BizAcuity

MARCH 18, 2019

Important issues like ensuring customer loyalty, retaining and attracting different types of customers or cross-selling products suited to them, fraud detection, application screening, have been areas of concern in the unsecured consumer lending business. Predictive analytics has played a pivotal role in streamlining the lending process.

Predictive Analytics 83

Predictive Analytics Analytics Cost-Benefit Data mining 83

Ontotext

AUGUST 31, 2023

The growth of large language models drives a need for trusted information and capturing machine-interpretable knowledge, requiring businesses to recognize the difference between a semantic knowledge graph and one that isn’t—if they want to leverage emerging AI technologies and maintain a competitive edge. What is a knowledge graph?

Modeling 64

Modeling Optimization Knowledge Discovery Enterprise 64

Ontotext

JUNE 15, 2023

Graph databases provide a way to store and analyze these relationships, allowing companies to gain insights into how information flows through social networks. Fraud detection: Fraud detection is becoming increasingly important for companies across many industries.

Machine Learning 52

Machine Learning Data Integration Big Data Testing 52

IBM Big Data Hub

AUGUST 9, 2023

In some cases, employees who click on the mock malicious link are brought to a landing page indicating that they fell prey to a simulated phishing attack, with information on how to better spot phishing scams and other cyberattacks in the future. Why phishing simulations are important Recent statistics show phishing threats continue to rise.

Testing 74

Testing Measurement Behavioral Analytics Reporting 74

Smart Data Collective

MARCH 7, 2021

Fortunately, there are a number of ways that AI technology can help address cybersecurity risks. The good news is that there are a lot of ways to mitigate these risks by using AI technology, such as with fraud scoring, automating the removal of rogue users and constant monitoring of internal resources.

Data-driven 129

Data-driven Software Technology Risk 129

IBM Big Data Hub

FEBRUARY 6, 2024

Through the development of cyber recovery plans that include data validation through custom scripts, machine learning to increase data backup and data protection capabilities, and the deployment of virtual machines (VMs) , companies can recover from cyberattacks and prevent re-infection by malware in the future.

Cost-Benefit 88

Cost-Benefit Testing Risk Strategy 88

AWS Big Data

NOVEMBER 14, 2023

With data streaming, you can power data lakes running on Amazon Simple Storage Service (Amazon S3), enrich customer experiences via personalization, improve operational efficiency with predictive maintenance of machinery in your factories, and achieve better insights with more accurate machine learning (ML) models.

IoT 55

IoT Data-driven Data Lake Data Strategy 55

Ontotext

MAY 2, 2024

Storage-centric approach In the storage-centric approach, people try to address data silos by throwing everything in a data lake or a data warehouse. Another challenge is the data models that represent how insights and analytics teams see the world. Our Value Proposition How does TopQuadrant fit into this picture?

Metadata 52

Metadata Data Lake Data-driven Enterprise 52

Sisense

AUGUST 6, 2020

In Augmented Apps , we examine how product teams are exploring AI and ML to make their products more intuitive and enhance the user experience. AI and machine learning (ML) are not just catchy buzzwords; they’re vital to the future of our planet and your business. Improving performance with AI.

Management 95

Management Machine Learning Key Performance Indicator Cost-Benefit 95

Cloudera

JUNE 7, 2022

Impressive, but dwarfed by the amount of unstructured data, cloud data, and machine data – another 50 ZB. Want to manage and analyze data of all types including machine, structured, transactional, and unstructured – anywhere? In fact, the total amount of data is expected to nearly triple by 2025.

IT 112

IT Data Architecture Unstructured Data Big Data 112

Domino Data Lab

MAY 20, 2021

In this article we discuss why fitting models on imbalanced datasets is problematic, and how class imbalance is typically addressed. Machine Learning algorithms often need to handle highly-imbalanced datasets. Merging the two results in a completely balanced dataset (50:50).

Machine Learning 136

Machine Learning Metrics Data mining Knowledge Discovery 136

Domino Data Lab

FEBRUARY 10, 2019

This blog post provides insights into why machine learning teams have challenges with managing machine learning projects. He also provides best practices on how to address these challenges. Why are Machine Learning Projects so Hard to Manage? Why is this?

Machine Learning 63

Machine Learning Modeling Deep Learning Metrics 63

Smart Data Collective

SEPTEMBER 26, 2019

However, people working in the gaming industry recognize how essential it is. Why is artificial intelligence necessary to enhance geolocation capabilities for gaming providers? Before the proliferation of artificial intelligence, gaming providers relied almost entirely on IP addresses to determine the location of their users.

Risk 67

Risk Metrics Technology Machine Learning 67

Ontotext

APRIL 3, 2024

Read about the latest use cases and trends in the Financial Services industry and learn how Generative AI and LLMs complement with key capabilities of knowledge graphs. The business why behind knowledge graph projects Peio Popov : Welcome, everyone! How do I make sure I can manage risk?” A graph can do that.

Technology 59

Technology Risk Digital Transformation Measurement 59

Smart Data Collective

FEBRUARY 25, 2021

Savvy small businesses recognize that AI technology can assist them with almost every aspect of their operations, including employee management, trend forecasting, fraud prevention and financial management. However, a very important aspect is how much you care about your customers. Do you love your customers?

Software 62

Software Machine Learning Marketing Technology 62

Sisense

FEBRUARY 18, 2021

Data scientists have developed multiple predictive analytics models, each tailored to different functions: Forecast models use multiple input types to estimate future outcomes: how long an engine component will last, how many in-store customers to expect on a given day, how much of an item you should keep in inventory, etc.

Predictive Analytics 66

Predictive Analytics Business Intelligence Analytics Manufacturing 66

CIO Business Intelligence

JUNE 23, 2022

Impressive, but dwarfed by the amount of unstructured data, cloud data, and machine data – another 50 ZB. Want to manage and analyze data of all types including machine, structured, transactional, and unstructured – anywhere? In fact, the total amount of data is expected to nearly triple by 2025. Jonathan Takiff / IDG.

IT 73

IT Data Architecture Unstructured Data Big Data 73

Smart Data Collective

APRIL 10, 2019

What is metadata and how is it used? Metadata typically contains the person’s name, address, phone number, credit card number, email address and even personal or business bank account numbers. Why a Cyber-Criminal Steals Metadata. How a Cyber-Criminal Steals Metadata. That is what this article is going to explain.

Metadata 66

Metadata Risk Technology Management 66

IBM Big Data Hub

JULY 7, 2023

But the loss of personal information in a data breach can also have significant consequences on an individual, including financial loss, identity theft, other fraud, emotional distress and even damage to reputation. Implement data classification based on how sensitive and valuable it is. Define sensitive data.

Risk 40

Risk Data Governance Data Lake Data-driven 40

O'Reilly on Data

NOVEMBER 28, 2023

And everyone has opinions about how these language models and art generation programs are going to change the nature of work, usher in the singularity, or perhaps even doom the human race. Our survey focused on how companies use generative AI, what bottlenecks they see in adoption, and what skills gaps need to be addressed.

Enterprise 281

Enterprise Modeling Testing Reporting 281

IBM Big Data Hub

APRIL 15, 2024

Data virtualization empowers businesses to unlock the hidden potential of their data, delivering real-time AI insights for cutting-edge applications like predictive maintenance, fraud detection and demand forecasting. Why choose data virtualization? With the rise of generative AI chatbots, foundation models now use this rich data set.

Analytics 64

Analytics Optimization Data-driven Metrics 64

IBM Big Data Hub

MAY 3, 2024

By following how the ADMT rules progress, organizations can better position themselves to comply as soon as the regulations take effect. Why is California developing new rules for ADMT and AI? Why is California developing new rules for ADMT and AI? How does the CPPA define ADMT?

Technology 65

Technology Advertising Risk Interactive 65

CIO Business Intelligence

APRIL 15, 2024

Tax preparation company H&R Block is no stranger to AI and machine learning (ML), having leveraged the technologies across its business for years. We believe these solutions are best when there’s a human in the loop, which is why wherever we deploy any generative AI solutions, we have options to bail out to humans,” he says.

Testing 74

Testing Experimentation Data Quality Machine Learning 74

Alation

DECEMBER 15, 2021

A data governance strategy consists of the background planning work that sets the holistic requirements for how an organization will manage data consistently. It creates the standards, processes, and documentation structures for how the organization will collect and manage data. Why is a Data Governance Strategy Needed?

Data Governance 52

Data Governance Strategy Metadata Data Strategy 52

Alation

NOVEMBER 22, 2022

This is why public agencies are increasingly turning to an active governance model, which promotes data visibility alongside in-workflow guidance to ensure secure, compliant usage. Why Is Data Governance In The Public Sector Important? To unlock your data’s value, you need a data governance program that addresses: Data ownership.

Data Governance 52

Data Governance Metadata Data-driven Unstructured Data 52