Birst BI

OCTOBER 30, 2018

A personal crystal ball that predicts your days ahead is what financial services firms everywhere want. Every day, these companies pose questions such as: Will this new client provide a good return on investment, relative to the potential risk? Is this existing client a termination risk? Will this next trade return a profit?

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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.

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Smarten

MAY 13, 2019

While one may think of fraud most commonly associated with financial and banking organizations or IT functions or networks, industries like healthcare, government and public sector are also at risk. Businesses that are proactive in identifying these risks can better optimize resources and respond to changing trends and patterns.

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Smarten

MAY 17, 2019

These are just some of the examples of use cases that effectively illustrate how your business can benefit from predictive analytics in real-world scenarios. The benefits of advanced analytics and assisted predictive modeling are too numerous to provide a complete list here. Loan Approval. Marketing Optimization.

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Smarten

MAY 16, 2019

In the interim, there is loss of productivity and the risk of crucial mistakes. Advanced analytics can help you to identify areas of dissatisfaction and understand the activities, processes, benefits, training and the work environment that encourages productivity and ensures employee satisfaction. Learn More: Human Resource Attrition.

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Smarten

SEPTEMBER 28, 2022

Apply Predictive Analytics to Specific Business Use Cases for Real Results! Gartner has predicted that, ‘Overall analytics adoption will increase from 35% to 50%, driven by vertical and domain-specific augmented analytics solutions.’ Predictive Analytics Using External Data. Quality Control.

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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.

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Smarten

MAY 15, 2019

With predictive analytics, the business can leverage data from various systems and software to take the guesswork out of production equipment maintenance and anticipate routine maintenance. The enterprise does not want to risk its reputation with unanticipated downtime or the loss of revenue for its customers. Loan Approval.

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Smarten

SEPTEMBER 14, 2018

Just Simple, Assisted Predictive Modeling for Every Business User! You can’t get a business loan, join with a business partner, successfully bid on a project, open a new location, hire the right employees or plan for the future without predictive analytics. No Guesswork!

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datapine

DECEMBER 4, 2019

Predictive & Prescriptive Analytics. Predictive Analytics: What could happen? 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. Approaches need to take this dynamic nature into mind.

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Smart Data Collective

MARCH 3, 2021

But if they use predictive analytics, they can determine how much each case pays out, considering factors as the number of previous cases filled with the same judge. When the risk of recurrence for a malpractice case is high, for example, they can convince the judge to be more generous in rewarding the client.

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CIO Business Intelligence

JUNE 7, 2022

Diagnostic analytics uses data (often generated via descriptive analytics) to discover the factors or reasons for past performance. Predictive analytics applies techniques such as statistical modeling, forecasting, and machine learning to the output of descriptive and diagnostic analytics to make predictions about future outcomes.

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Smarten

APRIL 17, 2018

How Can Assistive Predictive Modeling Help My Business Users? If you are wondering how and why predictive analytics software has expanded into the self-serve business user market, the reason is simple. Predictive Analytics Software should be easy to implement, easy to personalize and easy to use.

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datapine

MARCH 25, 2022

There is not a clear line between business intelligence and analytics, but they are extremely connected and interlaced in their approach towards resolving business issues, providing insights on past and present data, and defining future decisions. A fundamental differentiation factor is in the method each of them uses as a base.

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Smarten

MAY 16, 2019

Perhaps the greatest risk to a lending organization is that presented by loan applicants who are unprepared to fulfill the long-term obligation of paying off a loan. Predictive analytics can be a crucial piece of the puzzle in supporting the loan approval process and monitoring and managing loans throughout the life cycle of the contract.

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Smarten

JULY 30, 2018

How Can Predictive Analysis Tools Help My Hospital or Healthcare Organization? Hospitals and healthcare systems are turning to predictive analytics tools to plan and forecast and understand what, when and how to support patients.

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DataRobot

JANUARY 20, 2022

. — Snowflake and DataRobot AI Cloud Platform is built around the need to enable secure and efficient data sharing, the integration of disparate data sources, and the enablement of intuitive operational and clinical predictive analytics. Building data communities. . – Public sector data sharing. Grasping the digital opportunity.

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DataRobot Blog

MAY 25, 2022

The DataRobot AI Cloud Platform can also help identify infrastructure and buildings at risk of damage from natural disasters. DataRobot’s Explainable AI features like Feature Impact inform the user that the satellite imagery is the most important factor in determining damaged homes for the top-performing model. Learn more.

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Cloudera

JULY 18, 2018

Out-of-the-box advance analytics capabilities to eliminate 50-60% of costly ETL, data integration, visualization, and implementation. . Actionable healthcare analytics that allows organizations to conduct real-time “what if scenarios” against predictive models.

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Rocket-Powered Data Science

OCTOBER 6, 2023

Using the same statistical terminology, the conditional probability P(Y|X) (the probability of Y occurring, given the presence of precondition X) is an expression of predictive analytics. By exploring and analyzing the business data, analysts and data scientists can search for and uncover such predictive relationships.

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Smarten

NOVEMBER 19, 2018

The process of predictive analytics has come far in the past decade. Today’s self-serve predictive analytics and forecasting tools are designed to support business users and data analysts alike. What is Predictive Analytics? Can Predictive Analytics Help You Achieve Business Objectives?

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CIO Business Intelligence

MARCH 19, 2025

If sustainability-related data projects fail to demonstrate a clear financial impact, they risk being deprioritized in favor of more immediate business concerns. Insufficient resource allocation for ESG data initiatives Managing sustainability data requires robust governance, analytics capabilities and cross-functional collaboration.

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David Menninger's Analyst Perspectives

NOVEMBER 7, 2024

A major practical benefit of using AI is putting predictive analytics within easy reach of any organization. Predictive analytics applies machine learning to statistical modeling and historical data to make predictions about future outcomes.

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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.

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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. pharmacogenomics) and risk assessment of genetic disorders (e.g., Machine Learning and Predictive Modeling of Customer Churn.

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BizAcuity

APRIL 1, 2023

Anti-Money Laundering (AML) is increasingly becoming a crucial branch of risk management and fraud prevention. In fact, online casinos as an industry carries the biggest risk of money laundering. There are primarily two underlying techniques that can be leveraged for AML initiatives- Exploratory Data Analysis and Predictive analytics.

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BizAcuity

JANUARY 18, 2023

Anti-Money Laundering (AML) is increasingly becoming a crucial branch of risk management and fraud prevention. In fact, online casinos as an industry carries the biggest risk of money laundering. There are primarily two underlying techniques that can be leveraged for AML initiatives- Exploratory Data Analysis and Predictive analytics.

Machine Learning 52

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Sisense

MARCH 27, 2019

It also lets companies provide users with the data they need to complete their jobs more effectively, and even assists in predictive analytics. Finally, real-time BI helps better understand trends and create more accurate predictive models for organizations. Why is Real-Time BI Crucial for Organizations?

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Smarten

JUNE 8, 2022

What follows is a short list of sample use cases that leverage predictive analytics. These examples will help the reader to better understand how business users can leverage augmented analytics to perform tasks, refine results and make fact-based decisions on a daily basis.

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Cloudera

SEPTEMBER 7, 2023

But it’s also fraught with risk. This June, for example, the European Union (EU) passed the world’s first regulatory framework for AI, the AI Act , which categorizes AI applications into “banned practices,” “high-risk systems,” and “other AI systems,” with stringent assessment requirements for “high-risk” AI systems.

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IBM Big Data Hub

JUNE 29, 2023

This information is then used to build predictive models of an asset’s performance over time and help spot potential problems before they arise. One of the ways maintenance managers refine and improve predictive analytics to increase asset reliability is through the creation of a digital twin.

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Smarten

JANUARY 31, 2019

Put simply, business Intelligence uses historical data to reveal where the business has been, and managers can use this data to predict competitive response and discover what is changing in customer buying behavior and in sales.

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Smarten

JANUARY 27, 2023

Predictive Analytics – Your business users should not need to seek the advice of a data scientist to forecast and predict. True democratization should put the power in the hands of the users, help the organization to avoid delays while waiting for data and improve productivity for users and for IT.

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Smarten

APRIL 2, 2019

Smart Data Visualization allows users to view and analyze data to identify a problem and clarify a root cause and to interact easily with data discovery tools and analytics software to build a view that will tell a story using guided visualization and recommended data presentation so there is no need for assistance or delays.

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IBM Big Data Hub

DECEMBER 20, 2023

For instance, if data scientists were building a model for tornado forecasting, the input variables might include date, location, temperature, wind flow patterns and more, and the output would be the actual tornado activity recorded for those days. temperature, salary).

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Smarten

JUNE 28, 2018

Business Problem: A bank wants to group loan applicants into high/medium/low risk based on attributes such as loan amount, monthly installments, employment tenure, the number of times the applicant has been delinquent in other payments, annual income, debt to income ratio etc. Use Case – 1. Use Case – 2.

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Smarten

JUNE 27, 2018

For example, if an outlier indicates a risk or a mistake, that outlier should be identified and the risk or mistake should be addressed. All of these tools are designed for business users with average skills and require no special skills or knowledge of statistical analysis or support from IT or data scientists.

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