In the insurance industry, the claims process can often feel like a complex maze of manual processes for both customers and agents. However, new and advanced technologies are paving the way for smoother, more efficient claims handling. Claims processing automation at insurance companies can streamline routine tasks, reduce risk of errors, and enhance customer satisfaction. In this blog, we’ll explore three key areas to focus on for effective automation.

As technology evolves, the skills gap within IT departments continues to widen. New technologies are being adopted more quickly to keep pace with business changes, necessitating a constant update of skills. Yet, fully outsourcing these skills isn’t always the solution, as they are a core part of any IT organization. To effectively address this issue, we need a strategy that focuses on continually closing the skills gap.

Business leaders find themselves involved in a range of high-priority tasks, most of which require making critical decisions. Let’s say you’re the sales head of a global organization. You’re ready to make an important decision about next quarter’s sales strategy, but you must first look at the right data set. You know it exists somewhere in your organization’s databases, yet it’s not within the arm’s reach.

The OKR (Objectives and Key Results) framework is an approach used by companies that sets goals to improve alignment, engagement, and performance. OKRs became well-known after being used by Google and Intel and are now widely accepted across various industries, especially SaaS businesses.

One method that developers use to submit code modifications for evaluation is known as pull requests (PRs). We discussed the advantages of testing PRs in software development in a previous post but before merging into the main branch, they give team members a chance to talk about the changes, make suggestions, and accept or reject the code. To keep code quality high and make sure that the team reviews all changes, this approach is essential.

BigQuery data preparation is an AI-first solution that streamlines and simplifies the data preparation process as part of Gemini in BigQuery.

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.

Dynamic real estate market requires businesses to act fast and effectively. Having software that can automate most of your business and management-related tasks is a wise choice. When it comes to deploying a real estate analytics platform, you need to decide between two approaches — custom software and ready-made solutions.

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.

Our new per-minute, consumption-based pricing model offers granular control over your costs, eliminating the need to pay for peak users or connections. Whether you're building a proof of concept or handling massive user loads, our pricing model provides what you need to succeed - volume discounts, cost optimizations and business critical support. Consumption-based pricing has become widespread in SaaS and PaaS businesses, and it's easy to understand why: you should only ever pay for what you consume.