Analytics: 10 Gründe warum Projekte scheitern

  • Beitrags-Kategorie:Analytics

Warum scheitern Analytics-Projekte? Wir schauen uns 10 Gründe an.

1. The executive team doesn’t have a clear vision for its advanced-analytics programs
In our experience, this often stems from executives lacking a solid understanding of the difference between traditional analytics (that is, business intelligence and reporting) and advanced analytics (powerful predictive and prescriptive tools such as machine learning).
To illustrate, one organization had built a centralized capability in advanced analytics, with heavy investment in data scientists, data engineers, and other key digital roles. The CEO regularly mentioned that the company was using AI techniques, but never with any specificity.

In practice, the company ran a lot of pilot AI programs, but not a single one was adopted by the business at scale. The fundamental reason? Top management didn’t really grasp the concept of advanced analytics. They struggled to define valuable problems for the analytics team to solve, and they failed to invest in building the right skills. As a result, they failed to get traction with their AI pilots. The analytics team they had assembled wasn’t working on the right problems and wasn’t able to use the latest tools and techniques. The company halted the initiative after a year as skepticism grew.
First response: The CEO, CAO, or CDO—or whoever is tasked with leading the company’s analytics initiatives—should set up a series of workshops for the executive team to coach its members in the key tenets of advanced analytics and to undo any lingering misconceptions. These workshops can form the foundation of in-house “academies” that can continually teach key analytics concepts to a broader management audience.
2. No one has determined the value that the initial use cases can deliver in the first year
Too often, the enthusiastic inclination is to apply analytics tools and methods like wallpaper—as something that hopefully will benefit every corner of the organization to which it is applied. But such imprecision leads only to large-scale waste, slower results (if any), and less confidence, from shareholders and employees alike, that analytics initiatives can add value.

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