Documents

CASE STUDY: Lyft optimizes its business with unique anomaly detection

Explore how this ride-share leader uses Anodot to identify business risks in real time

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WHITE PAPER: Extending Competitive Advantage in Telecom

Explore this white paper prepared by EMA analysts on five use cases for applying AI analytics in telecom services

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EBOOK: Find and fix business incidents in real-time with AI-powered analytics

Explore how this ride-share leader uses Anodot to identify business risks in real time

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EBOOK: Solving Data Quality in Real Time with AI Analytics and Anomaly Detection

Immediately address data quality problems and save weeks of dealing with inaccurately reported data. AI Analytics and Anomaly Detection puts renewed trust in the quality of the data that directly impacts business priorities.

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ANALYST REPORT: No more Silos – How DataOps Technologies Overcome Enterprise Data Isolationism

This new report from Blue Hill Research takes a closer look at how enterprises deploy DataOps models to establish the free flow of data within their organization. It includes real-world case studies which demonstrate how organizations in various industries from retail and ecommerce to education are leveraging new technologies to break down silos.

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WHITE PAPER: Detecting the Business Incidents that Matter with Anomaly Detection

Learn how anomaly detection can remedy urgent problems faster and capture opportunities sooner.

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WHITE PAPER: How Three eCommerce & Retail companies are Harnessing AI to Gain Revenue

Learn how leading eCommerce and retailers are leveraging the power of machine learning – identifying problems and leveraging business opportunities faster.

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WHITE PAPER: How Five Leading Adtech companies used AI to save millions

Learn how leading adtechs like Rubicon Project, Uprise, and NetSeer are leveraging the power of machine learning – discovering outliers in time series data and turning them into valuable business insights.

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WHITE PAPER: The Build or Buy Dilemma For AI-Based Anomaly Detection

Leveraging the vast amount of business data available today to better meet customer needs and detect business incidents presents organizations with the challenge of whether to build their own anomaly detection system or buy one ready-made. Before organizations make this critical decision, it is important to weigh the benefits and challenges of each approach.

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WHITE PAPER: Ultimate Guide to Building a Machine Learning Anomaly Detection System, Part 3 – Correlating Abnormal Behavior

This 21-page white paper – part three of a three-part series – incorporates diagrams and charts to explain the process of identifying, ranking and correlating abnormal behavior.

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WHITE PAPER: Ultimate Guide to Building a Machine Learning Anomaly Detection System, Part 2 – Learning Normal Time Series Behavior

This 21-page white paper – part two of a three-part series – incorporates diagrams and charts to explain the process of learning the normal behavior of time series data. Topics include: A general framework for learning normal behavior, The importance of modeling seasonality, Can a seasonal pattern be assumed?, Example methods to detect seasonality, Real-time detection at scale requires online adaptive learning algorithms, The impact of the learning rate, Adapting the learning rate, and Other methods for learning normal behavioral patterns.

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WHITE PAPER: Ultimate Guide to Building a Machine Learning Anomaly Detection System, Part 1 – Design Principles

This 18-page white paper – part one of a three-part series – includes full color charts and use cases and discusses design principles of creating a machine learning-based anomaly detection system. Topics include: Why companies need anomaly detection, Types of machine learning, What is an anomaly, and Design principles.

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WHITE PAPER: Real-Time Correlation of Business Insights for Fintech

In this White Paper, Jason Bloomberg – a leading industry analyst and expert on agile digital transformation – takes a closer look at how real-time anomaly detection based on machine learning is a game changer for fintech companies.

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WHITE PAPER: Real-Time Anomaly Detection and Analytics for Today’s Digital Business

Jason Bloomberg – a leading industry analyst and globally recognized expert on agile digital transformation – takes a closer look at how real-time anomaly detection is a game changer for digital technology companies.

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CASE STUDY: Ensuring A Seamless Gaming Experience For Outfit7 Users

Outfit7’s lovable, digital characters raise a smile whenever they appear on screen. Anodot can identify any potential app performance areas, seamlessly correlating this data with other relevant metrics to ensure a smooth gaming experience.

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CASE STUDY: Netseer Sees Results with Anodot Real Time Business Incident Detection

As a leading adtech company, Netseer provides publishers with targeted ads, and bidding on advertising exchanges. Anodot provides real-time business insights to quickly identify performance issues.

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CASE STUDY: Anodot Finds “All the Anomalies Fit to Print” for Media Giant PMC

With millions of users across dozens of professional publications, PMC’s data science team uses Anodot to track business incidents and identify software bugs early, before small issues turn into major crises.

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CASE STUDY: eToro Detects Incidents and Gets to the Root Cause Faster with Anodot

eToro must be able to provide users with reliable market rates as quickly as possible. Find out how Anodot helps eToro monitor the health of every client request and stay ahead of potential problems to ensure they meet regulatory requirements.

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CASE STUDY: Anodot’s Eye on Data Protects Eyeview’s Revenue

With massive amounts of business-critical data and metrics, Eyeview recognized the need to improve its alerting method. Anodot AI-powered analytics crunches Eyeview’s data streams in real-time, providing accurate alerts on critical issues.

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CASE STUDY: Anodot’s useful ecommerce insights for Wix

Wix needed a real-time alert system that would indicate issues without manual threshold settings in the key metrics. Anodot’s AI-powered analytics provides the necessary insights to the company’s analysts.

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CASE STUDY: Credit Karma Adopts the Anodot Real-Time Business Incident Detection Dogma

Credit Karma researches and recommends credit cards, loans and insurance for credit profiles of more than 50 million members’ finances. In real time, Anodot identifies relevant and actionable incidents every day, so the team can deal with them quickly and appropriately.

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CASE STUDY: Rubicon Project Automates Real Time Business Incident Detection with Anodot

One of the largest ad exchanges in the world, Rubicon Project uses proprietary computing systems to automate the buying and selling of advertising. With Anodot’s correlated metrics, Rubicon can quickly pinpoint where to investigate.

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CASE STUDY: AI-powered Analytics Ensures Customer Satisfaction at LivePerson

Addressing some of the most challenging customer engagement problems, the LivePerson team needed to know when a problem was brewing, to ensure that its more than 18,000 customers were getting the most out of their solution. Find out how Anodot helps LivePerson to easily track in real time massive amounts of business-critical data, enabling their customers to get the most out of the LivePerson platform.

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REAL-TIME ANOMALY DETECTION & ANALYTICS FOR TELCOS

Even the tiniest drop in service quality or availability not only impacts customer trust, but can cost a telecom company millions of dollars. Anodot automatically analyzes the vast volumes of network data generated from multiple sources, helping Telcos to optimize service levels, minimize overhead costs and maximize profitability, ensuring customer loyalty and maintaining overall network performance.

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REAL-TIME ANOMALY DETECTION & ANALYTICS FOR E-COMMERCE

E-commerce sites are click- and usage-driven, and customer engagement and revenue are intricately tied together. Early detection of business incidents translates into an agile site that can respond to changes quickly for increased traffic, sales and revenue, optimized ad campaigns, and satisfied customers.

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REAL TIME ANOMALY DETECTION AND INSIGHTS FOR ADTECH

In programmatic advertising, every minute translates into tens of thousands of dollars, and Anodot gives advertising technology companies the crucial insights you need in real time.

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REAL-TIME BUSINESS INSIGHTS FOR MEDIA COMPANIES

Gain valuable insights from all of your data in real time, with Anodot time business incident detection and analytics.

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REAL-TIME ANOMALY DETECTION & ANALYTICS FOR FINTECH

As a financial technology company, you deal with a vast number of critical data streams that behave in complex ways, such as transactions, payments, money transfers or loans. Performing with the highest standard of availability and reliability is a crucial asset to securing customer trust.

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REAL-TIME ANOMALY DETECTION & ANALYTICS FOR THE TRAVEL INDUSTRY

Learn immediately if any of your key indicators are under- or over-performing, including numbers of passengers to and from specific cities/regions, brand or display advertising click-through rates, flight delays due to technical issues, customer service communication counts, average resolution time of support calls, number of bookings or cancellations, weather per geographic region and holidays per country.

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Network World: Anodot uses real-time analytics and anomaly detection to provide business insight

Linda Musthaler, Principal Analyst with Essential Solutions Corp. delves into the benefits of Anodot for companies that need real time business insights.

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Identify IOT Issues in Real Time with Anodot

A brief brochure about how to use Anodot to receive immediate alerts when things go wrong in factories, industrial settings and connected cars and buildings.

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Got Data? Get Insights. Fast.

A brief brochure about increasing business control with real time business incident detection, anomaly detection and analytics.

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Videos & Podcasts

VIDEO – Using AI analytics to detect real-time application issues

20:01

Anodot’s Solutions Engineer and AI specialist, Steven Kirkpatrick, presents how leading gaming companies are leveraging the powerful anomaly detection capabilities of Anodot’s AI Analytics to proactively address business incidents in real time, minimizing revenue loss and identifying opportunities.

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VIDEO – Optimized Incident Management for the Gaming Industry

3:53

King’s Product Owner of Business Intelligence, Lukasz Korbolewski, shares how King is using Anodot’s AI Analytics to proactively address business incidents in real time, minimizing revenue loss and identifying opportunities.

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VIDEO – Webinar – Autonomous Analytics in the Real World: Giving Your Data a Voice

38:38

Hear how to control your market with Autonomous Analytics in this informative webinar led by GigaOm’s William McKnight. Foursquare will share how they moved from a manual, reactive process to an automated and proactive application services management with Autonomous Analytics and you will hear their challenges, analytics journey, solutions, and customer benefits.

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VIDEO – The Real-time Journey from Raw Streaming Data to AI-based Analytics

36:07

Roy Ben-Alta, solution architect and principal business development manager at Amazon Web Services, and Anodot’s Chief Data Scientist Dr. Ira Cohen present various design patterns and share a solution implemented using Amazon Kinesis as a real-time event data processing pipeline that feeds Anodot’s AI-based analytics service, discovering and alerting on the anomalies in the data in real time and helping you avoid costly business incidents.

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VIDEO – Webinar – How A Rideshare Giant Uses AI To Detect Business Anomalies

59:25

Hear how Lyft and other data-driven organizations benefit from uncovering hidden insights in real time with AI solutions. Listen to: Kris Skrinak, AWS Partner Solutions Architect, AI/ML Global Segment Lead, A Ira Cohen, Chief Data Scientist and Co-Founder at Anodot, Gleb Mezhanskiy, Product Manager, Data, Lyft

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VIDEO – Webinar – AI-Based Anomaly Detection for Proactive Business Monitoring

1:03:09

Ready to move into the future of BI and AI-driven business monitoring? Listen to: Greg Kurzhals, Product Analyst at Pandora, Matt Aslett, Research Director for the Data Platforms and Analytics Channel at 451 Research, and Dr. Ira Cohen, Chief Data Scientist at Anodot

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VIDEO – Customer Success – LivePerson

1:59

Find out how Anodot helps LivePerson to easily track in real time massive amounts of business-critical data, enabling their customers to get the most out of the LivePerson platform..

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VIDEO – Optus and Anodot

19:13

Optus Networks’ Director of New Services Engineering, Jason Wong, presents how Optus Networks is leveraging the powerful anomaly detection capabilities of Anodot’s AI Analytitcs to proactively address business incidents in real time, protect revenue and keep their customers happy.

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VIDEO -Credit Karma and Anodot

26:08

Pedro Silva, Senior Product Manager at Credit Karma, discusses Credit Karma’s usage of Anodot’s AI-powered analytics and anomaly detection, sharing the benefits of easily tracking data, getting real-time alerts and altogether promoting a fair and healthy business monitoring.

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VIDEO – How AppNexus Leverages AI Analytics to Detect Business Incidents

15:03

In this session at White Hall’s BDA conference, AppNexus’ VP of Engineering, Travis Johnson, presented on how AppNexus is using Anodot’s AI Analytics for insights about unknown unknowns, to confidently and proactively address incidents quickly.

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VIDEO – Learning the learner: Using machine learning to monitor. . .machine learning?

37:47

In this session at O’Reilly’s Artificial Intelligence Conference, Dr. Ira Cohen, Anodot’s Chief Data Scientist and co-founder, presents how Anodot devised a way to intelligently monitor the performance of highly complex, unsupervised machine learning models with a “learning the learner” approach.

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VIDEO – A spike in sales is not always good news

24:02

In this session at the Strata Data Conference, Anodot’s Inbal Tadeski presents the importance of identifying relationships between time metrics so that they can be used for predictions, root cause diagnosis, and more.

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VIDEO – Real-Time Business Incident Detection

29:48

Uri Maoz, Anodot VP US Sales & Business Development, presents at the Innovation Enterprise Chief Data Officer Summit, June 2017

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VIDEO – Identify e-commerce losses and opportunities with machine learning

19:13

Nir Kalish, Anodot Sr Director Solution Engineering presents at the Big Data & Analytics for Retail Summit in Chicago, June 2017.

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VIDEO – The App Trap (Strata San Jose 2017)

39:40

In this session at Strata Data San Jose, Ira Cohen, Anodot’s Chief Data Scientist and co-founder, presents how to use anomaly detection to monitor all areas of your mobile app to fully optimize it.

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VIDEO – Rich Galan of Rubicon Project: The Need for Real-Time Anomaly Detection

11:07

Rich Galan of Rubicon Project presents the need for real-time anomaly detection at Innovation Enterprise CTO Conference.

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VIDEO – Disrupt the static nature of BI with Predictive Anomaly Detection – Anodot Meetup

30:22

Anodot’s Uri Moaz discusses how predictive anomaly detection can identify revenue-impacting business incidents in minutes(!) not days or weeks.

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VIDEO – Anomaly Detection on Google Analytics – Anodot Meetup

18:45

Corey Gilmore, Director of Data and Analytics at PMC, a large publishing company, presents how they identify anomalies in their Google Analytics using Anodot.

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VIDEO: Anomaly Detection in Adtech – Anodot Meetup

15:02

Greg Pendler of Netseer presents how his Adtech company is using Anodot to identify anomalies in real time, to keep their business working efficiently.

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PODCAST: The Analytics of Anomaly Detection with Anodot

Josh Bycer from Game Wisdom sat down with Anodot’s Ira Cohen and Rebecca Herson to discuss how things are changing with the mobile market, and the work Anodot is doing in the field of analytics.

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VIDEO: Analytics for Large Scale Time Series & Event Data (Strata New York 2016)

42:37

In this session from the O’Reilly Conference Strata New York, Ira Cohen, Anodot’s Chief Data Scientist and co-founder, outlines a system that performs real-time machine learning and analytics on streams at massive scale.

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DSC Webinar Series: Accurate Anomaly Detection with Machine Learning

DSC webinar hosted by Bill Vorhies, Data Science Central Editorial Director and featuring Ira Cohen, Anodot Chief Data Scientist discussing fundamental machine learning techniques for anomaly detection, requirements of an anomaly detection system in various use cases, issues and pitfalls to watch out for when implementing anomaly detection, and common use cases and examples.

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VIDEO: Berlin Buzzwords interview with Ira Cohen, Anodot Chief Data Scientist

03:53

Darya Niknamian, Dataconomy Media Content Marketing Manager, sat down with Ira Cohen, Anodot Chief Data Scientist, at Berlin Buzzwords 2016 and talked about his work, data science, robots, smart phones and VR glasses among other things.

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PODCAST: Semi-Supervised, Unsupervised, and Adaptive Algorithms for Large-Scale Time Series

O’Reilly Data’s Ben Lorica interviews Dr. Ira Cohen, Anodot’s co-founder and Chief Data Scientist about the challenges in building an advanced analytics system for intelligent applications at extremely large scale.

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VIDEO: Anodot Real Time Anomaly Detection

(1:29 minutes)

Brief explanation of Anodot’s Technology, demonstrating how even if you are an octopus you can’t access real time business insights using standard business intelligence tools.

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VIDEO: Anomaly Detection 101

(27 minutes)

Anodot’s Uri Maoz explains how real time anomaly detection can save your company millions of dollars, and what you should look for in this type of monitoring system for both your technical and your business metrics. Recorded at the CTO Summit.

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VIDEO: Discovering Real Time Anomalies in Large Scale Time Series Signals

(20:25 minutes)

Dr. Ira Cohen, Anodot’s co-founder and Chief Data Scientist presents at IGTcloud – Big Data, Analytics & Applied Machine Learning – Israeli Innovation Conference.

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