James Mesney_"Datameer's Big Data Analytics Platform"_April 9th_Data Enthusiasts London

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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.

Data & Analytics

Datameer is the only end-to-end big data analytics
application purpose-built for Hadoop, designed
to make big data easy for everyone.
Companies of all sizes like Citibank, British Telecom, Sky,
MachineZone and Workday use Datameer to integrate,
analyse and visualise all of their data to get new insights
faster than ever.
About Datameer!
3

From Data Ingestion to First Insight!
4
Datameer: 9X faster to Insights at 80% less cost
*
Simplicity
No Risk
Complex
High Risk
•  Self-service across business and IT
•  Point & click analytics
•  Stunning free-form data
visualization
•  End-to-end data security,
monitoring and management
•  Enterprise ready and proven
•  Open Standards
•  Extendable (R, Java, Python)
•  No ETL
•  No Data Modeling
•  No coding to integrate data

Telefonica
id’d
$150M/yr
in
Network
Op;misa;on

Visa
iden;ﬁed
&
prevented
$2b
in
poten;al
fraud

Sears
iden;ﬁed
$3m
in
EDW
Op;misa;on

End-to-End Big Data Analytics for Hadoop!
Drag-and-drop Visualization!
•  30+ visualization widgets
•  HTML5 – ready for mobile
•  Drill-through visualization
•  Free-form infographic design
•  View on any device
Point & Click Analytics!
•  250+ pre-built functions plus
plug-in API
•  Easy to use spreadsheet UI
•  Smart sampling
•  Data cleaning, proﬁling and analytics
•  Real-time preview
Wizard-led Data Integration!
5
•  55+ connectors + plug-in API
•  No ETL
•  Import and link to data

Datameer Differentiation!
Wizard-led
Data
Integration!
Enable business and IT
analysts to easily
integrate all data types,
role based security
Native on
Hadoop!
Reduce the
complexity for
fastest time to
insight
Self-service Big
Data!
Intuitive spreadsheet
interface
The only End-to-End
Big Data Analytics
Application!
Integrate, analyse and
visualise all of your
structured and
unstructured data
6

Datameer Customers!
Multiple data types
Compliance-driven demand heavy
Traditional BI/EDW
Desire to enable business analysts
(self-service)
Two Most Common Scenarios!
1.  Business has a use case and is looking for a way to solve their problem.!
2.  IT has built or wants to deploy Hadoop and is searching for a use case in the business.!
Multiple data types
Large investment in infrastructure
Heavy traditional BI/EDW
Desire to enable business analysts
(self-service)
Multiple data types and sources
Driven by HIPAA compliance and
other regulations
Heavy traditional BI/EDW
Desire to enable business analysts
(self-service)
Financial Services! Telecommunications! Healthcare!
7

Datameer Use Cases!
8
Back to Table of Contents! *This is an internal only document.!
EDW Optimization
New Product
Innovation
Customer
Analytics
Operational
Analytics

DEMO 
 
Q&A
9
Back to Table of Contents! *This is an internal only document.!
Thanks for your time!!
!
James Mesney!
Datameer Solution Engineer!
jmesney@datameer.com!

data pipeline, governance, and for growth and updating models regularly needs to be part of the AI strategy from the outset. This session will cover: Defining AI governance: What this means and how definitions of subjects like ethics and effectiveness can differ between organizations. Data governance: Companies must rely on an AI governance program to ensure only high-quality, unbiased and consistent data are used in training. AI is a growing necessity for enterprises / businesses; it provides an avenue for scaling quickly and efficiently. Best practices / implementation: how to implement AI that meets the requirements of the organization’s defined sets of governances. Planning the data pipeline and growing/updating the models: AI is not static in the real world; models must be frequently updated to maintain relevance and accuracy. 3 key takeaways or attendee benefits of the session: Understand how to assess your organization’s need for AI; how to identify the opportune areas for transforming processes, interactions, scaling, cost. How to start the implementation process. Defining data and AI governance and how to build the training data pipeline within that framework. Best practices for maintaining AI; how to use data to evaluate models and continuously iterate on them to reflect the real world.

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Harnessing the Power of Big Data at Freddie Mac

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Freddie Mac makes homeownership and rental housing more accessible and affordable. Operating in the secondary mortgage market, we keep mortgage capital flowing by purchasing mortgage loans from lenders so they in turn can provide more loans to qualified borrowers. Our mission to provide liquidity, stability, and affordability to the U.S. housing market in all economic conditions extends to all communities from coast to coast. We're using big data and advanced analytics to create powerful enhancements to better meet our customer’s needs: automated collateral evaluation, automated assessments for borrowers without credit scores, immediate certainty for collateral rep and warranty relief, and coming soon automated asset and income validation. We’re building tools to help our customers cut costs and give them rep and warranty relief sooner in the loan manufacturing process. We’ve designed Loan Advisor Suite with lenders to give our customers greater certainty, usability, reliability and efficiency. It's a simpler, better way to do business. More Tools - Access powerful solutions for every stage of the loan production process. More Loans - Increase output with automated data management and user-friendly controls. Less Risk = Get alerted to loan issues and take action the moment they occur. Hear the story of how ACE helped Freddie Mac reimagine the mortgage process and how HDP helped make it possible. Speaker Dennis Tally, Freddie Mac, Director

Neo4j Aura Enterprise

Neo4j

Danske Bank, the leader in mobile payments in Denmark, is innovating with AI. Danske Bank’s existing fraud detection engine is being enhanced with deep learning algorithms that can analyze potentially tens of thousands of latent features. Danske Bank’s current system is largely based on handcrafted rules created by the business, based on intuition and some light analysis. The system is effective at blocking fraud, but it has a high rate of false positives, which is expensive and inconvenient, and it has proved impractical to update and maintain as fraudsters evolve their capabilities. Moreover, the bank understands that fraud is getting worse in the near- and long-term future due to the increased digitization of banking and the prevalence of mobile banking applications and recognizes the need to use cutting-edge techniques to engage fraudsters not where they are today but where they will be tomorrow. Application fraud is an important emerging trend, in which machines fill in transaction forms. There is evidence that criminals are employing sophisticated machine-learning techniques to attack, so it’s critical to use sophisticated machine learning to catch fraud in banking and mobile payment transactions. Ron Bodkin and Nadeem Gulzar explore how Danske Bank uses deep learning for better fraud detection. Danske Bank’s multistep program first productionizes “classic” machine learning techniques (boosted decision trees) while in parallel developing deep learning models with TensorFlow as a “challenger” to test. The system was first tested in shadow production and then in full production in a champion-challenger setup against live transactions. Ron and Nadeem explain how the bank is integrating the models with the efforts already running, giving the bank and its investigation team the ability to adapt to new patterns faster than before and taking on complex highly varying functions not present in the training examples.

Building Confidence in Big Data - IBM Smarter Business 2013

IBM Sverige

Success with big data comes down to confidence. Without confidence in the underlying data, decision makers may not trust and act on analytic insight. You need confidence in your data – that it’s correct, trusted, and protected through automated integration, visual context, and agile governance. You need confidence in your ability to accelerate time to value, with fast deployments of big data appliances. Learn how clients have succeeded with big data by building confidence in their data, ability to deploy, and skills. Presenter: David Corrigan, Big Data specialist, IBM. Mer från dagen på http://bit.ly/sb13se

Advanced Analytics and Machine Learning with Data Virtualization

Denodo

Watch: https://bit.ly/2DYsUhD Advanced data science techniques, like machine learning, have proven an extremely useful tool to derive valuable insights from existing data. Platforms like Spark, and complex libraries for R, Python and Scala put advanced techniques at the fingertips of the data scientists. However, these data scientists spent most of their time looking for the right data and massaging it into a usable format. Data virtualization offers a new alternative to address these issues in a more efficient and agile way. Attend this webinar and learn: - How data virtualization can accelerate data acquisition and massaging, providing the data scientist with a powerful tool to complement their practice - How popular tools from the data science ecosystem: Spark, Python, Zeppelin, Jupyter, etc. integrate with Denodo - How you can use the Denodo Platform with large data volumes in an efficient way - How Prologis accelerated their use of Machine Learning with data virtualization

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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

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Lean Production Meets Big Data: A Next Generation Use Case

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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

Is Your Hadoop Environment Secure?

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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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Speaker: Marcel Kornacker As data is ingested into Apache Hadoop at an increasing rate from a diverse range of data sources, it is becoming more and more important for users that new data be accessible for analysis as quickly as possible—because “data freshness” can have a direct impact on business results. In the traditional ETL process, raw data is transformed from the source into a target schema, possibly requiring flattening and condensing, and then loaded into an MPP DBMS. However, this approach has multiple drawbacks that make it unsuitable for real-time, “at-source” analytics—for example, the “ETL lag” reduces data freshness, and the inherent complexity of the process makes it costly to deploy and maintain, and reduces the speed at which new analytic applications can be introduced. In this talk, attendees will learn about Impala’s approach to on-the-fly, automatic data transformation, which in conjunction with the ability to handle nested structures such as JSON and XML documents, addresses the needs of at-source analytics—including direct querying of your input schema, immediate querying of data as it lands in HDFS, and high performance on par with specialized engines. This performance level is attained in spite of the most challenging and diverse input formats, which are addressed through an automated background conversion process into Parquet, the high-performance, open source columnar format that has been widely adopted across the Hadoop ecosystem. In this talk, attendees will learn about Impala’s upcoming features that will enable at-source analytics: support for nested structures such as JSON and XML documents, which allows direct querying of the source schema; automated background file format conversion into Parquet, the high-performance, open source columnar format that has been widely adopted across the Hadoop ecosystem; and automated creation of declaratively-specified derived data for simplified data cleansing and transformation.

Webinar - Big Data: Power to the User

Datameer

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 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

Top 3 Considerations for Machine Learning on Big Data

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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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Imagine being able to use insights about your customer acquisition journey to design campaigns that improve conversion rates. What if you could identify points of failure along the customer acquisition path or during product usage? Today more than ever the role of the marketing and product executives have become data-driven. Business executives who leverage data to understand prospect and customer behavior have gained an edge over their peers. Big Data analytics is the key to unlocking insights from your customer behavior data and empowers you to combine and analyze all of your customer interaction data to drive customer acquisition and loyalty.

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Big data analytics is revolutionizing the way businesses are collecting, storing, and more importantly, analyzing data. However, the adoption of a big data analytics solution has its share of failures and false starts. Watch this webinar to learn how to navigate the most common obstacles of big data analytics. Datameer and MapR have worked with customers to identify and solve the common pitfalls organizations face when deploying Hadoop-based analytics. In this webinar, we will show you how to: • Find the balance between infrastructure and business use cases • Overcome challenges of using multiple tools that address big data analytics • Leverage all your resources (data scientists, IT and analysts) most effectively

Customer Case Studies of Self-Service Big Data Analytics

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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: -Customer examples and return on investment from self-service big data analytics -How business analysts can take advantage of Machine Learning -Best practices in self-service big data analytics

Getting Started with Big Data for Business Managers

Datameer

Big Data has become critical to the enterprise because of the massive amount of untapped data sources, and the potential to gain new insights that were previously not possible. So, how to get started with Big Data and Hadoop becomes a question more pertinent than ever before. Listen to leading analyst at Ovum, Tony Baer, as he discusses answers to the key questions around how to: Approach Big Data and associated business challenges -- Identify what types of new insights can be revealed by Big Data -- Staff for this undertaking and implement the technology necessary to be successful -- Take the first steps toward getting started with Big Data on Hadoop

Analyzing Unstructured Data in Hadoop Webinar

Datameer

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

BI, Hive or Big Data Analytics?

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Driven Inc.

Offloading ETL processes to Hadoop is often one of the first Big Data efforts because of the obvious ROI benefits. However, you have hundreds, maybe thousands, of legacy ETL processes to migrate which makes achieving the benefits of Hadoop and ROI a distant goal. What if you could automatically convert up to 70% of your existing ETL processes to run on Hadoop with no code changes? In this presentation you will see: - A detailed walk-through of migrating existing ETL processes to Hadoop without changing anything - How you can cut development time of new ETL process on Hadoop by up to 50% - How you can leverage your existing developers’ Java skills to turn them into Hadoop developers - Best practices for monitoring the performance of your ETL processes to ensure you meet your service level agreements

Hadoop and the Data Warehouse: When to Use Which

DataWorks Summit

In recent years, Apache™ Hadoop® has emerged from humble beginnings to disrupt the traditional disciplines of information management. As with all technology innovation, hype is rampant, and data professionals are easily overwhelmed by diverse opinions and confusing messages. Even seasoned practitioners sometimes miss the point, claiming for example that Hadoop replaces relational databases and is becoming the new data warehouse. It is easy to see where these claims originate since both Hadoop and Teradata® systems run in parallel, scale up to enormous data volumes and have shared-nothing architectures. At a conceptual level, it is easy to think they are interchangeable, but the differences overwhelm the similarities. This session will shed light on the differences and help architects, engineering executives, and data scientists identify when to deploy Hadoop and when it is best to use MPP relational database in a data warehouse, discovery platform, or other workload-specific applications. Two of the most trusted experts in their fields, Steve Wooledge, VP of Product Marketing from Teradata and Jim Walker of Hortonworks will examine how big data technologies are being used today by practical big data practitioners.

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From the early days of IT, organizations have grappled with the challenges of understanding how well their infrastructure is performing in support of the business. They have used a plethora of tools to detect, manage, and resolve problems that are causing disruption of services, but still struggle to achieve a unified, cross-domain understanding of what is happening across their IT infrastructure. Fortunately, over the past few years analytics platforms like Splunk, Elastic, and others have emerged to address requirements around IT Operations Analytics (ITOA). Now today the buzz is around AIOps – Artificial Intelligence Operations. But what is AIOps, and what can it do to help organizations address IT challenges. In this presentation you will get a better understanding of: What is Artificial Intelligence for IT Operations What are the required technologies for success at AIOps What challenges exist for achieving AIOPs

Similar to James Mesney_"Datameer's Big Data Analytics Platform"_April 9th_Data Enthusiasts London (20)

Data virtualization

Fueling AI & Machine Learning: Legacy Data as a Competitive Advantage

MongoDB World 2018: Partner Talk - IBM: Climbing the Ladder to AI

Turn Big Data into Big Value on Informatica and AWS

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Oil and gas big data edition

Creating an Exceptional Customer Experience with Master Data Management and B...

OC Big Data Monthly Meetup #6 - Session 1 - IBM

SD Big Data Monthly Meetup #4 - Session 1 - IBM

Shield db data security

Datameer Analytics Solution

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EMA Presentation: Driving Business Value with Continuous Operational Intellig...

What Does Artificial Intelligence Have to Do with IT Operations?

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