Process of switching to Data-Driven Requirements for User-Story creation. It has information about internal JA tools, which isn't useful for anyone outside the company.

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Data-Driven
User
Stories
VLAD MYSLA

2. Data collection is the process of
gathering and measuring information
on variables of interest, in an
established systematic fashion that
enables one to answer stated research
questions, test hypotheses, and
evaluate outcomes.
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Five W’s
1
Who
What
Where
When
Why
Regardless of the field of study or
preference for defining data
(quantitative, qualitative), accurate
data collection is essential to
maintaining the integrity of research.

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Hypothesis to User Stories
2
Who is your target audience?
What
Where
When
Why
MOST LIKELY
- SEO
- Mobile
- Subscribers

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Hypothesis to User Stories
3
Who is your target audience?
What you are going to provide?
Where
When
Why
MOST LIKELY
- UX
- Offer
- Speed

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Hypothesis to User Stories
4
Who is your target audience?
What you are going to provide?
Where this makes sense?
When
Why
MOST LIKELY
- SIP
- Funnel

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Hypothesis to User Stories
5
Who is your target audience?
What you are going to provide?
Where this makes sense?
When this makes sense?
Why
MOST LIKELY
- Random*
- Close site
- No actions
* - statistical distribution

7. 푛
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Hypothesis to User Stories
6
Who is your target audience?
What you are going to provide?
Where this makes sense?
When this makes sense?
Why might this idea work?
MOST LIKELY
Next, think through the data that can help answer your question, and develop a plan for it
the time someone says, “Ok, let’s begin.”? Or the time the real business of the meeting
starts? Does kibitzing count?
While the exercise is very much a how-to, each step also illustrwork, like consistently late-starting
meetings. Whatever it is, form it up as a question and write it down: “Meetings
always seem to start late. Is that really true?”
푥 + 푎 푛 =
푘=0
푛
푘
푥푘푎푛−푘
it the time someone says, “Ok, let’s begin.”? Or
starts? Does kibitzing count?

8. Data is everything
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9. 8
Data-Driven: Acceptance Criteria, Requirements, Definition of Done
Who might be interested in data?
Ops, Stakeholders, CEO, VP, PO, BI, Analytics, Developers, QA
Goals, Conversions, CTAs, Micro-Conversions, Navigation, Measured Activity
Client vs. Server, GA vs. CSRT vs. Event Sentry vs. NCV vs. Pixels
Goals, Metrics, Linked References, Performance, Errors, Tests
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What data should we collect?
Where data should be sent?
When it is enough data?
Why validate collected data?
Referenced API or Data might be broken, same as your code.. after somebody has changed it

10. Data collection: Google Analytics and Big Data
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GA
Premium Universal Google
Analytics
Big Data
In-house Hadoop
Data Base
Back-End API Measurement Protocol Outcome Events
Front-End API YES YES
Navigation Tracking YES YES
Event Tracking YES YES
Standard Reports Yes Yes
Custom Reports Yes By Request
SQL Queries Big Query YES
9

11. Su-Ha-Ri for Data: Su - discuss, validate, validate, repeat
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Use
Vocabulary
• Do not call
same things
differently
• Do not use
same term for
different things
Introduce
monitoring
• Integration
and Unit Tests
• Reports
• Alerts
Keep it
Raw
• Avoid
changing
original data
if it is collected
• Avoid data
changes after
it was saved
Include
References
• Avoid
duplicated
data collection
• Collect data
with references
on relevant
objects
10
1 2 3 4

12. Data-Driven: Questions you should always ask
11
client vs. server, redis vs. sql vs. solr, javascript vs. cookies vs. url vs. session
guest user can’t have subscriptions, cached page might contain wrong data
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Who owns the data?
“Who Owns What”, skype Chats, email BI team
What the data does or means?
real life vs. implemented data models vs. your vision
Where it is located?
When it should be used?
Why it is used that way?
hacks, assumptions and curious solutions vs. simple usage in general way

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