data-driven requirements for user-stories on justanswer
DESCRIPTION
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.TRANSCRIPT
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Data-Driven User Stories
VLAD MYSLA
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Five W’s1
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.
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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Hypothesis to User Stories2
Who is your target audience?
What
Where
When
Why
MOST LIKELY
- SEO- Mobile- Subscribers
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Hypothesis to User Stories3
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 Stories4
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 Stories5
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
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Hypothesis to User Stories6
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?”
it the time someone says, “Ok, let’s begin.”? Or starts? Does kibitzing count?
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Data is everythingScrum helps manage our teams when Lean helps us manage products
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Data-Driven: Acceptance Criteria, Requirements, Definition of Done8
Who might be interested in data?
What data should we collect?
Where data should be sent?
When it is enough data?
Why validate collected 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
Referenced API or Data might be broken, same as your code.. after somebody has changed it
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Data collection: Google Analytics and Big Data
GAPremium Universal Google
Analytics
Big DataIn-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
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Su-Ha-Ri for Data: Su - discuss, validate, validate, repeat
Use Vocabulary
• Do not call same things differently
• Do not use same term for different things
Introducemonitoring
• Integration and Unit Tests
• Reports
• Alerts
Keep itRaw
• 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
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Data-Driven: Questions you should always ask 11
Who owns the data?
What the data does or means?
Where it is located?
When it should be used?
Why it is used that way?
“Who Owns What”, skype Chats, email BI team
real life vs. implemented data models vs. your vision
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
hacks, assumptions and curious solutions vs. simple usage in general way
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Thank you
DI
GA
CSRT
HADOOP
BI
JA