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2017 State of Technology Training

2017 State of Technology Training

Pluralsight recently completed an in-depth survey of 300 enterprises...

IT Briefcase Exclusive Interview: Keeping Your (Manufacturing) Head in the Clouds

IT Briefcase Exclusive Interview: Keeping Your (Manufacturing) Head in the Clouds

with Srivats Ramaswami, 42Q
In this interview, Srivats Ramaswami,...

IT Briefcase Exclusive Interview: New Solutions Keeping Enterprise Business Ahead of the Game

IT Briefcase Exclusive Interview: New Solutions Keeping Enterprise Business Ahead of the Game

with Sander Barens, Expereo
In this interview, Sander Barens...

IT Briefcase Exclusive Interview: The Tipping Point – When Things Changed for Cloud Computing

IT Briefcase Exclusive Interview: The Tipping Point – When Things Changed for Cloud Computing

with Shawn Moore, Solodev
In this interview, Shawn Moore,...

Driving Better Outcomes through Workforce Analytics Webcast

Driving Better Outcomes through Workforce Analytics Webcast

Find out what’s really going on in your business...

Driving Business Analytics with the Consideration of Success Criteria

December 1, 2011 No Comments

Business analytics programs often center on “decision making.” These decisions might be high-level ones that the top brass of the company are using to steer corporate strategy, or they might be discrete operational decisions made by any number of individuals just trying to get their jobs done. But without the context of a yardstick for success, it is very difficult to objectively say that any decision was a good one.

At the same time, though, a common theme among data analysts and data managers is the concern that the usability of the data within an analytical system is sufficient to enable “good decision making.” That leaves us with a conundrum: the absence of success metrics prevents truly measuring decision quality, but the perception of poor or delayed decisions suggests the absence of success. This Gordian knot can be addressed with a quick slice of the knife by diverting from the implementation of the tools and techniques and considering what is actually meant by “success.”

A review of some of my early articles on straightforward analytics might suggest an idea. If you recall, we have spoken about high-level business drivers and how those can be broken down into more refined aspects of creating or improving value. At the highest level, we can focus on these four areas:

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DATA and ANALYTICS 

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