpretation of the black-box model fitted against the sample dataset. random_state=seed) y_train.value_counts().

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

Smarten

JUNE 25, 2018

Business Problem: A stock broker wants to analyze the price volatility of a stock as a measure of risk. Business Benefit: By analyzing the standard deviation or variance, one can measure the risk associated with a particular stock in terms of price fluctuations. 5) Standard Deviation/Variance. 6) Skewness/Histogram. About Smarten.

Statistics 40

Statistics Enterprise Measurement Business Intelligence 40

datapine

NOVEMBER 27, 2019

This is one of the major trends chosen by Gartner in their 2020 Strategic Technology Trends report , combining AI with autonomous things and hyperautomation, and concentrating on the level of security in which AI risks of developing vulnerable points of attacks. Industries harness predictive analytics in different ways.

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

Jet Global

SEPTEMBER 1, 2023

But we’re also seeing its use expand in other industries, like Financial Services applications for credit risk assessment or Human Resources applications to identify employee trends. Using the information from predictive analytics can help companies—and business applications—suggest actions that can affect positive operational changes.

Predictive Analytics 52

Predictive Analytics Risk Analytics Software 52

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