Wiley & sas business Series



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Big Data, Big Innovation full

   Common Characteristics 
 Organizations do function at this level. They just carry far more cost 
and operational risk than they need to. They also rarely manage to 
coalesce their latent capabilities into any real form of differentiation. 
 Some indicators of an organization operating at this level are:
 

   Personal tools.  Analysts use a wide variety of desktop-centric 
tools with choice largely defi ned by personal preference. 
 

  Data-focused effort.  Analysts spend signifi cant amounts of 
time trying to source, manage, and exchange data between 
semi-compatible tools. 
 

  Fragmented data.  Data is fragmented and centered around 
product, process, or at best, organizational silos. 


T H E   I N T E L L I G E N T   E N T E R P R I S E


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  Perpetual reinvention.  Team-members each create their own 
repository and usually start from scratch every time they have 
a new project. 
 

  Undefi ned  processes.  Processes are largely undefi ned, 
extremely manual, and require substantial effort to execute. 
 

  Unaware and overpaid.  Vendor management for analytics 
tools is limited and largely ineffectual, forcing higher-than- 
necessary licensing costs. 
 

  Selfi sh hoarding.  Analytical data is stored on individuals ’ per-
sonal computers and very little (if any) reuse occurs between 
analysts. 
 

  Big-chief syndrome.  Competencies are linked to the individ-
ual and skills are a source of job protection. 
 

  The haystack method.  Competencies are rarely applied across 
projects in a consistent way and fi nding value is as diffi cult as 
searching for a needle in a haystack.   

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