CIO Business Intelligence

DECEMBER 11, 2023

Now, it’s time to pay for it, and that’s putting a spotlight squarely on the chief financial officer (CFO), who has increasingly become the gatekeeper deciding which projects get funded and how significantly AI will play a role in enterprise strategy. For the CFOs at the center of that transformation, the stakes are higher than ever.

Finance 91

Finance Forecasting IoT Predictive Modeling 91

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. Let’s start with the models. And those experiments have paid off.

Experimentation 119

Experimentation Strategy Machine Learning Interactive 119

Smarten

NOVEMBER 26, 2018

Predictive Analytics used to involve a crystal ball but, today, there are other options and they are more widely accepted in the business community! And, with Assisted Predictive Modeling , your business users can leverage sophisticated tools, algorithms and techniques in a simple, intuitive environment to predict future results.

Predictive Analytics 58

Predictive Analytics Predictive Modeling Analytics Forecasting 58

CIO Business Intelligence

OCTOBER 13, 2023

Here are the top 11 roles companies are currently hiring for, or have plans to hire for, to directly address their emerging gen AI strategies. It’s a role that requires experience with natural language processing , coding languages, statistical models, and large language and generative AI models.

Deep Learning 143

Deep Learning Machine Learning Modeling Software 143

IBM Big Data Hub

SEPTEMBER 19, 2023

Overview: Data science vs data analytics Think of data science as the overarching umbrella that covers a wide range of tasks performed to find patterns in large datasets, structure data for use, train machine learning models and develop artificial intelligence (AI) applications.

Data Science 84

Data Science Data Analytics Prescriptive Analytics Analytics 84

DataRobot

OCTOBER 19, 2021

As a result, policymakers are left without enough time to create and implement targeted, localized strategies in time to prevent outbreaks. DataRobot helped combat this problem head on by applying AI to evaluate and predict resource allocation and identify the most impacted communities from a national to county level.

Forecasting 98

Forecasting Predictive Modeling Optimization Testing 98

Smarten

JUNE 29, 2018

This article focuses on the Independent Samples T Test technique of Hypothesis testing. What is the Independent Samples T Test Method of Hypothesis Testing? Let’s look at a sample of the Independent t-test on two variables. How Can the Independent Samples T Test Method Benefit an Organization? About Smarten.

Testing 40

Testing Statistics IT Business Intelligence 40

Smart Data Collective

SEPTEMBER 17, 2020

Financial institutions such as banks have to adhere to such a practice, especially when laying the foundation for back-test trading strategies. There is significant competition in the industry, and emerging tactics and strategies must be accepted to survive the market competition. The Role of Big Data.

Big Data 142

Big Data Finance Risk Management Risk 142

Domino Data Lab

OCTOBER 7, 2020

MANOVA, for example, can test if the heights and weights in boys and girls is different. This statistical test is correct because the data are (presumably) bivariate normal. Statistics developed in the last century are based on probability models (distributions). The accuracy of any predictive model approaches 100%.

Statistics 145

Statistics Testing Predictive Modeling Modeling 145

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. After cleaning, the data is now ready for processing.

Data Science 110

Data Science Statistics Recreation/Entertainment Predictive Analytics 110

DataRobot

JUNE 4, 2021

As new algorithms prove their merit, they are incorporated into DataRobot’s algorithm repository, and the platform continues to adapt and evolve even after models are deployed through a concept called continuous learning. Here, we have a model that predicts foot traffic for a chain of coffee shops. Data Source Comparison.

Forecasting 52

Forecasting Machine Learning Predictive Modeling Modeling 52

IBM Big Data Hub

JUNE 29, 2023

This is why asset reliability is such a critical component of business strategy. Preventive and predictive maintenance Both preventive and predictive maintenance are maintenance strategies used by business leaders to increase asset reliability. Before we dive into it, let’s take a look at some relevant terms.

Cost-Benefit 86

Cost-Benefit Measurement Management Predictive Analytics 86

Business Over Broadway

SEPTEMBER 27, 2018

In this paper, I show you how marketers can improve their customer retention efforts by 1) integrating disparate data silos and 2) employing machine learning predictive analytics. Your marketing strategy is only as good as your ability to deliver measurable results. genetic counseling, genetic testing).

Machine Learning 76

Machine Learning Data Integration Marketing Predictive Modeling 76

Smarten

JUNE 10, 2022

Recent studies have focused on the trends in business intelligence and augmented analytics, predicting that businesses will grow analytics within the enterprise with: Augmented Analytics to enable non-technical business users to create sophisticated data models. Assisted Predictive Modeling. Business Intelligence.

Slice and Dice 52

Slice and Dice Analytics Key Performance Indicator Business Intelligence 52

IBM Big Data Hub

FEBRUARY 14, 2024

Promote cross- and up-selling Recommendation engines use consumer behavior data and AI algorithms to help discover data trends to be used in the development of more effective up-selling and cross-selling strategies, resulting in more useful add-on recommendations for customers during checkout for online retailers.

Cost-Benefit 88

Cost-Benefit Insurance Unstructured Data Machine Learning 88

MLWhiz

NOVEMBER 30, 2014

As a result, click prediction systems are essential and widely used for sponsored search and real-time bidding. For this competition, we have provided 11 days worth of Avazu data to build and test prediction models. Can you find a strategy that beats standard classification algorithms?

Advertising 40

Advertising Predictive Modeling Metrics Testing 40

Birst BI

AUGUST 23, 2018

For example, there are a plethora of software tools available to automatically develop predictive models from relational data, and according to Gartner, “By 2020, more than 40% of data science tasks will be automated, resulting in increased productivity and broader usage by citizen data scientists.” [1]

Machine Learning 73

Machine Learning Data Science Testing Metrics 73

DataKitchen

SEPTEMBER 21, 2023

In the context of Data in Place, validating data quality automatically with Business Domain Tests is imperative for ensuring the trustworthiness of your data assets. Running these automated tests as part of your DataOps and Data Observability strategy allows for early detection of discrepancies or errors.

Testing 169

Testing Data Quality Predictive Modeling Metrics 169

Birst BI

OCTOBER 30, 2018

The credit scores generated by the predictive model are then used to approve or deny credit cards or loans to customers. A well-designed credit scoring algorithm will properly predict both the low- and high-risk customers. Integrate the data sources of the various behavioral attributes into a functional data model.

Predictive Analytics 73

Predictive Analytics Descriptive Analytics Analytics Metrics 73

Smarten

JUNE 20, 2018

The business can use this information for forecasting and planning, and to test theories and strategies. In prediction, the objective is to “model” all the components to some trend patterns to the point that the only component that remains unexplained is the random component. Linear Trend. Stationary/Stationarity.

Forecasting 40

Forecasting Statistics Business Intelligence Predictive Modeling 40

Smarten

JUNE 29, 2018

An Autoregressive Integrated Moving Average with Explanatory Variable (ARIMAX) model can be viewed as a multiple regression model with one or more autoregressive (AR) terms and/or one or more moving average (MA) terms. This article looks at the ARIMAX Forecasting method of analysis and how it can be used for business analysis.

Forecasting 40

Forecasting Enterprise IT Business Intelligence 40

Smarten

JUNE 26, 2018

Logistic regression makes use of one or more predictor variables that can be either continuous or categorical and predicts the target variable classes. Business Problem: A research agency wants to predict the likelihood of each election candidate being voted on by each voter and in turn devise a strategy to take proactive steps.

IT 40

IT Business Intelligence Predictive Modeling Statistics 40

Smarten

JUNE 29, 2018

For instance, if promotions and holiday seasons are significant factors, these factors should be given more focus when devising a marketing strategy. Business Problem: An agriculture production firm wants to predict the impact of the amount of rainfall, humidity, and temperature on the yield of particular crop. Use Case – 2.

Business Analysis 40

Business Analysis IT Sales Statistics 40

Smarten

JUNE 25, 2018

Sales team segments that are facing challenges based on any current discounting strategy can be identified and a deal negotiation strategy can be improved and optimized. Business Benefit: The business marketing team can focus on risky customer segments in an efficient way in order to avoid losing those customers.

Enterprise 40

Enterprise IT Business Intelligence Predictive Modeling 40

Smarten

JUNE 27, 2018

In this example, the segments that have a response rate higher than the overall response rate can be targeted first since they will require less effort to convert to a purchase, whereas a different marketing strategy must be devised for the lower segments (segments that have a response rate less than the overall rate).

IT 40

IT Business Intelligence Predictive Modeling Statistics 40

Birst BI

MAY 29, 2018

He published a paper proposing the development of a “relational database model” to address this issue. For example, a false positive or false negative for cancer diagnosis can result in tremendous emotional distress, additional unnecessary tests, and even death. Figure 6: Amazon Machine Learning to build and deploy Predictive Models.

Slice and Dice 74

Slice and Dice Analytics Business Intelligence Machine Learning 74

Smarten

APRIL 19, 2024

OpenAI – Azure OpenAI as the foundational entity for creating GPT models and is based on Large Language Models (LLM). GPT – Is based on a Large Language Model (LLM). Benefits include customized and optimized models, data, parameters and tuning. Open AI was developed by Microsoft.

Analytics 57

Analytics Predictive Modeling KPI Machine Learning 57

O'Reilly on Data

SEPTEMBER 19, 2023

Stage 2: Machine learning models Hadoop could kind of do ML, thanks to third-party tools. 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.” And it was good.

Machine Learning 247

Machine Learning Testing Modeling Cost-Benefit 247

Insight

AUGUST 5, 2020

This created a summary features matrix of 7472 recordings x 176 summary features, which was used for training emotion label prediction models. Prediction models An Exploratory Data Analysis showed improved performance was dependent on gender and emotion. up to 20% for prediction of ‘happy’ in females?

Predictive Modeling 98

Predictive Modeling Statistics Modeling Measurement 98

IBM Big Data Hub

JULY 5, 2023

But where do you start and how do you know which ALM strategy is right for you? The maintenance strategies that companies use most frequently are broken down into four stages of the asset lifecycle. It allows the company to run tests and predict performance based on simulations. What is asset lifecycle management (ALM)?

Strategy 57

Strategy Management IoT Cost-Benefit 57

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 112

Cost-Benefit Advertising Experimentation Software 112

IBM Big Data Hub

APRIL 5, 2024

Asset performance management (APM) strategy defines the balance between proactive and reactive maintenance tasks. Asset criticality defines whether a preventive or predictive task is justified in terms of cost and risk. The process of defining the optimum maintenance strategy is called reliability-centered maintenance.

Management 71

Management Risk Optimization Predictive Modeling 71

O'Reilly on Data

SEPTEMBER 15, 2020

In my book, I introduce the Technical Maturity Model: I define technical maturity as a combination of three factors at a given point of time. Outputs from trained AI models include numbers (continuous or discrete), categories or classes (e.g., spam or not-spam), probabilities, groups/segments, or a sequence (e.g.,

Cost-Benefit 293

Cost-Benefit Advertising ROI Machine Learning 293

Cloudera

SEPTEMBER 7, 2023

With the emergence of new creative AI algorithms like large language models (LLM) fromOpenAI’s ChatGPT, Google’s Bard, Meta’s LLaMa, and Bloomberg’s BloombergGPT—awareness, interest and adoption of AI use cases across industries is at an all time high. proprietary data, business strategies, methodologies, etc.

Insurance 79

Insurance Risk Data-driven Finance 79

Domino Data Lab

APRIL 21, 2021

We’ll use a gradient boosting technique via XGBoost to create a model and I’ll walk you through steps you can take to avoid overfitting and build a model that is fit for purpose and ready for production. This is to prevent any information leakage into our test set. 2f%% of the test set."

Statistics 69

Statistics Machine Learning Modeling Metrics 69

Alation

APRIL 14, 2022

And, as organizations progress and grow, “data drift” starts to impact data usage, models, and your business. Automatic sampling to test transformation. They strove to ramp up skills in all manner of predictive modeling, machine learning, AI, or even deep learning. A useful feature for exposing patterns in the data.

Metadata 59

Metadata Data Governance Modeling Data-driven 59

CIO Business Intelligence

AUGUST 15, 2023

We’re past the point of inflection: Information technology no longer merely supports or even drives an organization’s strategy; it has the power to transform and expand organizational missions and open up new strategic possibilities. They turned to artificial intelligence to help.

IT 91

IT Manufacturing IoT Cost-Benefit 91