Do you have a method for seeing all the data that passes through your organization? The need to democratize access to data and analytics, automate complex and tedious business processes, and amplify human output has led to analytic process automation (APA). Thousands of organizations across nearly every business and industry vertical use this software to accelerate data-driven business outcomes. This report examines the power of APA using technology, business, and real-world examples. If you're a technical business, analytics, or business intelligence leader, you'll learn how to use APA to tackle complex problems, increase productivity, and improve efficiency. You'll discover what APA means for your business and for you. This report explores: The importance of data: understand how data is transformed into information and insights for making business decisions Gathering data with APA: learn how APA differs from your current process Data democratization: grant data access to employees and empower them to analyze specific tasks and performance Data reporting: learn how APA blends data tables, fields, and values to help you search for insights at a granular level Analytics: explore new tools that use AI and ML to improve the analytic process

Professionals are challenged each day by a changing landscape of technology and terminology. In recent history, especially the last 25 years there has been an explosion of terms and methods born that automate and improve decision-making and operations.

More than half of the analytics and machine learning (ML) models created by organizations today never make it into production. Instead, many of these ML models do nothing more than provide static insights in a slideshow. If they aren’t truly operational, these models can’t possibly do what you’ve trained them to do. This report introduces practical concepts to help data scientists and application engineers operationalize ML models to drive real business change. Through lessons based on numerous projects around the world, six experts in data analytics provide an applied four-step approach—Build, Manage, Deploy and Integrate, and Monitor—for creating ML-infused applications within your organization. You’ll learn how to: Fulfill data science value by reducing friction throughout ML pipelines and workflows Constantly refine ML models through retraining, periodic tuning, and even complete remodeling to ensure long-term accuracy Design the ML Ops lifecycle to ensure that people-facing models are unbiased, fair, and explainable Operationalize ML models not only for pipeline deployment but also for external business systems that are more complex and less standardized Put the four-step Build, Manage, Deploy and Integrate, and Monitor approach into action

Business decisions today are tactical and strategic at the same time. How do you respond to a competitor’s price change? Or to specific technology changes? What new products, markets, or businesses should you pursue? Decisions like these are based on information from only one source: data. With this practical report, technical and non-technical leaders alike will explore the fundamental elements necessary to embark on a data analytics initiative. Is your company planning or contemplating a data analytics initiative? Authors Brett Stupakevich, David Sweenor, and Shane Swiderek from TIBCO guide you through several analytics options. IT leaders, product developers, analytics leaders, data analysts, data scientists, and business professionals will learn how to deploy analytic components in streaming and embedded systems using one of five platforms. You’ll examine: Analytics platforms including embedded BI, reporting, data exploration & discovery, streaming BI, and data science & machine learning The business problems each option solves and the capabilities and requirements of each How to identify the right analytics type for your particular use case Key considerations and the level of investment for each analytics platform