Collaborative Data UX Design - Virtually and Phyically

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Dataconomy Media

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Microsoft

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Fred Chiang

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clive boulton

Eric van tol

Catalina is a Data Scientist with a specialty in building out scalable data solutions for startups. Next DSS MIA Event - https://datascience.salon/miami/ There's a huge hype around the power of data science across industries. However, not all companies have been able to successfully build out their data science capabilities, and some are just starting to think about doing so. Just as each business is unique, each data science endeavor is unique. In this talk, we explore both the non-negotiables in building a data science culture and how to tailor your data science initiatives to match your business needs at different stages of your journey towards reaping the benefits of a data science culture.

Data Science Salon: Building a Data Science Culture

Formulatedby

Next DSS MIA Event - https://datascience.salon/miami/ For most data scientist building models is hard work, but deploying them into production and impacting business processes can be even harder. In fact, research shows that only about 10% of data science models get deployed into production, and those that do can take between 6 to 9 months to be deployed. This session will highlight the challenges that data scientist and organizations alike face when trying to deploy machine learning models and how to overcome these challenges. It will examine several use cases where models built in R and Python have been able to deliver impactful results across several industries.

Data Science Salon: Applying Machine Learning to Modernize Business Processes

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How to Transform IT Operations with Machine Learning - Apply Context

DevOps.com

Neo4j Innovation Lab, Stefan Wendin, Neo4j

Neo4j

A talk by Sebastian Herold (Scout24) and Arif Wider (ThoughtWorks) Abstract: More and more companies successfully migrate their monolithic applications to a Microservices architecture. However, maintaining a consistent and usable data landscape has only become more challenging by this: unstructured data, huge amounts of data, and hundreds of data sources. Having a centralized data team does not scale in this setting as it becomes the bottleneck between application developers and business analysts. We created a Data Manifesto of seven principles which break with traditional role separations and show a path how to deal with distributed data in a federal and scalable fashion. This leads to DataDevOps: a culture where application developers also own their data. Learn about the experiences we made with facilitating this cultural transformation at Scout24, the provider of Europe’s largest online markets for cars and real estate.

DataDevOps - A Manifesto on Shared Data Responsibility in Times of Microservices

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DataDevOps - A Manifesto on Shared Data Responsibility in Times of Microservices

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Data Governance tools can be used to advance your Data Governance program … or they can become the reason why Data Governance fails to meet people’s expectations. Tools can be developed internally or acquired from reliable vendors to attempt to address your organization’s needs. Sometimes the best environment is made up of a hybrid of tools developed internally and tools acquired. Join Bob Seiner for this month’s RWDG webinar where he will share tools that you can build yourself and talk about how the tools can be used to determine requirements to acquire outside tools. Tools developed internally at little or no cost have helped to solve many Data Governance problems. Several of these problems and their solutions will be described in detail during this webinar. In this webinar, Bob will discuss: • Several easy-to-build Data Governance tools • Customizing these tools to address specific issues • How internally developed tools can lead to tool acquisition • Knowing when it is time to acquire tools • Integrating DIY tools with acquired tools

RWDG Slides: Using Tools to Advance Your Data Governance Program

DATAVERSITY

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Big Data – Is it a hype or for real?

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KCC Software Ltd. & Easylearning.guru

This is a presentation on Hadoop for BI Professionals who want to upgrade their career path to BIG Data technologies. Hadoop for Business Intelligence Professionals is a definite upgrade in terms of career growth, scope of worth and organization influence. The PPT covers the following topics: ✓ What is BIG Data? ✓ What is Hadoop? Why is it so popular? ✓ Upgrading from BI to Hadoop ✓ Career Path ✓ Salary & Job Trends ✓ Hiring Companies ---------- Skillspeed is a live e-learning company focusing on high-technology courses. We provide live instructor led training in BIG Data & Hadoop featuring Realtime Projects, 24/7 Lifetime Support & 100% Placement Assistance. Email: sales@skillspeed.com Website: https://www.skillspeed.com

Hadoop for Business Intelligence Professionals

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Cwin16 tls-datalab for scientists

Capgemini

As a developer and consultant in customer projects you experience day-by-day that things are changing slow in the ERP world. New features and approaches are not known even ten years after the introduction to many developers. On the other side there are many new cool technologies and tools offered by SAP which the geeks want to learn and test. So on the one side you have to teach stuff like automated unit testing with ABAP Unit and on the other hand you rather would like to explore the latest HANA 2.0 features. I think it’s critical for SAP and the whole SAP Ecosystem to adopt faster and I think a strong community is a critical success factor for this. In this talk I want to show why!

SAP d-kom 2017 Karlsruhe - The Challenge of the „two speed“ SAP Ecosystem

Martin Fischer

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The 10 Best Data Analytics And BI Platforms And Tools In 2020

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Adelaide Office 365 User Group November 2023 Meeting

Archon Gnosis

Data plays a big role in building the kinds of experiences demanded by the market today. In this session, we’ll unpack what goes into building a data-driven app, case studies of how organizations have successfully overcome siloed data and analytics to bring new predictive features into their applications, and what your next steps for data should be on your digital transformation journey. Speaker: Les Klein, EMEA CTO Data, Pivotal

Data and its Role in Your Digital Transformation

VMware Tanzu

Business today is starting to understand the value of data, and some organisations are outperforming their competition by putting data at the heart of their thinking. Leveraging data to change business models, understand their customers and employees better and deliver new revenue streams is the driving force in this new data centric era. Jon Woodward - MSFT Dave Coplin - MSFT Mike Bugembe - JustGiving Gary Richardson - KPMG

Data Culture Keynote and Exec Track Birm Dec 8th

Jonathan Woodward

The Scout24 Data Landscape Manifesto is the formalization of our opinions on how a successful data-driven company should approach data. In a truly data-driven company, no manager, no salesperson, no engineer and no data scientist can do their job properly without easy access to large amounts of high-quality data. It is Sean's mandate to create a platform that encourages the production of high-quality data and enables engagement with data by all employees. He and his team are opinionated about how all producers and consumers of data need to be active participants in the data platform, to make data-driven decisions and to be responsible for the data they produce. And he built the data platform with 'nudges' that reward data usage that matches his vision for a data-driven company. In this talk, Sean will present the Scout24 Data Landscape Manifesto and will show how the strong opinions it contains enabled him to successfully migrate from a classic centralized data warehouse to a decentralized, scalable, cloud-based data platform at AutoScout24 and ImmobilienScout24 that is core to their analytics and machine learning activities.

The Scout24 Data Landscape Manifesto: Building an Opinionated Data Platform

Rising Media Ltd.

IoT at Scale

Vitaliy Rudnytskiy

Mobile of People and Internet of Things: State of the Union

Vitaliy Rudnytskiy

Overview of the Kaggle platform and its competitions

Alessandra Bilardi

From the Data Work Out event: Performant and scalable Data Science with Dataiku DSS and Snowflake Managing the whole process of setting up a machine learning environment from end-to-end becomes significantly easier when using cloud-based technologies. The ability to provision infrastructure on demand (IaaS) solves the problem of manually requesting virtual machines. It also provides immediate access to compute resources whenever they are needed. But that still leaves the administrative overhead of managing the ML software and the platform to store and manage the data. A fully managed end-to-end machine learning platform like Dataiku Data Science Studio (DSS) that enables data scientists, machine learning experts, and even business users to quickly build, train and host machine learning models at scale, needs to access data from many different sources and can also access data provided by Snowflake. Storing data in Snowflake has three significant advantages: a single source of truth, shorten the data preparation cycle, scale as you go.

Dataiku & Snowflake Meetup Berlin 2020

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Presentation "AI Product Manager" at the Digital Product School (on 10/22/2020) from Datentreiber. Content: • Overview over the AI product innovation cycle • AI Thinking: ideating and prioritizing the right use cases • AI Prototyping: testing critical hypotheses with experiments • AI Engineering: building scalable & user friendly AI applications • AI Management: maintaining AI solutions with DataOps • Outlook: how to become an AI product manager (links & more)

AI Product Manager

Im Bereich Marketing gibt es viele Daten. Doch nur wenige Unternehmen verwerten ihre Daten systematisch und planen die Verwendung von Analytik strategisch. Bei unserem Vortrag auf der Allfacebook Marketing Conference konnten Sie erfahren, wie Sie Ihr Unternehmen vom Datenverwalter zum Datenverwerter verwandeln, wie eine Datenstrategie Ihnen dabei hilft und welche Methode Sie dafür verwenden können.

Social Data Strategy: Mehr Wert aus Daten von Facebook & Co. erzeugen

Ihr Budget stagniert seit Jahren. Ihr Gehalt ebenso. Und Ihre Chefs sind der Meinung: Social Media Marketing hat keinen Nutzen. Doch Sie glauben weiterhin an Facebook, Instagram & Snapchat? Schlechte Nachricht: „An Gott glauben wir, alle anderen müssen Daten bringen.” (W.E. Deming). In dem abschließenden Vortrag erklärt Ihnen Datentreiber Martin Szugat, wie Sie mit der richtigen Datenstrategie Ihren Chefs beweisen, dass Ihre Inhalte auf Social Media einen wirtschaftlichen Nutzen bringen. Anhand verschiedener Praxisbeispiele erfahren Sie, welche analytischen Methoden Ihnen zur Verfügung stehen, um den Social Media Marketing ROI zu berechnen. Dabei lernen Sie außerdem die Methode des Data Thinkings kennen und frischen Ihre Statistikkenntnisse auf.

Social Media bringt's nicht? - Hold my beer.

Design Thinking zur Optimierung der Customer Journey

In den IT-Systemen vieler Unternehmen sind zahlreiche Datenschätze versteckt. Doch ohne eine Datenstrategie, welche die für das Unternehmen relevanten Verwertungsmöglichkeiten aufzeigt, bleiben diese Datenschätze unentdeckt und damit ungenutzt. Am Beispiel der datengetriebenen Optimierung des Produktportfolios für ein exemplarisches Unternehmen lernen Sie die visuelle und interaktive Methode des Datenstrategie-Designs kennen, mit der Sie in interdisziplinären Teams erfolgreiche Datenstrategien entwerfen können. In dem Webinar erfahren Sie außerdem, warum ein aktives Stammdatenmanagement kritisch für die Erschließung ihrer Datenlandschaft ist und wie Sie Ihre Daten aus einem Product Information Management System (PIM-System) analytisch verwerten können.

Erfolgreiche Datenstrategien für PIM & MDM entwerfen

Die ersten Schritte in das datengetriebene Marketing sind gemacht. Doch dann bleiben viele Unternehmen im Reporting stecken. Sie sammeln konsequent alle Kundendaten in der Data-Management-Platform, erfassen penibel Kennzahlen im Data-Warehouse und befüllen fleißig Ihren Data Lake. Doch sie fragen sich: wie gewinne ich aus all diesen Daten wirklich entscheidende Erkenntnisse? Und wie und vor allem wofür setze ich neue Technologien wie Künstliche Intelligenz ein? Den Unternehmen fehlt eine Datenstrategie: ein individueller Fahrplan, der Ihnen hilft, die richtigen Schritte in Richtung eines automatisierten und vor allem intelligenten Marketings zu gehen. Mit der Methode des Datenstrategie-Designs lernen Sie auf diesem Vortrag den Weg dahin kennen.

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„Eine Reise, tausend Meilen lang, mit einem ersten Schritt fing sie an!“ dichtete Laotse vor 2.600 Jahren. Das gilt auch heute noch und insbesondere für den digitalen Wandel des Marketings zu einem datengetriebenen Marketing. Für den Wandel reicht es nicht aus ein CRM, DMP oder eine andere Big Data-Lösung einzukaufen – das ist nicht der erste, sondern der letzte Schritt. Datengetriebenes Marketing beginnt bei den Mitarbeiter*innen im Kopf. Und mit der richtigen Datenstrategie sowie einem konsistenten Kennzahlenmodell zur Messung und Optimierung der Marketing-Ziele. In seiner Masterclass zeigt Datentreiber Martin Szugat wie Sie mit der Methode des Datenstrategie-Designs den richtigen Weg finden und die ersten Schritte in eine datengetriebene Zukunft gehen.

Data Driven Marketing - Die ersten Schritte in ein datengetriebenes Marketing

Unternehmen haben auf Facebook & Co. mit erheblichem Zeitaufwand und Budgeteinsatz teils beachtliche Reichweiten aufgebaut und stehen jetzt vor der Herausforderung Ihre Fans und Follower in qualifizierte Leads zu konvertieren. Verständlich, dass Marketing-Verantwortliche sich die Frage stellen: was kann, darf und soll ich mit den Daten aus den Social Media-Kanälen machen? In seiner Keynote zeigt Datentreiber Martin Szugat, was technisch machbar, rechtlich erlaubt und wirtschaftlich sinnvoll ist, um den Datenschatz aus Social Media zu heben und mit der richtigen Datenstrategie aus Fans Kunden zu machen.

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Nachdem Du in die Tiefen des Funnels hinabgestiegen bist, verschafft Dir Martin Szugat mit seiner Einführung zu Design Thinking für das datengetriebene Marketing einen Überblick über die ganzheitliche Customer Journey. In seinem Vortrag lernst Du, wie Du eine effektive Customer Journey entwirfst und dabei effizient im Team zusammenarbeitest. Er stellt Dir das Kennzahlenmodell AARRR vor: Aquisition, Activation, Retention, Referral und Revenue. Und gibt Dir einen Einblick in das Konzept der KPI-Treiberbäume. Außerdem erhälst Du einen Ausblick auf das (frei verfügbare) Datenstrategie-Designkit. Damit hast Du die Werkzeuge in der Hand, um Deinen Funnel erfolgreich zu gestalten und zu optimieren.

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Datenstrategie-Workshop

- Lernen Sie die Methode des Data Design Thinkings in der Praxis kennen, um für Ihr Unternehmen eine zielorientierte und individuelle Datenstrategie im Rahmen eines Data-Driven Marketings zu entwickeln. - Erschließen Sie das volle Potential Ihrer Daten, indem Sie Ihre Data Assets auf einer Landkarte verorten und bewerten. Dabei werden Sie neue Datenschätze und ungeahnte Geschäftspotentiale entdecken. - Verfeinern Sie Ihre Daten: „Daten sind das neue Öl.“ – Wie Öl müssen Sie Ihre Daten erst verfeinern, um daraus wertvolle Erkenntnisse zu gewinnen, die Sie für datengetriebene Entscheidungen nutzen können. - Verwerten Sie Ihre Daten mit Predictive & Prescriptive Analytics. - Entwerfen Sie neue Geschäftsmodelle, Wertschöpfungsketten und Vermarktungskonzepte für Ihre Kunden und Ihr Unternehmen.

Mit Design Thinking zum datengetriebenen Marketing

SELF-SERVICE.AI IN A NUTSHELL Background:> artificial intelligence enables SaaS companies to build intelligent self-service solutions for complex tasks such as customer service, personal scheduling, dynamic pricing, ad targeting etc. Objective:> provide a networking platform for AI-driven SaaS start-ups to present their product and team to high profile clients, partners and investors by organizing a start-up pitch competition. Audience:> start-ups from any country worldwide at any given stage with a Software-as-a-Service product that uses artificial intelligence (i.e. machine & deep learning, predictive & prescriptive analytics etc.) to provide a self-service solution for companies or consumers that solve a concrete business problem or serve a certain need. Examples:> existing AI-driven SaaS startups are e.g.: Clarifai, x.ai, Api.ai, Versium, Gpredictive, collectAI, trbo, DigitalGenius, DataMinr and many more to come.

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Videos wecken Emotionen – Daten sprechen den Verstand an. Zusammen sind sie eine wirksame Kombination, um Kunden für sich zu gewinnen. Daten helfen Ihrem Unternehmen herauszufinden, welche Zielgruppe sich für welche Inhalte interessieren und welche Inhalte in Käufe konvertieren. Die Daten sagen Ihnen aber auch, in welcher Form, auf welchem Medium, über welchen Kanal und zu welchem Zeitpunkt Sie Ihre Inhalte an den Konsumenten ausspielen sollten. Umgekehrt kann die Analyse der Inhalte und die Analyse des Nutzungsverhaltens der Inhalte selbst eine ergiebige Datenquelle für Kundeneinsichten sein. Und schließlich können Daten die Inhalte selbst personalisieren und so individuelle und nachwirkende Markenerlebnisse schaffen. Anhand verschiedener Beispiele zeigen wir Ihnen die unterschiedlichen Anwendungsfälle vom Zusammenspiel von Daten und Videos.

Video und Daten – eine explosive Marketing-Mischung

This project aims to predict whether a loan application will be approved or denied based on various factors such as applicant's income, credit score, loan amount, etc. Using a dataset containing historical loan application data, we employed machine learning algorithms to build a predictive model. The model was trained on features such as applicant's income, credit history, loan amount, loan term, and others. After training the model, we evaluated its performance using metrics like accuracy, precision, recall, and F1 score. The insights from this project can help financial institutions streamline their loan approval process and make informed decisions. Visit for more information: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/

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This project demonstrates a machine learning approach to detecting credit card fraud using advanced algorithms and techniques. The project utilizes a dataset containing various features such as transaction amount, merchant location, time of transaction, and others to build a predictive model. The presentation covers data preprocessing steps, feature engineering techniques, and the selection of machine learning algorithms such as logistic regression or random forest. It also discusses model evaluation metrics and the importance of fraud detection in financial institutions for safeguarding against fraudulent activities. Visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/

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Detecting Credit Card Fraud: A Machine Learning Approach

Boston Institute of Analytics

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