Registration & Networking in the Exhibition Area
January 23 | 08:00 - 08:45
Data Science New York
January 23-24, 2019
Conference Day 1
23 January
23 January
08:00
08:45
09:00
Turning Insights into Income - Growth Through Commercial Data Science
- Using Data Science to effectively examine which pipelines are still profitable and which are not
- Growing the market share by identifying weakness in both your firm and the competition
- Looking at alternative data: can you use public databases such as census data to understand where the enterprise should be investing more time and money?
Speaking:
Turning Insights into Income - Growth Through Commercial Data Science
January 23 | 09:00 - 09:30
- Using Data Science to effectively examine which pipelines are still profitable and which are not
- Growing the market share by identifying weakness in both your firm and the competition
- Looking at alternative data: can you use public databases such as census data to understand where the enterprise should be investing more time and money?
Speaking:
09:30
Why Big Data Efforts Are Failing to Deliver
- Organizations lack of Big Data Foundations
- Silos of knowledge
- Lack of finding people with the right "BIG" skill sets
- Tool and technology mismatch
Speaking:
Why Big Data Efforts Are Failing to Deliver
January 23 | 09:30 - 10:00
- Organizations lack of Big Data Foundations
- Silos of knowledge
- Lack of finding people with the right "BIG" skill sets
- Tool and technology mismatch
Speaking:
10:00
Research Paper: Loan Default Prediction in the Peer-to-Peer Market for Better ROI
- Presentation of a research paper on using AWS SageMaker to predict loan defaults in the peer-to-peer market
- Improving ROI through improved dataset identification and clasification
- Working with risk teams and regulators to explain the machine learning model and why credit was denied or approved
Speaking:
Research Paper: Loan Default Prediction in the Peer-to-Peer Market for Better ROI
January 23 | 10:00 - 10:30
- Presentation of a research paper on using AWS SageMaker to predict loan defaults in the peer-to-peer market
- Improving ROI through improved dataset identification and clasification
- Working with risk teams and regulators to explain the machine learning model and why credit was denied or approved
Speaking:
10:30
The Secret Sauce Behind Top Performing Data Science Teams
Building and growing a successful data science team is no easy feat.
This presentation will share best practices and examples based on experience of leading and growing an award-winning 40-people data science team from the grounds up, with focus on model building and experimenting.
Topics will include -
• Setting up technical infrastructure for model training
• Facilitating collaboration, knowledge sharing and transparency
• Ways to promote experimentation and ideation
Speaking:
The Secret Sauce Behind Top Performing Data Science Teams
January 23 | 10:30 - 10:45
Building and growing a successful data science team is no easy feat.
This presentation will share best practices and examples based on experience of leading and growing an award-winning 40-people data science team from the grounds up, with focus on model building and experimenting.
Topics will include -
• Setting up technical infrastructure for model training
• Facilitating collaboration, knowledge sharing and transparency
• Ways to promote experimentation and ideation
Speaking:
10:45
Coffee & Networking in the Exhibition Area
Coffee & Networking in the Exhibition Area
January 23 | 10:45 - 11:15
Speaking:
11:15
Panel Discussion: Customer Personalization - Leveraging Data Science and Technology to Deliver an Optimised Customer Journey
- Customers demand a personalized experience that delivers them value - organizations need to deliver this while optimizing their financial business metrics
- With productized Data Science (data, analytics, technology and product integration), this can be done at scale and at speed in many shapes and forms
- The perception of value of 1-1 personalization can be affected by individual customer's perception of a company's leveraging "their data" in ways they find infringing on their privacy.
- With GDPR, a customer can do more than just choose not to be a customer, they can demand you act – working with the customer is key
Speaking:
Panel Discussion: Customer Personalization - Leveraging Data Science and Technology to Deliver an Optimised Customer Journey
January 23 | 11:15 - 11:45
- Customers demand a personalized experience that delivers them value - organizations need to deliver this while optimizing their financial business metrics
- With productized Data Science (data, analytics, technology and product integration), this can be done at scale and at speed in many shapes and forms
- The perception of value of 1-1 personalization can be affected by individual customer's perception of a company's leveraging "their data" in ways they find infringing on their privacy.
- With GDPR, a customer can do more than just choose not to be a customer, they can demand you act – working with the customer is key
Speaking:
11:45
Cast Study: How Condé Nast are Boosting Both Subscription and Advertising Revenue Using Data Science Products
Speaking:
Cast Study: How Condé Nast are Boosting Both Subscription and Advertising Revenue Using Data Science Products
January 23 | 11:45 - 12:15
Speaking:
12:15
How to Drive Institutional Change to Increase Profit & Productivity as a Data Science Leader
- We all know the value of data science to an organization, but leading the drive for real institutional change is not as easy as it seems
- This session will examine the techniques and pitfalls when using the data as a tool for widespread change
- It will use real world examples and case studies to provide you with the tools you need to be the force for change at your company
Speaking:
How to Drive Institutional Change to Increase Profit & Productivity as a Data Science Leader
January 23 | 12:15 - 12:45
- We all know the value of data science to an organization, but leading the drive for real institutional change is not as easy as it seems
- This session will examine the techniques and pitfalls when using the data as a tool for widespread change
- It will use real world examples and case studies to provide you with the tools you need to be the force for change at your company
Speaking:
12:45
Panel Discussion: How to Make Sure Your Synthetic Dataset Produces Usable Results
- Privacy, data ownership, and data-hungry algorithms – synthetic datasets are becoming a major asset to any Chief Data Scientist
- Synthetic datasets take time to build and are difficult to get right – what models are working in the real world?
- Research and the future: will we ever see perfect synthetic datasets?
Speaking:
Panel Discussion: How to Make Sure Your Synthetic Dataset Produces Usable Results
January 23 | 12:45 - 13:15
- Privacy, data ownership, and data-hungry algorithms – synthetic datasets are becoming a major asset to any Chief Data Scientist
- Synthetic datasets take time to build and are difficult to get right – what models are working in the real world?
- Research and the future: will we ever see perfect synthetic datasets?
Speaking:
1:15
Lunch & Networking in the Exhibition Area
Lunch & Networking in the Exhibition Area
January 23 | 13:15 - 14:15
Speaking:
Track A | People
2:15
Discussion Group 1A: Hiring the Unicorn: What Makes a Great Data Scientist?
Speaking:
Discussion Group 1A: Hiring the Unicorn: What Makes a Great Data Scientist?
January 23 | 14:15 - 15:00
Speaking:
Track B | Data Science Transformation
2:15
Discussion Group 1B: Data Science Advocacy: Educating and Working with the Business Stakeholders
Speaking:
Discussion Group 1B: Data Science Advocacy: Educating and Working with the Business Stakeholders
January 23 | 14:15 - 15:00
Speaking:
Track C | Law and Ethics
2:15
Discussion Group 1C: Discrimination and Privacy - What are the Key Legal and Ethical Considerations for the Chief Data Scientist?
Speaking:
Discussion Group 1C: Discrimination and Privacy - What are the Key Legal and Ethical Considerations for the Chief Data Scientist?
January 23 | 14:15 - 15:00
Speaking:
Track A | People
3:00
Discussion Group 2A: Structuring Data Science Teams for Maximum Efficiency
Speaking:
Discussion Group 2A: Structuring Data Science Teams for Maximum Efficiency
January 23 | 15:00 - 15:45
Speaking:
Track B | Data Science Transformation
3:00
Discussion Group 2B: Transforming Traditional Industries into Data Science Champions
Speaking:
Discussion Group 2B: Transforming Traditional Industries into Data Science Champions
January 23 | 15:00 - 15:45
Speaking:
Track C | Law and Ethics
3:00
Discussion Group 2C: Data Science’s Impact on the Workforce - Job Loss, Creation and Evolution
Speaking:
Discussion Group 2C: Data Science’s Impact on the Workforce - Job Loss, Creation and Evolution
January 23 | 15:00 - 15:45
Speaking:
3:45
Coffee & Networking in the Exhibition Area
Coffee & Networking in the Exhibition Area
January 23 | 15:45 - 16:15
Speaking:
4:00
Building the Homo Deus – How Augmenting Workers with AI Will Lead to a More Productive Workforce
- The enormous effort devoted to developing “artificial intelligence” that mimics human decision-making may be misplaced and even counterproductive for many organizations.
- For knowledge work, in particular, we should not strive for AI systems to replace humans, since we run the risk of reifying the cognitive and organizational biases that already exist.
- Instead, we should harness the power of computers to perform tasks that human experts are not very good at accomplishing (such as situational awareness), leaving more time in the workday for human experts to do what they are good at (thinking creatively and identifying causal relationships).
- By building joint human-machine platforms, we can efficiently share knowledge across an organization and prioritize what additional information to collect.
- This approach can also ease the transition from today’s workplace to a future one, by starting with expertise-driven causal explanations and by requiring AI methods to be understandable.
In addition to describing a future workflow in which we leverage the best of human and machine intelligence, this presentation also points out the challenges in moving to this future. Specifically, the presenter will discuss key technical and cultural gaps that will need to be overcome if we are to get greater value out of the data we possess and collect and to enable data-driven decision-making at scale. These gaps represent priority areas for research funding, which business leaders should support.
Speaking:
Building the Homo Deus – How Augmenting Workers with AI Will Lead to a More Productive Workforce
January 23 | 16:00 - 16:30
- The enormous effort devoted to developing “artificial intelligence” that mimics human decision-making may be misplaced and even counterproductive for many organizations.
- For knowledge work, in particular, we should not strive for AI systems to replace humans, since we run the risk of reifying the cognitive and organizational biases that already exist.
- Instead, we should harness the power of computers to perform tasks that human experts are not very good at accomplishing (such as situational awareness), leaving more time in the workday for human experts to do what they are good at (thinking creatively and identifying causal relationships).
- By building joint human-machine platforms, we can efficiently share knowledge across an organization and prioritize what additional information to collect.
- This approach can also ease the transition from today’s workplace to a future one, by starting with expertise-driven causal explanations and by requiring AI methods to be understandable.
In addition to describing a future workflow in which we leverage the best of human and machine intelligence, this presentation also points out the challenges in moving to this future. Specifically, the presenter will discuss key technical and cultural gaps that will need to be overcome if we are to get greater value out of the data we possess and collect and to enable data-driven decision-making at scale. These gaps represent priority areas for research funding, which business leaders should support.
Speaking:
4:45
Panel Discussion: Writing the Trust Contract – How Do You Help Foster Trust in the Data Science Process and its Results?
- While most enterprise leaders view data science as an integral part of the modern business, forming a trust contract is still an incredibly difficult task
- How do we overcome the mistrust in both the complex process and the fact data science is often iterative?
- What can we do to make sure business leader’s understand data science’s value beyond the “magic wand” projects?
Speaking:
Panel Discussion: Writing the Trust Contract – How Do You Help Foster Trust in the Data Science Process and its Results?
January 23 | 16:45 - 17:15
- While most enterprise leaders view data science as an integral part of the modern business, forming a trust contract is still an incredibly difficult task
- How do we overcome the mistrust in both the complex process and the fact data science is often iterative?
- What can we do to make sure business leader’s understand data science’s value beyond the “magic wand” projects?
Speaking:
5:15
5:30
Drinks Reception & Networking in the Exhibition Area
Drinks Reception & Networking in the Exhibition Area
January 23 | 17:30 - 18:30
Speaking:
Conference Day 2
24 January
24 January
08:15
Registration, Coffee & Networking in the Exhibition Area
Registration, Coffee & Networking in the Exhibition Area
January 24 | 08:15 - 08:45
Speaking:
08:45
09:00
Interactive Spatio Temporal Intelligence - a new Frontier for Analytics and Data Science
Speaking:
Interactive Spatio Temporal Intelligence - a new Frontier for Analytics and Data Science
January 24 | 09:00 - 09:30
Speaking:
09:30
Utilizing Data Science Techniques to Improve Enterprise Risk Management Models
Speaking:
Utilizing Data Science Techniques to Improve Enterprise Risk Management Models
January 24 | 09:30 - 10:00
Speaking:
10:00
Panel Discussion: Shrinking the Haystack - Combatting Fraud and Insider Threat with Advanced Monitoring Solutions for High Fidelity Alerts
- Insider threat is a very small proportion of employee behaviour, so how can we help our models to differentiate between legitimate access and illegal activity?
- Can cognitive computing shrink the haystack to identify insider threat and fraud in real time?
- Different techniques used to improve the detection rates and filter out the noise of false positives at scale
Speaking:
Panel Discussion: Shrinking the Haystack - Combatting Fraud and Insider Threat with Advanced Monitoring Solutions for High Fidelity Alerts
January 24 | 10:00 - 10:30
- Insider threat is a very small proportion of employee behaviour, so how can we help our models to differentiate between legitimate access and illegal activity?
- Can cognitive computing shrink the haystack to identify insider threat and fraud in real time?
- Different techniques used to improve the detection rates and filter out the noise of false positives at scale
Speaking:
10:30
Coffee & Networking in the Exhibition Area
Coffee & Networking in the Exhibition Area
January 24 | 10:30 - 11:00
Speaking:
Track A | Data
11:00
Discussion Group 3A: What Are The Most Effective Strategies For Scaling Your Data Science Infrastructure?
Speaking:
Discussion Group 3A: What Are The Most Effective Strategies For Scaling Your Data Science Infrastructure?
January 24 | 11:00 - 11:45
Speaking:
Track B | Methodologies & Creativity
11:00
Discussion Group 3B: How Do You Prevent Group Think in a Data Science Team?
Speaking:
Discussion Group 3B: How Do You Prevent Group Think in a Data Science Team?
January 24 | 11:00 - 11:45
Speaking:
Track A | Data
11:45
Discussion Group 4A: Legacy Data, Legacy Systems, And Legacy Processes – How Can We Minimize Time Spent On Data Governance?
Speaking:
Discussion Group 4A: Legacy Data, Legacy Systems, And Legacy Processes – How Can We Minimize Time Spent On Data Governance?
January 24 | 11:45 - 12:30
Speaking:
Track B | Methodologies & Creativity
11:45
Discussion Group 4B: Deep Learning ROI – Where are the Benefits and Draw Backs Compared to Traditional ML Techniques?
Speaking:
Discussion Group 4B: Deep Learning ROI – Where are the Benefits and Draw Backs Compared to Traditional ML Techniques?
January 24 | 11:45 - 12:30
Speaking:
12:30
Lunch & Networking in the Exhibition Area
Lunch & Networking in the Exhibition Area
January 24 | 12:30 - 13:30
Speaking:
Track A | Data
1:30
Discussion Group 5A: How Can We Limit Bias in Our Data?
Speaking:
Discussion Group 5A: How Can We Limit Bias in Our Data?
January 24 | 13:30 - 14:15
Speaking:
Track B | Building the Data Science Team
1:30
Discussion Group 5B: What Does the Future Hold for the Chief Data Scientist Role?
Speaking:
Discussion Group 5B: What Does the Future Hold for the Chief Data Scientist Role?
January 24 | 13:30 - 14:15
Speaking:
Track A | Data
2:15
Discussion Group 6A: Outside the Box - How Do You Encourage Creative and Unusual Use of Data?
Speaking:
Discussion Group 6A: Outside the Box - How Do You Encourage Creative and Unusual Use of Data?
January 24 | 14:15 - 15:00
Speaking:
Track B | Building the Data Science Team
2:15
Discussion Group 6B: Leading Global Data Science Teams
Speaking:
Discussion Group 6B: Leading Global Data Science Teams
January 24 | 14:15 - 15:00
Speaking:
3:00
Coffee & Networking in the Exhibition Area
Coffee & Networking in the Exhibition Area
January 24 | 15:00 - 15:30
Speaking:
3:30
The Bionic Newsroom: AI-powered journalism
Speaking:
The Bionic Newsroom: AI-powered journalism
January 24 | 15:30 - 16:00
Speaking:
4:00
Q&A with Advisory Board
- Open Q&A with the advisory board
Speaking:
Q&A with Advisory Board
January 24 | 16:00 - 16:30
- Open Q&A with the advisory board
Speaking:
4:30
4:45
End of Main Conference
End of Main Conference
January 24 | 16:45