Wiley & sas business Series

   The Need for Governance 
 Coordinating all the groups involved in business analytics can be 
extremely challenging. The greater the overhead needed to coordi-
nate activities, the harder it is for organizations to leverage business 

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analytics as a real competitive rather than just a useful source of 
insight. 
 Productivity and effi ciency comes from:
 
◼
  Defi ning an engagement model that identifi es the handover 
points between individuals 
 
◼
 Establishing standardized development and deployment processes  
Getting this right helps drive process effi ciencies, ensure quality con-
trol, and simplify the application of competencies across new prob-
lems. It also ensures that governance is tailored appropriately. Too 
much, and innovation suffers; too little, and operational risks increase. 
 It ’s a key part of an effective operating model. The major focus in 
this area is on managing workfl ow and facilitating collaboration and, 
just like integration and asset management, this occurs at two levels:
    1. 
 
Coordinating the development of analytical assets 
   2. 
 
Coordinating the deployment of analytical assets  
As with asset management, there is no reason why both of these activ-
ities can ’t take advantage of a common technology platform and man-
agement approach. It ’s important to remember, however, that while 
the vast majority of their requirements overlap, the level of emphasis 
placed on specifi c requirements varies between the two. 
 Achieving best practice within this process requires at a minimum:
 
◼
  Establishing a clear operating model that outlines roles, respon-
sibilities, and handover points 
 
◼
 Documenting and following standardized processes 
 
◼
 Having well-defi ned points of ownership with the power to 
make decisions 
 
◼
 Ensuring a high degree of quality through explicit quality con-
trol activities   
 Unclear processes almost always create highly variable outcomes 
and process ineffi ciencies. It ’s hard for an organization to drive con-
tinuous improvement when everyone follows a different process. 
Some people will naturally do things more effi ciently than others. 
Unfortunately, when everyone does things differently, it 
’s almost 

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impossible to replicate those effi ciencies. Having standard processes 
not only increases agility through making sure everyone has clarity 
on how to execute but also ensures that everyone benefi ts when team 
members fi nd new effi ciencies. 
 This does not necessarily mean that activities need to be defi ned to 
the lowest level of detail possible—a certain degree of pragmatism and 
realism needs to be applied when working out an appropriate level 
of granularity. It ’s also true that too much rigidity stifl es innovation; 
when people aren ’t given the freedom to experiment, improvements 
tend to be the fi rst thing that suffers. 
 Underpinning these processes are roles and responsibilities. To be 
effective, everyone must be crystal clear on what they are respon-
sible for delivering as well as when they need to get involved. This 
helps provide certainty as well as reduce transaction costs. By link-
ing roles to activities, the workfl ow system itself can automatically 
notify stakeholders when their interaction is required. If a champion 
model has been submitted by an analyst, the next logical step would 
be for the information management team to deploy that model 
into production and validate the predictions against the original 
model. Ideally, the system itself handles all the necessary notifi ca-
tions based on a combination of asset registration or workfl ow and 
process management. 
 Finally, effective governance requires a high degree of quality con-
trol. When it comes to dealing with operational systems, repeatability 
and transparency are critical. Every process must be exhaustively tested 
prior to moving it into production lest it fail and cost the organiza-
tion real money. Minimizing risk involves ensuring that standard tests 
are applied, making sure that appropriate signoffs are followed, and 
ensuring that outputs and predictions are the same in production as in 
development. While the checks will vary between information assets 
and analytical assets, the need for a high degree of quality control is 
a constant.