Analyzing Unstructured Data in Hadoop Webinar

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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

Technology Education

© 2014 Datameer, Inc. All rights reserved.
Analyzing Unstructured Data in
Hadoop!

View Recording !!

You can view the recording of this webinar
at:

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Analyzing-Unstructured-Data-in-Hadoop-
On-Demand.html

© 2013 Datameer, Inc. All rights reserved.
Matt Schumpert @datameer
Senior Director, Solutions Engineering

Matt has been working in the enterprise
infrastructure software space for over 14 years in
various capacities, including sales engineering,
strategic alliances and consulting.

Matt currently runs the pre-sales engineering team at
Datameer, supporting all technical aspects of
customer engagement from initial contact through
roll-out of customers into production.

Matt holds a BS in Computer Science from the
University of Virginia.
#datameer @datameer
About Our Speaker!

Agenda!
•  Market & Data trends
•  Tuning into new channels
•  The good news
•  The rise of wrangling
•  Analytics requirements
•  Bringing order to chaos
•  Use Cases

Market & Data Trends!
•  Data volumes will grow 800% in 5 years
•  Unstructured data is growing 62% faster
•  80% of all data will be unstructured in 2019
•  “Big Unstructured Data” requires new tech.
•  85% of the Fortune 500 will be unable to exploit
Big Data for competitive advantage through 2015
Source: Gartner

Market & Data Trends!
•  ‘Multi-structured’ is the word of the day
•  Mainstream IT tools broadening the base
•  Competitive advantage lies outside your firewall!
S U

Tuning Into New Channels!
•  Public & social data is available by the firehose
•  The new discipline: connecting, filtering, switching
•  Find the right keywords, dictionaries, segments
•  Learn from, but don’t emulate search engines
•  Beware of point solutions

The Good News!
•  All data has structure
•  Storage is cheap (Hadoop ~= $300 / TB)
•  Processing is cheap (“free”)
•  Unstructured data compresses well
•  Data APIs abound
•  Public data blossoming (data.gov, etc.)

The Rise of Wrangling!
•  A ‘record’ is no longer a record
•  Event streams need different angles of attack
•  Explode, project, align, window, search
•  New companies/technologies specializing in it
Source: Gartner

Analytics Requirements (1)!
•  A scalable Big Data foundation (Hadoop)
•  Schema-on-read
•  Data profiling & cleansing
•  Fast, visual iteration over samples
Source: Gartner

Analytics Requirements (2)!
•  Text mining, without programing
•  Helper functions for semi/un-structured formats
•  Data connectors, new visualizations
•  Patience, and a an culture of data discovery

Datameer:!
End-to-End Big Data Analytics!

Bringing Order to Chaos!
•  ‘Big Data Visualization’ is an oxymoron
•  Rich, detailed summaries are the goal
•  ‘It’s the analytics, stupid’

For more information!

http://www.datameer.com

" @datameer
" mschumpert@datameer.com

Learn more
Contact
#datameer @datameer

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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

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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: -Continuously visualize a profile of your data to identify inconsistencies, incompleteness and duplicates in your data -Visualize machine learning and data mining, including clustering, decision tree analysis, column correlations and recommendations -Create self-service visualizations for business and IT users

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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: *Hadoop security landscape *Hadoop encryption, masking, and access control *Customer examples of securing hadoop environments

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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

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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

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Watch this recording at: http://info.datameer.com/Online-Slideshare-Lean-Production-Meets-Big-Data.html Sensor and robotic data will usher in a new wave of big data opportunities and use cases in 2014. This will be especially true in manufacturing as economic pressure to continuously improve operational efficiencies continue to challenge the bottom line. Join Lean Production Expert Conny von Ludwig (former Porsche Consulting & Lufthansa), and Datameer CEO, Stefan Groschupf as they discuss: -the current manufacturing challenges and how big data analytics can improve these inefficiencies -how to reduce production outages by 10% -a next gen big data analytic use case focused on manufacturing sensor data

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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

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View the full recording of this deck here: http://info.datameer.com/Slideshare-Top-3-Things-to-Consider-for-Machine-Learning-on-Big-Data.html Machine learning is powerful but requires coding and access to all the relevant datasets to get full insights. With new Big Data analytic tools, business users can now use machine learning to gain a competitive edge. Based on best practices and customer experiences, join Datameer and Caserta Concepts as we discuss what to look for and what value organizations get out of Machine Learning on Big Data. This webinar will provide: *an overview of challenges and tools available today *use cases for machine learning on hadoop *capabilities to look for *comparison of available solutions

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http://www.datameer.com With data scientists in short supply, it’s surprising that much of their precious time is spent doing ”data plumbing”—preparing data or servicing business users rather than doing actual data science. Hadoop and self-service Smart Analytics is changing this reality. Join Matt Schumpert, Director of Solution Engineering at Datameer, as he walks through a real-world use case and discusses the evolution toward self-service data science, freeing data scientists to the hard work we so badly need.

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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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2. View Recording !! You can view the recording of this webinar at: http://info.datameer.com/Online-Slideshare- Analyzing-Unstructured-Data-in-Hadoop- On-Demand.html

3. © 2013 Datameer, Inc. All rights reserved. Matt Schumpert @datameer Senior Director, Solutions Engineering Matt has been working in the enterprise infrastructure software space for over 14 years in various capacities, including sales engineering, strategic alliances and consulting. Matt currently runs the pre-sales engineering team at Datameer, supporting all technical aspects of customer engagement from initial contact through roll-out of customers into production. Matt holds a BS in Computer Science from the University of Virginia. #datameer @datameer About Our Speaker!

4. Agenda! •  Market & Data trends •  Tuning into new channels •  The good news •  The rise of wrangling •  Analytics requirements •  Bringing order to chaos •  Use Cases

5. What we learned in 2010… (or before)!

6. Market & Data Trends! •  Data volumes will grow 800% in 5 years •  Unstructured data is growing 62% faster •  80% of all data will be unstructured in 2019 •  “Big Unstructured Data” requires new tech. •  85% of the Fortune 500 will be unable to exploit Big Data for competitive advantage through 2015 Source: Gartner

7. Market & Data Trends! •  ‘Multi-structured’ is the word of the day •  Mainstream IT tools broadening the base •  Competitive advantage lies outside your firewall! S U

8. Tuning Into New Channels!

9. Tuning Into New Channels! •  Public & social data is available by the firehose •  The new discipline: connecting, filtering, switching •  Find the right keywords, dictionaries, segments •  Learn from, but don’t emulate search engines •  Beware of point solutions

10. The Good News! •  All data has structure •  Storage is cheap (Hadoop ~= $300 / TB) •  Processing is cheap (“free”) •  Unstructured data compresses well •  Data APIs abound •  Public data blossoming (data.gov, etc.)

11. The Rise of Wrangling! •  A ‘record’ is no longer a record •  Event streams need different angles of attack •  Explode, project, align, window, search •  New companies/technologies specializing in it Source: Gartner

12. Analytics Requirements (1)! •  A scalable Big Data foundation (Hadoop) •  Schema-on-read •  Data profiling & cleansing •  Fast, visual iteration over samples Source: Gartner

13. Analytics Requirements (2)! •  Text mining, without programing •  Helper functions for semi/un-structured formats •  Data connectors, new visualizations •  Patience, and a an culture of data discovery

14. Datameer:! End-to-End Big Data Analytics!

15. Enterprise Integration!

16. Bringing Order to Chaos! •  ‘Big Data Visualization’ is an oxymoron •  Rich, detailed summaries are the goal •  ‘It’s the analytics, stupid’

17. Industry Use Cases! •  Retail: Competitive pricing through web scraping •  MFG: Product sentiment through Twitter •  FSI: Brand preferences from Facebook “likes” •  Gov: Nefarious behavior through email seizure!

18.

19. For more information! http://www.datameer.com " @datameer " mschumpert@datameer.com Learn more Contact #datameer @datameer

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