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Exam Code: 00M-229 Practice test 2022 by team
IBM SPSS Predictive Analytics Sales Mastery v1
IBM Predictive plan
Killexams : IBM Predictive plan - BingNews Search results Killexams : IBM Predictive plan - BingNews Killexams : RemSense Technologies marries virtualplant system with IBM’s asset management portal

RemSense Technologies Ltd (ASX:REM) has reached a new development milestone after integrating its virtualplant asset visualisation system with partner IBM’s Maximo Application Suite (MAS).

The technology company says the two-way integration allows users to access RemSense’s industry-leading digital twin solution alongside IBM’s asset management system.

Ultimately, this makes simultaneous asset management more efficient — users can rapidly comprehend assets from the MAS portal within RemSense’s visually accurate virtual environment.

Additionally, users can monitor MAS plant asset data directly within virtualplant’s photorealistic environment.

This provides valuable background and insight as companies supervise their assets remotely, helping them engage in predictive maintenance when it counts.

Where to from here?

It won’t be long until industry gets to see the integration in action. RemSense and IBM plan to make their debut at the upcoming WA Mining Conference and Exhibition in Perth.

The maiden demonstration will take place at IBM’s booth (#8132) this week on October 12 and 13.

The companies are also working with several prospective adopters in Australia’s mining capital.

‘One click’ access

RemSense managing director and CEO Steve Brown said the IBM integration allowed users from either side to get the best of both worlds.

“The benefits of this integration will enable virtualplant and MAS users to fully benefit from the visualisation of assets through a ‘one click’ access for companies and contractors, from anywhere at any time,” he explained.

“We are also delighted to be working with IBM to launch our joint corrosion inspection and reporting function based on virtualplant’s high-resolution curated dataset and IBM’s extensive experience in visual analytics.”

IBM ANZ’s business unit executive for sustainability software, David Small, said the company was really excited about the integration’s evolution.

“The visualisation of assets will provide immense value to our clients and creates a unique experience to navigate and analyse data in a human-centric environment,” he said.

Mon, 10 Oct 2022 11:20:00 -0500 en text/html
Killexams : Top Predictive Analytics Solutions

Understanding complex events in today’s business world is critical. The explosion of data analytics—and the desire for insights and knowledge—represents both an opportunity and a challenge.

At the center of this effort is predictive analytics. The ability to transform raw data into insights and make informed business decisions is crucial. Today, predictive analytics plays a role in almost every corner of the enterprise, from finance and marketing to operations and cybersecurity. It involves pulling data from legacy databases, data lakes, clouds, social media sites, point of sale terminals and IoT on the edge.

It’s critical to select a predictive analytics platform that generates actionable information. For example, financial institutions use predictive analytics to understand loan and credit card applications, and even grant a line of credit on the spot. Operations departments use predictive analytics to understand maintenance and repairs for equipment and vehicles, and marketing and sales use it to gauge interest in a new product.

Top predictive analytics platforms deliver powerful tools for ingesting and exporting data, processing it and delivering reports and visualizations that guide enterprise decision-making. They also support more advanced capabilities, such as machine learning (ML), deep learning (DL), artificial intelligence (AI) and even digital twins. Many solutions also provide robust tools for sharing visualizations and reports. 

Types of Predictive Models

Predictive models are designed to deliver insights that guide enterprise decision-making. Solutions incorporate techniques such as data mining, statistical analysis, machine learning and AI. They are typically used for optimizing marketing, sales and operations; improving profits and reducing costs; and reducing risks related to things like security and climate change.

Also see: Best Data Analytics Tools 

Four major types of predictive models exist:

Regression models

A regression model estimates the relationship between different variables to deliver information and insight about future scenarios and impacts. It is sometimes referred to as “what if” analysis.

For example, a food manufacturer might study how different ingredients impact quality and sales. A clothing manufacturer might analyze how different colors increase or decrease the likelihood of a purchase. Models can incorporate correlations (relationships) and causality (reasons).

Classification models

These predictive models place data and information in categories based on past histories and historical knowledge. The data is labeled, and an algorithm learns the correlations. The model can then be updated with new data, as it arrives. These models are commonly used for fraud detection and to identify cybersecurity attacks.

Clustering models

A cluster model assembles data into groups, based on common attributes and characteristics. It often spots hidden patterns in systems. In a factory, this might mean spotting misplaced supplies and equipment and then using the data to predict where it will be during a typical workday. In retail, a store might send out marketing materials to a specific group, based on a combination of factors such as income, occupation and distance.

Time-Series models

As the name implies, a time-series model looks at data during a given period, such as a day, month or year. Using predictive analytics, it’s possible to estimate what the trend will be in an upcoming period. It can be combined with other methods to understand underlying factors. For instance, a healthcare system might predict when the flu will peak—based on past and current time-series models.

In more advanced scenarios, predictive analytics also uses deep learning techniques that mimic the human brain through artificial neural networks. These methods may incorporate video, audio, text and other forms of unstructured data. For instance, voice recognition or facial recognition might analyze the tone or expression a person displays, and a system can then respond accordingly.

Also see: Top Business Intelligence Software 

How to Select a Predictive Analytics Platform

All major predictive analytics platforms are capable of producing valuable insights. It’s important to conduct a thorough internal review and understand what platform or platforms are the best fit for an enterprise. All predictive analytics solutions generate reports, charts, graphics and dashboards. The data must also be embedded into automation processes that are driven by other enterprise applications, such as an ERP or CRM system.

Step 1: Understand your needs and requirements

An internal evaluation should include the types of predictive analytics you need and what you want to do with the data. It should also include what type of user—business analyst or data scientist—will use the platform. This typically requires a detailed discussion with different business and technical groups to determine what types of analytics, models, insights and automations are needed and how they will be used.

Step 2: Survey the vendor landscape

The capabilities of today’s predictive analytics platforms is impressive—and companies add features and capabilities all the time. It’s critical to review your requirements and find an excellent match. This includes more than simply extracting value from your data. It’s important to review a vendor’s roadmap, its commitment to updates and security, and what quality assurance standards it has in place. Other factors include mobile support and scalability, Internet of Things (IoT) functionality, APIs to connect data with partners and others in a supply chain, and training requirements to get everyone up to speed.

Step 3: Choose a platform

Critical factors for vendor selection include support for required data formats, strong data import and export capabilities, cleansing features, templates, workflows and embedded analytics capabilities. The latter is critical because predictive analytics data is typically used across applications, websites and companies.

A credit card check, for example, must pull data from a credit bureau but also internal and other partner systems. The APIs that run the task are critical. But there are other things to focus on, including the user interface (UI) and usability (UX). This extends to visual dashboards that staff uses to view data and understand events. It’s also vital to look at licensing costs and the level of support a vendor delivers. This might include online resources and communities as well as direct support.

Top Predictive Analytics Platforms

Here are 10 of the top predictive analytics solutions:

Google Looker

Key Insight: The drag-and-drop platform is adept at generating rich visualizations and excellent dashboards. It ranks high on flexibility, with support for almost any type of desired chart or graphics. It can generate valuable data for predictive analytics by connecting to numerous other data sources, including Microsoft SQL and Excel, Snowflake, SAP HANA, Salesforce and Amazon Redshift. It also includes powerful tools for selecting parameters, filtering data, building data-driven workflows and obtaining results. While the platform is part of Google Cloud and it is optimized for use within this environment, it supports custom applications.


  • The highly flexible platform runs on Windows, Macs and Linux. It offers strong mobile device support.
  • Generates strong data-driven workflows and supports custom applications.
  • Users deliver the platform High Score for its interface and ease of use.


  • Lacks some customization features found in other analytics solutions.

IBM SPSS Modeler

Key Insight: The predictive analytics platform is designed to put statistical data to work across a wide array of industries and use cases. It includes powerful data ingestion capabilities, ad hoc reporting, predictive analytics, hypothesis analysis, statistical and geospatial analysis and 30 base machine learning components. IBM SPSS Modeler includes a rich set of tools and features, accessible through sophisticated dashboards.


  • Offers excellent open-source extensibility through R and Python, along with support for numerous data sources, including flat files, spreadsheets, major relational databases, IBM Planning Analytics and Hadoop.
  • Visual drag-and-drop interface speeds tasks for data analysts and data scientists.
  • Works on all major operating systems, including Windows, Linux, Unix and Mac OS X.


  • Not as user friendly as other platform analytics platforms. Typically requires training to use it effectively.

Qlik Sense

Key Insight: Qlik Sense is a cloud-native platform designed specifically for business intelligence and predictive analytics. It delivers a robust set of features and capabilities, available through dashboards and visualizations. Qlik Sense includes AI and machine learning components; embedded analytics that can be used for websites, business applications and commercial software; and strong support for mobile devices. The solution supports hundreds of data types, and a broad array of analytics use cases.


  • Offers powerful drag-and-drop interface to enable fast modeling.
  • A “Smart Search” feature aids in uncovering and connecting complex data relationships.
  • Provides rich visualizations along with highly interactive and powerful dashboard features.


  • Can be pricy, particularly with additional add-ons.


Key Insight:  The widely used CRM and sales automation platform includes powerful analytics and business intelligence tools, including features driven by the company’s AI-focused Einstein Analytics. It delivers insights and suggestions through specialized AI agents. A centralized Salesforce dashboard offers charts, graphs and other insights, along with robust reporting capabilities. There’s also deep integration with the Tableau analytics platform, which is owned by Salesforce.


  • Powerful features and highly customizable views of data.
  • Integrates with more than 1,000 platforms. Strong data ingestion capabilities.
  • A generally intuitive UI and strong UX make the platform powerful yet relatively easy to use.


  • No trial or free version available.

SAP Analytics Cloud

Key Insight: Formerly known as BusinessObjects for cloud, SAP Analytics Cloud can pull data from a broad array of sources and platforms. It is adept at data discovery, compilation, ad-hoc reporting and predictive analytics. It includes machine learning and AI functions that can guide data analysis and aid in modeling and planning. A dashboard offers flexible options for displaying analytics data in numerous ways.


  • Accommodates large data sets from a diverse range of sources.
  • Offers highly customizable, interactive charts and other graphics.
  • Includes powerful ML and AI components.


  • Not as visually rich as other platforms.

SAS Visual Analytics

Key Insight: The low-code cloud solution is designed to serve as a “single application for reporting, data exploration and analytics.” It imports data from numerous sources; supports rich dashboards and visualizations, with strong drill-down features; includes augmented analytics and ML; and includes robust collaboration and data sharing features. The platform also includes natural language chatbots that aid business users and other non-data scientists in content creation and management.


  • Delivers fast performance, even with large data volumes.
  • Supports a wide array of predictive analytics use cases.
  • Includes smart algorithms that accommodate predictive analytics without the need for programming skills.


  • Expensive, particularly for smaller organizations.

Tableau Desktop

Key Insight: The enormous popularity of Tableau is based on the platform’s powerful features and its ability to generate a wide range of appealing and useful charts, graphs, maps and other visualizations through highly interactive real-time dashboards. The platform offers support for numerous predictive analytics frameworks, including regression models, classification models, clustering models, time-series models. Non data scientists typically find its user interface accommodating and easy-to-learn.


  • Excellent UI and UX. Most users find it easy to generate useful analytics models and visualizations.
  • Supports flexible, scalable and highly customizable predictive analytics use cases.
  • Tight integration with Salesforce CRM. Generates excellent visualizations.


  • Sometimes pulls considerable compute resources, including CPUs and RAM. It can also perform slowly on mobile devices.

Teradata Vantage

Key Insight: Teradata Vantage delivers powerful predictive analytics capabilities, including the ability to use data from both on-premises legacy hardware sources and multicloud public frameworks, including AWS, Azure and Google Cloud. The solution also works across virtually any data source, including data lakes and data warehouses. It supports sophisticated AI and ML functionality and includes no-code and low-code drag and drop components for building models and visuals. The company is especially known for its fraud prevention analytics tools, though it offers numerous other predictive tools and capabilities.


  • Highly flexible. Powerful SQL engine supports broad, deep and complex queries.
  • Accommodates huge workloads, almost any type of query, and delivers fast and efficient data processing.
  • Generates dynamic visualizations and an easy-to-use yet powerful interface.


  • Some users complain about a lack of documentation and training materials.

TIBCO Spotfire

Key Insight: TIBCO Spotfire offers a powerful platform for performing predictive analytics. It connects to numerous data sources and includes real-time feeds that can be highly filtered and customized. The solution is designed for both business users and data scientists. It includes rich visualizations and supports customizations through R and Python.


  • TIBCO offers a strong API management framework.
  • Delivers rich visualizations and immersive interactive visual analytics.
  • Strong support for real-time decision making across multiple industries and use cases.


  • Customizations can be time consuming and difficult.


Key Insight: The analytics cloud delivers highly flexible self-service predictive analytics. The query engine is designed to search on virtually any data format and understand complex table structures over billions of rows. It offers powerful search and filtering capabilities that extend to natural language queries. ThoughtSpot also provides a powerful processing engine that generates a range of visualizations, including social media intelligence. The solution includes a machine learning component that ranks the relevancy of results.


  • The platform gets High Score from users for its user interface and ease of use.
  • ThoughtSpot delivers a high level of flexibility, including an ability to search Snowflake, BigQuery and other cloud data warehouses in real time.
  • Natural language search reduces the need for complex SQL input.


  • Users complain that data source connectivity isn’t as robust as other analytics platforms.

Also see: Top Cloud Companies

Predictive Analytics Vendor Comparison Chart

Company Key Product Pros Cons
Google Looker Excellent drag-and-drop interface with appealing visualizations and a high level of flexibility Support is almost entirely online
IBM SPSS Modeler Excellent open-source extensibility; strong drag-and-drop functionality Not as user friendly as other analytics solutions
Qlik Qlik Sense Powerful and versatile platform with strong modeling features Some complaints about customer support
Salesforce Salesforce Highly customizable; strong integration with other platforms and data sources Expensive
SAP Analytics Cloud Supports extremely large datasets and has powerful capabilities Visualizations are not always as appealing as other platforms
SAS Visual Analytics High performance platform that supports numerous data types and visualizations May require customization
Tableau Tableau Desktop Outstanding UI and UX, with deep Salesforce/CRM integration Can drain CPUs and RAM
TIBCO Spotfire Powerful predictive analytics platform with excellent visual output Steep learning curve
Teradata Advantage Highly flexible with a powerful SQL engine; generates rich visualizations Some users complain about an aging UI.
ThoughtSpot ThoughtSpot Flexible, powerful capabilities with a strong UI and UX Visualizations sometimes lag behind competitors
Sun, 25 Sep 2022 06:33:00 -0500 en-US text/html
Killexams : Systecon to Continue Predictive Maintenance Tool Deployment Under Army Contract Modification

Systecon has received a contract modification from the U.S. Army to continue deploying an analytics tool that could enable the service to perform predictive maintenance and Strengthen operational readiness of its fleet of military vehicles and related platforms, GovCon Wire has learned.

The company said Friday it will support a brigade level-operational unit by fielding the predictive analytics software prototype developed through the Army’s Prognostics and Predictive Maintenance project.

In May, Systecon announced that it was selected by the Army to facilitate the deployment of the technology to provide updates on maintenance needs of the military branch’s vehicles and associated hardware.

During a one-year performance period, the company developed the prototype in a Microsoft Azure GovCloud environment using all data available to the Army Materiel Command Analysis Group.

The prototype uses artificial intelligence algorithms and conducts topological data analysis to present actionable information through a customized dashboard. The platform-agnostic algorithms use kinematic data, external condition variables and data derived from platform sensors to detect system failures and determine the equipment’s remaining useful life.

Systecon said the predictive analytics model built through the PPMx initiative recorded a training accuracy of 95 percent with an error rate of 3 percent when it comes to predicting fully mission-capable and nonmission-capable occurrences.

Mon, 10 Oct 2022 19:05:00 -0500 en-US text/html Killexams : Predictive Analytics Market 2022 Major Impacting Facts, Prominent Investment, Future Scenarios, Growth And Forecast 2026

"IBM (US), Microsoft (US), Oracle (US), SAP (Germany), SAS Institute (US), Google (US), Salesforce (US), AWS (US), HPE (US), Teradata (US), Alteryx (US), FICO (US), Altair (US), Domo (US), Cloudera (US), Board International (Switzerland), TIBCO Software (US), Hitachi Vantara (US), Qlik (US), Happiest Minds (India), Dataiku (US), RapidMiner (US), Biofourmis (US)."

Predictive Analytics Market by Solution (Financial Analytics, Risk Analytics, Marketing Analytics, Web & Social Media Analytics), Service, Deployment Mode, Organization Size, Vertical, and Region - Global Forecast to 2026

The global Predictive Analytics Market size to grow from USD 10.5 billion in 2021 to USD 28.1 billion by 2026, at a Compound Annual Growth Rate (CAGR) of 21.7% during the forecast period. Various factors such as increasing use of AI and ML and acquisitions and product launches in this market are expected to drive the adoption of Predictive Analytics software and services.

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The spread of COVID-19 has been disrupting the world, businesses, and economies and has impacted the way of living of the masses and approaches adopted by enterprises for their business management. The ability of enterprises to sustain this pandemic has become new normal for them as they shift their focus from growth opportunities and concentrate on implementing measures to mitigate the impact of COVID-19. The upcoming analytics projects are kept on hold owing to the pandemic. Several companies are competing with each other to gain a single project. Businesses have already started their efforts to return to normal and are facing multiple challenges in terms of the impact of the pandemic on their customer base and operations. Meeting customer expectations in terms of optimization of processes and increased security concerns due to the presence of various connected networks, rise in connectivity issues, and decline in industrial and manufacturing operations are the key challenges faced by businesses. New practices such as work from home and social distancing are creating the requirement of the remote health monitoring of infected patients, smart payment technologies, and the development of digital infrastructures for large-scale technology deployments. With an increased focus on health, there has been a rise in the demand for health-related wearable devices. For instance, in August 2020, Fitbit announced ~34% growth in its smartwatch sales in the second quarter of 2020.

The services segment to hold higher CAGR during the forecast period

The services segment is further divided into professional services and managed services. These services are an integral step in deploying Predictive Analytics solutions and are taken care of by solution and service providers. The demand for professional services is expected to rise due to a rise in tailored demands from customers. Customers are coming up with customization requirements in already installed predictive analytics solutions for enhancing overall performance. The customization requirements include enhanced real-time insights for precise alert systems, enhanced security measures, improved digital Customer Experience (CX), and the capability to extract the maximum potential from the rising enterprise data. The growing adoption of Predictive Analytics solutions is expected to boost the adoption of professional and managed services.

According to SAS Institute, predictive analytics is the use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data. The goal is to analyze current data to make predictions about future events.

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Some of the key players operating in the Predictive Analytics market include IBM (US), Microsoft (US), Oracle (US), SAP (Germany), SAS Institute (US), Google (US), Salesforce (US), AWS (US), HPE (US), Teradata (US), Alteryx (US), FICO (US), Altair (US), Domo (US), Cloudera (US), Board International (Switzerland), TIBCO Software (US), Hitachi Vantara (US), Qlik (US), Happiest Minds (India), Dataiku (US), RapidMiner (US), Biofourmis (US), In-med Prognostics (India), Aito.Ai (Finland), Symend (US), Onward Health (India), Unioncrate (US), CyberLabs (Brazil), Actify Data Labs (India), Amlgo Labs (India), and Verimos (US). These Predictive Analytics vendors have adopted various organic and inorganic strategies to sustain their positions and increase their market shares in the global Predictive Analytics market.

SAP is a leading provider of enterprise application solutions and services. It is also a leading experience management, analytics, and BI company. Its solutions are compliant with GDPR. They enable enterprises to build intelligent AI- and ML-based software to unite human expertise with machine-generated insights. The company segments its diverse portfolio into applications, technology, and services; intelligent spend group; and Qualtrics. It works on an intelligent enterprise framework, which includes experience, intelligence, and operations business models. In the predictive analytics market, SAP offers the SAP Predictive Analytics solution. The solution inherits its data acquisition and data manipulation functionalities from SAP Lumira.

Teradata is a data intelligence company catering to more than 75 countries. It also caters to several industries, including communications, media and entertainment, financial services, life sciences, healthcare, retail, energy, travel and transportation, and manufacturing. Its broad customer base comprises O2 Czech Republic, BMW, Siemens Healthineers, Vodafone, American Red Cross, and US Bank. It has partnered with various companies, including Accenture, Alation, AlphaZetta, Alteryx, General Dynamics Information technology, Erwin, HCL Technologies, and Informatica, to deliver end-to-end analytics solutions. Its vast product portfolio consists of various categories, including Software, Cloud, Ecosystem Management, Hardware, and Applications. The company offers the Teradata Vantage solution in the predictive analytics market. It is a business intelligence platform powered by the cloud.

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Thu, 06 Oct 2022 08:26:00 -0500 text/html
Killexams : Better training, new tool bring loan fraud to light

Some dealership staff might intentionally send suspicious loan applicants to a lender partner hoping to close the deal. But according to antifraud provider Point Predictive, many dealers don't even realize when they're transmitting a fishy loan.

Better training and lender communication could help dealerships avoid being an unwitting accomplice to fraud or having to buy back fraudulent auto debt, according to Point Predictive Chief Fraud Strategist Frank McKenna.

McKenna's company has launched BorrowerCheck, a tool to help dealerships spot fraud. But it's also training retailers to notice it themselves, McKenna said.

McKenna: Don’t be an unwitting accomplice.

"They've experienced it, but they just don't know it," McKenna said.

Seventy percent of auto loans that default within the first six months — a sign of potential fraud, most lenders say — have evidence of misrepresentation on the initial application, according to Point Predictive's 2022 Auto Fraud Trends report.

But lenders who encounter fraud might simply absorb the loss rather than charge it back to the dealership. Point Predictive's 2021 Fraud Sentiment Study found a fifth of the lenders it polled didn't require partner dealerships to buy back early defaults or fraudulent loans.

"If you're working with those lenders that don't, you would never know," McKenna said.

McKenna said Point Predictive can teach dealerships how to spot fake identification and suss out synthetic fraud indicators on credit reports.

He said they've begun working with stores in a dealership group and have caught issues including synthetic fraud, forged pay stubs and phony employers — items "dealers haven't seen before or maybe haven't had much knowledge of," McKenna said.

Lenders are growing more aggressive on chargebacks, according to McKenna. He said this causes dealerships pain, but it also raises awareness of the issue and leads surprised retailers to say, " 'I don't even know; I don't see this stuff later,' " he said. " 'What's risky, what's not? I don't know.' "

Fri, 14 Oct 2022 15:08:00 -0500 en text/html
Killexams : How People Rate Pizza, Jobs and Relationships Is Surprisingly Predictive of Their Behavior

We’re constantly being asked how we feel about nearly every aspect of our lives. Pop-up questionnaires collect data about common experiences like doctor’s visits, restaurant meals or trips to the cell phone store. And they can even pry into bigger life questions. How do you feel on a scale of, say, 1 to 10 about a job, a spouse, your health.

Despite the ubiquitous presence of “like” scales everywhere we look, such ratings perplex scientists because they are wholly subjective and so thought to be of unclear relevance and accuracy. Scientists, as a result, have been slow to take stock of these surveys.

A new study published October 3 in the journal Proceedings of the National Academy of Sciences (PNAS) has found that human feelings can accurately be expressed numerically and have more predictive power for how we behave than formal studies of socioeconomic factors like household income and employment status. “These ‘made up’ numbers actually carry a huge amount of information, even though we don’t know how humans achieve this,” says study co-author Andrew Oswald, a professor of economics and behavioral science at the University of Warwick.

Oswald and colleagues gathered information from three large data sets of nearly 700,000 people in Germany, Australia and the United Kingdom. Participants were asked annually over a three-decade period how they felt on a numerical scale about their job, spouse, health and home. Using the data collected, researchers constructed statistical models to show how people felt and the actions they took as a result of their reported feelings. The study found that ratings of life satisfaction had a direct linear relationship to actions people subsequently take. “The paper shows the link between the feelings I report today and my actions tomorrow,” says Oswald.

Participants who rated their job satisfaction as a 2 out of 7, for example, had a 25 percent probability of quitting their job in the next quarter. Those who rated their job satisfaction a 6 out of 7 had only a 10 percent probability of quitting. The same was true across other measures like marriage, health and housing. Similarly, those who rated their marriages lower were more likely to get divorced, and those who rated their health positively were less likely to end up in the hospital.

Previous research has also shown that data about feelings predict human outcomes, but not in such a linear fashion; the degree of satisfaction or lack thereof served as a good predictor of future actions. For example, a 2001 study published in the American Journal of Psychiatry found that those who numerically rated their lives lower had a higher risk of suicide over a 20-year period. A PNAS study co-authored by Oswald in 2012, found that life satisfaction in adolescence was correlated with higher reported incomes in adulthood.

Additionally, human measurement of feelings goes beyond psychology and extends into the realm of economics. Economists have previously been critical of feelings data because they deemed them unscientific and unreliable. Instead they use metrics like gross domestic product (GDP) and interest rates to predict human behaviors. But this new research shows that it may be time to more readily embrace feelings in economics. “Our work provides scientific evidence that using data on feelings is extremely valuable and we need to bring it into the center of economics and social policy making,” Oswald says. This study showed that socioeconomic factors—including household income, relative income, employment status, homeownership status, household size, number of children, marital status and education—had a lesser probability of predicting human behavior than data on feelings.

But while the study has shown that numbers can quantify feelings, researchers are still a bit perplexed as to why estimates of seemingly subjective feelings can be such good predictors of future actions. According to Oswald, a number of factors could be at play. Humans are well versed in comparative thinking and have the ability to scale their own life satisfaction against that of their neighbors. “If you’ve seen a huge mountain, you know whether or not you’re living next to a hill,” Oswald says. We’re also accustomed to using measuring devices for other aspects of life like temperature, distance and weight, so it shouldn’t be too surprising that we’re able to measure our feelings in a similarly accurate way for life-defining events such as relationships and a career. “Humans are somehow able to look inside themselves and know intuitively how to scale their feelings with others so that they can come up with numbers that are truly meaningful,” Oswald says.

Study co-author Caspar Kaiser, a research fellow at the Wellbeing Research Center at the University of Oxford, says that it may also be because we exercise these mental muscles every day. We communicate our feelings all the time, and we do it in a scaled fashion. This could be why it comes out in the data more accurately than in objective markers. “These days we’re asked to rate nearly everything from movies to restaurants to podcasts and this is just an extension of something we’re already doing,” he says.

Ori Heffetz, an economics professor at Cornell University and the Hebrew University of Jerusalem, who was not involved in the study, says that this research shows that feelings data shouldn’t be underestimated even if they’re more difficult to study. “As economists it’s easy to count money but we need to study what’s important, not just what’s easy. Scientists who ignore this do so at their own risk,” he says. “If you want to understand people’s behavior, you have to understand their perceptions, feelings and expectations about their own reality.”

Looking ahead, Kaiser hopes that this same data can be studied in lower-income countries so that it can be applied universally to places with varied levels of economic development. But more than anything else he’s interested in studying why feelings work so well. “While we know that humans have a remarkable ability to encode their feelings along a cardinal scale, we still don’t know for sure how it’s done,” he says.

Sun, 16 Oct 2022 05:09:00 -0500 en text/html
Killexams : Point Predictive, CreditMiner tech helps dealers combat fraud

New technology from Point Predictive and a CreditMiner-TransUnion partnership allow dealerships to take a more active role in preventing borrower fraud.

BorrowerCheck for Dealers is "the first in a planned series of products for dealers" leveraged from the fraud analytics firm's vast database of records including income reports, loan applications, fake employers and fraudulent loans. It is a resource previously only available to lenders.

Dealerships can check the customer's name, address and Social Security number and see if more proof is necessary, according to Point Predictive. The company also has created a new phone verification mechanism.

Luna: Secrets within a phone

CreditMiner's new "Identify" software leverages TransUnion's ability to check a customer's driver's license image against a selfie and the records on the person ostensibly described in the license. It also evaluates the credibility of the customer's phone. Ken Luna, CreditMiner strategic partnerships vice president, said lenders had been using these TransUnion capabilities, but they weren't prevalent among dealerships.

The launch of this new tool comes amid growing fraud trends reported by both TransUnion and Point Predictive.

More fraud

Earlier this year, Point Predictive said its fraud team found more than 16,600 suspicious auto loan applications in 2021, a 260 percent increase from 2020. These questionable 2021 loan applications sought a combined $309 million worth of financing for vehicles. And that's just part of what Point Predictive called a potentially $7.7 billion issue facing the industry last year.

Merchant: Rise of synthetic fraud

Frank McKenna, Point Predictive chief fraud strategist, on Sept. 6 told Automotive News nearly every dealership his company has talked to is reporting more fraud.

"They're telling us that their fraud rates have increased pretty significantly," he said.

Retailers who have never seen fraud before are reporting multiple cases — and those incidents primarily end with indirect lenders forcing dealerships to repurchase the loans, he said. Buying back a loan can set the dealership back tens of thousands of dollars, a loss that would require multiple car sales to cover, he said.

"Those dealerships now are getting hit with a lot more of those," McKenna said. "They're looking for ways to stop it."

McKenna said these chargebacks arise when lenders discover identity theft, which he said has become more sophisticated. The fraudsters have "really good fake IDs" and "know everything about the customer," he said.

This knowledge has reached the point scammers can answer verification questions such as their victim's residence in a given year and last auto lender, McKenna said. He said fraudsters will obtain this information from sources such as Credit Karma.

Another growing auto loan fraud trend involves scammers who create a phony identity rather than steal an existing one, a crime known as synthetic fraud.

"Synthetic fraudsters look like real people with great credit scores and well-established employment, which makes it very difficult for dealership personnel to identify," TransUnion Senior Vice President Satyan Merchant said in a July statement.

Merchant in January said the synthetic fraud rate in auto lending had grown nearly 30 percent from the first quarter of 2021.

"Incidence of synthetic fraud in auto lending has grown faster than any other financial sector as we emerge from the pandemic," he said in a statement.

Another problem involves what Point Predictive calls "fraud for car," when consumers who need transportation lie about their income or employment to qualify for a car loan. Such lies can also arise in "fraud for profit" cases, according to the company.


Point Predictive had previously focused on serving auto lenders, who would alert dealerships or request stipulations after a questionable application had been submitted. With the new BorrowerCheck system, dealerships can tap the same Point Predictive database and perform such due diligence themselves. A dealership could recognize which additional documentation, such as a customer's employment, would be necessary before even submitting an application to the lender, Point Predictive said.

"Dealers play a vital role on the front line, helping to validate for lenders whether a prospective borrower is being truthful on their applications for financing," Point Predictive CEO Tim Grace said in a statement.

BorrowerCheck also gives dealerships a verification system that even Point Predictive's lenders didn't have before, according to McKenna. Given the issue with traditional identity validation questions, the company opted to focus on a consumer's phone number, which he said seemed more difficult to fake.

Point Predictive's software checks records for signs the phone number provided by a loan applicant is associated with the person they're purporting to be. If the phone number seems legitimate, Point Predictive lets the dealership send a one-time passcode to that phone and request confirmation of it by the applicant.

In all but one instance of identity theft tracked by Point Predictive, the fraudster used a different phone number than the one belonging to the person they're claiming to be.

McKenna said dealerships running these initial checks could speed the process for legitimate customers. The one-time passcode is faster than verification questionnaires, he said. Dealerships can recognize applicants who will need additional documentation and request it before even submitting the loan rather than waiting for a lender to demand it later, he said.

Better deals

The screening also permits retailers to send better deals to lenders, reducing the risk of buying back early defaults and preserving the relationship with lenders who judge dealers on loan quality. (Point Predictive noted in its BorrowerCheck announcement Aug. 30 that it sells lenders the DealerCheck service to conduct such monitoring.)

"Our customers' experience is vitally important to us," Ryan Morris, vice president of corporate finance at Southern California-based Mossy Automotive Group, said in a statement. "We believe limiting fraud risk exposure and protecting our dealership reputation while prioritizing our customers' experience is the key to our continued success. Lowering our risk of buy-back demands will also increase our bottom line."

CreditMiner and TransUnion already had partnered on one industry facing tool released this year. The Synthetic Fraud Score announced in January rates the probability of an applicant being a scammer on a scale of 100-1,000.

Luna said a monthlong pilot program found 3 percent of prospective borrowers receiving soft credit checks through CreditMiner had synthetic fraud scores exceeding 500, which meant a more than 50 percent chance of a scam. (Some legitimate customer behavior, such as a recent divorce or a move, can cause false positives, he said.)

While 3 percent doesn't sound like much, "it really is," Luna said.

If the score is high enough, CreditMiner recommends a dealership use its new Identify software to scrutinize the person and their ID more closely, Luna said.

The new software checks the authenticity of the driver's license, compares the personal information on it to TransUnion records and examines whether the ID photo matches the selfie taken by a customer as part of the system. It also checks the phone to see if the device has been tied to suspicious activity.

"It's not just the individual," Luna said. "It's that phone."

CreditMiner has seen interest from large dealership groups in adopting the product and said one planned to just skip the synthetic score phase and just run the Identify check on every consumer.

Fri, 14 Oct 2022 17:36:00 -0500 en text/html
Killexams : Healthcare Predictive Analytics Global Market Report 2022


Major players in the healthcare predictive analytics market are Allscripts Healthcare Solutions, Cerner Corporation, IBM Corporation, Cotiviti, Inc. , Oracle Corporation, Health Catalyst, Inovalon, Inc.

New York, Oct. 07, 2022 (GLOBE NEWSWIRE) -- announces the release of the report "Healthcare Predictive Analytics Global Market Report 2022" -
, Optum, Inc., Citiustech, McKesson Corporation, Medeanalytics, Information Builders Inc., IQVIA, Truven Health Analytics, Inc., and Health Fidelity, Inc.

The global healthcare predictive analytics market is expected to grow from $10.69 billion in 2021 to $13.67 billion in 2022 at a compound annual growth rate (CAGR) of 27.9%. The healthcare predictive analytics market is expected to reach $33.20 billion in 2026 at a CAGR of 24.8%.

The healthcare predictive analytics market consists of sales of healthcare predictive analytics solutions and services by entities (organizations, sole traders and partnerships) that refer to software solutions used for analysing and processing patient data to deliver data-based high-quality care, precise diagnoses, and individualized treatments, by healthcare organisations, hospitals, and doctors.Predictive analytics in healthcare is an advanced method for improving patient outcomes.

By examining data and results from previous patients, machine learning algorithms can be programmed to provide insights into the best treatment for current patients.

The main components of healthcare predictive analytics include services, software, and hardware.The services offered through healthcare predictive analytics solutions include detecting the early signs of the patients’ condition deterioration, risk scoring for chronic illnesses, preventing patient suicide and self-harm, reducing hospital readmissions rates and other services.

The delivery models of healthcare predictive analytics include stand-alone and integrated, and they are used for operations management, financial, population health management, and clinical. The end-users of predictive analytics services include healthcare payers and healthcare providers.

Noth America was the largest region in the healthcare predictive analytics market in 2021.Asia Pacific is expected to be the fastest-growing region in the forecast period.

The regions covered in the healthcare predictive analytics market report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, Middle East and Africa.

The healthcare predictive analytics market research report is one of a series of new reports that provides Healthcare predictive analytics market statistics, including healthcare predictive analytics industry global market size, regional shares, competitors with a healthcare predictive analytics market share, detailed healthcare predictive analytics market segments, market trends and opportunities, and any further data you may need to thrive in the healthcare predictive analytics industry. This healthcare predictive analytics market research report delivers a complete perspective of everything you need, with an in-depth analysis of the current and future scenario of the industry.

The increasing adoption of electronic health records over manual records is driving the growth of the healthcare predictive analytics market.An EHR is a digital representation of a patient’s medical history that the physician keeps track of throughout time.

It may include all critical administrative and clinical data relevant to that person’s care under a specific provider, such as demographics, progress notes, problems, medications, vital signs, past medical history, immunizations, laboratory data, and radiology reports.Electronic health records are increasingly used because it is a digital version of manual records and is not vulnerable to wear and tear or don’t get destroyed easily.

For instance, in 2022, according to’s report on ’Adoption of Electronic Health Records by Hospital Service Type 2019-2021’, between 2019 and 2021, 86% of general acute care hospitals, 40% of rehabilitation hospitals, and 23% of specialty hospitals used a 2015 Edition certified electronic health record (EHR). Thus, the increasing adoption of electronic health records will propel the healthcare predictive analytics market.

Technological advancement is a key trend gaining popularity in the healthcare predictive analytics market.The companies operating in the healthcare predictive analytics are focusing on incorporating innovative technologies such as artificial intelligence to Strengthen productivity, lowers costs, and improves patient care and health outcomes.

In September 2020, EVERSANA, a US-based life sciences commercial services company, launched ACTICS.ACTICS is the technology-enabled approach that helps life science enterprises maximize their commercial success.

ACTICS offers the cloud-based solution that pharmaceutical innovators require, to Strengthen actions in the product and patient journeys by combining the power of AI-driven predictive analytics with ready-to-deploy real-time commercial services.

In January 2022, Huron, a Chicago-based consulting firm offering services to the healthcare, life sciences, commercial, and higher education industries, acquired Perception Health Inc. for an undisclosed amount. Through this acquisition, clients will receive data insights from Huron and Perception Health across the care continuum to better detect and manage risks for patients and communities. Huron’s healthcare predictive analytics and data capabilities are strengthened by the acquisition, enabling Huron to assist clients in finding patterns and insights that will Strengthen patient care and help them make better data-driven decisions. Perception Health Inc. is a US-based disease prediction platform with precise data sets that deliver healthcare professionals access to a previously unavailable and actionable predictive dimension.

The countries covered in the healthcare predictive analytics market report are Australia, Brazil, China, France, Germany, India, Indonesia, Japan, Russia, South Korea, UK, and USA.
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Mon, 10 Oct 2022 22:35:00 -0500 en-US text/html
Killexams : Predictive Maintenance (PdM) Software Market 2022 Share, Size, Company Profiles, Trends, Growth, Segments, Landscape and Demand by Forecast to 2026

The MarketWatch News Department was not involved in the creation of this content.

Oct 03, 2022 (The Expresswire) -- Global “Predictive Maintenance (PdM) Software Market” (2022-2026) research report covers of opportunities, segmentation of the global Predictive Maintenance (PdM) Software market based growth, size, share. The report establishes a solid foundation for the users who wish to enter into the global market in terms of drivers, restraints, developments, top trends, and landscape estimation. The report further offers a detailed overview of leading companies encompassing their successful industrial strategies, contribution, recent techniques in present and future contexts. It reports also covers monetary and exchange fluctuations, import-export trade, and global market status in a market pattern. It helps the reader understand the business strategies, new collaborations that players are highlights in the market. The report provides a significant microscopic look at the market.

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Market Overview:

The global Predictive Maintenance (PdM) Software market size is projected to reach USD million by 2026, from USD million in 2021, at a CAGR of during 2022-2026. Predictive Maintenance (PdM) Software market growth in the Asia Pacific is primarily driven by countries, such as China, Japan, India, and Singapore.

With industry-standard accuracy in analysis and high data integrity, the report makes a brilliant attempt to unveil key opportunities available in the global Predictive Maintenance (PdM) Software Market Share to help players in achieving a strong market position. Buyers of the report can access Checked and reliable market forecasts, including those for the overall size of the global Predictive Maintenance (PdM) Software market in terms of revenue.

The Predictive Maintenance (PdM) Software market growing trend of rising demand for comfortable and innovative Predictive Maintenance (PdM) Software solutions and the need for energy-efficient solutions will create a market opportunity for the Predictive Maintenance (PdM) Software market.- Further, with the ongoing technological evolution, such as Predictive Maintenance (PdM) Software systems increasing projects worldwide coupled with consumer preferences for better Predictive Maintenance (PdM) Software is favoring the growth of the market.- Moreover, the rising concerns over global climate change have led to the implementation of various rules and regulations pertaining to energy efficiency.

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List of TOP KEY PLAYERS in Predictive Maintenance (PdM) Software Market Report are -

● Microsoft
● GE Digital
● Schneider
● Hitachi
● Siemens
● Intel
● RapidMiner
● Rockwell Automation
● Software AG
● Cisco
● Bosch.IO
● Dell
● Augury Systems
● Senseye
● T-Systems International
● TIBCO Software
● Fiix
● Uptake
● Sigma Industrial Precision
● Dingo
● Huawei

Global Predictive Maintenance (PdM) Software Scope and Market Size: -

Predictive Maintenance (PdM) Software market analysis is segmented by players, region (country), by Type and by Application. Players, stakeholders, and other participants in the global Predictive Maintenance (PdM) Software market will be able to gain the upper hand as they use the report as a powerful resource. The segmental analysis focuses on revenue and forecast by Type and by Application for the Predictive Maintenance (PdM) Software Market Forecast period 2015-2026.

The Predictive Maintenance (PdM) Software Market is Segmented by Types:

● Cloud Based
● On-premises

The Predictive Maintenance (PdM) Software Market is Segmented by Applications:

● Industrial and Manufacturing
● Transportation and Logistics
● Energy and Utilities
● Healthcare and Life Sciences
● Education and Government
● Others

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Geographically, this report is segmented into several key regions, with sales, revenue, market share and growth Rate of Predictive Maintenance (PdM) Software in these regions, from 2021 to 2027, covering

● North America (United States, Canada and Mexico) ● Europe (Germany, UK, France, Italy, Russia and Turkey etc.) ● Asia-Pacific (China, Japan, Korea, India, Australia, Indonesia, Thailand, Philippines, Malaysia and Vietnam) ● South America (Brazil, Argentina, Columbia etc.) ● Middle East and Africa (Saudi Arabia, UAE, Egypt, Nigeria and South Africa)

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● To gain insightful analyses of the market and have comprehensive understanding of the global market and its industrial landscape. ● To identify and analyses the profile of leading players operating in the global Predictive Maintenance (PdM) Software market. ● To understand the most affecting driving and restraining forces in the market and its impact in the global market. ● Learn about the market strategies that are being adopted by leading respective organizations. ● To understand the future development and plan for the market.

Besides the standard structure reports, we also provide custom research according to specific requirements.

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● Market Size and Growth Rate for Historical and Forecast Period ● The global Predictive Maintenance (PdM) Software market research report comprises a global viewpoint with respect to the demand and supply analysis ● Globally, the constraints, challenges, innovations, drivers, and patterns affecting Ambient Lighting market expansion in these critical sectors are investigated ● Porter’s Five Forces Model, PESTLE Analysis, and SWOT Analysis are among the tools used in the report to provide qualitative analysis ● The Predictive Maintenance (PdM) Software market study provides analysis on market size in terms of both consumption volume, production volume, revenue, global trends, import-export, value chain, distributors, pricing, segments trends analysis, etc. for both the regional and worldwide market

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Outline: The team of experienced research analysts at Research Reports World has thoroughly evaluated the primary and secondary information related to the global Predictive Maintenance (PdM) Software market. The organization offers a bunch of trending industry reports on the portal, of which the recently published report is the global Predictive Maintenance (PdM) Software market report. The publishers of the Predictive Maintenance (PdM) Software report particularly focused on the research-based services to offer crucial information for business executives and investors. Utilizing such pivotal knowledge can help to opt for precise business-related decisions.

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● Predictive Maintenance (PdM) Software Market report provides comprehensive analysis of the market with the help of up-to-date market new opportunities, overview, outlook, challenges, trends, market dynamics, size and growth, competitive analysis, major competitors analysis. ● Report recognizes the key drivers of growth and challenges of the key industry players. Also, evaluates the future impact of the propellants and limits on the market. ● Uncovers potential demands in the Predictive Maintenance (PdM) Software Market. ● Predictive Maintenance (PdM) Software Market report provides in-depth analysis for changing competitive dynamics Provides information on the historical and current market size and the future potential of the market.

An exhaustive and professional study of the global Predictive Maintenance (PdM) Software market report has been completed by industry professionals and presented in the most particular manner to present only the details that matter the most. The report mainly focuses on the most dynamic information of the global market.

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Major Points from Table of Contents:

Detailed TOC of Global Predictive Maintenance (PdM) Software Market Report 2021

1 Predictive Maintenance (PdM) Software Market Overview

1.1 Predictive Maintenance (PdM) Software Product Scope

1.2 Predictive Maintenance (PdM) Software Segment by Type

1.3 Predictive Maintenance (PdM) Software Segment by Application

1.4 Predictive Maintenance (PdM) Software Market Estimates and Forecasts (2015-2026)

2 Predictive Maintenance (PdM) Software Estimates and Forecasts by Region

2.1 Global Predictive Maintenance (PdM) Software Market Size by Region: 2015 VS 2021 VS 2026

2.2 Global Predictive Maintenance (PdM) Software Market Scenario by Region (2015-2021)

2.3 Global Market Estimates and Forecasts by Region (2022-2026)

2.4 Geographic Market Analysis: Market Facts and Figures

3 Global Predictive Maintenance (PdM) Software Competition Landscape by Players

3.1 Global Top Predictive Maintenance (PdM) Software Players by Sales (2015-2021)

3.2 Global Top Predictive Maintenance (PdM) Software Players by Revenue (2015-2021)

3.3 Global Predictive Maintenance (PdM) Software Market Share by Company Type (Tier 1, Tier 2 and Tier 3) and (based on the Revenue in Predictive Maintenance (PdM) Software as of 2020)

3.4 Global Predictive Maintenance (PdM) Software Average Price by Company (2015-2021)

3.5 Manufacturers Predictive Maintenance (PdM) Software Manufacturing Sites, Area Served, Product Type

3.6 Manufacturers Mergers and Acquisitions, Expansion Plans

4 Global Predictive Maintenance (PdM) Software Market Size by Type

4.1 Global Predictive Maintenance (PdM) Software Historic Market Review by Type (2015-2021)

4.2 Global Market Estimates and Forecasts by Type (2022-2026)

4.2.3 Global Price Forecast by Type (2022-2026)

Get a demo Copy of the Predictive Maintenance (PdM) Software Market Report 2022

5 Global Predictive Maintenance (PdM) Software Market Size by Application

5.1 Global Predictive Maintenance (PdM) Software Historic Market Review by Application (2015-2021)

5.2 Global Market Estimates and Forecasts by Application (2022-2026)

6 North America Predictive Maintenance (PdM) Software Market Facts and Figures

6.1 North America Predictive Maintenance (PdM) Software by Company

6.2 North America Predictive Maintenance (PdM) Software Breakdown by Type

6.3 North America Predictive Maintenance (PdM) Software Breakdown by Application

7 Europe Predictive Maintenance (PdM) Software Market Facts and Figures

8 China Predictive Maintenance (PdM) Software Market Facts and Figures

9 Japan Predictive Maintenance (PdM) Software Market Facts and Figures

10 Southeast Asia Predictive Maintenance (PdM) Software Market Facts and Figures

11 India Predictive Maintenance (PdM) Software Market Facts and Figures

12 Company Profiles and Key Figures in Predictive Maintenance (PdM) Software Business

13 Predictive Maintenance (PdM) Software Manufacturing Cost Analysis

13.1 Predictive Maintenance (PdM) Software Key Raw Materials Analysis

13.1.1 Key Raw Materials

13.1.2 Key Raw Materials Price Trend

13.1.3 Key Suppliers of Raw Materials

13.2 Proportion of Manufacturing Cost Structure

13.3 Manufacturing Process Analysis of Predictive Maintenance (PdM) Software

13.4 Predictive Maintenance (PdM) Software Industrial Chain Analysis

14 Marketing Channel, Distributors and Customers

14.1 Marketing Channel

14.2 Predictive Maintenance (PdM) Software Distributors List

14.3 Predictive Maintenance (PdM) Software Customers

15 Market Dynamics

15.1 Predictive Maintenance (PdM) Software Market Trends

15.2 Predictive Maintenance (PdM) Software Drivers

15.3 Predictive Maintenance (PdM) Software Market Challenges

15.4 Predictive Maintenance (PdM) Software Market Restraints


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