A key struggle of data integration at an enterprise level can be summarized by the Last Mile Problem, which is often used to describe the large increase in difficulty and cost of transporting physical goods from distribution centres to customers. Data sitting in corporate databases faces a similar reality – different user segments will have different requirements of said data. The data therefore needs to be transformed and blended before it can be useful. Using FME and FME Server, we can streamline and automate business logic to shorten that ‘last mile’ between data centres and business applications. In this talk, we will cover the typical life cycle of a ‘last mile’ ETL and the organizational cultural changes that can supercharge improvement efforts.

1. The Last Mile Problem of
Geospatial Data

2. Presenter
The
Peak
of
Data
Integration
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Grace Cai
Digital Product Owner
Shell Canada Ltd.

3. Shell – a sense of our scale
Customers
32 million
Cash flow
$45.1 billion
from operating activities
served every day through 46,000
Shell-branded retail stations
R&D
$815 million
spent on Research and
Development
All metrics accurate as of end 2021
Employees
82,000
at December 31, 2021
Countries
70+
in which we operate

4. The
Peak
of
Data
Integration
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23
Agenda
1. Define the Last Mile Problem of Data
2. Describe the life cycle of a Last Mile ETL
3. When to create Last Mile ETLs

5. The
Peak
of
Data
Integration
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23
Last Mile Problem in the Physical World
Describes the large leap in difficulty and/or
cost to move people, goods, or services
between hubs and their final destination
● Transportation
● Supply Chain
● Telecommunications

6. The
Peak
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Data
Integration
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Last Mile Problem of Data
Barriers preventing data turning into insight that informs decisions:
• Trust in data
• Opaque ETLs
• Integration into existing workflow/decision making
• Solving the right problem

7. The
Peak
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Data
Integration
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What’s makes the Last Mile ETL special?
• It’s not meant for everyone, embrace the niche and chaos!
• 80% 100% solutions
• Truly agile solutions
• Cheap

8. The
Peak
of
Data
Integration
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The Last Mile ETL Life Cycle
● Discover
● Negotiate
● Build, Test, Deploy
● Socialize & Integrate
● Measure
https://xkcd.com/1098/

9. The
Peak
of
Data
Integration
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When NOT to create a Last Mile ETL
● A technological band-aid to a
process issue
● Leads to data silos or ‘bubbles of
excellence’
● Generate ‘nice to have’ insights
● Costs more than it returns
https://xkcd.com/1319/

10. The
Peak
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Integration
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Examples of Last Mile ETLs
● Dashboard feeders
● Report generators
● ‘Healing ETLs’
○ make doing the right thing,
the easiest thing

11. The
Peak
of
Data
Integration
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Replication
● Do not lift and shift
● Transfer of knowledge rather than code
● Build strong relationships across teams

12. Takeaways
● Find your niche and embrace the chaos
● Embed your ETLs into business processes
● Sometimes it’s okay to not scale
● Be your own Product Manager

13. ThankYou!
Lujia.Cai@shell.com

14. The
Peak
of
Data
Integration
20
23
“…data science projects fail not because of poor technology
or lack of available data. Nor is it because of a lack of talent or
technical skills. It's because of the last mile problem. Data
scientists are struggling to deploy their models into business
processes and applications where they are used by the
business.”
Bill Waid
Chief Product and Technology O cer at Fico
Solving The Last Mile Problem For Data Science Project Success