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Collaborative design for data driven projects
- 1. COLLABORATIVE DESIGN FOR DATA DRIVEN PROJECTS Carlos Prieto @MasterPrieto
- 2. Carlos Prieto PhD Survivor Human Data Interaction Have a love/hate relationship with vegemite.
- 3. The hard truth The price of light is less than the cost of darkness. Arthur C. Nielsen, Market Researcher & Founder of ACNielsen
- 5. Chile -10% Bookings since last Update
- 10. Peter MerholzKristin Skinner https://www.oreilly.com/ideas/realizing-the-potential-of-design
- 17. GANs Generative Adversarial Networks
- 19. We are using data as a proxy for behavior
- 21. 1 Choice of measurement metrics for the project Metrics of predictability Efficiency metrics Productivity metrics
- 23. 3 Data analysis
- 25. Inductive Qualitative: Hermeneutic, Semiotics or Narrative and Metaphor
- 28. Use Data to Identify Your Actual Pain Points
- 29. Use Data to Identify New Opportunities
- 30. 4 Make decisions Everyone on board Delegate actions Collect more data
- 32. Data are just summaries of thousands of stories – tell a few of those stories to help make the data meaningful. Chip & Dan Heath, Authors of Made to Stick, Switch
- 33. Prices 1st Qtr 2nd Qtr 3rd Qtr 4th Qtr Prices 1st Qtr 2nd Qtr 3rd Qtr 4th Qtr
- 35. 0 1 2 3 4 5 Category 1 Category 2 Category 3 Category 4 Employers for young workers Series 3 Series 2 Series 1 0 1 2 3 4 5 6 Category 1 Category 2 Category 3 Category 4 Employers for young workers Series 3 Series 2 Series 1
- 37. Insights Collaboration helps with biased and decentralized sources of data. Make it easy for everyone to understand. Analysis is never finished only abandoned.
- 38. We have some PhD scholarship for 2018. The topics are: 1. Multimodal analytics 2. Student-facing Dashboards 3. Human-Centred Design for LA 4. Interoperability Do your PhD in Sydney! More info at: utscic.edu.au
Editor's Notes
- 0:00 Hello everyone, first of all thank you for inviting me to design research today. It’s really good to see how your work have such a huge impact in Australia innovation. Now, I will like to talk to you about the challenge of collaborative design for data driven projects. Why collaboration and data must be in the same sentence and how non strategic collaboration have an impact in users life.
- 1 min 00:00 My name is Carlos Prieto and I’m a PhD survivor at the connected intelligence centre in UTS. I’m interested in understand the relationship humans have with data. I had work as a consultant in UX design for companies like booking.com, world bank, kayak and running research projects in universities.
- 30 sec 01:30 The price of light is less than the cost of darkness, When it comes to innovation, Companies can’t afford not having data shaping their products. We collect, share and make information our first ally between departments. Data driven products are new to the innovation ecosphere, giving use interesting insight every day.
- 3 min Let me tell you a story from back in the days when a huge hospitality/travel company started to deploy their system in latin America. To avoid saying names anything and not breach my NDA lets just refer to our client “The travel company” The company was looking for a new version to run in most latin Spanish countries. After a few weeks, some updates started to make into the new improved version completely translated into Spanish.
- 3 Min The problem started when in one particular country, Chile. The platform started to register less bookings since the last update. For a huge company like this, identifying specific issues is a big task that can’t be done without data-analytics systems. Marketing suggested to be the lack of online campaigns. Designers suggested to renew the front page and make it more localized. Bussines analyst suggestes that chile may have a problem in terms of taxes over hospitality services.
- 30 sec
- 1 Min
- 1 Min
- 2 Min 12:00 The must important is how data could not tell us how a context cultural term was the problem. As more advance data driven products are, we still have a long run in to situating context and human traits in our methods.
- 3 Min This is a traditional double diamond that our teams use frequently not that far from others people representations. There is an underneath problem when after many iterations( wich are very common for big companies) data produced becomes repetitive. Sometimes never analysed and constantly only using what is align with the company’s views. We have to come with a way to provide and analyse data collected from different departments as an agile flow. Data becomes a self participant with such a strong voice that not many want to deal with it.
- 3 Min So we implemented what we call the data analytics pipe. Let’s take the idea that participants should be able to come and grab relevant data for their current task. Data must be understandable, available and open to new analysis. Departments representatives bring their own views on what to look for and many times clash with others stakeholders interest.
- 1 Min Facebook uses deep neural networks – the foundation stones of deep learning – to decide which adverts to show to which users. The lack of collaboration with users through participatory supervision makes the product out of touch of what users actually want. Is interesting how data is e
- 1 Min 14:00 Sometimes is funny as people exposing themselves. Or realizing how creepy it is being traked all the time.
- Other times is disregarding the human side of using social networks and showing a dead relatives in your year review becomes a national topic Instead of how designers and engeineers are responsible of how the product behaves.
- 2 Min 16:00 COMPAS (Correctional Offender Management Profiling for Alternative Sanctions) Developer Northpointe Based on fairness 137 questions How often did you feel bored? How many times did you move in the last twelve months? Ranking 1-10 Black people are incarcerated at six times the rate white people.
- 2 Min 18:00
- 1Min 19:00 Insilico Medicine, a Baltimore-based biotech research company, One suggests new molecules that may have cancer-fighting properties; the other eliminates those suggestions based on known treatments.
- 2 Min 21:00
- 1 Min 22:00
- 1 Min 24:00
- 2 Min 26:00 (aka, what you are interested in measuring)
- 2 min 28:00 (aka, how you will make the measurements)
- 2 min 30:00 (aka, which information you are searching for) This is where the best and most difficult part comes in. The data you have collected are mostly raw, numerous and extremely detailed. To obtain information that can be useful in the decision-making process, you must filter, group together and process.
- 30 Sec deductive means reasoning from the particular to the general. If a causal relationship or link seems to be implied by a particular theory or case example, it might be true in many cases.
- 30 sec 31:00 An inductive approach is concerned with the generation of new theory emerging from the data.
- 30 Sec 32:00 Now, The current image shows what can be interpreted as noise for someone looking for a character. Lets take 30 seconds to play and find waldo in this same image. Please raise your hand once you find it. Alright, so what we did is a quick way to explain how different people withing the same objective takes different paths on analysing what they see.
- 30 Sec 33:00 Some of you started left to right
- (aka, how you will make the measurements)
- Its your duty to communicate your insight and corroborate with others. Not everyone have the same literacies.
- Dead to PieCharts The problem with pie charts is that they force us to compare areas (or angles), which is pretty hard. However, a chart meant to visualize size differences that forces you to do math to figure out size differences is not much better than a table, although it’s prettier.
- The introduction of elements for triggering management automatisms is anything but simple and must be approached gradually. The previous example of release of an application in test mode, started up automatically depending on the measured level of faults in the code, is an ideal simplification.