This blog post is the first in a 6-part series that outlines a framework for implementing a Data and Analytics Program. The intention is not to delve into the
specific details of execution because there are many options that can work but to spell out the foundation and key
principles upon which a successful program is built. The information presented is an amalgam of details culled from multiple years of
research, experience, and conversations with others who have undertaken such an
endeavor. The guidelines and advice are based upon industry standards and best
practices along with lessons learned and the latest emerging technologies and
techniques.
Introduction
The first thing to recognize and accept is that
Data and Analytics is not an IT or even a technology initiative. It is a
business initiative that requires technical know-how and support. The concept of Data and Analytics needs to be deeply embedded in the organization at all levels so that
information and insights are actively sought, shared, and acted upon. Focusing
solely on the technologies involved and centralizing the initiative within IT
without significant collaboration are two very common reasons given for why Data
and Analytics programs fail. That said, this 6-part series is not going to be yet another "Top 10 Reasons Why Things Fail" story. Instead, we will look at how a company can implement a successful Data and Analytics
program and the roles that various colleagues play.
In part 2 of the series, we will start by spelling out our ambitions...
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