Rocket-Powered Data Science

SEPTEMBER 25, 2022

This is all fueled and facilitated by data flows across processes, products, and people’s activities, used in synergy with computational models and simulations of the system being digitally twinned. All phases of the MVT process are discussed: strategy, designs, pilot, implementation, test, validation, operations, and monitoring.

Diagnostic Analytics 214

Diagnostic Analytics Prescriptive Analytics Internet of Things IoT 214

Jen Stirrup

FEBRUARY 9, 2024

For example, India is also using AI to enhance weather forecasting and climate modelling. The Indian government is testing AI-powered climate models to improve weather forecasts across the country [3]. Artificial Intelligence is one way forward to tackling the issues of climate change.

Forecasting 104

Forecasting Measurement Testing Technology 104

DataRobot Blog

MAY 26, 2022

Last time , we discussed the steps that a modeler must pay attention to when building out ML models to be utilized within the financial institution. In summary, to ensure that they have built a robust model, modelers must make certain that they have designed the model in a way that is backed by research and industry-adopted practices.

Risk 52

Risk Modeling Metrics Business Objectives 52

Rocket-Powered Data Science

SEPTEMBER 20, 2021

The data scientists need to find the right data as inputs for their models — they also need a place to write-back the outputs of their models to the data repository for other users to access. The BI team may be focused on KPIs, forecasts, trends, and decision-support insights. That’s data democratization.

Data Science 285

Data Science Forecasting Business Intelligence Sales 285

The Unofficial Google Data Science Blog

APRIL 19, 2021

by LEE RICHARDSON & TAYLOR POSPISIL Calibrated models make probabilistic predictions that match real world probabilities. To explain, let’s borrow a quote from Nate Silver’s The Signal and the Noise : One of the most important tests of a forecast — I would argue that it is the single most important one — is called calibration.

Modeling 122

Modeling IT Metrics Testing 122

CIO Business Intelligence

JULY 5, 2022

Research firm Gartner defines business analytics as “solutions used to build analysis models and simulations to create scenarios, understand realities, and predict future states.”. Data analytics is used across disciplines to find trends and solve problems using data mining , data cleansing, data transformation, data modeling, and more.

Business Analytics 132

Business Analytics Prescriptive Analytics Data mining Diagnostic Analytics 132

CIO Business Intelligence

DECEMBER 11, 2023

For example, by tapping into real-time data with AI-enabled analytics, CFOs will be able to develop multiple scenarios for capital allocation, offering more forward-looking projections and more accurate forecasts.

Finance 100

Finance Forecasting IoT Predictive Modeling 100

Smarten

JULY 23, 2019

Put simply, predictive analytics is a method used to forecast and predict the future results and needs of an organization using historical data and a comprehensive set of data from across and outside the enterprise. Predictive Modeling allows users to test theories and hypotheses and develop the best strategy.

Modeling 63

Modeling Predictive Analytics Predictive Modeling IT 63

AWS Big Data

OCTOBER 26, 2023

Data analysts and database developers want to use this data to train machine learning (ML) models, which can then be used to generate insights on new data for use cases such as forecasting revenue, predicting customer churn, and detecting anomalies. We also need an offline feature store to store features in feature groups.

Modeling 62

Modeling Data Warehouse Machine Learning Testing 62

Smart Data Collective

JUNE 15, 2021

However, it is important to make sure that you understand the potential role of AI and what business model to build around it. However, even the most brilliant idea built around AI technology can fail without a proper business model. Without a good business model, you won’t understand customer needs and how to build your startup.

Modeling 91

Modeling Advertising Cost-Benefit Machine Learning 91

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. You must detect when the model has become stale, and retrain it as necessary. The Core Responsibilities of the AI Product Manager. The AI Product Development Process.

Marketing 362

Marketing Experimentation Metrics Testing 362

DataRobot

OCTOBER 19, 2021

Many available forecasts provide less than four weeks notice at the state and county level. Additionally, these forecasts often miss surges in community transmission until it is too late to change course. Traditional predictive models do not account for anomaly detection on data reporting issues (e.g., reporting backlogs).

Forecasting 98

Forecasting Predictive Modeling Optimization Testing 98

Corinium

JUNE 6, 2019

Fractal’s recommendation is to take an incremental, test and learn approach to analytics to fully demonstrate the program value before making larger capital investments. It is also important to have a strong test and learn culture to encourage rapid experimentation. Therefore, understanding customers for cross and up-sell is paramount.

Insurance 250

Insurance Analytics Forecasting Deep Learning 250

Smart Data Collective

JANUARY 27, 2021

A number of new predictive analytics algorithms are making it easier to forecast price movements in the cryptocurrency market. Conversely, if predictive analytics models suggest that the value of a cryptocurrency price is likely to decrease, more investors are likely to sell off their cryptocurrency holdings.

Machine Learning 139

Machine Learning Forecasting Predictive Analytics Marketing 139

IBM Big Data Hub

FEBRUARY 13, 2024

This data is fed into generational models, and there are a few to choose from, each developed to excel at a specific task. Generative adversarial networks (GANs) or variational autoencoders (VAEs) are used for images, videos, 3D models and music. Autoregressive models or large language models (LLMs) are used for text and language.

Enterprise 123

Enterprise Modeling Testing Data-driven 123

Domino Data Lab

MARCH 3, 2021

It’s often stated that nothing changes inside an enterprise because you’ve built a model. In some cases, data science does generate models directly to revenue, such as a contextual deal engine that targets people with offers that they can instantly redeem. But what about good decisions?

Metrics 93

Metrics Modeling Sales Data Science 93

Smart Data Collective

OCTOBER 8, 2021

Modern machine learning and back-testing; how quant hedge funds use it. Similarly, hedge funds often use modern machine learning and back-testing to analyze their quant models. Here, the models get tested using historical data to evaluate their profitability. Mathematical Model-based Strategies.

Machine Learning 114

Machine Learning Testing Strategy Modeling 114

CIO Business Intelligence

JULY 18, 2023

It was when Nvidia reported strong results for the three months to April 30, 2023, and forecast that its sales could jump by 50% in the following fiscal quarter, that its stock market valuation soared, catapulting it into the exclusive trillion-dollar club alongside well-known tech giants Alphabet, Amazon, Apple, and Microsoft.

Manufacturing 98

Manufacturing Forecasting Software Marketing 98

Smarten

JUNE 26, 2018

This article provides a brief explanation of the ARIMA method of analytical forecasting. What is ARIMA Forecasting? This analytical forecasting method is suitable for instances when data is stationary/non stationary and is univariate, with any type of data pattern, i.e., level/trend/seasonality/cyclicity. About Smarten.

Forecasting 40

Forecasting Enterprise IT Sales 40

Smarten

JUNE 29, 2018

This article looks at the ARIMAX Forecasting method of analysis and how it can be used for business analysis. What is ARIMAX Forecasting? This method is suitable for forecasting when data is stationary/non stationary, and multivariate with any type of data pattern, i.e., level/trend /seasonality/cyclicity. About Smarten.

Forecasting 40

Forecasting Enterprise IT Business Intelligence 40

CIO Business Intelligence

FEBRUARY 20, 2024

Others are weighing the advantages of subscription-based business models where industrial equipment, automation, and processes are delivered as a service. In the face of increased competition, shrinking profit margins, and increasing ESG obligations, manufacturers are looking for ways to make products better, faster, and with less waste.

Manufacturing 92

Manufacturing Sales Risk Management Forecasting 92

datapine

MAY 2, 2023

Your Chance: Want to test a professional logistics analytics software? In a recent move towards a more autonomous logistical future, Amazon has launched an upgraded model of its highly-successful KIVA robots. Your Chance: Want to test a professional logistics analytics software? Where is all of that data going to come from?

Big Data 275

Big Data Cost-Benefit Internet of Things Optimization 275

DataRobot Blog

AUGUST 18, 2022

Challenges in inventory management, demand forecasting, price optimization, and more can result in missed opportunities and lost revenue. Retailers and manufacturers that incorporate AI in supply chain management greatly enhance their ability to forecast demand, manage inventory, and optimize price. Francois Fressin.

Manufacturing 98

Manufacturing Cost-Benefit Forecasting Data-driven 98

CIO Business Intelligence

JUNE 7, 2022

To ensure robust analysis, data analytics teams leverage a range of data management techniques, including data mining, data cleansing, data transformation, data modeling, and more. It is frequently used for economic and sales forecasting. What are the four types of data analytics? It is frequently used for risk analysis.

Data Analytics 133

Data Analytics Diagnostic Analytics Management Analytics 133

DataRobot

OCTOBER 18, 2021

They designed experiments that tested real-life human behaviors against those expected from economic theory. There is a wealth of research showing again and again that evidence-based algorithms are more accurate than forecasts made by humans. Haggling isn’t the only way that humans don’t behave the way that economic theory suggests.

Forecasting 145

Forecasting Digital Transformation Cost-Benefit Data-driven 145

AWS Big Data

FEBRUARY 1, 2023

Storage and redundancy – Due to the heterogeneous data stores and models, it was challenging to store the different datasets from various business stakeholder teams. This will make launching and testing models simpler. The response times for these data sources are critical to our key stakeholders.

Optimization 52

Optimization Forecasting Data Lake Metadata 52

Cloudera

JANUARY 5, 2021

In today’s retail environment, retailers realize that building demand forecasts simply based upon historical transaction, promo, and pricing data alone is not good enough. Retail supply chains are a recognized and proven source of ROI when data analytics are leveraged to improve forecast accuracy and product availability.

Forecasting 91

Forecasting Cost-Benefit Sales Unstructured Data 91

Smarten

JUNE 21, 2018

This article provides a brief explanation of the Holt-Winters Forecasting model and its application in the business environment. What is the Holt-Winters Forecasting Algorithm? The Holt-Winters algorithm is used for forecasting and It is a time-series forecasting method. 2) Double Exponential Smoothing Use Case.

Forecasting 40

Forecasting Enterprise IT Predictive Modeling 40

Smarten

DECEMBER 5, 2022

Self-serve assisted predictive modeling provides recommendations and suggestions based on data volume, type and other parameters, so users will be directed to the appropriate analytical techniques and receive clear, concise results that are easy to understand and apply for confident decisions. Paired Sample T Test. ARIMAX Forecasting.

Predictive Analytics 52

Predictive Analytics Predictive Modeling Forecasting Testing 52

CIO Business Intelligence

APRIL 9, 2024

Previously head of cybersecurity at Ingersoll-Rand, Melby started developing neural networks and machine learning models more than a decade ago. One SCADA engineer, for example, started using generative AI “to create synthetic data for testing our SCADA systems,” Melby says. “As

Digital Transformation 97

Digital Transformation Machine Learning Data Lake Software 97

CIO Business Intelligence

NOVEMBER 14, 2022

A DSS leverages a combination of raw data, documents, personal knowledge, and/or business models to help users make decisions. According to Gartner, the goal is to design, model, align, execute, monitor, and tune decision models and processes. Model-driven DSS. They emphasize access to and manipulation of a model.

Data mining 137

Data mining Data-driven Statistics OLAP 137

Smarten

MAY 31, 2022

When combined with Citizen Data Scientist initiatives, the adoption and use of predictive modeling and forecasting techniques can be a boon to any enterprise. Team members who have access to augmented analytics and assisted predictive modeling can plan better, predict more accurately and dependably meet goals and objectives.

Predictive Analytics 67

Predictive Analytics Analytics Predictive Modeling Prescriptive Analytics 67

Smarten

DECEMBER 3, 2023

It also augments the expert and citizen data scientists by automating many aspects of data science, machine learning, and AI model development, management and deployment.’ ‘You Solution capabilities included self-serve data preparation , smart data visualization and predictive analytics for forecasting, etc.

Key Performance Indicator 52

Key Performance Indicator Analytics IT Predictive Analytics 52

Cloudera

APRIL 9, 2021

Specifically, we’ll focus on training Machine Learning (ML) models to forecast ECC part production demand across all of its factories. This integration is key in assuring that models evolve with the data – to avoid, for example, model drift. The third video in the series highlighted Reporting and Data Visualization.

Machine Learning 98

Machine Learning Forecasting Manufacturing Predictive Analytics 98

CIO Business Intelligence

SEPTEMBER 22, 2022

Seeing that remote working continues to be a pressing issue still finding its footing after nearly three years in beta testing, the work surrounding feasible solutions seems to compound as time goes on, with some intending a full return to office while others have forged the company future on remote models.

Uncertainty 109

Uncertainty Forecasting Visualization Big Data 109

Smarten

APRIL 10, 2023

When an enterprise selects an assisted predictive modeling solution, it can satisfy the needs of business users, IT, and data scientists and achieve impressive results for the organization.’ Data Scientists can create and re-purpose analytical models and focus on strategic initiatives.

Predictive Analytics 52

Predictive Analytics Predictive Modeling Analytics Prescriptive Analytics 52