Book Image

IBM Cloud Pak for Data

By : Hemanth Manda, Sriram Srinivasan, Deepak Rangarao
3 (1)
Book Image

IBM Cloud Pak for Data

3 (1)
By: Hemanth Manda, Sriram Srinivasan, Deepak Rangarao

Overview of this book

Cloud Pak for Data is IBM's modern data and AI platform that includes strategic offerings from its data and AI portfolio delivered in a cloud-native fashion with the flexibility of deployment on any cloud. The platform offers a unique approach to addressing modern challenges with an integrated mix of proprietary, open-source, and third-party services. You'll begin by getting to grips with key concepts in modern data management and artificial intelligence (AI), reviewing real-life use cases, and developing an appreciation of the AI Ladder principle. Once you've gotten to grips with the basics, you will explore how Cloud Pak for Data helps in the elegant implementation of the AI Ladder practice to collect, organize, analyze, and infuse data and trustworthy AI across your business. As you advance, you'll discover the capabilities of the platform and extension services, including how they are packaged and priced. With the help of examples present throughout the book, you will gain a deep understanding of the platform, from its rich capabilities and technical architecture to its ecosystem and key go-to-market aspects. By the end of this IBM book, you'll be able to apply IBM Cloud Pak for Data's prescriptive practices and leverage its capabilities to build a trusted data foundation and accelerate AI adoption in your enterprise.
Table of Contents (17 chapters)
Section 1: The Basics
Section 2: Product Capabilities
Section 3: Technical Details

Financial planning and analytics

The customer challenge was that an online distribution company wanted to scale its planning operations and bring cross-organizational collaboration into the planning process. They had challenges in scaling their existing planning solution, which also lacked effective collaboration capabilities.

Traditionally, all businesses do Financial planning and analytics (FP&A). This involves multiple finance teams using hundreds of interconnected spreadsheets for planning and reporting. This is a very time-consuming, error-prone process and limits the amount and number of scenarios that can be used. In this era, organizations are looking to go beyond finance to a more collaborative and integrated planning approach known as FP&A. This involves multiple teams throughout the organization all collaborating with the goal of gaining forecast accuracy and the ability to quickly respond to changes in market conditions.

The planning analytic service in CP4D...