Webinar - Big Data: Power to the User
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What is the value of big data? How does a user get that value? Before, analysts would have to wait months relying on IT for a new report or make changes to an existing one. Now, analysts are able to shrink that time down to days or even minutes. On top of that, analysts can ask questions that were not possible before. In this webinar, we’ll show you how this analysis is possible and the value that has been achieved by customers. In this session, you will learn: How analysts get value out of big data How to visualize data at every step of analysis How analysts can do big data analytics without IT, in one product
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To achieve these benefits organisations need to embrace the changing reality of the new data driven society and make a break from the beliefs and best practices inherent in traditional Business Intelligence programmes.
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2. Setting expectations with the executive team
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4. Framing the boundaries of our data science
5. Pragmatic data governance
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CLEANSE
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Location Hub Analytics quickly processes and analyzes your data, objectively revealing meaningful patterns and trends
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Self-Service Analytics Framework - Connected Brains 2018
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Big Data LDN 2017: How to leverage the cloud for Business Solutions
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DMTI Spatial Location Hub Analytics: big data, analytics, visualization
Similar to Webinar - Big Data: Power to the User
Online Fraud Detection Using Big Data Analytics Webinar
With the ease and convenience of the internet, shopping online has never been faster and simpler than with a click of a button. But with this convenience, lurks the consequence for online fraud.
Companies and merchants lose valuable time and money to online thieves scamming the web. Learn how to identify patterns with Datameer and Trustev as they demonstrate how to take control of the situation and combat combat against suspicious activity by using big data analytics.
In this webinar, you will take away:
*An understanding of the complexities and challenges of online fraud today
*Best practices for merchants and companies to protect themselves from fraud
*A demonstration of fraud reporting, prevention and prediction
Instant Visualizations in Every Step of Analysis
Surveys reveal that concerns about data quality can create barriers for companies deploying Analytics and BI initiatives.
How can you readily identify and correct data quality issues at every step of your big data analysis to ensure accurate insights into customer behavior? In this webcast, we'll discuss how IT and business users can leverage self-service visualizations to quickly spot and correct data anomalies throughout the analytic process.
In this webinar, you will learn how to:
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Customer Case Studies of Self-Service Big Data Analytics
Self-service tools empowers business users to rapidly gather, analyze and visualize data from broad, diverse data sources. Analyzing these sources provides new answers and new business opportunities for those smart enough to answer the new questions.
Free your IT staff from the need to respond to routine report requests. Business users can now rely on the rapid delivery of advanced self-service BI and data visualization capabilities to solve complex problems and capitalize on new opportunities.
From these slides, you will learn:
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Fight Fraud with Big Data Analytics
You can view the full presentation of this webinar here: http://info.datameer.com/Slideshare-Fighting-Fraud-this-Holiday-Season.html
In 2012, retailers lost $3.5 billion in revenue to online fraud. These losses spike by a substantial estimated 20% during the holiday season.
Join Datameer and Hortonworks in this webinar to learn how Big Data Analytics can be used to identify new fraud schemes during peak fraud season.
In this webinar, you will learn about:
current challenges in identifying fraud
what to look for in a big data solution addressing fraud
how big data analytics can identify credit card fraud
best practices
Making Hadoop based analytics simple for everyone to use
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Transform Banking with Big Data and Automated Machine Learning 9.12.17
Banks are rich in valuable data and can build and maintain a competitive advantage by identifying and executing on high-value machine learning projects leveraging the rich data available.This webinar will describe use cases fit for big data and machine learning in the banking sector (commercial, consumer, regulatory, and markets) and the impact they can have for your organization.
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Analyzing Unstructured Data in Hadoop Webinar
Unstructured data is growing 62% per year faster than structured data. According to Gartner, data volumes are set to grow 800% in aggregate over the next 5 years, and 80% of it will be unstructured data.
This on-demand webinar will highlight and discuss:
How applying big data analytics to unstructured data can help you gain richer, deeper and more accurate insights to gain competitive advantages
The sources of unstructured data which include email, social media platforms, CRM systems, call center platforms (including notes and speech-to-text transcripts), and web scrapes
How monitoring the communications of your customers and prospects enables you to make time-sensitive decisions and jump on new business opportunities
Understanding Customer Buying Journey with Big Data
Big Data enables you to combine the vast amount of customer behavior data being generated from mobile, web, social media, transaction systems, Ads and turn them into new insights that drive customer acquisition and retention.
This talk will highlight and showcase how leading edge companies are leveraging big data to:
Combine customer interaction data to understand customer buying journey
Understand high-value customer behavior beyond profile segmentation
Identify the most common path to customer churn
Perform market basket analysis to help with cross-sell and up-sell
Speakers:
Matt Schumpert
Director of Product Management
Matt has been working in the enterprise infrastructure software space for 15 years in various capacities, including product management, sales engineering, sales leadership and strategic alliances. An early employee of Big Data pioneer Datameer, Matt is currently focused on driving the strategic direction and technology strategy for Datameer’s flagship Big Data analytics product. Previously, Matt built the sales engineering organization at Datameer, managed strategic partnerships at Oracle & BEA Systems in the US an Europe, and drove sales of business rules management software at ILOG (now IBM). Matt holds a BS in Computer Science from the University of Virginia.
Standing Up an Effective Enterprise Data Hub -- Technology and Beyond
Federal organizations increasingly are focused on creating environments that enable more data-driven decisions. Yet ensuring that all data is considered and is current, complete, and accurate is a tall order for most. To make data analytics meaningful to support real-world transformation, agency staff need business tools that provide user-friendly dashboards, on-demand reporting, and methods to manage efficiently the rise of voluminous and varied data sets and types commonly associated with big data. In most cases, existing systems are insufficient to support these requirements. Enter the enterprise data hub (EDH), a software architecture specifically designed to be a unified platform that can economically store unlimited data and enable diverse access to it at scale. Plan to attend this discussion to understand the key considerations to making an EDH the architectural center of your agency’s modern data strategy.
Webinar - Introducing Datameer 4.0: Visual, End-to-End
In Big Data projects, analysts often spend 80% of their time preparing data for analysis. In addition, users don’t have a good understanding of their data quality.
Today there are multiple tools that assist with integration, data preparation, analysis and visualization. However, data quality continues to be one of the biggest challenges businesses face when deploying big data analytics.
People need to profile their data and ensure data quality to get accurate insights and make informed business decisions. Join Datameer as we address these pain points with visualizations at every step.
This webinar will highlight and showcase:
-How visual data profiling reduces the guesswork in the data wrangling process
-Enhanced interactive data mining capabilities reduce time to insight
-A demonstration of the new 4.0 features and functions
The Journey to Success with Big Data
Keynote from Big Data World Show Singapore, April 2015.
• How is data driving change?
• Where are the opportunities, across industries?
• What is required to gain value from data?
• How can you get started today?
James Mesney_"Datameer's Big Data Analytics Platform"_April 9th_Data Enthusia...
Datameer provides an end-to-end big data analytics platform designed to simplify analytics on Hadoop. The platform allows business and IT users to integrate, analyze, and visualize both structured and unstructured data. It features drag-and-drop visualization, point-and-click analytics, and wizard-led data integration. Companies use Datameer to get insights from their data faster and at lower costs than traditional methods.
Four Key Considerations for your Big Data Analytics Strategy
This document discusses considerations for big data analytics strategies. It covers how big data analytics have evolved from focusing on structured data and batch processing to also including real-time, multi-structured data from various sources. It emphasizes that discovery is key and requires visual exploration of granular data details. Native big data analytics platforms are needed that can handle real-time streaming data and provide self-service capabilities through customizable applications. The document provides examples of how various companies are using big data analytics for applications like cybersecurity, customer analytics, and supply chain optimization.
Predictive analytics from a to z
This will be an engaging, fast-paced and informative presentation and discussion of the latest tools and trends in predictive analytics. The webinar will include a demo of the PMML capabilities in Alpine Data Labs Chorus 4.0 and instant deployment of predictive models via Zementis solutions.
On this webinar you’ll come away with the following knowledge:
Quickly start your very own Alpine Chorus 4.0 advanced analytics project and export to PMML with ease.
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Chanchal Chatterjee PARTNERS 2017 Oct24
The document discusses Teradata's AI solutions and differentiations. It outlines common enterprise AI use cases like fraud detection, predictive maintenance, and recommender systems. It then details how Teradata's tight integration of AI solutions with its analytics platforms provides faster data access, enables new processes, and achieves better performance than alternatives. Specific performance differentiations in fraud detection over machine learning and rule engines are shown. Example solutions for fraud detection/prevention and IoT are also summarized.
Why spatial data governance is critical to your business strategy
Data is everywhere and we never saw such amount of data to handle. An estimated 80% of this data contains location information!
Your organization needs to evaluate your data in context with relevant business factors and gain spatial insights for confident business decisions.
Precisely, with its expertise and wide portfolio, can support you to:
Govern your spatial data with a strong data governance strategy
Elaborate metrics and enhance analytics to accelerate insights and reduce business risksEnrich your data to unlock new insights and empower better-informed decisionsData Integrity – a journey to increase trust in data, accelerate data-driven transformation and produce better business results – is the key to transform your company into a successful data-driven one.
Discover in our webcast how governing spatial data provides trusted insights for confident business decisions.
Big data-analytics-ebook
Big data analytics is the process of examining large and varied data sets -- i.e., big data -- to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful information that can help organizations make more-informed business decisions.
Complement Your Existing Data Warehouse with Big Data & Hadoop
To view the full webinar, please go to: http://info.datameer.com/Slideshare-Complement-Your-Existing-EDW-with-Hadoop-OnDemand.html
With 40% yearly growth in data volumes, traditional data warehouses have become increasingly expensive and challenging.
Much of today’s new data sources are unstructured, making the structured data warehouse an unsuitable platform for analyses. As a result, organizations now look at Hadoop as a data platform to complement existing BI data warehouses, and a scalable, flexible and cost-effective solution for data storage and analysis.
Join Datameer and Cloudera in this webinar to discuss how Hadoop and big data analytics can help to:
-Get all the data your business needs quickly into one environment
Shorten the time to insight from months to days
Extend the life of your existing data warehouse investments
Enable your business analysts to ask and answer bigger questions
The Future of Data Management: The Enterprise Data Hub
The document discusses the enterprise data hub (EDH) as a new approach for data management. The EDH allows organizations to bring applications to data rather than copying data to applications. It provides a full-fidelity active compliance archive, accelerates time to insights through scale, unlocks agility and innovation, consolidates data silos for a 360-degree view, and enables converged analytics. The EDH is implemented using open source, scalable, and cost-effective tools from Cloudera including Hadoop, Impala, and Cloudera Manager.
EIS-Webinar-MDM-Personalization-2023-03-15.pdf
The Increasing Criticality of MDM for Personalization for Customers and Employees
Master data management seems to be one of those perennial, evergreen programs that organizations continue to struggle with.
Every couple of years people say, “we're going to get a handle on our master data” and then spend hundreds of thousands to millions and tens of millions of dollars working toward a solution.
The challenge is that many of these solutions are not really getting to the root cause of the problem. They start with technology and begin by looking at specific data elements rather than looking at the business concepts that are important to the organization.
MDM programs are also difficult to anchor on a specific business value proposition such as improving the top line. Many initiatives are so deep in the weeds and so far upstream that executives lose interest and they lose faith in the business value that the project promises. Meanwhile frustrated data analysts, data architects and technology organizations feel cut off at the knees because they can't get the funding, support and attention that they need to be successful.
We've seen this time after time and until senior executives recognize the value and envision where the organization can go with control over its data across domains, this will continue to happen over and over again. Executives all nod their heads and say “Yes! Data is important, really important!” But when they see the price tag they say, “Whoa hold on there, it's not that important”.
Well, actually, it is that important.
We can't forget that under all of the systems, processes and shiny new technologies such as artificial intelligence and machine learning lies data. And that data is more important than the algorithm. If you have bad data your AI is not going to be able to fix it. Yes there are data remediation applications and there are mechanisms to harmonize or normalize certain data elements. But looking at this holistically requires human judgment: understanding business processes, understanding data flows, understanding dependencies and understanding of the entire customer experience ecosystem and the role of upstream tools, technologies and processes that enable that customer experience.
Until we take that holistic approach and connect it to business value these things are not going to get the time, attention and resources that they need.
Seth Earley, Founder & CEO, Earley Information Science
Dan O'Connor, Senior Product Manager at inriver
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Why Use Hadoop for Big Data Analytics?
Datameer offers the first data analytics solution built on Hadoop that helps business users access, analyze and use massive amounts of data. Hadoop provides key breakthroughs over traditional solutions by enabling linear scalability from 1 to 4000 servers, overcoming limitations of storage and compute through use of commodity hardware and open source software, and not requiring rigid data models or specialized hardware or software.
Why Use Hadoop?
Datameer offers the first data analytics solution built on Hadoop that helps business users access, analyze and use massive amounts of data. Hadoop provides key breakthroughs over traditional solutions by enabling linear scalability from 1 to 4000 servers, overcoming limitations of storage and compute through use of commodity hardware and open source software, and not requiring rigid data models or specialized hardware or software.
BI, Hive or Big Data Analytics?
The document describes a proof of concept (POC) technical solution for a real estate company to analyze large amounts of web activity and customer data. The POC proposed loading one year of data from six tables into an Amazon cloud Hadoop environment and using Datameer for data discovery and analytics. The goals were to set up the cloud environment, load the search analytics data, and allow the business to perform analytics with acceptable performance and gain new insights. High-level and detailed descriptions of the technical solution are provided.
Is Your Hadoop Environment Secure?
How do you protect the data in big data analytics projects?
As big data initiatives focus on volume, velocity or variety of data, often overlooked in the big data project is the security of the data. This is especially important for financial services, healthcare and government or anytime sensitive data is analyzed.
This webinar highlights:
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Lean Production Meets Big Data: A Next Generation Use Case
The document discusses how big data and analytics can help optimize business processes using lean principles. It provides an overview of lean production concepts like identifying and eliminating waste. Big data is presented as a new approach to gain insights from process data that can help pinpoint improvement opportunities. The speaker demonstrates how Datameer's software allows users to easily analyze data from multiple sources and measure key performance indicators to drive continuous process improvements.
The Economics of SQL on Hadoop
Watch the recorded event at: http://info.datameer.com/Slideshare-
Economics-SQL-Hadoop.html
As organizations clamor to utilize their new investments in Hadoop ecosystems AND leverage their existing analytical infrastructures, many rush to integrate SQL as a data access layer to leverage existing skill sets and get started faster.
However, this approach relegates Hadoop to a data management and processing platform rather than the storage and compute engine optimized for analytical workloads it was purpose-built to be.
These slides by EMA and Datameer, will discuss the technical limitations of SQL on Hadoop and propose alternative ways to fully maximize Hadoop investments.
You will understanding:
*how SQL negates the inherent benefits of Hadoop
*why technological paradigm changes can sometimes be good
*use cases when SQL on Hadoop makes sense
Top 3 Considerations for Machine Learning on Big Data
This document discusses considerations for machine learning on big data. It provides background on speakers Karen Hsu and Elliott Cordo. It then covers drivers and challenges of big data, including how companies like Amazon and Netflix have leveraged big data analytics. Alternatives to machine learning on big data like data mining, traditional BI, and visualization are discussed. Example use cases and key criteria around ease of use and quality for algorithms like clustering, column dependencies, and decision trees are presented. Best practices for machine learning on big data are provided for clustering, recommendations, and overall analytics processes. The document concludes with a polling question and call to action.
Best Practices for Big Data Analytics with Machine Learning by Datameer
Don't forget! You can watch the full Datameer recording here:
http://info.datameer.com/Online-Slideshare-Big-Data-Analytics-Machine-Learning-OnDemand.html
Learn through industry use cases, how to empower users to identify patterns & relationships for recommendations using big data analytics.
How to do Data Science Without the Scientist
1. The document discusses making data science and analytics easier and more accessible for non-experts. It outlines challenges with traditional tools like requiring coding skills, long cycle times, and siloed data and applications.
2. The author proposes simplifying data integration and preparation, generating rather than writing code, and moving computation to the data to address these challenges.
3. A demo is presented of Datameer's product which aims to simplify and speed up the analytics process for users without advanced data science skills.
How to do Predictive Analytics with Limited Data
http://www.datameer.com It is frustrating: even with petabytes of data on a Hadoop cluster, one still encounters situations where there’s a lack of key data for a wide variety of big data analytic use cases. You might have billions of clicks on your web site, but only a few users choose to rate a product. There might be millions of text documents on your cluster, but it is too expensive to have someone categorize more than a tiny fraction of them. In principle, this is where predictive modeling could help. For instance, one could learn a model to predict user ratings so you can better serve product recommendations based on those expected ratings. Or, one could create a model to automatically categorize text documents, saving countless hours and dollars. The main problem is that there is only a limited amount of training material (i.e. user ratings, categorized documents) and it is thus hard to generate good models.
As it turns out, recent research on machine learning techniques has found a way to deal effectively with such situations with a technique called semi-supervised learning. These techniques are often able to leverage the vast amount of related, but unlabeled data to generate accurate models. In this talk, we will give an overview of the most common techniques including co-training regularization. We first explain the principles and underlying assumptions of semi-supervised learning and then show how to implement such methods with Hadoop.
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みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
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Webinar - Big Data: Power to the User
- 2. View Recording You can view the recording of this webinar at: http://info.datameer.com/Online-Slideshare- Big-Data-Power-to-the-User-OnDemand.html
- 3. About Our Speaker Karen Hsu @karenhsumar With over 15 years of experience in enterprise software, Karen Hsu has co- authored 4 patents and worked in a variety of engineering, marketing and sales roles. Most recently she came from Informatica where worked with the start-ups Informatica purchased to bring data quality, master data management, B2B and data security solutions to market. Karen has a Bachelors of Science degree in Management Science and Engineering from Stanford University. #datameer @karenhsumar !
- 4. Agenda ■ Business Value of Big Data Analytics ■ Technology ■ Example ■ Q&A #datameer @karenhsumar !
- 5. © 2013 Datameer, Inc. All rights reserved. Business Value of Big Data Analytics Customer Analytics Fraud & Compliance EDW Optimization Operational Analytics Increase Customer conversion by 3x Identify $2B in potential fraud 98% OpEx savings $1M+ CapEx savings Save $22,000 / minute #datameer @karenhsumar !
- 6. © 2013 Datameer, Inc. All rights reserved. How We Think About Big Data BEFORE! TODAY! Human Capital Human Capital Cost Hardware Costs Hardware Costs Low Data Complexity High Data Complexity #datameer @karenhsumar !
- 7. © 2013 Datameer, Inc. All rights reserved. Time for Decision Time to Insight The Problem! #datameer @karenhsumar !
- 8. © 2013 Datameer, Inc. All rights reserved. BI vs. Big Data Analytics! ETL Data Warehouse Business Intelligence Slow! Static! Complex! 18 months #datameer @karenhsumar !
- 9. © 2013 Datameer, Inc. All rights reserved. BI vs. Big Data Analytics! ETL Data Warehouse Business Intelligence Raw Data! Flume/Sqoop/Hive! Database/Tableau! 12 months Fast! Dynamic! Simple! Raw Data Hadoop Drag & Drop Spreadsheet Less than 4 weeks #datameer @karenhsumar !
- 10. © 2013 Datameer, Inc. All rights reserved. Big Data Ecosystem & Evolution Infrastructure Platform Hardware Vertical Solution #datameer @karenhsumar !
- 11. © 2013 Datameer, Inc. All rights reserved. Use Case Categories! Customer Analytics Fraud Compliance Operational Analytics EDW Optimization #datameer @karenhsumar !
- 12. © 2013 Datameer, Inc. All rights reserved. Lean Analytics Process 1. Integrate! 3. Analyze! 4. Visualize! 2. Prepare! Identify! Use Case! Deploy! #datameer @karenhsumar !
- 13. © 2013 Datameer, Inc. All rights reserved. Product #datameer @karenhsumar !
- 14. © 2013 Datameer, Inc. All rights reserved. Wizard-led Data Integration! Point-and-click Analytics! Self-Service Big Data Analytics! Drag-and-Drop Visualization! • No ETL! • 50+ Connectors + plug-in API! • Spreadsheet UI! • 240+ pre-built functions! • HTML5 support! • View on any device!
- 15. © 2013 Datameer, Inc. All rights reserved. Add-on: Smart Analytics! Drag & Drop Smart Analytics
- 16. © 2013 Datameer, Inc. All rights reserved. Enterprise Integration! #datameer @karenhsumar !
- 17. Example 1. Integrate 3. Analyze 4. Visualize 2. Prepare Identify Use Case Deploy #datameer @karenhsumar !
- 18. Integrate 3. Analyze 4. Visualize 2. Prepare 1. Integrate Identify Use Case Deploy #datameer @karenhsumar !
- 20. Prepare 1. Integrate 3. Analyze 4. Visualize Identify Use Case Deploy 2. Prepare Profile Identify Outliers Enrich Convert Transform #datameer @karenhsumar !
- 22. Analyze 1. Integrate 4. Visualize 2. Prepare Identify Use Case Deploy 3. Analyze #datameer @karenhsumar !
- 24. Visualize #datameer @karenhsumar 1. Integrate 3. Analyze 2. Prepare Identify Use Case Deploy 4. Visualize
- 26. Deploy 1. Integrate 3. Analyze 4. Visualize 2. Prepare Identify Use Case Deploy #datameer @karenhsumar !
- 28. Addressing Data Scientist Challenges in Workflow Multiple tools One platform Reproduce results Collaborate + Track Not for business Ease of Use #datameer @karenhsumar !
- 29. Contact Learn more For more information @karenhsumar khsu@datameer.com http://www.datameer.com/solutions/use- cases.html #datameer @karenhsumar !
- 30. Appendix