The Analytical DELTA

In their book Analytics at Work – Smarter Decision Better Results, Thomas Davenport, Jeanne Harris and Robert Morison explained the five success factors for implementing analytics initiatives: DELTA.

  • D for accessible, high-quality data: Good data is the prerequisite for everything analytical.  It’s “clean” in terms of accuracy and format.  Its meaning and use are commonly understood.  It’s integrated and consistent.  It’s accessible in data warehouses, or else easily found, filtered, and formatted on the fly.  Perhaps most fundamentally, it represents and measures something new, something important, or something important in a new way.
  • E for an enterprise orientation:
  1. Major analytics applications, those that really improve performance and competitiveness, invariably touch multiple parts of the enterprise
  2. If your applications are cross-functional, it doesn’t make sense to manage your key resources – data, analysts and technology – locally.
  3. Without an enterprise perspective, chances are you’ll have many small analytical initiatives but few, if any, significant ones.
  • L for analytical leadership: Organizations that really capitalize on analytics in their business decisions, processes, and customer relationships have a special kind of leadership.  Their senior managers are not just committed to the success of specific analytical projects; they have a passion for managing by fact.  Their long-term goal is not just to apply analytics in useful areas of the business, but to become more analytical in decision-making styles and methods across the enterprise.
  • T for strategic targets: An analytical target may be strong customer loyalty, highly efficient supply chain performance, more precise asset and risk management, or even hiring, motivating, and managing high-quality people.  Companies need targets because they cannot be equally analytical about all aspects of their businesses, and analytical talent isn’t plentiful enough to cover all bases.
  • A for analysts: Analysts have two chief functions: they build and maintain models that help the business hit its analytical targets, and they bring analytics to the organization at large by enabling businesspeople to appreciate and apply them.

Source: Davenport, T.H., Harris, J.G. & Morison, R. Analytics at Work – Smarter Decision Better Results. Harvard Business Press; Boston. 2010.