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Many Factors Drive Analytical Data Infrastructure (ADI) Plans and Decisions

Introduction

As organizations plan, develop, and execute a business intelligence (BI) and analytics strategy, they also need to include an assessment of their analytical data infrastructure (ADI) options.

We define ADI as: a set of technology components for integrating, modeling, managing, storing, and accessing the data sets that serve as sources for analytic/BI consumers, e.g., analytic/business applications, tools, and users.

Developing a business and technical strategy for cross-functional, multiple-use-case BI and analytics projects is more difficult than ever due to the:

  • Range of innovation
  • Variety of ADI platform capabilities
  • Diversity of use cases
  • Absence of corporate standards and governance

When deciding on ADI priorities and selection criteria, important factors to consider include:

  • Scale and performance
  • Security and privacy capabilities
  • Data modeling and management capabilities for relevant data types
  • Data-integration support
  • Analytical features embedded in the ADI
  • Development features and services
  • Deployment options

Executive Summary

  • The two primary factors driving most ADI platform decisions are (a) security of the data and analytic workloads and (b) scale and performance that can meet digital business transformation needs.
  • The current ADI “standard” for most organizations can’t adequately address new and existing BI and analytic use cases, associated data workflows, and multiple business preferences and priorities.
  • Different business functions have different priorities, deployment preferences, and BI and analytics use cases—all of which will complicate ADI platform decisions.
  • Many organizations are still evaluating whether to—or when to—migrate business processes and related BI and analytic tools and data infrastructure workflows to cloud services.

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