Data fabric technology can help in a significant way, which is why it’s becoming a go-to method of choice for data management. Data fabric technology integrates information scattered across disparate sources and creates a unified data model through a virtual data layer. Data fabrics let you leave your existing data architecture intact, pulling directly from your current data sources and updating them in real time. So why is a data fabric approach the solution to your complex data challenges?

Agencies typically have older procurement systems or too many systems. In either case, they are data entry intensive and error-prone, negatively impacting the procurement process and outcomes. The Office of Federal Procurement Policy’s Procurement Acquisition Lead Time memo from 2021 specifically calls for government modernization by “leveraging technology to modernize operations and help the workforce move from low- to high-value activities.”

Low-code gives developers the flexibility to automate and streamline large portions of workflows, applications, and end-to-end processes while still allowing room for high-code development. This dual capability accelerates project timelines and enhances developer productivity. That’s why low-code development is often used as a supplement to high-code approaches; but it can also be the primary driver of development efforts.

At the heart of BPA is automation, which drives efficiency and reduces manual effort. Key elements to look for include: Effective data management is crucial for informed decision-making and operational efficiency. The right platform will offer a strong data fabric architecture to make this possible. A data fabric combines data management, integration, automation, and low-code development tools to build impactful and data-rich digital solutions.

In the contemporary business landscape, the integration of artificial intelligence (AI) into business processes is no longer a futuristic concept—it's a present-day reality. Organizations across various industries are leveraging AI to streamline operations, enhance decision-making, and drive innovation.

Processes are the foundation of your organization. They lay the groundwork for how your business operates, from product development and customer service to procurement and financial operations—and everything in between. Even work that isn’t formally defined in a process is likely a part of one. If you’ve ever tried to map or document a business process, you’ve probably learned that there’s no straightforward way to get it done.

Business observability is like having x-ray vision for your business systems. It allows you to see and understand the inner workings of your operations through the data they generate—logs, metrics, and traces. Unlike traditional monitoring that detects issues, observability dives deeper to reveal why problems occur and their potential business impacts. Without it, you’re flying blind, hoping nothing breaks. It’s like driving a car without a dashboard.

Every organization faces incidents. These could be simple, such as customers being temporarily locked out of online accounts. Or they could be major incidents that damage your reputation, like a bad customer interaction that gets on the news. Or they could be damaging in another deep way, like a security breach or safety incident in manufacturing. Unplanned events are inevitable. How you handle them will make or break your organization.

An operational data fabric focuses explicitly on integrating and managing disparate data in the context of operational systems and processes. It’s tailored to meet the needs of operational data management, often in real time or near real time, to support business operations. Let’s break down some key features of operational data fabric: To fully appreciate the transformative power of operational data fabric, it’s important to understand how it compares to data fabric.

Due to market pressures and disruptions, life sciences organizations need to reexamine their supply chain operations and how to more safely and efficiently bring products to market. However, deciding where to begin implementing change can be daunting. Here are some best practices for achieving streamlined end-to-end supply chain orchestration.