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3. Gaming sector example

This company is in the business of selling game consoles of various sorts and Internet 

games, and it also has a big Exadata data warehouse that’s analysing that information. 

It is looking to augment its Exadata data warehouse with business decision analysis, 

and want to use it for what you might expect: better understand what the customers 

are doing out in the games. ‘They want to understand relationships between custom-

ers. One of the really interesting things in games is that people play games with each 

other. And you want to understand the social networks of people who are playing with 

each other because it’s likely that if one person in that network wants to do something, 

the others will want to do the same thing. So they can use that information to better 

upsell information in this game space.’ According to the company, integrating data 

alone does not generate value. Analytic modelling and tools to interpret the output is 

needed. They say ‘Advanced analytic models are needed to enable   data-  driven  opti-

mization (for example, of employee schedules or shipping networks) or predictions (for 

instance, about flight delays or what customers will want or do given their buying histo-

ries or  Web-  site  behavior)’. A plan is also needed to identify where models will create 

additional business value, who will need to use them, and how to avoid inconsisten-

cies and unnecessary proliferation as models are scaled up across the enterprise. 

They also note the importance of decision support tools. They say:

the output of modeling may be strikingly rich, but it’s valuable only if managers and, 

in many cases, frontline employees understand and use it. Output that’s too com-

plex can be overwhelming or even mistrusted. What’s needed are intuitive tools 

that integrate data into day-to-day processes and translate modeling outputs into 

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Part 3  Implementation

tangible business actions: for instance, a clear interface for scheduling employees, 

 fi ne-  grained  cross-  selling suggestions for  call-  center agents, or a way for marketing 

managers to make  real-  time decisions on discounts.  

 A key design issue is how the information about these transactions can usefully be 

applied to improve the effectiveness and business contribution of these systems. In 

 Chapter   12    we describe how web analytics systems are used to analyse and improve 

the effectiveness of  sell-  side e-commerce systems. 

 While independent analytics systems are available, larger companies are increas-

ingly finding that to apply the insights available they have to integrate web analytics 

information with internal sales and ordering systems to form data warehouses.   

  Design for digital technology projects 

 The   

design

  element of creating a digital business system involves specifying how the system 

should be structured and how the  end-  user functionality and ‘back-end’ integration with 

other projects will be implemented.   

 In the two  Focus on  sections that follow we look at two aspects of design that are of great 

importance to how digital business systems are perceived by customers – security and inter-

face design. Before that, we consider the overall architectural design of digital business 

systems.