[SEASON 1 EP 9] The Death of Data Viz, Cross-cultural AI, and AI Auditing
In our penultimate episode of the season, Triveni and Will explore the data world’s shifting attitude toward standalone data visualizations (are they dying? Who are they for?), how to respond to global AI practices (what are global AI standards? How do different countries vary in their AI approaches?), and the feasibility of an AI audit. We’ll also see how Spark fits into the infrastructure of our data science systems.
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Learn more about the articles referenced in this episode below:
Standalone Data Visualization is Dead...and I Couldn’t Be More Excited by Matthew Miller (Biztory)
IDC: Asia-Pacific spending on AI systems will reach $.5 billion this year, up 80% from 2018 by Catherine Shu (TechCrunch)
High-Stakes AI Decisions Need to Be Automatically Audited by Oren Etzioni and Michael Li (WIRED)
RecordedAug 30 201926 mins
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Rata Jacquemart, Project Leader, AI Consulting, Dataiku
It's vital for Retail & CPG industries to adapt to rapidly changing consumer demands in challenging and ever-changing environments. So, join us for our new virtual series highlighting the use cases capable of boosting your business in the present, and in the near, post-Covid, future!
In our first edition, Rata Jacquemart, Project Leader, AI Consulting at Dataiku, will discuss Pricing & Marketing optimization and cover numerous topics, including; how to build your discount optimization engine, personalized promotions, and media performance assessment tool's, helping you to start making a change within your organization.
As Darwin said, “It is not the strongest of the species that survives, nor the most intelligent; it is the one most adaptable to change.”
Speaker Bio: Rata Jacquemart is a data science project leader at Dataiku, helping customers in various industries building real & measurable business impacts with data science. She holds a PhD in Robotics & applied Mathematics. Before joining Dataiku, she was a data scientist for multiple top companies as the Boston Consulting Group (BCG GAMMA), fifty-five and Telenor where she specialized in retail/ e-commerce, CPG and Telco industries. Before starting her data science journey, she was a researcher in robotics and remote sensing leading projects in using satellite data for Agriculture and Meteorology applications.
[IMPORTANT NOTICE] Due to unforeseen circumstances today’s meetup with Salman Shams from NHS England will be indefinitely postponed - the new date will be confirmed in the near future. Apologies for any inconvenience caused, we look forward to seeing you at our next meetup with Conor McCabe from ASOS.
Data or information is now the driving force behind everything we do. Whether it is realising how many district nurses we need to serve each specific area within NW London, or finding out what best we can do for a prosthetic limb so that the user feels more in control and feels it is their own arm. Machine learning, analysis of data, and extrapolating using known markers has helped scientists, data analysts, and businesses decide what direction they want to take in providing the right kind of services and products for their clients. This presentation will focus mainly on how data analysis in the NHS, being a youth worker with Tower Hamlets, and being a research scientist doing a PhD can all be bound together using the same thread that is data.
Speaker Bio: Salman Shams, PhD. is currently working as a data analyst for the National Health Service. He has a PhD in medical engineering which involved using machine learning methods to increase the efficiency of prosthetic limbs. until recently he was also a youth engagement worker with the London borough of Tower Hamlets where he worked for eight years and in that time was able to recognise the potential of having data and information on young people who are currently not in education employment or training and how this helps local authorities manage their services better catering to those young people.
Data has always been the foundation of the banking industry. What has changed in recent years, of course, is the amount of data available and the speed at which it is processed, as well as the need to quickly respond to market changes.
Join Dataiku, Deloitte Omnia, and Snowflake on September 2nd @ 12 p.m. ET where we’ll showcase how Dataiku and Snowflake are simplifying this process by removing the need to move data from where it is securely stored with scalable model inference inside Snowflake’s processing engine. We’ll highlight how this enables users to run large or complex jobs using data contained in or being sent to Snowflake without significant slow down, allowing you to do more in less time.
Tanvir Ansari, Data Science SME, Deloitte Omnia
Frank Pacione, Sales Engineer, Snowflake
Steve Franks, Solution Architect, Dataiku
Please note that by registering for this event you agree that your personal data will be shared with Dataiku's partners Snowflake and Deloitte. They may contact you with information that might be of interest to you.
Snowflake Privacy Terms: https://www.snowflake.com/privacy-policy/
Deloitte Privacy Terms: https://www2.deloitte.com/us/en/legal/privacy.html
Take part in the webinar to discover what are the benefits of a Data Science approach in Forecasting.
Forecasting has been used since the 1950s in anticipating risks and making decisions. But in the era of AI and algorithms, older modeling techniques fail to integrate the amounts of data sources needed to produce results that are accurate enough for modern business.
This webinar provides an overview of Forecasting addressed through Dataiku. You will be shown the example of sales forecasting to illustrate, in a concrete way, the steps to follow to combine business expertise with Data Science techniques. You will then be able to understand how to refine your forecasts, automate them, and multiply them in many use cases applicable to Forecasting.
Given that it costs 5-10 times more to acquire a new customer than to retain an existing one, it seems obvious that all businesses should engage in some level of churn prevention.
Because of its business impact and its relative ease in execution, for many types of business, churn prediction is a great first project to tackle with machine learning and AI.
In this webinar, Vincent De Stoecklin, Customer Success Director at Dataiku, will:
> Explain how data science and machine learning can help leverage churn prevention
> Deep-dive into a churn prediction project (from design to production)
> and demo a churn analysis on Dataiku DSS.
What options does data science offer to support marketing projects? How can the success of campaigns and marketing activities be measured and continuously improved using data? In this webinar we show the top 3 use cases “Churn Prediction”, “Segmentation” and “Recommendation Engine” using real examples from companies that make data-driven decisions.
Determining a Health Care Provider's (HCP) propensity to prescribe is crucial to improve sales force effectiveness and grow sales in the pharmaceuticals industry. During this workshop, we'll show you how to use Dataiku to identify physicians with the highest propensity to prescribe a product using a machine learning model built in a low-code manner. We will be using historical sales data, marketing campaign data, calls and event attendance data in order to build and deploy a robust prediction pipeline. The HCP propensity model can be used in many different lines of business to improve decision process around next best action, marketing messaging, sales targeting, and more.
Marc Damez-Fontaine (PwC), Igor Girard-Carrabin (ArianeGroup)
What are the actual challenges in supply chain management? How can AI improve business performance? And what are the pitfalls and best practices to analyze the data of your supply chain?
Listen to Marc Damez-Fontaine, Director Data & AI at PwC, present different levels of AI technologies that can help supply chain operations.
Right after we will hear from Igor Girard-Carrabin, Supply Chain Engineer at ArianeGroup, who will provide feedback on concrete use cases of AI for supply chain management within the European Commission funded SESAME project.
The SESAME project aims at improving European launchers' manufacturing and operations through digital technologies, processes and methods.
- Marc Damez-Fontaine (Director Data & AI at PwC)
- Igor Girard-Carrabin (SESAME Project Manager at ArianeGroup)
Please be aware that by registering for this webinar, you agree to have your personal information shared with Dataiku's partner PwC.
The value of data science multiplies when it is used and applied across the organization. Successful data science should impact the business — and that requires data scientists to not only collaborate with one another but also with data consumers of differing technical expertise and experience.
In this latest Data Science Central webinar, learn how Pfizer’s data science team is implementing collaboration in the right context — from processes to team structure and tools, like the Alation Data Catalog and Dataiku — to help make a greater impact with data science. And, see how the Alation Data Catalog and Dataiku work together for seamless data science collaboration.
Fraud in the healthcare industry is on the rise globally and APAC is no exception. There has been a considerable increase in fraud/deceptive activities in the APAC region owing to the growing penetration of the internet and an increase in the use of mobile internet.
Preventing Fraud has the potential to make medicine better, more affordable, and more accessible.
During the webinar, we will cover the following:
Define what is Healthcare fraud & evaluate what can be done to detect and prevent fraud
Deep dive 4 different options to combat fraud
Finally, see how to combine the traditional and Machine-Learning Based methods within a Machine Learning Framework
Participate in a data science project showcase followed by Q&A with one of our Sales Engineering director.
The question regarding whether one should buy or build an in house AI platform continues to pop up across EMEA in a wide variety of different organisations, from the exciting small start-ups to the large stock exchange listed companies who challenge the need to buy an AI platform.
During this session, Ryan Morris, Account Executive at Dataiku will address this question and will cover:
1)AI platform in itself
2) 6 key considerations that should be tabled when evaluating whether to build or to buy
Dataiku features Apps, the ability to distribute your analytics project to a much broader audience such as subject matter experts and business analysts.
In this session, Dr. Robert Coop, phData’s General Manager of Machine Learning, will demonstrate how Apps can be used to allow end-users to classify emotions expressed by people in videos using deep learning. This talk will demonstrate how to take a complex project and to package it into an application that enables users to benefit from the results of deep-learning emotion classification without having to understand the analytic process.
Data architecture is the foundation of every organization’s data strategy, but it's not just something for CIOs and data architects either - everyone at data-powered organizations can benefit from understanding the ways data moves between teams and flows into data projects to yield insights. In our Crash Course, we’ll cover key architecture terms and highlight different priorities regarding security and scalability. Additionally, we’ll discuss ways to strategize and align architectural concerns with business priorities.
Jesse Bishop works with a wide variety of Fortune 500 clients and specializes in helping large organizations operationalize their AI workflow. Jesse is an Insight Data Science Fellow in New York City. He previously worked for the Federal Trade Commission developing models to predict the impact of mergers in a wide variety of industries including Energy, Semiconductors, and E-commerce. Jesse earned his Ph.D. in Applied Microeconomics from the University of Minnesota.
Christina Hsiao is a technical evangelist for Dataiku based in the US. In her role, Christina is able to share her passion for applied data science through writing and by speaking with customers, partners, and organizations interested in solving business problems with the powerful combination of people, data, and technology. Prior to joining Dataiku, she spent nearly a decade at SAS, mainly specializing in Natural Language Processing and text analytics. Christina holds a bachelor’s degree in Mechanical Engineering from Stanford University.
7:05pm: Foundational Data Science for Personalized Communications w/ Nike
Email communication is one of the most common activities on PCs and mobile devices and this holds true now more than ever. Nike invests a lot of effort into Email communication. Although the cost of sending one email may be small, the cost builds up as the number of emails aggregates. Also, the user engagement governs the reputation of Nike IP addresses and the KPIs. Therefore, it is important to identify the campaigns relevant to consumers with a high propensity of engagement.
The goal of the Personalized Communications team is to serve the Nike consumers with the most relevant campaign emails at the right time with the right frequency. In this talk, Ankit will address important questions/topics relevant to the Personalization Communications team. He will also answer questions that explain how Nike is currently handling personalized communications, what’s working well and what’s not, and how to build the data foundation for Personalized Communications. Finally, Ankit will do a deep-dive into some of the data science models that the team is currently working on.
Ankit has 4+ years of experience informing business decisions through data science and statistical modeling. He is currently working as Sr. Data Scientist at Personalization and Data Science team at Nike. His previous experiences include working in Ad Tech and Finance industry.
While randomised control trials like A/B tests are the gold standard for causal inference, there are many situations where they’re not appropriate or even possible to run. Geo-experiments (where we apply the treatment to specific geographic regions) can act as a quasi-experimental alternative when conventional A/B tests aren’t feasible. In this presentation we’ll introduce geo-experiments, the common statistical models used to construct synthetic controls for geo-experiments, as well as some of the methods we use at ASOS to accelerate the pace of geo-experiments we run.
Speaker Bio: Conor is a Machine Learning Scientist with a background in statistics working on the marketing science team at ASOS. His work in the marketing science space has involved developing experimentation frameworks to streamline online testing as well as machine learning methods for digital ads optimisation.
Data in itself is useless. It’s just the raw material. In order to get the value from the data we need context information to transform data into information, into knowledge, and into action. And that’s what enterprises and even humans want to achieve.
Metadata is critical to enable both human and machine to understand, interpret and finally use data. Instead of diving into solutions for gathering and using metadata, with this webinar we will understand the relevance of metadata for your business.
Please join us on 10th August to learn:
1) What is metadata?
2) Why is it needed for AI?
3) What are the benefits and challenges?
4) and finally what makes data really valuable?
2:05pm: Data Science in Compliance and Fraud Detection w/ Spotify
Data Science is an emerging function in a variety of industries and a greater number of data scientists have begun working on personalization, recommendations, or sales optimizations.
The cost of compliance has also been expanding in most industries and especially in the technology sector. This is a result of the Public’s attention shift from traditional frauds to antitrust, conflict of interest, and data privacy. While there are plenty of opportunities to leveraging DS/ML to solve such problems, the complex nature of such compliance and fraud detection issues stymies data practitioners from being able to grow the data science practice. The “language barrier” of communication between the company’s DS/business/compliance appears to hide the low hanging fruits.
In this talk, Harry will share his experience in connecting data science with compliance, examples of the DS/ML use cases, and key takeaways for our two groups of the audience (Data Scientists and Business Stakeholders.)
• Hidden Data Science Opportunity in Compliance and Finance
• Career Journey: from a fraud investigator to a data scientist
• Opportunities that often get overlooked in compliance/fraud functions
• How to speak two languages: recommendations on connecting data science resources with compliance business
Disclaimer: All views, thoughts, & opinions expressed in the webinar belong solely to the panelists, & not to the panelists’ employer, organization, committee, other group or individual.
Christina Hsiao, Sr. Product Marketing Manager and Du Phan, Research Scientist at Dataiku
Explainable AI has become a powerful mitigating force that enables modelers, regulators, and laypeople to have more trust and confidence in machine learning models. However, it’s not always easy for data scientists to implement models that are explainable.
In this webinar, Dataiku’s Christina Hsiao, senior product marketing manager, and Du Phan, research scientist, will discuss fairness and explainability concepts both generally and in Dataiku. They will also discuss the research and reasoning behind why Dataiku’s R&D team chose the path they did for the features developed, the trade-offs between white-box and black-box models, and how Dataiku helps data teams build models in a way that is collaborative and sustainable for the business.
Dataiku is the centralized data platform that moves businesses along their data journey from analytics at scale to enterprise AI. By providing a common ground for data experts and explorers, a repository of best practices, shortcuts to machine learning and AI deployment/management, and a centralized, controlled environment, Dataiku is the catalyst for data-powered companies.
Customers like Unilever, GE, BNP Paribas, Santander use Dataiku to ensure they are moving quickly and growing exponentially along with the amount of data they’re collecting. By removing roadblocks, Dataiku ensures more opportunity for business-impacting models and creative solutions, allowing teams to work faster and smarter.