Introduction
Data and analytics functions aren’t new. Since the 1950s, data-driven decision-making processes have been used by organizations to improve business performance and gain an edge over the competition[1]. From the 1950s through the 1990s data and analytics functions were generally the domain of a few large companies. Beginning in the early 2000s breakthroughs in information management and widespread adoption of the World Wide Web, data, and the technology to store, process, and analyze it became available to almost every organization regardless of size.
In more recent years the extraordinary promise of data science, machine learning, and artificial intelligence has resulted in a desperate scramble to build data science teams in every organization. In many cases, the rationale seems to be “If you build it, value will come”. As a result, many organizations are adopting technology and analytical capabilities faster than their capacity to adapt. The rapid pace of change in technology, knowledge, skills, and abilities is creating confusion about the capabilities and value potential of data science.
Challenges are to be expected in a nascent field. Over time business managers and data and analytics functions will learn how to work together and add value, but until then the haphazard adoption of data and analytics functions across so many organizations and industries has the potential to harm business performance, careers, and the credibility of data and analytics at a monumental scale. It doesn’t have to be this way. Data and analytics functions will improve the performance of almost any organization with proper planning, execution, and integration.
This series of articles aims to identify and provide solutions to many of the challenges organizations, business managers, and data & analytics professionals face when developing data & analytics functions. The recommendations contained are primarily targeted at data & analytics teams and functions that support the business performance of an organization rather than groups that develop customer-facing products. You will also notice that I use the term data & analytics or D&A functions or teams. This is to cover the many names given to roles from data scientist to marketing analyst and so many more[2].
The series will cover the following topics with the potential for deeper dives into subtopics:
- Chapter 1: From Data to Value - Wherein we explore process-supported alignment of leaders and D&A teams.
- Chapter 2: All Models Are Wrong, Some Are Useful - Wherein we explore a hypothesis-driven approach to building practical intelligence, tools, and resources.
- Chapter 3: System of Organizational Intelligence - Wherein we explore a system that enables the continuous advancement of applied intelligence.
