O'Reilly on Data

DECEMBER 12, 2019

If a model is going to be used on all kinds of people, it’s best to ensure the training data has a representative distribution of all kinds of people as well. Interpretable ML models and explainable ML. The debugging techniques we propose should work on almost any kind of ML-based predictive model.

Machine Learning 282

Machine Learning Modeling Testing Risk Management 282

CIO Business Intelligence

SEPTEMBER 24, 2024

AI-powered optimisation algorithms can dynamically adjust resource levels by leveraging usage patterns and performance metrics to provide computing power when it’s needed and scale it back when demand is low. PwC AI-powered predictive models are essential to forecasting peak usage and scaling resources.

Predictive Modeling 115

Predictive Modeling Machine Learning Forecasting Data-driven 115

Rocket-Powered Data Science

JULY 6, 2021

Customer purchase patterns, supply chain, inventory, and logistics represent just a few domains where we see new and emergent behaviors, responses, and outcomes represented in our data and in our predictive models. And the goodness doesn’t stop there.

Machine Learning 279

Machine Learning Data-driven Deep Learning IoT 279

Occam's Razor

NOVEMBER 18, 2014

Short story #2: Predictive Modeling, Quantifying Cost of Inaction. You can hover over each box to get a sense of the key metrics. Short story #2: Predictive Modeling, Quantifying Cost of Inaction. Thank goodness for predictive models. Short story #1: Treemaps, Sunbursts, Packed Trees, Oh My!

Visualization 145

Visualization Slice and Dice Predictive Modeling Metrics 145

CIO Business Intelligence

JULY 5, 2022

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 also involves data mining, statistical analysis, predictive modeling, and the like, but is focused on driving better business decisions.

Business Analytics 136

Business Analytics Prescriptive Analytics Data mining Diagnostic Analytics 136

Juice Analytics

APRIL 21, 2021

There are many choices: Dashboards Reports Self-service BI tools Predictive models One-off analyses using slides Spreadsheet models It is a confusing array of ways to deliver data to these data consumers. How much will the raw data be enhanced with analysis, modeling, and pre-digested insights?

Advertising 133

Advertising Data-driven Dashboards Predictive Modeling 133

Smart Data Collective

SEPTEMBER 17, 2020

Big Data can efficiently enhance the ways firms utilize predictive models in the risk management discipline. It can come in handy when tracking, analyzing, and sharing metrics connected with employee performance. It improves the response timeline in the system and consequently boosts efficiency. Client Data Accessibility.

Big Data 145

Big Data Finance Risk Management Risk 145

Smart Data Collective

APRIL 10, 2020

You can even develop predictive models for identifying how many returns are likely to occur in different areas, optimizing logistics and reducing shipping costs that would otherwise be impossible to determine in advance.

Big Data 145

Big Data Cost-Benefit Metrics Marketing 145

Juice Analytics

JULY 6, 2018

Here are 25 more lessons we've learned (the hard way) about what's easy and what's hard when it comes to telling data stories: Easy: Picking a good visualization to answer a data question Hard: Discovering the core message of your data story that will move your audience to action Easy: Knowing who is your target audience Hard: Knowing what motivates (..)

Slice and Dice 90

Slice and Dice Predictive Modeling Visualization Data-driven 90

BizAcuity

NOVEMBER 13, 2024

This approach involves everything from identifying key metrics to implementing analytics systems and designing dashboards. Advanced Analytics and Predictive Insights The real value of data lies in its ability to forecast trends and identify opportunities.

Consulting 52

Consulting Data-driven Cost-Benefit Business Intelligence 52

DataKitchen

JUNE 19, 2023

It involves tracking key metrics such as system health indicators, performance measures, and error rates and closely scrutinizing system logs to identify anomalies or errors. The above image shows an example custom ‘data in use’ test of a predictive model and API. Donkey: Oh, they have layers.

Testing 100

Testing Data Quality Metrics Cost-Benefit 100

Juice Analytics

MARCH 11, 2021

3) That’s where our data visualization and user experience capabilities helped them turn this data into a web-based analytical tool that focused users on the metrics and peer groups they cared about. Predictive models to take descriptive data and attempt to tell the future.

Predictive Modeling 128

Predictive Modeling Visualization Reporting Interactive 128

Birst BI

OCTOBER 30, 2018

Descriptive analytics techniques are often used to summarize important business metrics such as account balance growth, average claim amount and year-over-year trade volumes. The credit scores generated by the predictive model are then used to approve or deny credit cards or loans to customers. Accounts in use.

Predictive Analytics 73

Predictive Analytics Descriptive Analytics Analytics Metrics 73

DataKitchen

JANUARY 17, 2022

The business analysts creating analytics use the process hub to calculate metrics, segment/filter lists, perform predictive modeling, “what if” analysis and other experimentation. Despite the complexity, mission-critical analytics must be delivered error-free under intense deadline pressure. Requirements continually change.

Experimentation 130

Experimentation Data Lake Predictive Modeling Marketing 130

CIO Business Intelligence

DECEMBER 19, 2024

While crucial, if organizations are only monitoring environmental metrics, they are missing critical pieces of a comprehensive environmental, social, and governance (ESG) program and are unable to fully understand their impacts. of survey respondents) and circular economy implementations (40.2%).

IT 52

IT IoT Machine Learning Optimization 52

Business Over Broadway

SEPTEMBER 27, 2018

Analytics in these types of projects may be less valuable due to lack of generalizability (to the other customers) and poor models (e.g., underspecified) due to omitted metrics. Machine Learning and Predictive Modeling of Customer Churn. segmentation on steroids).

Machine Learning 76

Machine Learning Data Integration Marketing Predictive Modeling 76

BizAcuity

MARCH 8, 2019

Through workforce analytics, companies can get a comprehensive view of their employees designed to interpret historical trends and in creating predictive models that lead to insights and better decisions in the future. Derives metrics for benchmark interpretation and trends.

Analytics 73

Analytics Prescriptive Analytics Cost-Benefit Metrics 73

CIO Business Intelligence

MAY 26, 2022

Expectedly, advances in artificial intelligence (AI), machine learning (ML), and predictive modeling are giving enterprises – as well as small/medium-sized businesses – a never-before opportunity to automate their recruitment even as they deal with radical changes in workplace practices involving remote and hybrid work.

Advertising 128

Advertising Machine Learning Software Measurement 128

datapine

MARCH 25, 2022

While some experts try to underline that BA focuses, also, on predictive modeling and advanced statistics to evaluate what will happen in the future, BI is more focused on the present moment of data, making the decision based on current insights.

Business Intelligence 264

Business Intelligence Analytics Statistics Dashboards 264

The Unofficial Google Data Science Blog

DECEMBER 4, 2019

Over the life of the forecast, the data scientist will publish historical accuracy metrics. But due to the long time lag between forecasts and actuals, these metrics alone are insufficient. Every forecast update will include metrics to provide insight on change drivers, and will flag significant gaps between different model forecasts.

Forecasting 115

Forecasting Data Science Statistics Uncertainty 115

Smarten

NOVEMBER 22, 2019

Augmented analytics and tools like Smart Visualization and Self-Serve Data Preparation , as well as Assisted Predictive Modeling can provide guidance and auto-suggestions and recommendations to make users more comfortable in adopting analytics and achieving positive outcomes.

Predictive Modeling 69

Predictive Modeling Data-driven Forecasting Visualization 69

CIO Business Intelligence

MAY 28, 2022

Expectedly, advances in artificial intelligence (AI), machine learning (ML), and predictive modeling are giving enterprises – as well as small/medium-sized businesses – a never-before opportunity to automate their recruitment even as they deal with radical changes in workplace practices involving remote and hybrid work.

Advertising 119

Advertising Machine Learning Software Measurement 119

CIO Business Intelligence

MARCH 5, 2024

As copilot technology capabilities are changing rapidly, leaders should frequently identify metrics and evaluate strategies. DePaul’s Dumiak adds, “While in Excel, I can ask Microsoft Copilot to summarize tables and give me charts, and suddenly, it’s created pivot tables without ever having to learn the command generation sequence.”

Advertising 124

Advertising Cost-Benefit Experimentation Reporting 124

Smarten

APRIL 12, 2023

Smarten CEO, Kartik Patel says, ‘Smarten SnapShot supports the evolving role of Citizen Data Scientists with interactive tools that allow a business user to gather information, establish metrics and key performance indicators.’

Snapshot 52

Snapshot Key Performance Indicator KPI Business Intelligence 52

AWS Big Data

OCTOBER 9, 2024

Use functional queries to compare high-level aggregated business metrics between the source on-premises database and the target data lake. Functional data parity is the third step in the overall data validation framework, where you have the flexibility to continue similar business metrics validation driven by an aggregated SQL query.

Data Quality 111

Data Quality Data Lake Data Warehouse Metrics 111

Smarten

APRIL 19, 2024

Anomaly Alerts KPI monitoring and Auto Insights allows business users to quickly establish KPIs and target metrics and identify the Key Influencers and variables for the target KPI.

Analytics 59

Analytics Predictive Modeling KPI Machine Learning 59

Paul Blogs on BI

MARCH 25, 2020

Firstly, let’s talk about the data and the metrics being used to track the pandemic. The three main metrics being tracked in this pandemic are: Confirmed Cases. As more testing becomes available this first metric will increase significantly. Total Deaths. Total recovered.

Business Intelligence 51

Business Intelligence Measurement Metrics Cost-Benefit 51

AWS Big Data

AUGUST 21, 2024

Monitor the ComputeCapacity metric under AWS/Redshift-Serverless and Workgroup in Amazon CloudWatch. The goal is to provide the best price-performance balance based on your choices. Monitoring You can monitor the RPU scaling in the following ways: Review the RPU capacity used graph on the Amazon Redshift console.

Optimization 104

Optimization Data Lake Data Warehouse Cost-Benefit 104

Sisense

AUGUST 11, 2019

Typically, this involves using statistical analysis and predictive modeling to establish trends, figuring out why things are happening, and making an educated guess about how things will pan out in the future. BA primarily predicts what will happen in the future. What About “Business Intelligence”?

Business Intelligence 75

Business Intelligence Business Analytics Analytics OLAP 75

DataRobot

NOVEMBER 29, 2021

As with model accuracy, there are many metrics one can use to measure bias. These metrics can be grouped into two categories: bias by representation and bias by error. Bias by representation examines if the outcomes predicted by the model vary for protected features. Watch webinar with DataCamp on Responsible AI.

Technology 52

Technology Metrics Predictive Modeling Modeling 52

Sisense

APRIL 10, 2020

Using XG-Boost to model the text data resulted in an almost identical score for Python and R. There are many performance metrics to evaluate performance of Machine Learning models. This metric can be used in classification analyses to identify a model’s ability to predict a desired attribute, based on the training data.

Deep Learning 98

Deep Learning Data Science Machine Learning Visualization 98

DataRobot Blog

DECEMBER 15, 2022

While AI-powered forecasting can help retailers implement sales and demand forecasting—this process is very complex, and even highly data-driven companies face key challenges: Scale: Thousands of item combinations make it difficult to manually build predictive models. A variety of models are been trained in parallel.

Forecasting 81

Forecasting Modeling ROI Machine Learning 81

Smarten

FEBRUARY 17, 2022

If a user is presenting a recommendation or suggestion in a staff meeting, the organization must be willing to support the idea that the user must base his/her recommendation on data acquired from the augmented analytics solution, and require that reporting and suggestions are based on facts and metrics rather than opinion and guesswork.

Predictive Modeling 78

Predictive Modeling Digital Transformation Data Science Enterprise 78

MLWhiz

NOVEMBER 30, 2014

In online advertising, click-through rate (CTR) is a very important metric for evaluating ad performance. 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.

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

Domino Data Lab

AUGUST 22, 2019

GloVe and word2vec differ in their underlying methodology: word2vec uses predictive models, while GloVe is count based. then the model is predicting that the input x belongs to one class, whereas if it outputs anything less than 0.5, Natural Language Processing.] At the time—in 2014—the three were colleagues working.

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

IBM Big Data Hub

JUNE 29, 2023

In order to take a proactive approach to asset reliability, maintenance managers rely on two widely used metrics: mean time between failure, (MTBF) and mean time to repair (MTTR). Both KPIs help predict how assets will perform and assist managers in planning preventive and predictive maintenance. How does asset reliability work?

Cost-Benefit 80

Cost-Benefit Measurement Predictive Analytics Management 80