Data elements are the fundamental building blocks of EDI documents. They represent individual information within a transaction set, such as city, state, country, item number, quantity, and price. Each data element is defined by its data type, which specifies whether it’s numeric, alphanumeric, a date, or a time. Additionally, the definition includes details like minimum and maximum length requirements and any applicable code values.

Implementing a modern, integrated data architecture can help you break down data silos, which cause C-suite decision-makers to lose 12 hours a week. Furthermore, more than 60% of organizations agree that data silos represent a significant business challenge. The solution is a data architecture that eliminates silos, and that’s where the data mesh vs. data fabric debate comes in. While both data architectures work to eliminate data silos, they differ in their approaches (more on that later).

Cloud operations were once the logical next step for streamlining business processes, but now they have become the go-to solution for maximizing operational efficiency. With the advent of ‘cloud-first’ architectures and strategies, cloud computing is the rule, not the exception.

Businesses handle massive amounts of data daily. To make informed decisions, this data must be optimized for accuracy and consistency, which requires effective data management. However, for best results, data management must be coupled with data governance, which provides essential frameworks to manage data quality and security. Data stewardship takes it a step further.

Twelve years ago, a Wakefield Research survey revealed that 1 in 3 Americans thought cloud computing was somehow related to the weather. Fast forward to today, 67% of enterprise infrastructure in the US is cloud-based. Given that 92% of enterprises already have a multi-cloud strategy in place or in the works, it’s evident that embracing cloud migration is no longer just an option but a strategic necessity.

When Facebook acquired WhatsApp in 2014, they had to integrate an enormous amount of data—450 million monthly active users generating billions of messages, photos, and videos daily—into Facebook’s systems. This data migration required precise planning and execution to ensure a smooth transition and prevent data loss while maintaining the accuracy and integrity of the information.

Mergers and acquisitions (M&A) have become a stepping stone to corporate growth strategies. Companies worldwide are actively turning to these deals to expand market reach and drive financial performance. The latest data from EY-Parthenon confirms this trend, with M&A activity projected to surge by 12% in 2024. While the idea of combining companies is undeniably exciting, a critical yet often overlooked factor that can either make or break a deal is data governance.

Mergers and acquisitions (M&As) are strategic business activities where two or more companies join forces by combining their assets, operations, and management structures, often resulting in a unified entity or allowing one company to absorb another. These transactions are typically pursued to enhance competitive advantage, expand market share, or achieve operational efficiencies.

In the high-stakes world of business, mergers and acquisitions (M&A) represent a strategic move for companies to accelerate growth, diversify offerings, and enhance market presence. As reported by Bain & Company, the world of strategic M&A witnessed 27,000 deals announced, totaling approximately $2.4 trillion in the year 2023. M&A deals, whether they involve acquiring a competitor, entering a new market, or merging with a complementary business, reshape industries.

Every organizational activity or interaction today generates data. This quickly creates large amounts of data at organizational and departmental levels, but data generation is only the beginning. No matter how much raw data you have at your disposal, you can only leverage it fully if you know how to process it correctly for your requirements. You can process data flows using one of two approaches: batch processing or batch processing.