Contextual inquiry as a research method has gained its popularity these years among user experience practitioners. As a user researcher, we face excessive user data that are collected from field studies. Most of us review and analyze the field data by looking for trends of users’ responses and behaviors. For example, “Affinity diagram” has been commonly used to group and analyze the field data to identify any trends. However, in many cases, it is not enough to draw our conclusions based on a few “Aha!” moments. We should also consider the rich and “random” data that are not obvious to form trends, and abstract hidden implications from them. How we could accomplish it, however, has remained as a challenge. In this presentation, I will start with a case study from our own work, and demonstrate how we found the hidden implications from our data. Then we will explore and discuss strategies and techniques from different perspectives.

1. Wei Zhou, Oracle
UPA 2014
London

2. Agenda
 Topic Introduction (5 min)
 Case Study Presentation (20 min)
 Summary of Techniques Learned(5 min)
 Group Discussion for Cases and Possible Solutions
Using the Techniques (25 min)
 Wrap Up (5 min)

3. Topic Introduction
 Contextual inquiry is a user research method to collect field
data through inquiry and observations in the real
environment users live in or work in.
 The output from contextual inquiry is not structured data. It
does not always show direct links to new user requirements
and design issues.
 Analyzing and understanding the field data become a
discovery process. And it is always a challenge to find design
implications from such rich and unstructured data.

4. Topic Introduction and Challenges
 “Affinity diagram” has been used as a way to organize notes
and insights from field studies.
 From “Affinity diagram”, we could cluster the data into
trends.
 However, in many cases, it is not enough to draw our
conclusions based on a few “Aha!” moments.
 We should try to abstract hidden design implications from
the data that don’t seem to form a trend as well.

5. CASE STUDY
Oracle Financial User Experience: Finding the
Design Implications From the Differences

6. Site Visits Study for Designing Credit
Management and Collections System
• We visited 3 companies and interviewed 40+ users
at their sites.
• User Profiles:
• Credit manager
• Credit analyst
• Collections manager
• Collections analyst

7. Different Data for Credit Analysts
Credit Analyst – Release an order hold
Contact: Wei Zhou
From Schreiber Foods Inc
Open Crystal Report (Order holds
report for the entire team) from email
to retrieve case folder #, credit
exposure and customer’s name
(see Screenshot)
Go to EBS
Oracle Navigator
(see Screenshot)
Go to Oracle Collection
IEXH (See Screenshot)
Are credit exposure and order
behavior or pattern changed?
Thru “Application” link
Thru SFI Collections user role and Collection
Search Customer Name to find the customer with order holds
Crystal Report was sent periodically in
the morning, and then 3pm, 4:30pm…
Crystal Report was opened up in Excel
Go to Intranet
Start to retrieve customer information to
evaluate before doing anything on the
order hold
Select the customer
found in search result
(See Screenshot)
Frustration with Oracle Search, had to
put “%” in the beginning and the end of
the string to get result, otherwise, may
fail. Google search style is desired
Search Result has scrunched columns
every time and couldn’t view the whole
customer names. Hard to move the
column splitter.
Go to Customer
Detailed View (default
was “transaction”
Go to “General Tab” to
check credit, and WAP
(See Screenshot)
Ignores Transaction tab, go to General
Tab.
Click “Trend” button to see
Trend Information such as
historical WAPs and balances
(See Screenshot)
Upon closing the Trend dialog, May
also go to “Trend” tab to get
additional information
(See Screenshot)
Yes
Go to Order
Management Apps to
check the order
Here, user had to go back to EBS,
switch user role to OM view.
Collections view and OM can’t be open
at the same time!
No
2
Example: Company 1
Each company has their own
work flow and steps!
We also collected a lot of
screenshots from each
company.

8. Similarities From the Differences
 What is the ultimate goal? Release a held order!
 What are the reasons of those different steps?
 Review held orders
 Check customers’ credits, order history and payment
info
 Update credit recommendations for customers after
obtaining approvals, such as
 Release an order hold
 Increase/decrease credit limits
 If it is a new customer, check public credit information
and set up new account

9. Analysis of Differences
 Credit held orders were reviewed in different tools.
 Company 1 users: Crystal Report received by email
 Company 2 users: ExpressOS & BPCS system
 Company 3 users: GMIL
 Credit review was conducted in different third-party
applications
 Company 1 users: DNBi, SeaFax and SEC/Edgar
 Company 2 users: Global Credit
 Company 3 users: DNBi
 Held order release was completed in different tools.

10. Analysis of Differences
 Different authorities to release a held order
 Company 1 users: Credit analysts have authority to
release the orders.
 Company 2 users: Credit analysts provide hard-copy
credit review and obtain physical signatures of
management before releasing any orders, higher $
amount needs further approval.
 Company 3 users: Credit analysts have authority to
release the orders under certain $ amount. If an order $
amount is over the limit, escalate to manager to release
it.

11. Finding the Design Implications from
Differences
 Let’s drill deeper into the consolidated data
 Are there any reasons why no trends were seen at all?
 Why are they different?
 Are these differences actually related to a same topic?

12. Finding the Design Implications from
Differences
 the reason for the differences is because there were no
integrated solutions in the market to help our users!
 Users struggled with too many separate applications
to complete a single task. It is crucial to provide
appropriate seamless integration within a complete
task flow such as release a credit hold.

13. Another Example – Enrollment and Payment for
Continuing Education
 One University we visited, we found the different
schools and departments have its own customized or
home grown systems for Enrollment and Payment.
 They said they needed to apply different discounts for
the students who are government employees and
military family, plus ad hoc, random programs etc.
 The payments become more complicated due to
different discount programs and different ways to
apply the discounts.
 We observed 4 different departments’ current systems.

14. Another Example – Enrollment and Payment for
Continuing Higher Education
 Although they are all different, but yet not so different!
 Why are they so different? - We noticed the differences
are different design solutions, not the underlying
problems to solve!
 What are the underlying reasons? - apply various
discounts and be able to collect payments.
 We believe we can provide a simple streamlined
system for Continuing Higher Education.

15. Summary of the Technique
 Document and Analyze “Differences”
 Find “Similarities” from “Differences”
 Drill deeper beyond the surface of the data, ask:
 Are there any reasons why no trends were seen at all?
 Why are they different?
 Is underlying problem the same?
 Are these differences actually related to a same topic?
 Are these differences just different design solutions?

16. Group Discussion
 What kinds of data are difficult to analyze using affinity
diagrams? And why?
 What kinds of data have been ignored or discarded during
field data analysis? And why?
 What are the challenges and strategies to abstract hidden
design implications from field data?
 How many different perspectives could we analyze
consolidated field data? By similarities, by differences and
what else?
 Is there a method or process that we could come up with to
guide us step by step to find hidden design implications?

17. Wrap Up
 Summary of our discussions
 Any concerns?
 Contact: wei.x.zhou@oracle.com