Dataiku is an end-to-end collaborative data science platform and its pipeline graph framework allows users to design and manage any calculation, from the most basic to complex. This visual, intuitive interface enables easy analysis and management of the intricacies involved in financial calculations.
In this webinar, we’ll present a few standard modeling techniques in finance that you would typically do in Excel. In that sense, this is a financial modeling cooking show using Dataiku. Like any cooking show, it gives recipes as the starting point — you can then use this template in the future to produce results that suit your unique taste and needs.
What you will learn:
• How Dataiku mitigates the known limitations in Excel and similar tools
• How shortcuts and formulaic expressions in Excel can be easily replicated and optimized in Dataiku using Dataiku plugins
• An example of a financial statement simulation and automation
• Package a Dataiku Flow into a Dataiku App for broader audience consumption
RecordedJul 28 202144 mins
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David Behar, Senior Data Scientist @ Dataiku; John McCambridge, AI Solutions Manager @ Dataiku
When managing stock portfolios of hundreds or thousands of stocks, it is necessary but challenging or impossible to follow the news happening for each of them. The volume of potentially market-moving information is too large to handle manually.
In this session, we'll show you how news headlines can be pulled and linked with market data to learn which news items are most impactful. As a result of this model, fresh news headlines loaded in real-time are linked with firms in the market and transformed into volatility scores. This score enables the user to focus on the stocks deemed more likely to exhibit anomalous moves as a result.
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.
Triveni Gandhi, Senior Data Scientist @ Dataiku; Sophie Dionnet, General Manager, Business Solutions @ Dataiku
Join us for a look into how Dataiku can be used in identifying key drivers of adoption for pharmaceuticals, resulting in better targeted marketing campaigns. During this session, we'll show you how to use Dataiku to prepare and blend multiple data sources containing a variety of data, build regression models to correlate marketing outreach, investigate trends, and much more. A Dataiku application on top of the pipeline allows users to scale analytics to new product lines quickly and effectively. Learn how to adapt this starter project to your specific needs in order to make smarter marketing decisions.
Chris Dr. Chris Marshall, IDC and Richard Jones, VP and GM, APAC at Dataiku
Exclusive with IDC
Since the release of the 2020 IDC InfoBrief and with the release of the 2021 edition, we have seen nearly twice as many companies starting their AI journey. Plus, those that had already started are showing more AI maturity, experimenting less and executing more repeatedly and in a more coordinated way.
Despite rising demand, AI projects remain difficult to execute.
In this webinar, we will investigate the latest trends as well as the different strategies enterprises adopt toward AI, when they work and when they don't.
During this exchange, Dr. Chris Marshall, VP Big Data, Analytics & AI at IDC, will review regional patterns in deploying AI while Richard Jones, General manager APAC at Dataiku, will discuss the challenges facing organizations when deploying their AI solutions.
Bring your questions because we will be LIVE and make sure you are registered.
What if the biggest revolution in AI were already underway, and no one noticed? Over the last 10 years, plenty of attention has been given to the meteoric development of AI (and AI-adjacent) technologies, and rightfully so. The advances up and down the stack have been remarkable but the coming years could be even more transformative thanks to a change in the way that AI is developed and applied.
We call this new approach “Everyday AI” and it represents a significant shift in the thinking about AI and its place in the world. This talk will outline what we mean by Everyday AI, how organizations can best take advantage of it, and what it means for data scientists, data engineers, business analysts, and leaders from the shop floor all the way up to the C-suite.
Many organizations believe that they need to have all their data ducks lined up before they attempt AI analytics. They believe they need to have conquered traditional or BI analytics first, including data catalogs, data lineage, master data management, big data, etc. before planning for AI. While this conventional thinking has merits, it results in high opportunity costs and carries risks. Join Jerry Hartanto, AI Strategist at Dataiku, to debunk this common assumption and uncover how organizations can establish capabilities for traditional analytics and experiment with AI analytics, leveraging analytics capabilities frameworks and tools that excel for both traditional and AI analytics.
1.) Jan Rosenzweig, Portfolio Manager @ Morgan Hill 2.) John McCambridge, AI Solutions Manager @ Dataiku
Tail risk is an inevitable component of financial portfolios. Popularized by Taleb in his famous ‘Black Swan’, tail risk is usually something one only finds out about when it is already too late. When the markets crash, all participants panic-sell, driving prices ever lower and correlations ever higher. The resulting losses are invariably much larger than predicted, and often larger than the capitalization that those institutions affected can bear.
This talk will present some new approaches to managing tail risk by using methods from machine learning. Morgan Hill has developed a suite of methods ranging from fat-tailed orthogonalization, tail-risk allocation to tail risk hedging. Whilst providing new levels of protection for tail risk events the methods also apply to optimising a portfolio for maximum returns within a given level of risk.
We shall present some intuitive explanations of how and why these methods work, and give a demonstration of the software implementation.
-Jan Rosenzweig, Portfolio Manager @ Morgan Hill
-John McCambridge, AI Solutions Manager @ Dataiku
Please be aware that by registering for this webinar, you agree to have your personal information shared with Dataiku's partner Morgan Hill. They may contact you with information that could be of interest to you.
Hillorie Farace di Villaforesta (Head of Cloud Alliances) Sudhir Hasbe (Senior Director of Product Management)
Deriving sustainable business value from AI initiatives can be challenging. Many puzzle pieces need to click into place perfectly. If you can’t make the pieces fit, your organization may not realize and sustain the business value you need from your AI investment.
Are you facing any of these challenges?
No strategic priority among AI investments
Team resource and skill constraints
A lack of alignment with your company’s policies and values
An inability to scale AI effectively and efficiently
Join us to learn from Sudhir Hasbe, senior director of product management at Google Cloud (Analytics), and Hillorie Farace di Villaforesta, head of cloud alliances at Dataiku, to learn how to face these challenges head-on.
Find out how to:
Securely scale and govern AI projects
Systematize AI and analytics for greater productivity
Empower and upskill technical and business teams
Deploy more AI in the business while managing costs
Gain more business value from AI
Hillorie Farace di Villaforesta (Head of Cloud Alliances at Dataiku)
Sudhir Hasbe (Senior Director of Product Management at Google Cloud)
Several companies have defined a data strategy that serves as a compass on the way to becoming a data-driven organization. However, the best data strategy will fail if there is no data culture in the company.
Based on the BARC Data Culture Framework, Dr. Carsten Bange, Founder and CEO of BARC will discuss which aspects companies can prioritise in order to create a positive data culture.
At any given time, organizations are attempting to transform their business (think business process, digital, management, organizational, and cultural transformations) with the common end goals of operational change, business model innovation, and domain expansion. Now is the time to use AI-enabled solutions to drive business transformation, but how is that done in practice? Join Jerry Hartanto, AI Strategist at Dataiku, for an overview of how AI mitigates business transformation risks, accelerates the time to value, and drives tangible outcomes (before it’s too late!).
Doug Bryan, AI Strategist, Dataiku / Aaron McClendon, Head of Data Science, Aimpoint Digital
Efficient supply chain management is essential for organizations to provide the right products and services to their customers in the right place and at the right time. In this webinar, Dataiku and Aimpoint Digital will share how teams are effectively developing, deploying, and automating scalable Demand Forecasting models, helping to significantly improve their supply chain analytics initiatives and harmonize the demand-driven supply chain vision.
Triveni Gandhi, Data Scientist & Paul-Marie Carfantan, AI Governance Manager
Responsible AI is a topic of growing interest for data practitioners for both research and the industry, especially in the context of ML Fairness. While implementing standardized fairness techniques into existing pipelines can be a challenge, Dataiku offers strategic resources for data scientists and analysts to seamlessly incorporate ML Fairness techniques into their workflows.
Join Triveni Gandhi, Senior Industry Data Scientist, and Paul-Marie Carfantan, AI Governance Manager, for this webinar to learn about practical applications of ML Fairness and how they support broader Governance, Responsible AI, and MLOps concepts in the organization.
1.) Rohit Bhattacharjee, Data Science Team Lead at Maaloomatiia, 2.) Layla Sabbouh, Data Scientist at Maloomatiia
Logistics and supply chain executives who manage complex worldwide operations are under pressure to meet production demand, reduce costs and maintain high standards of customer satisfaction, all in the face of increasing tensions in global trade and commerce as well as supplier and vendor constraints.
One of the best ways forward is to develop AI-enabled solutions and applications to make the supply chain more agile and resilient. Some of the most promising use cases with the highest ROI revolve around AI for Inventory Management, AI for Mitigating Supply Chain Risks, and AI for Production Schedule Optimization. All three algorithms improve visibility, predictability, and flexibility while making recommendations and adjustments in real-time.
Some of the substantial benefits that AI on Dataiku offers to the supply chain sector are listed below:
· 15-20% reduction in inventory holding costs
· 5-7% increase in OTIF performance
· 3-5% increase in product availability
In this webinar, we shall discuss the high-level capabilities of each of these applications while taking you through an end-to-end demo of the same on Dataiku. All in all, this is a very attractive value proposition, and we look forward to having you join us.
-Rohit Bhattacharjee, Data Science Team Lead @ Maaloomatiia
-Layla Sabbouh, Data Scientist @ Maloomatiia
Please be aware that by registering for this webinar, you agree to have your personal information shared with Dataiku's partner Maaloomatiia. They may contact you with information that could be of interest to you.
Claire Gubian, Global Head of Business Transformation @ Dataiku with Guests Mike Gualtieri and Asha Dinesh from Forrester
In this exclusive, invite-only webinar with Dataiku featuring Forrester, see why we're past the stage of experimentation and POCs with AI — today, ROI is a must. But how can you guarantee business value from AI initiatives?
- Mike Gualtieri, VP & Principal Analyst at Forrester, will discuss the state of the market and trends in how today's businesses are driving value.
- Asha Dinesh, Consultant at Forrester, will dive into the results of The Total Economic Impact™ Of Dataiku study.
- Claire Gubian will uncover why Everyday AI is the path forward to ROI from AI and unpack some examples of businesses that have been successful with their AI initiatives.
- 15-minute Q&A with the experts from Dataiku & Forrester.
Minosh Salam, Director, Business and Strategy @ DataQraft, Umut Şatir Gürbüz, Senior Sales Engineer @ Dataiku
This webinar will talk about the Must-Know Trends that will define where Enterprise AI is headed next.
Democratized Data Quality, AI Governance, Self-Service Analytics, MLOps, Responsible AI, Edge Computing are some of the popular concepts that we will discuss in detail. In the second part of the webinar we will showcase the platform's capabilities with a live demo.
- Minosh Salam, Director, Business and Strategy at DataQraft
- Umut Şatir Gürbüz, Senior Sales Engineer at Dataiku
Please be aware that by registering for this webinar, you agree to have your personal information shared with Dataiku's partner DataQraft. They may contact you with information that could be of interest to you.
Dr. Emma Beauxis-Aussalet, Sarah-Jane van Els & Triveni Gandhi
As we saw in episode 1 of this series, the bias inherent in historical data is often not correctable by simply collecting more or more representative data. If nobody from a certain group has ever applied for this kind of loan or that type of job, there may simply be no data to collect. If we accept defeat on this, there is a real risk AI models will refuse to make predictions on these groups with missing data, reinforcing the problem that got us here in the first place. One solution with promise is synthetic data, generated by combining the data of real cases to produce anonymised cases with properties that match the underlying population, “filling in the gaps” in historical data. In this session, we discuss a concrete use case developed by the ICAI lab in collaboration with Randstad and explore the promise and limits of this approach.
Dr. Emma Beauxis-Aussalet is an assistant professor of ethical computing at the Vrije Universiteit Amsterdam (VU). She is also lab manager of the Civic AI Lab. In 2019 Emma obtained her doctorate at Utrecht University with a dissertation on AI bias, for her work at the Centrum Wiskunde & Informatica (CWI). With her multidisciplinary experience, she has been researching computational methods, statistics, user interfaces and data visualizations that enable transparent and controllable AI systems. Modelling and visualizing AI errors is one of her main research topics. For her achievements in this field, she was named one of the 100 Brilliant Women in AI Ethics in 2021. She also received the 3rd WomENcourage Prize for her contributions to the development of AI literacy and bias awareness in lectures and workshops.
Sarah-Jane is a recent MSc Information Sciences graduate with a BSc in Business Administration from the Vrije Universiteit Amsterdam. She conducted her master thesis at Randstad Groep Nederland, researching synthetic data to identify bias in recommender systems for recruitment.
Dataiku is the world’s leading platform for Everyday AI, systemizing the use of data for exceptional business results. Organizations that use Dataiku elevate their people (whether technical and working in code or on the business side and low- or no-code) to extraordinary, arming them with the ability to make better day-to-day decisions with data.
More than 450 companies worldwide use Dataiku to systemize their use of data and AI, driving diverse use cases from fraud detection to customer churn prevention, predictive maintenance to supply chain optimization, and everything in between.