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.

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.

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.

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.

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.

Machine learning operations (MLOps) is a practice that focuses on the operationalization of machine learning models. It involves automating and streamlining the lifecycle of ML models, from development and training to deployment and monitoring. Much like data operations (DataOps), MLOps aims to improve the speed and accuracy of the data you’re accessing and analyzing.

The Snowflake AI Data Cloud has democratized data for thousands of customers, removing data silos and powering data sharing and collaboration use cases. Many customers have been able to unlock enormous value from their data with Snowflake, including safely collaborating on sensitive data using Snowflake Data Clean Rooms and Data Governance features. However, some highly sensitive data has remained off-limits due to regulatory requirements and privacy concerns — until now.

Adtech and martech companies are engaged in a fierce battle for audience attention. Customers are bombarded with thousands of ads and marketing messages every day, and the average attention span is plummeting, so it’s no wonder they tune out — or turn on ad blockers. But it’s not all doom and gloom. The global adtech market is expected to grow at a rate of 22.4% through 2030, and martech’s projected growth rate is 18.5% through 2032.

Being able to create and share insightful data-led stories to support your dashboards and reports is a critical capability in today’s modern workplace. You want your data to be accessible for more people, and ensure everyone gets value from your investments. Data storytelling tools bring valuable context to the ‘why’ behind the results, while inspiring your audience to care about and act on insights.