CIO Business Intelligence

MAY 9, 2024

They should lead the efforts to tie AI capabilities to data analytics and business process strategies and champion an AI-first mindset throughout the organization. For companies with a central strategy function, the CAIO will be a key partner in driving success.” Artificial Intelligence, IT Leadership

Risk Management 138

Risk Management Risk Cost-Benefit Technology 138

CIO Business Intelligence

MAY 21, 2024

With this model, patients get results almost 80% faster than before. Next, Northwestern and Dell will develop an enhanced multimodal LLM for CAT scans and MRIs and a predictive model for the entire electronic medical record.

IT 131

IT Cost-Benefit Experimentation Forecasting 131

IBM Big Data Hub

MAY 18, 2023

” The new Forrester Wave™ report details how IBM compares with other vendors in the AI-decisioning landscape based on current offering, strategy and market presence scores. Intelligent , helping businesses to take predictions and scores from external data science services into account and apply them to make decisions.

Reporting 91

Reporting Scorecard Predictive Modeling Machine Learning 91

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. Data scientist As companies embrace gen AI, they need data scientists to help drive better insights from customer and business data using analytics and AI.

Deep Learning 143

Deep Learning Machine Learning Modeling Software 143

Smarten

FEBRUARY 17, 2022

Where many businesses fail in implementing the Citizen Data Scientist initiative, it is typical to find that the business has simply decided to deploy the strategy without much planning or culture change.

Predictive Modeling 76

Predictive Modeling Digital Transformation Enterprise Data Science 76

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. Machine Learning and Predictive Modeling of Customer Churn.

Machine Learning 76

Machine Learning Data Integration Marketing Predictive Modeling 76

O'Reilly on Data

DECEMBER 12, 2019

You’ve even discovered a few problems with your ML model. That’s where remediation strategies come in. We discuss seven remediation strategies below. Data augmentation. ML models learn from data to become accurate, and ML models require data that’s truly representative of the entire problem space being modeled.

Machine Learning 191

Machine Learning Modeling Testing Risk Management 191

Sisense

SEPTEMBER 9, 2019

We were most interested in exploring if it was possible to predict the return rate on a new product based on historical return rates of products with similar features. Then we ran Kraken’s machine learning and predictive modeling engine to get the results.

Predictive Analytics 75

Predictive Analytics Predictive Modeling Forecasting Data-driven 75

Cloudera

MAY 12, 2021

Use cases for this category could include but are not limited to: people analytics and reporting; employee recruiting, retention and development; employee resource groups; diversity, equality and inclusion strategy; supplier diversity, and more. DATA FOR GOOD. A popular category last year, and no doubt this year too.

Digital Transformation 65

Digital Transformation Machine Learning Optimization Data Lake 65

IBM Big Data Hub

AUGUST 28, 2023

With nearly 5 billion users worldwide—more than 60% of the global population —social media platforms have become a vast source of data that businesses can leverage for improved customer satisfaction, better marketing strategies and faster overall business growth. What is text mining?

Data mining 56

Data mining Machine Learning Deep Learning Marketing 56

Birst BI

AUGUST 23, 2018

What if some of these data science tasks could be automated using AI, increasing data science productivity to tackle more AI use cases? Automating data science tasks leaves room to build more AI applications with the same amount of data science resources. Source: Gartner (April 2018).

Machine Learning 73

Machine Learning Data Science Testing Metrics 73

CIO Business Intelligence

MAY 26, 2022

Not to mention the advanced insights and predictive modeling that should drive all major and minor decisions, as well as personalized engagement with stakeholders of all types, and so on. . All aspects of a business are dependent upon and differentiated by the sophistication of the underpinning technical capability.

Informatics 100

Informatics Manufacturing Predictive Modeling Technology 100

CIO Business Intelligence

MAY 28, 2022

Not to mention the advanced insights and predictive modeling that should drive all major and minor decisions, as well as personalized engagement with stakeholders of all types, and so on. . All aspects of a business are dependent upon and differentiated by the sophistication of the underpinning technical capability.

Informatics 95

Informatics Manufacturing Predictive Modeling Technology 95

Domino Data Lab

OCTOBER 7, 2020

In our next blog we discuss how we try to avoid these problems in applied data analysis of high dimensional data. Data Has Properties. Statistics developed in the last century are based on probability models (distributions). The accuracy of any predictive model approaches 100%. P >> N) ).

Statistics 145

Statistics Testing Predictive Modeling Modeling 145

Rocket-Powered Data Science

JULY 13, 2023

Beyond the early days of data collection, where data was acquired primarily to measure what had happened (descriptive) or why something is happening (diagnostic), data collection now drives predictive models (forecasting the future) and prescriptive models (optimizing for “a better future”).

Analytics 167

Analytics Machine Learning Data Science Deep Learning 167

Birst BI

MAY 29, 2018

Today, BI vendors are already introducing AI capabilities in their products for data preparation, data discovery, and data science. Figure 6: Amazon Machine Learning to build and deploy Predictive Models. Mona Patel works in Birst’s Product Strategy team.

Slice and Dice 74

Slice and Dice Analytics Business Intelligence Machine Learning 74

Rocket-Powered Data Science

JULY 6, 2021

2) MLOps became the expected norm in machine learning and data science projects. MLOps takes the modeling, algorithms, and data wrangling out of the experimental “one off” phase and moves the best models into deployment and sustained operational phase. And the goodness doesn’t stop there.

Data-driven 279

Data-driven Machine Learning IoT Deep Learning 279

O'Reilly on Data

MARCH 20, 2019

The objective here is to brainstorm on potential security vulnerabilities and defenses in the context of popular, traditional predictive modeling systems, such as linear and tree-based models trained on static data sets. If an attacker can receive many predictions from your model API or other endpoint (website, app, etc.),

Modeling 222

Modeling Machine Learning Predictive Modeling Consulting 222

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

Rocket-Powered Data Science

FEBRUARY 12, 2021

The “Next” part of the report probes organizations’ forward-looking strategies and goals over the next one to two years. Interest in AI is high and growing, specifically in the areas of smart analytics, customer-centricity, chatbots, and predictive modeling. The average ROI from RPA/IA deployments is 250%.

ROI 234

ROI Digital Transformation Reporting Machine Learning 234

Domino Data Lab

JULY 9, 2019

Chris Wiggins , Chief Data Scientist at The New York Times, presented “Data Science at the New York Times” at Rev. Wiggins also indicated that data science, data engineering, and data analysis are different groups at The New York Times. Session Summary. Transcript. Feel free to email me.

Data Science 112

Data Science Machine Learning Advertising Modeling 112

Cloudera

NOVEMBER 18, 2020

In fact, each of the 29 finalists represented organizations running cutting-edge use cases that showcase a winning enterprise data cloud strategy. With ultra-personalized marketing at the heart of their strategy, OVO built its first contextual offer engine, OVO UnCover.

Internet Publishing and Broadcasting 118

Internet Publishing and Broadcasting Data-driven Broadcasting Digital Transformation 118

IBM Big Data Hub

DECEMBER 20, 2023

ML is a computer science, data science and artificial intelligence (AI) subset that enables systems to learn and improve from data without additional programming interventions. With repetition, the agent learns the best strategies. Here, we’ll discuss the five major types and their applications.

Machine Learning 77

Machine Learning Modeling Deep Learning Predictive Modeling 77

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.” That’s why it’s a likely next step in Analyzing Data for Fun and Profit. And it was good.

Machine Learning 247

Machine Learning Testing Modeling Cost-Benefit 247

Domino Data Lab

APRIL 21, 2021

There are different approaches to this strategy, with the two most commonly used being random oversampling and SMOTE. In this context, offsetting the threshold in a way that reduces the false negatives at the expense of false positives becomes a viable strategy. Knowledge and Data Engineering, IEEE Transactions on, 21, 1263-1284.

Statistics 69

Statistics Machine Learning Modeling Metrics 69

Alation

APRIL 14, 2022

The vision of big data freed organizations to capture more data sources at lower levels of detail and in vastly greater volumes. For example, data science always consumes “historical” data, and there is no guarantee that the semantics of older datasets are the same, even if their names are unchanged.

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.

IT 98

IT Manufacturing IoT Cost-Benefit 98

Rocket-Powered Data Science

SEPTEMBER 20, 2021

The way that I explained it to my data science students years ago was like this. The semantic layer delivers data insights discovery and usability across the whole enterprise, with each business user empowered to use the terminology and tools that are specific to their role. That’s data democratization. That’s empowering.

Data Science 286

Data Science Forecasting Business Intelligence Sales 286

Rocket-Powered Data Science

OCTOBER 6, 2023

Which pricing strategies lead to the best business revenue? Now that we have described predictive and prescriptive analytics in detail, what is there left? That is not a totally clean separation and distinction, but it might help to clarify their different applications of data science. ” “Just 26.5%

Business Analytics 157

Business Analytics Prescriptive Analytics Analytics Diagnostic Analytics 157

Domino Data Lab

OCTOBER 23, 2019

Eighty percent of this problem is collecting the data and then transforming the data. The other 20 percent is ML- and data science–related tasks like finding the right model, doing EDA, and feature engineering. Gathering the Data. there is a list of data sources to extract and transform. In Figure 6.1,

Statistics 59

Statistics Machine Learning Testing Modeling 59

Smarten

MARCH 13, 2024

For a number of years, Gartner and other technology research and analysis firms have predicted and monitored the growth of this phenomenon. In fact, Gartner predicted that, ‘…40% of data science tasks will be automated, resulting in increased productivity and broader usage by citizen data scientists.’

Analytics 52

Analytics Predictive Modeling Strategy Enterprise 52

The Unofficial Google Data Science Blog

MARCH 31, 2016

These predictive posterior distributions have many uses such as in multi-armed bandit problems. Thompson sampling is an "explore/exploit" algorithm which was designed for Bayesian models, and its strategy is to randomly select a choice (called an "arm") with probability proportional to the posterior of that choice being the best option.

Modeling 40

Modeling Statistics Advertising Testing 40

Domino Data Lab

AUGUST 20, 2019

So, if we need to dump some data, how do we do it? There are three major strategies: manual techniques based on our knowledge of the problem and the data, random sampling techniques that use coin flipping to keep or discard data, and model-based techniques that attempt to keep features that interact well with our learning model.

Testing 68

Testing Modeling Interactive Measurement 68

datapine

JANUARY 31, 2022

Although the oil company has been producing massive amounts of data for a long time, with the rise of new cloud-based technologies and data becoming more and more relevant in business contexts, they needed a way to manage their information at an enterprise level and keep up with the new skills in the data industry.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

datapine

NOVEMBER 27, 2019

In 2020, BI tools and strategies will become increasingly customized. It is not only important to gather as much information possible, but the quality and the context in which data is being used and interpreted serves as the main focus for the future of business intelligence. Source: Business Application Research Center *.

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

Depict Data Studio

SEPTEMBER 17, 2019

Chris Lysy is a professional data designer and illustrator constantly exploring the overlap between contemporary design practice, digital communications, and data science. When not developing creative communication strategies for researchers, David is also one half of the team behind? Chris Lysy. Kylie Hutchinson.

Visualization 40

Visualization Consulting Dashboards Reporting 40

datapine

DECEMBER 4, 2019

Moreover, as most predictive analytics capabilities available today are in their infancy — they have simply not been used for long enough by enough companies on enough sources of data – so the material to build predictive models on was quite scarce. Last but not least, there is the human factor again.

Business Intelligence 212

Business Intelligence Prescriptive Analytics Analytics Predictive Analytics 212