Analytics-Enabled Experiences: The New Secret Weapon
0 likes199 views
The document discusses the essential role of analytics-enabled experiences in transforming data into valuable insights for organizations, particularly in the context of adapting office spaces post-COVID-19. It emphasizes the importance of understanding data's context, creating a dedicated data science team, and redesigning workplace experiences to meet evolving employee expectations. The text also highlights future trends in office design and the necessity for organizations to prioritize human metrics over traditional building metrics.
Analytics-Enabled Experiences: The New Secret Weapon
- 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?
- 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
- 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
- 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
- 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 Your feedback is important to us. Don’t forget to rate and review the sessions.