AI can offer transformative business value. But you need the right combination of capabilities. Appian is continuing its history of providing practical value to enterprises across industries with the new AI capabilities in our 24.2 release—from 11 new generative AI skills that help you optimize mission-critical processes to the release of our Enterprise Copilot that gives users instant answers to their questions. This post will cover the latest enhancements to our AI offerings.

Electronic Data Interchange (EDI) is a popular communication method that enterprises use to exchange information accurately and quickly with trading partners. EDI transmits data almost instantaneously — serving as a fast and efficient mode for exchanging business documents. ANSI X12 vs. EDIFACT are the two most common EDI standards used for EDI, but they have different structures, styles, and usage.

A database schema, or DB schema, is an abstract design representing how your data is stored in a database. Database schemas can be visually represented using schema diagrams, such as the one below: A database schema diagram visually describes the following: Database schemas are at the heart of every scalable, high-performance database. They’re the blueprint that defines how a database stores and organizes data, its components’ relationships, and its response to queries.

Local governments aim to serve the needs of their communities by fostering a safe, prosperous, and vibrant environment where residents and businesses can thrive. But too often, inefficient processes and manual, paper-based tasks consume valuable time and resources. This leads to delays, errors, and inconsistencies in the delivery of essential services, eroding citizen satisfaction and trust.

Two related concepts often come up when data teams work on data governance: data provenance and data lineage. While they may seem similar at first glance, there are fundamental differences between the two concepts. Data provenance covers the origin and history of data, including its creation and modifications. On the other hand, data lineage tracks the data’s journey through various systems and processes, highlighting its flow and transformation across different data pipeline stages.

Data observability is a process that actively monitors an organization’s data for accuracy, health, and usefulness. It is the ability of an organization to have comprehensive visibility over its entire data landscape, including data pipelines, infrastructure, and applications. Data observability allows the organization to quickly identify, control, prevent, remediate, and fix data outages, all within agreed service level agreements (SLAs).

3. Elevate risk governance to the next level. NatWest is the UK's leading business bank and second-largest retail mortgage provider. Lengthy change implementation processes hindered risk governance at the bank. Initiating change projects or launching products and applications meant navigating layers of policy checks and approvals, which could take up to 73 days.

Customer experiences matter. And many businesses struggle to improve them. More often than not, a bad process is to blame: manual tasks, data spread across too many systems, or even just old ad hoc workflows that linger on because that’s how it’s always been done. But the cost of ignoring these issues is high.

The regulatory and business demands of financial services require teams to collaborate with consistency and accuracy—a difficult task when it comes to enterprise risk management (ERM). Business units often complete their risk management strategic objectives independently, and since each may have its own taxonomies and processes, human errors may go unnoticed, increasing potential risk.

Data provenance is a method of creating a documented trail that accounts for data’s origin, creation, movement, and dissemination. It involves storing the ownership and process history of data objects to answer questions like, “When was data created?”, “Who created the data?” and “Why was it created? Data Provenance is vital in establishing data lineage, which is essential for validating, debugging, auditing, and evaluating data quality and determining data reliability.