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

Enabling trust with data quality

Data quality is a cornerstone of business operations in the organization, and it is affected by the way data is entered, stored, and managed. The reason for a lack of confidence in information is because information is pervasive across the organization. We are dealing with fragmented silos of data that were accumulated through many years, without data quality measures and without being organized in a way that makes sense to the business.

High-quality data is essential for high-quality outcomes for any analytics we perform using the data. Data quality entails the following:

  • Completeness: Having a good understanding of all the related data assets.
  • Accuracy: Common data problems such as missing values, incorrect reference data, and so on that must be eliminated so that we have consistent data.
  • Availability: Data must be available on demand.
  • Timeliness: The up-to-dateness of the data is crucial to making the right decisions.