What if your document processing system could do more than categorize documents and extract data, no matter the format? That’s exactly what you can do with intelligent document processing (IDP) software. IDP tools adapt to varying structures and formats and understand content to summarize lengthy documents, identify anomalies, and flag errors. The best part? IDP software continuously improves in accuracy the more you use it.

From artificial intelligence (AI) to machine learning (ML) to conversational chatbots, the tools we use to interact with and consume information are rapidly changing thanks to powerful new technologies that make understanding our data more accessible than ever. One particularly influential field is natural language technology (NLT) and its branches.

Python is one of the most popular programming languages and choosing the right Integrated Development Environment (IDE) is essential for an efficient workflow. Whether you are a beginner or an experienced developer, choosing the right and best IDE is important because each developer’s needs are unique, whether working on scientific projects, professional applications, or simple scripts. Let’s check out the top 5 best Python IDE as of 2024.

The financial document processing domain has undergone a 360-degree shift in the past decade. It was at the brink of the 1980s when software providers began releasing document management systems aimed at helping companies save time, money, and effort in regard to financial document processing. What started as simple document management systems using Optical Character Recognition to digitize printed financial documents has evolved into advanced, AI-powered solutions.

The headless data architecture is the formalization of a data access layer at the center of your organization. Encompassing both streams and tables, it provides consistent data access for both operational and analytical use cases. Streams provide low-latency capabilities to enable timely reactions to events, while tables provide higher-latency but extremely batch-efficient querying capabilities. You simply choose the most relevant processing head for your requirements and plug it into the data.

Your docs are a lot like your family—not in the corporate jargony “we are a family” way, but more in the “can’t live with them, can’t live without them” way. Yes, these docs are crucial in more ways than one, but teams that regularly work with them know that the time they spend searching for, cleansing, and prepping their docs can be better utilized elsewhere.

The Salesforce Data Cloud is a powerful tool that enables businesses to collect, unify, and analyze customer data from multiple sources in real time. As data analysts, the Salesforce Data Cloud provides numerous opportunities for deep insights, better decision-making, and delivering personalized customer experiences.

From social media and Google reviews to sensors and artificial intelligence (AI) assistants, development teams today have access to so much user data, often called big data, that it sometimes feels like a blessing and a curse.

In today’s data-driven world, understanding and leveraging customer data is essential for any business. CRM Analytics (CRMA), or Tableau CRM, is Salesforce's advanced analytics platform, enabling businesses to gain insights from their CRM data. It is designed for data analysts to dig deeper into customer data, generate actionable insights, and help drive business decisions.

Imagine your development team just rolled out a new feature that fixes a bug but now some parts of your software that used to work flawlessly have stopped working. This is a common risk in Agile development, where changes come fast and often. Since Agile moves quickly with constant updates, regression testing is a key part of making sure the software stays reliable. With each new update, there’s a risk that changes might impact parts of the software that were already working well.