Talk from GIS-Pro 2016 on automating field data collection. Covers workflow design best-practices, how to assess requirements, and build iterative processes.
http://www.fulcrumapp.com/
3. field data automation ✦ fulcrumapp.com/gispro2016
About me
Coleman McCormick
12 years in mapping / GIS
7 years developing software products
Work with companies + organizations worldwide on
business process, data management, software
5. field data automation ✦ fulcrumapp.com/gispro2016
What we do
Software company in St Petersburg, FL est. 2009
Mobile form builder — web, iOS, Android
Workforce management
Automation & streamlining of data collection
SaaS subscription service
Worldwide customer base — 100+ countries
6. field data automation ✦ fulcrumapp.com/gispro2016
Field collection use cases
• Asset management
• Damage assessments
• Inspections
• Public works
• Regulatory audits
• On and on…
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Agenda
Goals and objectives in collecting data
Brief history of data collection
A look at workflow
Friction and its causes
Techniques
Some examples of automation in action
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Ideals + Goals
i need to collect some data.
what does “success” look like?
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What’s the ideal process?
• Think about goals and objectives
• What questions need answering?
• Who are the stakeholders? (Who’s asking the
questions?)
• Interviews — understand the “why” of the requirements
• Paint the picture — what does success look like?
• Tasking your field workforces
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Start by defining the successful final result,
and work backwards!
bit.ly/amazon-product-dev
Need consensus on goals from bottom to top
“First write the press release, then build the
product”
Meeting Mission Objectives
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On-Demand
• Leadership wants answers to questions now, not 3, 6,
or 12 months later
• Reporting on specific data views — the data I want,
when I want it
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On-Demand
• Event-driven decision making
• Push and pull to get answers — Notifications vs. self-
service reporting, hybrid environments
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Other key goals
• Cost savings — fieldwork is expensive; room for huge
savings
• Reliability — critical data can’t be wrong, costs incurred
with unreliable inputs or systems
• Speed — decisions can’t be made without the data in
hand!
• Compliance — proof of work, audit history
• No duplication of effort
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a rapid evolution in technology
A Brief History
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Pen and paper, notes, transcription
pain
time between collection + decision = wide gap
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Start at the beginning
• What questions need answering?
• What data do I need to get those answers?
• Who are the stakeholders?
• How will we get the needed info?
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Easy pitfalls
• Overengineering
• Minimize the “nice to haves”
• KISS
• YAGNI — “you aren’t gonna need it”
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• What’s the aggregate burden of capturing everything?
• If something slips through the cracks, which thing will
it be?
• Weighting expectations against the goals
• Classify things by “order of importance”
• Better yet — If you can’t envision how some
information will be used, why bother with it?
“If our team is going to be on site,
let’s just get everything we can…”
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A quick story about our approach to
workflow design…
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• JIFX field experiments — Camp Roberts, CA
• Goal: Field test technologies / tools for disaster relief scenarios
• Work with FEMA to field test mobile tools for damage
assessment data capture
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Friction
the source of our data management pains
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Automation =
the removal of friction
“Alexa, get me an Uber”
Tap a button →
Perform an action
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Common sources of friction
• Technology — tools are too complicated, systems
over-designed, “solution looking for a problem”
• Work — complexity of the work, steps in the
workflow
• Human factors — compliance, need-to-know,
organizational silos
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It’s not all bad, but should reduce what we can control
There is always some friction in the workflow
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Treat the sources, not the
symptoms.
If not, your workflow becomes
a patchwork of workarounds.
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Iteration
• Incremental reflection + improvement
• Look at whole workflow, seek frictions, test new
methods to reduce, re-deploy, re-test
• Scrum!
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A Few Techniques
demonstrating ways to reduce friction
(with tech)
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Some samples of what this
looks like
• Autofilling data
• Real-time QA
• StreetView integration
• Capturing weather data
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Automation in Action
examples from the field
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Automation in action
• A few diverse examples:
• Comcast — telecom
• HALO Trust — humanitarian aid
• Desert Channels — wildlife
• Century Engineering — civil engineering
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The HALO Trust
fulcrumapp.com/cases/halo-trust
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fulcrumapp.com/cases/desert-channels-queensland
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Century Engineering
fulcrumapp.com/cases/century-engineering
