Systems | Development | Analytics | API | Testing

Data analytics is the science of analyzing raw data to draw conclusions about it. The process involves examining extensive data sets to uncover hidden patterns, correlations, and other insights. With today’s technology, data analytics can go beyond traditional analysis, incorporating artificial intelligence (AI) and machine learning (ML) algorithms that help process information faster than manual methods.

Online Analytical Processing (OLAP) is a computing technology through which users can extract and query data to analyze it from different points of view. These typically high-speed and complex queries are performed on massive data volumes stored in a data lake, data warehouse, or other large-scale repositories. Typical OLAP applications include business intelligence (BI) and forecasting and reporting.

Think about the monotonous task of extracting data from sources like spreadsheets and emails and typing it into various systems. Repetitive tasks and manual workflows lurking inside business processes slow down teams, introduce human error, and erode employee engagement. Slow, ineffective processes can also kill an organization’s time to market and customer satisfaction.

Medical affairs has grown to become a core pillar of the pharmaceutical industry, linking the work of research and development and commercial operations. However, for leaders in the industry, navigating the process and data complexities and communication silos involved can prove challenging.

Data governance covers processes, roles, policies, standards, and metrics that help an organization achieve its goals by ensuring the effective and efficient use of information. It sets up the processes and responsibilities necessary to maintain the data’s quality and security across the business. Data governance manages the formal data assets of an organization.

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.