IBM Big Data Hub

MAY 6, 2024

In this chapter of the IBM series on clinical trial innovation, we spotlight key strategies for boosting recruitment speed, helping to ensure diversity, and harnessing digital advancements. It’s crucial to evaluate the feasibility of trials and refine protocols using evidence-based strategies.

Advertising 57

Advertising Data-driven Optimization Strategy 57

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

Smarten

NOVEMBER 26, 2018

With the right predictive analytics tool, your business can hypothesize, test theories, discover the effects of a possible price increase, discover and address changing buying behavior and develop appropriate competitive strategies.

Predictive Analytics 58

Predictive Analytics Predictive Modeling Analytics Forecasting 58

Jedox

MAY 17, 2022

What Predictive Analytics Cannot Forecast. Predictive Analytics Example in Finance. A Brief History of Predictive Analytics. No industry has attempted to do more with predictive analytics than the financial services industry. What is Predictive Analytics? What Predictive Analytics Cannot Forecast.

Predictive Analytics 81

Predictive Analytics Finance Forecasting Analytics 81

IBM Big Data Hub

SEPTEMBER 19, 2023

How effectively and efficiently an organization can conduct data analytics is determined by its data strategy and data architecture , which allows an organization, its users and its applications to access different types of data regardless of where that data resides.

Data Science 88

Data Science Data Analytics Prescriptive Analytics Analytics 88

DataRobot

NOVEMBER 24, 2021

Whatever its requirements, applying data-driven AI strategies can help. But it had trouble predicting sellout volume at scale and automating the necessary modeling and forecasting. Now, its data scientists of any skill level can quickly build and deploy accurate, world-class predictive models in minutes.

Cost-Benefit 98

Cost-Benefit ROI Data-driven Predictive Modeling 98

Smarten

JUNE 8, 2022

If a business wishes to optimize inventory, production and supply, it must have a comprehensive demand planning process; one that can forecast for customer segment growth, seasonality, planned product discounting or sales, bundling of products, etc.

Predictive Analytics 52

Predictive Analytics Predictive Modeling Forecasting Optimization 52

DataRobot

JUNE 4, 2021

Although the DataRobot platform makes leading edge AI technology available to customers, often the most bang-for-the-buck strategy for improving model accuracy is not necessarily to focus on the shiny new algorithm. Forecasting In-Store Foot Traffic. Here, we have a model that predicts foot traffic for a chain of coffee shops.

Forecasting 52

Forecasting Machine Learning Predictive Modeling Modeling 52

datapine

AUGUST 14, 2019

Companies surely need data scientists to help them empower their analytics processes, build a numbers-based strategy that will boost their bottom line, and ensure that enormous amounts of data are translated into actionable insights. connecting data sources and predicting future outcomes. perfect for statistical computing and design.

Data Science 214

Data Science Statistics Business Intelligence Visualization 214

Smarten

MAY 13, 2019

Self-serve, assisted predictive modeling and predictive analytics can help you to identify the customers who are most likely to leave and allow you to develop processes and strategies, as well as new marketing, new products and services, and other strategies that will improve customer retention and reduce customer churn.

Predictive Analytics 51

Predictive Analytics Analytics Predictive Modeling Advertising 51

Sisense

MARCH 24, 2020

Predict: Lastly, look to forecast trends in supply and demand and track fast-moving changes in leading indicators. To foster the art of the possible, below are examples of how regular businesses use analytics to maximize customer revenue, reduce costs, forecast outcomes, and drive efficiency. Insights over instinct.

Analytics 94

Analytics Sales Forecasting Business Intelligence 94

DataRobot Blog

AUGUST 10, 2022

NHS organisations are used to using workforce KPIs to manage staffing levels, but the real opportunity is being able to identify staff that are at risk of leaving post and to implement strategies to retain their much needed talent. Enabling population health management and reducing health inequalities.

Forecasting 52

Forecasting Experimentation Data-driven Risk 52

Smarten

JUNE 10, 2022

Predictive Modeling to support business needs, forecast, and test theories. Assisted Predictive Modeling. Users can apply predictive analytics to any use case using forecasting, regression, classification, clustering and other algorithms. Business Intelligence. Dashboards.

Slice and Dice 52

Slice and Dice Analytics Key Performance Indicator Business Intelligence 52

Sisense

SEPTEMBER 9, 2019

We fed Kraken (BigSquid’s predictive analytics engine) information about historical warranty costs, claims, forecasts, historical product attributes, and attributes of the new products on the roadmap. Then we ran Kraken’s machine learning and predictive modeling engine to get the results. Lessons Learned. Be patient!

Predictive Analytics 75

Predictive Analytics Predictive Modeling Forecasting Data-driven 75

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 92

Cost-Benefit Insurance Unstructured Data Machine Learning 92

FineReport

APRIL 9, 2024

The implementation of robust healthcare data management strategies is imperative to mitigate the risks associated with data breaches and non-compliance. The integration of clinical data analysis tools empowers healthcare providers to leverage predictive analytics for proactive decision-making.

Digital Transformation 52

Digital Transformation Business Intelligence Data-driven Visualization 52

Smarten

JUNE 20, 2018

The business can use this information for forecasting and planning, and to test theories and strategies. For example, the decision to the ARIMA or Holt-Winter time series forecasting method for a particular dataset will depend on the trends and patterns within that dataset. Linear Trend.

Forecasting 40

Forecasting Statistics Business Intelligence Predictive Modeling 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

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”). Source: [link]

Analytics 166

Analytics Machine Learning Data Science Deep Learning 166

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. A sound ALM strategy ensures compliance no matter where data is being stored. What is asset lifecycle management (ALM)?

Strategy 61

Strategy Management IoT Cost-Benefit 61

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

Jet Global

JUNE 20, 2023

As a product manager, you need to use all the tools at your disposal to add value to their strategies and goals. Some of the reports a product manager is expected to produce—and deliver with short turnaround times—are accurate sales forecasts and predictive models outlining customer needs.

Management 52

Management Forecasting Risk Sales 52

DataRobot

NOVEMBER 15, 2021

DataRobot and Palantir have partnered to create a custom framework that will empower retailers and manufacturing companies to take on a more nimble strategy to demand forecasting, eliminating the time and resources spent on manual data cleansing and one-off manual modeling. Find out more. Learn more.

Manufacturing 52

Manufacturing Forecasting Machine Learning Data-driven 52

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. Usage in a business context. BI and BA Use-Case Scenarios?

Business Intelligence 264

Business Intelligence Analytics Statistics Dashboards 264

Cloudera

SEPTEMBER 7, 2023

proprietary data, business strategies, methodologies, etc. In this blog series, well dive into the advantages of Trusted AI and the broader ramifications of AI adoption, exploring how financial institutions can bootstrap and evolve their AI strategies, from initial steps to what mature AI adoption looks like. Track market trends.

Insurance 78

Insurance Risk Data-driven Finance 78

IBM Big Data Hub

DECEMBER 20, 2023

Supervised machine learning Supervised machine learning is a type of machine learning where the model is trained on a labeled dataset (i.e., Supervised learning is commonly used for risk assessment, image recognition, predictive analytics and fraud detection, and comprises several types of algorithms.

Machine Learning 86

Machine Learning Modeling Deep Learning Predictive Modeling 86

FineReport

APRIL 24, 2024

This understanding empowers them to make well-informed choices based on a comprehensive comprehension of the presented information, ultimately leading to more effective strategies and outcomes. This foresight empowers organizations to proactively prepare for upcoming shifts or developments based on credible analytical forecasts.

Visualization 52

Visualization Dashboards Interactive Key Performance Indicator 52

Smarten

DECEMBER 5, 2022

Gartner predicts that, ‘90% of corporate strategies will explicitly mention information as a critical enterprise asset and analytics as an essential competency.’. ARIMAX Forecasting. ARIMA Forecasting. Holt-Winters Forecasting. Naïve Bayes. Multiple Linear Regression. Paired Sample T Test. Hierarchical Clustering.

Predictive Analytics 52

Predictive Analytics Predictive Modeling Forecasting Testing 52

Rocket-Powered Data Science

SEPTEMBER 20, 2021

The BI (business intelligence) analysts need to find the right data for their visualization packages, business questions, and decision support tools — they also need the outputs from the data scientists’ models, such as forecasts, alerts, classifications, and more.

Data Science 285

Data Science Forecasting Business Intelligence Sales 285

Rocket-Powered Data Science

OCTOBER 6, 2023

Decades (at least) of business analytics writings have focused on the power, perspicacity, value, and validity in deploying predictive and prescriptive analytics for business forecasting and optimization, respectively. Which pricing strategies lead to the best business revenue? ” “Just 26.5% ” “91.9%

Business Analytics 157

Business Analytics Prescriptive Analytics Analytics Diagnostic Analytics 157

DataRobot Blog

JUNE 24, 2022

In addition, traditional analytical approaches are not flexible enough in generating alternative scenarios that allow investment analysts to test market shifts or to compare predictive-model outputs to the forecasts of traditional sources of market information. Forecasting the Real Estate Market Using DataRobot.

Forecasting 52

Forecasting Machine Learning Marketing Data-driven 52

datapine

NOVEMBER 27, 2019

In 2020, BI tools and strategies will become increasingly customized. Accordingly, the rise of master data management is becoming a key priority in the business intelligence strategy of a company. Predictive analytics is the practice of extracting information from existing data sets in order to forecast future probabilities.

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

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

datapine

DECEMBER 4, 2019

We mentioned predictive analytics in our business intelligence trends article and we will stress it here as well since we find it extremely important for 2020. Predictive analytics is the practice of extracting information from existing data sets in order to forecast future probabilities.

Business Intelligence 212

Business Intelligence Prescriptive Analytics Analytics Predictive Analytics 212

Smarten

MAY 30, 2023

When a business plans to launch an augmented analytics or business intelligence solution, it must carefully plan for user adoption – especially if the launch of this solution is part of a larger strategy for self-serve business user analytics and the Citizen Data Scientist approach to data use across the enterprise.

Slice and Dice 52

Slice and Dice Business Intelligence Cost-Benefit Dashboards 52

Data Science and Beyond

JANUARY 8, 2017

However, it’s important to remember that all models are wrong (but some models are useful) , and that the lifetime value of active customers is unknowable since it involves forecasting of uncertain quantities. I’ve successfully used the package on a real dataset and have contributed some small fixes and improvements.

Statistics 100

Statistics Modeling Predictive Modeling Marketing 100

Jet Global

JULY 17, 2023

How to Handle Missing Data Values Data teams can use a number of strategies to handle missing data. The first common strategy for dealing with missing data is to delete the rows with missing values. Finally, you can use classification or regression models to predict missing values.

Predictive Analytics 52

Predictive Analytics Reporting Software Strategy 52