Tracking and analyzing how our individual products come together has always been an elusive problem for Steelcase. Our problem can be thought of in the following way: “we know how many Lego pieces we sell, yet we don’t know what Lego set our customers buy.” The Data Science team took over this initiative, which resulted in an evolution of our analytics journey. It is a story of innovation, resilience, agility and grit. The effects of the COVID-19 pandemic on corporate America shined the spotlight on office furniture manufacturers to solve for ways on which the office can be made safe again. The team would have never imagined how relevant our work on product application analytics would become. Product application analytics became an industry priority overnight. The proposal presented this year is the story of how data science is helping corporations bring people back to the office and set the path to lead the reinvention of the office space. After groundbreaking milestones to overcome technical challenges, the most important question is: What do we do with this? How do we scale this? How do we turn this opportunity into a true competitive advantage? The response: stop thinking about this work as a data science project and start to think about this as an analytics-enabled experience. During our session we will cover the technical elements that we overcame as a team to set-up a pipeline that ingests semi-structured and unstructured data at scale, performs analytics and produces digital experiences for multiple users. This presentation will be particularly insightful for Data Scientists, Data Engineers and analytics leaders who are seeking to better understand how to augment the value of data for their organization

1. Analytics-enabled experiences:
The new secret weapon
Jorge Lozano
Data Science
Kevin Tian
Data Engineering

2. Agenda
▪ So, what’s our problem?
▪ Turning data into
experiences
▪ How do you make it
happen?
▪ Where to go from here?

3. We unlock human
promise by creating
great work
experiences
wherever work
happens.

4. A century old organization.

5. So, what’s our problem?

6. “We know how many Lego pieces we sell, yet we don't
know what Lego set our customers buy."
A century old organization with a century old
problem.

7. Products Product Application
(Setting)
Sales Order
A century old organization with a century old
problem.

8. The effects of the COVID-19 pandemic on corporate America
shined the spotlight on office furniture manufacturers to
solve for ways on which the office can be made safe again.

9. Turning data into experiences

10. How is data science helping corporations bring
people back to the office and set the path to lead
the reinvention of the office space?

11. Information
Data
Distance
Division
Configuration
Orientation

12. 78% of workstations do not
have enough distance and
division to comply with standard
social distance measures.
What would happen if I add division?
What would happen if I add distance?

15. How do we turn this opportunity into a true competitive
advantage?
Stop thinking about this work as a data science project and
start to think about this as an analytics-enabled experience.
Experiences
Data

16. Space Scan:
Analytics-Enabled
Experience

17. It is not until you make it an experiences that you change
the game.
Data Information Insights Experiences
Experiences
Insights
Information
Data
“Start with a clearly
defined problem and let
that lead your data
needs.”
Product Coordinates
“Bring context to your
data. Understand what it
can and cannot tell you.
How can you enrich it?”
Frameworks
“The power of data
begins to surface when
you can make inference
and predictions”
Assessments
“Nothing is ever real
until you can experience
it”
Digital Experience

18. Enabling competencies

19. How did we do this?
Organizational
structure
Technical
competencies

20. Organizational structure
You need a Data Science team, not a team of Data Scientists.
Data Scientist Data Engineer ML Engineer Analytics Translator UX/UI Designer Product Owner

21. David Allen
If the only tool you have is a
hammer, it's hard to eat spaghetti.

22. Technical Competencies
Legacy
System
• Model deployment
• Deep Learning
• Distributed training

23. Unlock Data Value
Descriptive Diagnostic Predictive Prescriptive
Experiences
Insights
Information
Data
The way you know you are moving from one step to another is based on what
the data can do for you.

24. Data/Descriptive
• Our big data problem
• Variety: json, xml, pdf, dwg, etc.
• Veracity:
• json
• xml
Descriptive
Data

25. Data Veracity Challenge
XML
JSON

26. Data/Descriptive
• Our big data problem
• Variety: json, xml, pdf, dwg, etc.
• Veracity:
• json
• xml
• Deliver descriptive analytics faster
• Reduced time from days to minutes
Descriptive
Data

27. Information/Diagnostic
• Enable exploratory data analysis
• Making data accessible for exploration
• Built-in visualization
• Collaborative Notebooks
• Cross-team collaboration
• No more ”it works on my machine…”
• Diagnostic analytics with quality control
Diagnostic
Information

28. Insight/Predictive
• Scale on demand
• 16 machines X 1 day vs 1 machine X 16 days
• More machines will not solve all your problems
• Accelerating the success and failure
Predictive
Insights

29. Accelerating the success and failure

30. Insight/Predictive
• Scale on demand
• 16 machines X 1 day vs 1 machine X 16 days
• More machines will not solve all your problems
• Accelerating the success and failure
• e.g. where we were counting “headrests” as if they were
seats
• Space Scan Model: Detect high risk setting
• Question: How can we embed the model into current
process?
Predictive
Insights

31. Experiences/Prescriptive
• Digital Experiences embedded in the dealer’s native
tools
• Containerization
• Plug in on our dealer’s tools that sends the data to the model, executes
and provides a report back.
• Edge deployment
• Faster response time
• Reduced data privacy concern
Prescriptive
Experiences

32. Where are we headed next?

33. People will return to the office, but they will expect
something different.
▪ The office is here to stay, but its role is set to change
▪ Few executives think company culture will survive a
purely remote working set up
▪ Leading organizations are set on bringing their
employees back
▪ It's time to put human metrics ahead of building
metrics.
▪ People have new needs and expectations, requiring
shifts in the way we think about buildings and the
workplace.
▪ New design principles will prevail

34. How to prepare for the office revolution?
data
Data Strategy
Products
Business Models
Customer Experiences
Organizational Performance
Talent
Value generation from a digital thread
unlocks potential across our business
today & tomorrow

35. How to prepare for the office revolution?
data
Data Strategy
“Near real time” data collection
Self-service analytics
Catch the trend
Value generation from a digital thread
unlocks potential across our business
today & tomorrow

36. Thanks!

37. Feedback
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