Unlock the Power of Your Workday Data With Simba Drivers

For many organizations, Workday is a core system housing vital data on HR, payroll, finance, and more. However, extracting and utilizing that data for analysis can be a challenge. The new Simba Workday ODBC and JDBC Drivers simplify this process, enabling seamless access to Workday data through standard database interfaces. With Simba Drivers, you can effortlessly integrate Workday data into your analytics tools, ETL workflows, or custom applications, unlocking its full potential for decision-making.

Your Complete Guide to Mortgage Document Processing with AI

Businesses across various sectors want to leverage AI to increase efficiency, reduce cost, enhance customer experience, or do all that in one go. The mortgage industry is feeling it, too, thanks to the several potential areas where AI technologies can impact. For instance, AI can help mortgage lenders by: In fact, according to a Fannie Mae survey, mortgage lenders believe compliance, underwriting, and property valuation are all ripe for AI integration.

Intelligent document processing (IDP) in logistics and transportation

Documentation forms an integral part of operations in almost every industry. Take logistics and transportation, for example, where companies process hundreds of thousands of documents daily to keep the goods in motion and the supply chain functional. So, what are logistics companies doing to handle such a vast number of documents? More importantly, how can they use the intelligent document processing (IDP) technology to manage their documents and extract the data they need?

The AI Tipping Point: What Financial Leaders Need to Know for 2025

AI is proving that it’s here to stay. While 2023 brought panic and wonder, and 2024 saw widespread experimentation, 2025 will be the year that financial services enterprises get serious about AI's applications. But it’s complicated: AI proofs of concept are graduating from the sandbox to production, just as some of AI’s biggest cheerleaders are turning a bit dour.

Luggage lost in a world of streaming data

The need to democratize and share data inside and outside your organization, as a real-time data stream, has never been more in demand. Treating real-time data as a product, and adopting Data Mesh practices, is the way forward. Here, we explain the concept through a real-life example of an airline building applications that process data across different domains.

Luggage lost in a world of streaming data

Democratizing and sharing data inside and outside your organization, as a real-time data stream, has never been more in demand. Treating data as-a-product and adopting Data Mesh practices is leading the way. Here, we explain the concept through a real-life example of an airline building applications that process data across different domains.

Top 15 Website Analytics Tools for 2025

Whether you’re struggling to pinpoint why your bounce rate is off the charts, understand which pages drive conversions, or optimize the user journey, the right website analytics tool can make or break your strategy. But with countless platforms claiming to be the best, choosing one feels overwhelming. That’s why we’ve handpicked the top 15 website analytics tools for 2025 to help you track the metrics that matter and grow your business smarter—not harder.

Confluent Introduces Enterprise Data Streaming to MongoDB's AI Applications Program (MAAP)

Today, Confluent, the data streaming pioneer, is excited to announce its entrance into MongoDB’s new AI Applications Program (MAAP). MAAP is designed to help organizations rapidly build and deploy modern generative AI (GenAI) applications at enterprise scale.

Resource Allocation Policy Management - A Practical Overview

As organizations evolve – onboarding new team members, expanding use cases, and broadening the scope of model development, their compute infrastructure grows increasingly complex. What often begins as a single cloud account using available credits can quickly expand into a hybrid mix of on-prem and cloud resources that come with different associated costs and are tailored to diverse workloads.

Improving Data Pipeline Reliability with On-Call Data Teams

A big part of data teams’ responsibilities is dealing with the unpredictable. Data pipelines don’t always run without incident: you need to rerun processes and fix data processing issues—in other words, put out data fires—to keep stakeholders happy. For every significant roadblock, additional time and effort is given over to investigation and post-mortem reports to make sure the incident doesn’t reoccur. But naturally, they keep happening.