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How Becoming AI-Driven Helps Fend off Disruption from Big Tech and Startups

Ian Wilson, former Global Head of AI at HSBC, will share 25 years of lessons on how enterprise boards and executive leadership teams can transform their organizations in order to fend off disruption from traditional competitors, Big Tech, and new entrants into their market.

Ian Wilson is the former Global Head of Artificial Intelligence at HSBC. Prior to this role, Ian developed significant leadership experience as a board advisor, founder, CEO, and executive director from startups and scale-ups to major global organizations. His journey through the industry has taken him across three continents, from military AI to AI product companies startups and eventually to defining enterprise AI strategy and operating models for Global organizations. Ian has deep knowledge and experience of building AI Centers of Excellence, delivering large scale production-grade enterprise AI solutions, and making AI transformation clear and understandable to all levels of business. He is a speaker at global events and a video content author on enterprise AI transformation. Ian lives in Cambridge with his family, enjoys travel and the outdoors and speaks rusty but fluent Japanese.

Presented by: Productive AI LLC
Recorded Jul 27 2021 41 mins
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Presented by
Ian Wilson, Former Global Head of Artificial Intelligence at HSBC
Presentation preview: How Becoming AI-Driven Helps Fend off Disruption from Big Tech and Startups

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    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.
  • Dataikuでつくるデータパイプライン③ AIのビジネス活用に不可欠なMLOpsの実現 Nov 18 2021 8:00 am UTC 25 mins
    宮崎 真 データサイエンティスト@Dataiku
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    本セッションでは、モデルのデプロイから、運用時のモニタリング、再学習まで、AIを実際にビジネスに適用する際に不可欠なMLOpsをDataikuでどのように実現できるか、デモを交えてご紹介します。

    ■ 講師プロフィール
    宮崎 真 www.linkedin.com/in/makoto-miyazaki
    Dataikuのデータサイエンティスト。製薬や自動車など、様々な業界のクライアント企業がデータドリブンな組織になるための支援をより現場に近いところでしています。2019年にDataikuに入社する前は日本経済新聞の記者として約6年間、東京から地方まで取材現場を駆け回っていました。

    Dataikuについてお問い合わせはこちらよりお願いいたします。
    https://www.dataiku.com/ja/お問い合わせ/
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    Speakers:
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    -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.
  • AI Trends: Driving Agility and Efficiency in the Enterprise Oct 25 2021 8:00 am UTC 60 mins
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    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.

    Speakers:
    - 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.
  • Deep Dive into Synthetic Data Generation for Bias Mitigation Oct 21 2021 2:00 pm UTC 60 mins
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    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.

    Speaker bios:
    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でつくるデータパイプライン② クリック操作でできるデータ分析とモデル開発 Oct 21 2021 8:00 am UTC 25 mins
    松島 七衣 シニアセールスエンジニア@Dataiku
    データパイプラインとは、データの取込からデータ準備、分析・可視化までの一連のプロセスを繰り返し利用できるように自動化するためのフローです。DataikuではこのフローをE2Eで、高度にビジュアル化されたGUIを使い、コーディングをすることなく組み立てることが可能です(※PythonやR、SQLやHiveを使ったコーディングも、もちろん可能です)。
    本セッションでは、データ分析からAutoMLを使った機械学習モデルの開発、モデル出力結果の確認まで、コーディングなしで行えること、デモでご紹介します。ビジネスユーザーでも簡単に分析やモデルの開発が可能なため、スキル不足のお悩みをお持ちの企業やチームにも最適です。ぜひご覧ください。
    スクリーン リーダーのサポートが有効になっています。


    ■ 講師プロフィール
    松島 七衣 https://www.linkedin.com/in/nanae-matsushima/
    プリセールスエンジニアとして、日本市場立ち上げを推進。
    Dataiku入社前は、富士通株式会社を経て、Tableau SoftwareでSales Engineerとして6年半従事。日本法人の初期フェーズから、規模・業界問わず多くのお客様に対して、製品の提案と技術支援を実施。
    経産省主催データ分析コンテストの初回可視化部門で優勝経験を持つ。


    Dataikuについてお問い合わせはこちらよりお願いいたします。
    https://www.dataiku.com/ja/お問い合わせ/
  • Interactive Document Intelligence With NLP Oct 20 2021 6:00 pm UTC 75 mins
    Amanda Milberg, Data Scientist @ Dataiku
    Many firms have a large document corpus made up of both digitized and raw images. Now more than ever, financial institutions are turning towards unstructured data sources to capture additional attributes in order to, ultimately, adjust or confirm their analyses and discover new trends and insights. Many organizations rely on individuals to read sections of these documents or search for relevant materials in an ad hoc manner, with no systematic way of categorizing and understanding the information and trends.

    Join us for this Dataiku session on interactive document intelligence, where we will showcase a modular and reusable pipeline to rapidly and automatically digitize documents, extract text, and consolidate data into a unified and searchable database. We will focus on NLP techniques applied to prepare, categorize, and analyze textual data based on themes of interest (in this project: ESG), with additional theme modules available. Lastly, we will demo a purpose-built dashboard to provide business users with a simple and interactive tool to analyze high-level trends and drill down into aggregated insights.
  • Leveraging AI in HR for Predictive Performance Oct 20 2021 4:00 pm UTC 60 mins
    Dominick Rocco, General Manager of Machine Learning, phData / Doug Bryan, AI Strategist, Dataiku
    HR is an often overlooked but rich source of valuable AI use cases such as writing better job postings, identifying key attributes of successful new hires, and attrition management. There is potential for huge benefits when it comes to AI in HR to support collaborative teams and employee retention in addition to keeping job listings competitive.

    Join Dataiku and phData as we walk you through a use case of a human resources team at a major medical device manufacturer that needed a more robust data analytics solution as they looked for ways to accurately predict manager performance. phData built a fully functioning model that delivers measurable business value, complete with visualizations and executive dashboards.
  • AI for Modern Supply Chain analytics by Eulidia & Dataiku Oct 19 2021 9:00 am UTC 60 mins
    1.) Arnaud Canu, Chief Technology Officer @ Eulidia, 2.) Cédric Jacques, Partner @ Eulidia
    Difficultés d’approvisionnement, rupture de stocks, allongement des délais de livraison, insatisfaction des clients... Plus que jamais, La crise COVID a mis en lumière la nécessité pour les entreprises de superviser et d’optimiser leurs chaînes d’approvisionnement et de distribution, pour prévenir les perturbations éventuelles et élaborer les plans d’actions opérationnels.

    Pour répondre à des enjeux de plus en plus complexes, Eulidia est convaincu que l'intelligence artificielle se positionne comme un moteur essentiel de la transformation de la Supply Chain et comme un levier d’innovation et de compétitivité pour les acteurs du marché qui décident d’investir !

    Avec le concours de Dataiku, ce webinar vous éclairera sur les étapes clés de ce programme de modernisation
    -Modern AI : Retours d’expérience
    -Flexibilité et performance apportée par une plate-forme Analytics telle que Dataiku / Snowflake 
    -Les clés de la modernisation de la Supply Chain
    -Supply Chain Analytics : quels cas d’usages et quels bénéfices ? 

    Orateurs:
    -Arnaud Canu, Chief Technology Officer @Eulidia,
    -Cédric Jacques, Partner @Eulidia

    En vous inscrivant à ce webinaire, vous acceptez que vos informations soient partagées avec les partenaires de Dataiku et Eulidia.
  • Dataiku and AWS: Delivering Enterprise AI at Scale Recorded: Oct 15 2021 51 mins
    Michael Ernest, Director of Solution Architecture at Dataiku
    In this demo we'll walk through a DSS project that features a variety of integrations with the AWS platform as well as several key services. The demonstration will highlight key performance benefits built into DSS when operating in the AWS environment, and integrations with AWS services, including Redshift, EMR, and EKS.
  • Dataikuでつくるデータパイプライン① クリック操作でできるデータ取込とデータ準備 Recorded: Oct 14 2021 25 mins
    松島 七衣 シニアセールスエンジニア@Dataiku
    データパイプラインとは、データの取込からデータ準備、分析・可視化までの一連のプロセスを繰り返し利用できるように自動化するためのフローです。DataikuではこのフローをE2Eで、高度にビジュアル化されたGUIを使い、コーディングをすることなく組み立てることが可能です(※PythonやR、SQLやHiveを使ったコーディングも、もちろん可能です)。
    本セッションでは、データパイプラインのうち、データサイエンティストが7-8割の時間を費やすと言われるデータ取込とデータ準備について、コーディングなしで行えること、デモでご紹介します。コーディングを得意としない方、そして簡単な操作はGUIでサクサク進めたいという方、ぜひご覧ください。

    ■ 講師プロフィール
    松島 七衣 https://www.linkedin.com/in/nanae-matsushima/
    プリセールスエンジニアとして、日本市場立ち上げを推進。
    Dataiku入社前は、富士通株式会社を経て、Tableau SoftwareでSales Engineerとして6年半従事。日本法人の初期フェーズから、規模・業界問わず多くのお客様に対して、製品の提案と技術支援を実施。
    経産省主催データ分析コンテストの初回可視化部門で優勝経験を持つ。


    Dataikuについてお問い合わせはこちらよりお願いいたします。
    https://www.dataiku.com/ja/お問い合わせ/
  • Maximizing ROI from AI Initiatives with Dataiku (featuring Forrester) Recorded: Oct 12 2021 51 mins
    Jerry Hartanto, AI Strategist & Evangelist @ Dataiku with Guest Asha Dinesh, Market Impact Consultant @ Forrester &
    When it comes to building a modern AI platform, organizations shouldn’t spend time, energy and resources cobbling together tools across the AI lifecycle, which ultimately results in losing the larger picture of the full data pipeline (not to mention adds technical debt).

    This webinar unpacks the results of The Total Economic Impact™ Of Dataiku study (conducted by Forrester Consulting and commissioned by Dataiku) that quantifies and solidifies some of the benefits that Dataiku customers experience in leveraging one, central platform to systemize the use of data for Everyday AI, including:

    - 423% ROI over three years (with a payback period of < 6 months)
    - 75% time savings for data engineers and data scientists
    - 90% reduction in manual, repeated reporting tasks
  • NLP The Visual Way Recorded: Oct 12 2021 42 mins
    Shobana Muthukrishnan, Data Scientist, Dataiku
    Do you want to know what your customers are talking about your airline? What services do they like? And what puts them off? We developed a workflow in Dataiku DSS that uses NLP to determine the sentiment behind the tweets and a webapp that allows the end user to view the results. We will walk you through how we used the tweets, did some text cleaning, built models to classify the sentiment, and if time permits - also web scraping to extract airline online reviews.
  • 【機械学習x深層学習】ラーメン好きのフランス人にNLPで日本のラーメン屋をオススメしてみた Recorded: Oct 7 2021 25 mins
    宮崎 真 データサイエンティスト@Dataiku
    構造化データの分析も非構造化データの解析も、一つの分析ワークフローで組み立てられて、しかもデプロイまで一気に行える、そんなプラットフォームがあれば、モデルを本番利用する機会がますます増えると思いませんか。Dataikuなら簡単です!
    今回のお題は、フランスでも大人気のラーメンです。ラーメン店@フランスのクチコミを元に、フランス人の同僚が気に入りそうな日本のラーメン店を紹介するモデルを開発しデプロイします。面倒なテキストデータの準備もビジュアルレシピとコードレシピでサクサクです。

    ■ 講師プロフィール
    宮崎 真 www.linkedin.com/in/makoto-miyazaki
    Dataikuのデータサイエンティスト。製薬や自動車など、様々な業界のクライアント企業がデータドリブンな組織になるための支援をより現場に近いところでしています。2019年にDataikuに入社する前は日本経済新聞の記者として約6年間、東京から地方まで取材現場を駆け回っていました。
    今回のお題は、フランスでも大人気のラーメンです。ラーメン店@フランスのクチコミを元に、フランスからの旅行者が気に入りそうな日本のラーメン店を紹介するモデルを開発しデプロイします。面倒なテキストデータの準備もビジュアルレシピとコードレシピでサクサクです。

    Dataikuについてお問い合わせはこちらよりお願いいたします。
    https://www.dataiku.com/ja/お問い合わせ/
  • Succeeding with Mature MLOps feat. TDWI Recorded: Oct 6 2021 59 mins
    Dan Darnell, Head of Product Marketing Dataiku and James Kobielus, Senior Research Director, Data Management
    Organizations everywhere are automating the development, deployment, monitoring, and governance of mission-critical machine learning (ML) and other artificial intelligence (AI) applications.
    Operational data science is a collaborative process that increasingly goes under the name of MLOps. Organizations are bringing the latest MLOps into their data science workflows to augment the productivity of data engineers, statistical modelers, and other highly skilled personnel. Mature enterprise MLOps processes leverage cloud-native infrastructure to scale the deployment, monitoring, and management of statistical models and code builds into production applications.

    Join Dan Darnell from Dataiku and TDWI’s senior research director James Kobielus for this webinar to learn how enterprises can succeed in using mature MLOps practices across their entire data science pipelines to speed deployment of their most sophisticated AI applications.
    Key topics that he will discuss include:
    - Business opportunities that are driving demand for MLOps
    - Key investments in data ingestion, cleansing, preparation, and modeling technologies that are essential for organizations to succeed with MLOps
    - Challenges that organizations face when implementing MLOps within their established data science processes
    - Principal operational metrics that organizations must monitor and track to ensure the success of their MLOps initiatives while mitigating associated operational, legal, and regulatory risks
  • [Virtual Meetup] Personalised Recommendations w/ Skyscanner Recorded: Sep 30 2021 38 mins
    Maria Prosviryakova - Senior ML Engineer at Skyscanner
    Looking for places to travel next in these uncertain times? Interested in finding great deals in safe destinations? Skyscanner's personalised recommendations can save your precious decision time! In this talk, Maria Prosviryakova, Senior Machine Learning Engineer at Skyscanner, will share the journey from a simple yet impactful collaborative filtering model to deep learning-powered destinations recommendations. Maria will touch upon the architecture of the real-time recommender system that relies on ML pipelines and MLflow and is orchestrated using Apache Airflow. She will also discuss challenges faced on the road to production, and how the personalised recommendations increased Skyscanner's engagement metrics.

    Speaker bio:
    Maria Prosviryakova is currently working as a Senior ML Engineer at Skyscanner. Maria holds an MS degree in Statistics and has 7+ years of experience working as a data scientist across different industries and locations: finance in New York, e-commerce in Buenos Aires and the travel industry in Barcelona. She now specialises in recommender systems and ML in production.
  • 海外事例からわかる『AI成功ストーリー』 Recorded: Sep 30 2021 25 mins
    ウィリアム・ホン 日本・韓国地域営業統括@Dataiku
    DXの一環として多くの企業がAI活用を試みる一方で、成功するのはわずか10%と言われています。本セッションでは、AI活用がビジネス貢献を実現した、グローバルの製造、金融、製薬業の企業の事例をご紹介します。これらの事例の共通点は、データサイエンティストからビジネスユーザーまで、企業全体がエンドツーエンドで利用可能なAIプラットフォームを導入し、データ活用の文化を広げて成功したことです。

    ■ 講師プロフィール
    ウィリアム・ホン https://sg.linkedin.com/in/yongmin-william-hong-74a170ab
    Dataikuアジア太平洋本部で日本・韓国の地域営業統括として、顧客とパートナーのAI成長を促す役割を担っている。前職の日本IBMでは、主要アカウントを管理するチームリーダーにつき、主にAI、クラウド、ブロックチェーンの戦略的パートナーシップを牽引。

    Dataikuについてお問い合わせはこちらよりお願いいたします。
    https://www.dataiku.com/ja/お問い合わせ/
  • Key Trends to Staffing the AI Enterprise Recorded: Sep 28 2021 51 mins
    Conor Jensen, VP of Data Science, Americas
    Only 27% of data professionals say their organization has formal training and education to help staff understand the roles data, machine learning, and/or AI play within the business, according to a Dataiku AI maturity survey. What’s standing in their way? In this webinar, Conor Jensen, VP of Data Science, Americas at Dataiku will unpack key challenges and trends for staffing the AI enterprise, including this aforementioned lack of formal upskilling programs, difficulty hiring data talent, a lack of specificity around the business’s AI needs, and more.
Everyday AI, Extraordinary People
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.

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  • Title: How Becoming AI-Driven Helps Fend off Disruption from Big Tech and Startups
  • Live at: Jul 27 2021 3:00 pm
  • Presented by: Ian Wilson, Former Global Head of Artificial Intelligence at HSBC
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