Name: TigerGraph 101 - Part One - An Introduction to Graph
Start: 2022-01-27T13:17:00Z
End: 2022-01-27T13:18:06.000Z
Location: BrightTALK
Rating: 0

TigerGraph is a platform for advanced analytics and machine learning on connected data. Based on the industry’s first and only distributed native graph database, TigerGraph’s proven technology supports advanced analytics and machine learning applications such as fraud detection, anti-money laundering (AML), entity resolution, customer 360, data operations, digital twin, recommendations, knowledge graph, cybersecurity, supply chain, IoT, and network analysis. 

This channel showcases TigerGraph's technology and how it helps organisations tap into their data to gain key insights into the business decisions and processes driving innovation, growth and cost optimisation.  

For more information visit www.tigergraph.com

A digital twin of your data operations and supply chain looks like a network and should be directly represented as a network. Graph databases are specifically designed to do this.

A graph-powered digital twin allows you to connect all internal and external data components into a digital network that models data lineage and dependency and then facilitates rapid root cause analysis to inform decisions. With TigerGraph, you get network-wide analytics and real-time capabilities, so operations and strategic planning are all working from exactly the same model. This enables continuous evolution without ever losing control and with an entirely data-driven basis for prioritization and execution. As a result, TigerGraph helps in providing valuable insights leading to faster and smarter decision-making and great customer-centricity.

What you will learn:

1. Introduction to graph analytics and machine learning on connected data
2. How to take your data analytics to the next level by using TigerGraph for
Data operations
Supply chain optimisation
    - Risk management with advanced analytics
    - Powerful what-if scenarios
3. Customer success stories

Putting graph analytics at the heart of your data operations and supply chain

Fraud is a board-level concern for banks around the world. The fact that existing fraud detection software doesn't stop enough fraud leads to massive losses for banks, losses they can find difficult, if not impossible, to recover.

In this video, Harry Powell, TigerGraph's Head of Industry Solutions, talks about the limits of machine learning, their effects on fraud detection solutions, and how TigerGraph's graph analytics and machine learning are already being used by tier 1 banks in the U.S. to create a more effective fraud detection capability.

To learn more, visit https://www.tigergraph.com/solutions/fraud-detection.

Overcoming the Accuracy Barrier in Banking Fraud Detection

The Technical University of Denmark (DTU), a leading university in the areas of technical and natural sciences, is part of a major project across Denmark and Sweden to map genetic material for everyone with childhood cancer, with the long-term goal of improving the treatment of acute lymphoblastic leukaemia. This presentation will discuss how the project combines graph analytics with the fields of AI, machine learning, and translational bioinformatics to create models that can predict the risk of relapse and toxicity within acute lymphoblastic leukaemia treatments. By linking together various other data points about the patient's life, illness, and treatment, clinical personnel can understand to a much greater extent why children get cancer and provide earlier diagnosis and far more effective treatment.

Improve Treatment of Lymphoblastic Leukeamia using Graph Analytics with AI & ML

Sie möchten wissen, wie Sie sich wirksamer gegen Finanzbetrug schützen und bei Angriffen umgehend richtig reagieren können?
Um den Kampf gegen professionellen Betrug zu gewinnen braucht es vor allem eins: die Bereitschaft moderne Technologien zu implementieren und anzuwenden.
7 der 10 weltweit führenden Finanzinstitute nutzen TigerGraph's Graph Analytics und Machine Learning Technologie in den Bereichen Betrugserkennung, AML und Credit Risk Assessment. 

Inhalte des Webinars:
Einführung Graph Analytics & Machine Learning mit Connected Data
Wie Sie verdächtige Muster in Zahlungsströmen in Echtzeit identifizieren 
Warum relationale Datenbanken und Graphdatenbank-Anbieter der 1. Generation, aufgrund Größe und Komplexität des Problems, Schwierigkeiten hatten ein Echtzeit-Lösungen bereitzustellen
TigerGraph Kundenbeispiele aus AML und Betrugsbekämpfung

Finanzbetrug bekämpfen: Wirksamer Schutz durch Graph Analytics+Machine Learning

Now is the best time to learn the fundamentals of graph & graph analytics, and take advantage of the next frontier for data science and analytics.

Watch part 4 of our 4-part series, covering:

- Querying and beyond

Resource list and the slide deck here: https://bit.ly/3kRz0Tw

TigerGraph 101 - Part Four - Querying and Beyond

Now is the best time to learn the fundamentals of graph and take advantage of the next frontier for data science and analytics. 

Watch part 3 of our 4-part series, which covers Schema Modeling and Data Loading:

- What should you think about while designing your schema?
- How your data drives schema over use-case
- Data loading methods and advantages

Resource list and the slide deck here: https://bit.ly/3kRz0Tw

Coming up...

Part 4 - Querying and Beyond

TigerGraph 101 - Part Three - Schema Modeling and Data Loading

Now is the best time to learn the fundamentals of graph and take advantage of the next frontier for data science and analytics. 

Watch part 2 of our 4-part series, covering data acquisition and preparation.

Resource list and the slide deck here: https://bit.ly/3kRz0Tw

Coming up...

Part 3 - Schema Modeling and Data Loading
Part 4 - Querying and Beyond

TigerGraph 101 - Part Two - Data Acquisition and Graph Preparation

It's a familiar story.  The pandemic forced every organization to change tack and operate differently.  In this session, Harry Powell explains how Jaguar Land Rover put graph analytics to work in order to respond to the pandemic and ensure the smooth continuation of its sensitive supply chain. 

From placing orders, to understanding features, to ensuring parts were delivered on time and in the right place, to working with suppliers and its factories, Jaguar Land Rover made use of graph analytics to connect the dots and run their operations in new conditions imposed by the pandemic.

Questions that the business could ask included:
- What suppliers are at the biggest risk?
- What are JLR's alternative production plans?
- What options do JLR's customers have

Watch this session to understand more about this supply chain case study.

COVID-19 Broke My Forecast!  A Supply Chain Case Study with Jaguar Land Rover

2021 has given rise to sophisticated ransomware-style attacks and increased cyber threats. Organizations are fighting the onslaught of threats from inside, from known threat actors, nation-states, etc.  

This sessions focuses on how machine learning and graph analytics work together to increase threat detection accuracy, decrease alert fatigue and false positives. 

With automation, security analysts can focus on the events that really matter. In addition, enhanced threat response management satisfies compliance demanded by regulators. Automation of threat analysis is key to your SOC teams’ success.

This webinar covers:
- New reporting rules for cyber security and other impacts on compliance 
- How to utilize watch list data and breach data in real-time with new technologies like master data matching, graph analytics, and machine learning.
- Capabilities in visualization and human intelligence technologies that will increase throughput and provide valuable data analytics.
- How to implement practical methods of cyber identification, complex dependency and case management using "human-in-the-loop" technology for higher accuracy and process streamlining

Real-Time Threat Detection and Incident Response with Graph Analytics & ML

A digital twin of your data operations and supply chain looks like a network and should be directly represented as a network. Graph databases are specifically designed to do this.

A graph-powered digital twin allows you to connect all internal and external data components into a digital network that models data lineage and dependency and then facilitates rapid root cause analysis to inform decisions. With TigerGraph, you get network-wide analytics and real-time capabilities, so operations and strategic planning are all working from exactly the same model. This enables continuous evolution without ever losing control and with an entirely data-driven basis for prioritisation and execution. As a result, TigerGraph helps in providing valuable insights leading to faster and smarter decision-making and great customer-centricity.

Content of this session includes:

- Introduction to graph analytics and machine learning on connected data 
- Data operations 
- Supply chain optimisation 
- Risk management with advanced analytics 
- Powerful what-if scenarios

Putting Graph Analytics at the heart of your Data Operations & Supply Chain

A supply chain case study.

The automotive supply chain is one of the most complex and global in the world; the average car is made up of some 4,500 parts produced by 100s of suppliers, relying on forecasts issued years in advance.

This sessions will show you how, using graph analytics, Jaguar Land Rover (JLR) has reduced query time across their entire supply chain from 3 weeks to just 45 minutes.  And how JLR accurately plans in response to supply and demand uncertainties around the Covid-19 pandemic.

Accelerating Supply Chain Planning at Jaguar Land Rover From 3 Weeks to 45 Mins

Fighting Money Laundering with Machine Learning, Graph Database and Analytics.  

This session covers how graph analytics creates new business value for banks, credit institutes, insurance providers and other financial institutions.

Financial crime is a graph-based opportunity, so this is where graph analytics can help with your endeavours to detect fraudulent activity.  Graph analytics will also help with other programmes around customer journey, customer 360, customer segmentation and product recommendation.

Learn why 7 out of the top 10 global banks already depend on TigerGraph and graph analytics to detect fraud and to deliver outstanding customer experiences.

Combatting Financial Crimes with Graph Databases and Analytics

TigerGraph is a platform for advanced analytics and machine learning on connected data. Based on the industry’s first and only distributed native graph database, TigerGraph’s proven technology supports advanced analytics and machine learning applications such as fraud detection, anti-money laundering (AML), entity resolution, customer 360, recommendations, knowledge graph, cybersecurity, supply chain, IoT, and network analysis.

Watch this video to understand more about TigerGraph and graph analytics, and how to get started today for free.

What is TigerGraph?

“By 2025, graph technologies will be used in 80% of data and analytics innovations, up from 10% in 2021, facilitating rapid decision-making across the enterprise.” * – Rita Sallam, Distinguished VP and Fellow, Gartner Research

Now is the best time to learn the fundamentals of graph and take advantage of the next frontier for data science and analytics. Watch the 4-part series now.  

Watch part 1 of our 4-part series, covering:  

- An introduction to graph 
- How graph compares to relational databases
- Real-world use cases for graph 
- How to create your first graph using TigerGraph Cloud's GraphStudio

Resource list and the slide deck here: https://bit.ly/3kRz0Tw

Coming up... 

Part 2 - Data Acquisition and Graph Preparation
Part 3 - Schema Modeling and Data Loading 
Part 4 - Querying and Beyond

*  Gartner, Inc.: Graph Steps Onto the Main Stage of Data and Analytics: A Gartner Trend Insight Report. Published 14 December 2020.

TigerGraph 101 - Part One - An Introduction to Graph

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TigerGraph 101 - Part One - An Introduction to Graph

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