18
Dec
   

It’s been about two months since Teradata launched the Aster Big Analytics Appliance and since then we have had the opportunity to showcase the appliance to various customers, prospects, partners, analysts, journalists etc. We are pleased to report that since the launch the appliance has already received the “Ventana Big Data Technology of the Year” award and has been well received by industry experts and customers alike.

Over the past two months, starting with the launch tweetchat, we have received numerous enqueries around the appliance and think now is a good time to answer the top 10 most frequently asked questions about the new Teradata Aster offering. Without further ado here are the top 10 questions and their answers:

WHAT IS THE TERADATA ASTER BIG ANALYTICS APPLIANCE?

The Aster Big Analytics Appliance is a powerful, ready to-run platform that is pre-configured and optimized specifically for big data storage and analysis. A purpose built, integrated hardware and software solution for analytics at big data scale, the appliance runs Teradata Aster patented SQL-MapReduce® and SQL-H technology on a time-tested, fully supported Teradata hardware platform. Depending on workload needs, it can be exclusively configured with Aster nodes, Hortonworks Data Platform (HDP) Hadoop nodes, or a mixture of Aster and Hadoop nodes. Additionally, integrated backup nodes are available for data protection and high availability

WHO WILL BENEFIT MOST BY DEPLOYING THE APPLIANCE?

The appliance is designed for organizations looking for a turnkey integrated hardware and software solution to store, manage and analyze structured and unstructured data (ie: multi-structured data formats). The appliance meets the needs of both departmental and enterprise-wide buyers and can scale linearly to support massive data volumes.

WHY DO I NEED THIS APPLIANCE?

This appliance can help you gain valuable insights from all of your multi-structured data. Using these insights, you can optimize business processes to reduce cost and better serve your customers. More importantly, these insights can help you innovate by identifying new markets, new products, new business models etc. For example, by using the appliance a telecommunications company can analyze multi-structured customer interaction data across multiple channels such as web, call center and retail stores to identify the path customers take to churn. This insight can be used proactively to increase customer retention and improve customer satisfaction.

WHAT’S UNIQUE ABOUT THE APPLIANCE?

The appliance is an industry first in tightly integrating SQL-MapReduce®, SQL-H and Apache Hadoop. The appliance delivers a tightly integrated hardware and software solution to store, manage and analyze big data. The appliance delivers integrated interfaces for analytics and administration, so all types of multi-structured data can be quickly and easily analyzed through SQL based interfaces. This means that you can continue to use your favorite BI tools and all existing skill sets while deploying new data management and analytics technologies like Hadoop and MapReduce. Furthermore, the appliance delivers enterprise class reliability to allow technologies like Hadoop to now be used for mission critical applications with stringent SLA requirements.

WHY DID TERADATA BRING ASTER & HADOOP TOGETHER?

With the Aster Big Analytics Appliance, we are not just putting Aster and Hadoop in the same box. The Aster Big Analytics Appliance is the industry’s first unified big analytics appliance, providing a powerful, ready to run big analytics and discovery platform that is pre-configured and optimized specifically for big data analysis. It provides intrinsic integration between the Aster Database and Apache Hadoop, and we believe that customers will benefit the most by having these two systems in the same appliance.

Teradata’s vision stems from the Unified Data Architecture. The Aster Big Analytics Appliance offers customers the flexibility to configure the appliance to meet their needs. Hadoop is best for capture, storing and refining multi-structured data in batch whereas Aster is a big analytics and discovery platform that helps derive new insights from all types of data. Hadoop is best for capture, storing and refining multi-structured data in batch. Depending on the customer’s needs, the appliance can be configured with all Aster nodes, all Hadoop nodes or a mix of the two.

WHAT SKILLS DO I NEED TO DEPLOY THE APPLIANCE?

The Aster Big Analytics appliance is an integrated hardware and software solution for big data analytics, storage, and management, which is also designed as a plug and play solution that does not require special skill sets.

DOES THE APPLIANCE MAKE DATA SCIENTISTS OR DATA ANALYSTS IRRELEVANT?

Absolutely not. By integrating the hardware and software in an easy to use solution and providing easy to use interfaces for administration and analytics, the appliance allows data scientists to spend more time analyzing data.

In fact, with this simplified solution, your data scientists and analysts are freed from the constraints of data storage and management and can now spend their time on value added insights generation that ultimately leads to a greater fulfillment of your organization’s end goals.

HOW IS THE APPLIANCE PRICED?

Teradata doesn’t disclose product pricing as part of its standard business operating procedures. However, independent research conducted by industry analyst Dr. Richard Hackathorn, president and founder, Bolder Technology Inc., confirms that on a TCO and Time-to-Value basis the appliance presents a more attractive option vs. commonly available do-it-yourself solutions. http://teradata.com/News-Releases/2012/Teradata-Big-Analytics-Appliance-Enables-New-Business-Insights-on-All-Enterprise-Data/

WHAT OTHER ASTER DEPLOYMENT OPTIONS ARE AVAILABLE?

Besides deploying via the appliance, customers can also acquire and deploy Aster as a software only solution on commodity hardware] or in a public cloud.

WHERE CAN I GET MORE INFORMATION?

You can learn more about the Big Analytics Appliance via http://asterdata.com/big-analytics-appliance/  – home to release information, news about the appliance, product info (data sheet, solution brief, demo) and Aster Express tutorials.

 

Join the conversation on Twitter for additional Q&A with our experts:

Manan Goel @manangoel | Teradata Aster @asterdata

 

For additional information please contact Teradata at http://www.teradata.com/contact-us/



26
Apr
By Paul Barrett in Digital Marketing, Social Media, Teradata Aster on April 26, 2012
   

What questions are important to you about Social Media?

John Lovett from Web Analytics Demystified just published a new white paper on Social Analytics. Lovett, who has written the book on Social Analytics (literally), lays out a compelling vision for Deeper Social Analytics for companies.  He clearly presents the value of companies to go beyond surface level analytics of likes, followers and friends and challenges the CMO to ask deeper and more important questions.

I love the three key questions presented in the paper that really hit the c-suite.  These are

  • What is the Audience?
  • What is the Activity?
  • What is the Action?

These 3 questions provide a framework to share social media initiatives with business leaders and strip away all the non-business related questions that become so distracting in understanding the impact of social media on the enterprise.

Although we are still in the early, Black and White TV stage of social analytics, Teradata Aster has been heavily influenced by our customers’ needs in the social space.  Customers like LinkedIn,  Gilt Groupe, MySpace, and Myzinga have redefined how consumers interact with each other through music, shopping, and content.  Attensity running on Aster promises to bring together big data and social analytics that starts to deliver on Mr. Lovett’s proposition of deeper social analytics.

Teradata’s strategy to marry big data analytics and marketing applications with its industry-leading database solutions is steeped with the concept of deeper analytics.  In social analytics, we have identified 10 key business questions that should be asked about every social post.   In a market where posts can go viral, impact brand, customer perception, and revenue, being able to quickly and effectively navigate deeper social analytics becomes a mission critical capability.

Beyond John’s questions, the 10 key questions are:

  • What was said?
  • What is it about? (ie,  product, service, brand, experience)
  • Is it a common sentiment?
  • What are the trends on this topic?
  • Who said it?
  • What is their value?
  • How engaged are they?
  • What is their influence?
  • How do I respond?
  • Was my response effective?

To effectively answer these questions CMO’s need a set of marketing technologies.  These include:

  • A social listening platform that analyzes social timelines for owned, embassy and public feeds.  These tools identify what was said, what it is about and if it is a common sentiment.
  • The second level demands a customer hub to understand who posting and what the customer relationship is with them and to measure the customer value of that relationship.
  • The third level requires social network analytics and the ability to find implicit and explicit social connections.  This helps illuminate how engaged customers are and their level of influence – or who influences them
  • The fourth level is where integrated marketing management and customer facing marketing applications come it.  Once you understand what was said, who said it and the potential impact – how do you respond?  Is it a one-on-one conversation, a social discussion or was a bigger issue identified that may result in marketing campaigns?

Are these the right business questions to ask?  What else do you want to know about social media posts in your business?



13
Apr
By Mayank Bawa in Analytics, Business analytics, Teradata Aster on April 13, 2012
   

We live in interesting times!

In the past 30 years, data was used to record business events and report on business events. Over the last 5 years, data has gotten closer to business. Now data is being used to record business events, report on business events as well as influence business events. We now realize that the more data we record, the more comprehensively data can influence business events.

Hence the excitement of “big data” - it is a business opportunity for each line of business - to influence business events to have favorable outcomes.

The responsibility for technologists is to provide the right platforms and tools to make influencing business easy and simple.

There are TWO relentless forces that are playing out in the big data space to which technology has to respond.

The first force is the diversity of data. As we record more data, we end up having different formats of data to manage. About 20% is relational, but we also have text, emails, PDF, Twitter feeds, Facebook profiles, social graphs, CDRs, Apache logs, JSON formats, …

The second force is the richness of analytics. As we influence more business, we end up having richer analytics to perform. About 20% is SQL, but we also have time series analysis, statistical analysis, geo-spatial analysis, graph analysis, sentiment analysis, entity extraction, …

Note that I am not saying MapReduce doesn’t have a diverse set of analytics to do: MapReduce is a way of programming to do analysis - time series, statistical, geo-spatial - each require different MapReduce programs to be written.

Today, the platforms and tools for big data are very complex. They expect lines of business owners to write programs to manage different forms of big data, to write sophisticated programs to analyze big data, to master the management and administration of big clusters and be self-sustaining in managing data quality. This last point is very important - data values change over time. We have to keep values consistent, otherwise our analysis will be wrong and our influence on business will be negative - garbage in, garbage out rule of computing.

As a result, big data is in danger of entering the DIY (do it yourself) space. A line of business is now expected to support big clusters = big administration = big programs = big friction = low influence.

We have to acknowledge these challenges as technologists. If we let big data solutions be a DIY solution, only pockets of enterprise will embrace big data - the rest of the non-technology savvy business leaders will be left out of the opportunity.

We have to simplify this equation. We need to enable line of business owners to benefit from big data a lot more easily. We have to make it simpler for business leaders to get from big data to big analytics.

Our goal, big data = small clusters = easy administration = big analytics = big influence.

This entails solving the following problems:

[1] Make platform and tools to be easier to use to manage and curate data. Otherwise, garbage in = garbage out, and you will get garbage analytics.

[2] Provide rich analytics functions out of the box. Each line of programming cuts your reachable audience by 50%.

[3] Provide tools to update or delete data. Otherwise, data consistency will drift away from truth as history accumulates.

[4] Provide applications to leverage data and find answers relevant to business. Otherwise the cost of DIY applications is too high to influence business - and won’t be done.

At Teradata Aster, we are continuing to lead the big data revolution. We have led the revolution for the past 5 years, and helped shape the market and technologies. We are convinced that the path to big data success is to connect it with Big Analytics in the coming 5 years.



15
Mar
By Steve Wooledge in Analytics, MapReduce, Teradata Aster on March 15, 2012
   

Yesterday I presented at the Los Angeles Teradata User Group on the topic of “Data Science: Finding Patterns in Your Data More Quickly & Easily with MapReduce”. One point discussed was the common misnomer that big data is about volume, which is certainly part of the issue organizations are facing. However, the big story in big data is the complexity and additional processing required to make “unstructured” data actionable through analytics. This is where procedural frameworks like MapReduce can help. Here is a great post by Teradata’s own Bill Franks about unstructured data which helps describe the requirements unstructured data demands in the context of analytics.

As Franks notes, “the thought of using unstructured data really shouldn’t intimidate people as much as it often does.” Read more to learn why.