Leading Analyst Firm, EMA, Validates Cisco’s Big Data Warehouse Expansion Solution

Enterprise Management Associates (EMA) is a leading industry analyst firm that provides deep insight across the full spectrum of IT and data management technologies. EMA analysts, including Shawn Rogers who guides EMA’s Business Intelligent Research group, leverage a unique combination of practical experience, insight into industry best practices, and in-depth knowledge of current and planned vendor solutions to help their clients achieve their goals.

Shawn recently published an EMA Impact Brief on Cisco Big Data Warehouse Expansion.   In it he examines the impact this solution brings to traditional data warehouse environments by better managing data, system growth and extending the analytic value delivered from a data warehouse investment.

Cisco Big Data Warehouse Expansion is a new offering that combines hardware, software and services to help customers control the costs of their ever-expanding data warehouses by offloading infrequently used data to low-cost big data stores.  Analytics are enriched as more data is retained and all data remains accessible.

BDWE

In his Impact Brief, Shawn makes three important observations about how enterprises can benefit:

Cost Control: A common cost to traditional data warehouse environments is planning for the exponential growth of data. The Cisco solution analyzes the system, identifies data not in use (cold data) and provides a workflow and tools to offload the data onto Hadoop avoiding upgrade costs and extending the life of the data warehouse.

Improved Performance: By implementing an ongoing strategy to offload data from the primary system to Hadoop, the Cisco Big Data Warehouse Expansion solution frees up resources providing for better overall system performance. Additionally, Cisco deploys data virtualization technology that adds a layer of optimization for fast queries and simplified access spanning the original warehouse and the new Hadoop data store. Further, Cisco UCS servers are optimized for Hadoop workloads.

Added Analytic Value: Many companies are forced by the economics of data management to implement aggressive Information Lifecycle Management (ILM) policies removing data from critical systems to avoid costs. The Cisco solution helps customers keep more data online and available for deeper and more insightful analytics, therefore, adding value to the overall environment.”

The report ends with the following conclusion:

“EMA recommends that organizations that manage mature data warehouses investigate offloading as a strategy to expand and extend their data warehouse investment.”

If Shawn’s counsel makes as much sense to you as it does to me, then let’s get started.

Click here to download and read the entire report.

Learn More

To learn more about Cisco Data Virtualization, check out our page.

To learn more about EMA research, analysis, and consulting services for enterprise line of business users, IT professionals and IT vendors at http://www.enterprisemanagement.com.

New Horizons, New Possibilities: Announcing Data Virtualization Day 2014

With Big Data, the Cloud and the Internet of Everything transforming our world, the possibilities are staggering.  And status quo means falling behind.  These truths drive Data Virtualization Day 2014’s theme: New Horizons, New Possibilities.  

This year marks the fifth anniversary of Data Virtualization Day, the premier data virtualization industry event, where IT leaders stay informed of the latest trends and meet fellow innovators.

As host of Data Virtualization Day 2014, I would like to personally invite the entire data virtualization community to come together on October 1st, 2014, at the historic Waldorf Astoria in New York City, New York.

Our objective is to help you explore these new horizons and imagine these new possibilities.  In particular, Data Virtualization Day will address:

  • Why Big Data, the Cloud and the Internet of Everything are transforming business and IT?
  • How is data virtualization advancing to meet these opportunities?
  • What new data virtualization-based solutions are on the horizon?
  • Who is achieving significant business outcomes today?

Maintaining the Data Virtualization Day tradition, the all-day agenda will feature three data virtualization user case studies, presented by those users.  Past speakers have included CTO, CDO, and other executives from Goldman Sachs, Pearson, Comcast, NYSE, Franklin Templeton, Bank of America, Qualcomm, Sky Broadcasting and more.  And we are lining up a power-packed line up again this year to provide you with complete, insiders’ views into their advanced data virtualization deployments including business challenges, organizational transformation, technology adoption, and more.

Beyond the user speakers, industry analysts and Cisco executives will provide insights into trends and solutions that you can use as you map for your business’s data integration strategy.

And furthering the Data Virtualization Day tradition, winners of the 2014 Data Virtualization Leadership Awards will also be announced.  These are the best of the best.  Enjoy as we celebrate their accomplishments.

So join me, along with data virtualization’s vanguard, to explore these new horizons and imagine these new possibilities.  Registration is easy.  And the event is free.  Register now. 

Learn More

To learn more about Cisco Data Virtualization, check out our page.

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Data Abstraction: The Lingua Franca for Data Silos

Enterprises are seeking ways to improve their overall profitability, cut costs, reduce risk and more through better leverage of their data assets.

Significant volumes of complex, diverse data spread across various technology and application silos make it difficult for organizations to achieve these business outcomes. To further complicate matters, there is a range of problems such as

  • Separate access mechanisms, syntax, and security for each source
  • Lack of proper structure for business user or application consumption and reuse
  • Incomplete or duplicate data
  • And a mixture of latency issues

Data abstraction overcomes these challenges by transforming data from its native structure and syntax into views and data services that are much easier for business intelligence and analytics developers to use when creating new decision-making applications.

Enterprises can approach data abstraction three ways:

  • Manual data abstraction
  • Data warehouse schemas
  • Data virtualization

Of the three approaches, data virtualization is the superior solution for data abstraction because it enables the most flexibility and agility when you need to provide simple, consistent, business–formatted data from different data locations and sources.

As a complement to Cisco’s Data Virtualization software and services, Cisco also provides data abstraction best practices that help you accelerate your data abstraction activities. Composed of three distinct layers (application layer, business layer and physical layer), these best practices support a data reference architecture that rationalizes multiple, diverse data silos for a range of BI and analytic applications. The architecture aligns closely with analyst best practices mapped out by both Forrester and Gartner on the topic of data virtualization. Using these best practices will enable your company to access the right data for the business, gain agility and efficiency, maintain end-to-end control, and increase security of your data across all your data silos.

To learn more about data abstraction best practices using Cisco Data Virtualization, check out our white paper.

Call for Nominations: 2014 Data Virtualization Leadership Awards

The Data Virtualization Leadership Awards recognize organizations for data virtualization initiatives with significant business impact, agility and innovation.

  • Has your organization executed a data initiative with significant business impact, agility or innovation that should be celebrated?
  • Do you know a champion whose efforts have driven data virtualization success in your organization and beyond?

If so, please submit a nomination for the 2014 Data Virtualization Leadership Awards.

The deadline to submit is only 3 weeks away on July 9 so don’t delay.

Award Categories

  • High Impact Award: Recognizing the depth and breadth of the data virtualization impact on the business and to IT.
  • Agility Award: Recognizing speed and flexibility in applying data virtualization for improved business agility.
  • Innovative Solutions Award: Recognizing innovation in executing demanding data integration projects in complex environments.
  • Data Virtualization Champion Award: Recognizing promotion of data virtualization’s value across their organization and in the broader business and IT community.
  • Peter Green 3-3-3 Award: Superior usage and outcomes in practice of the 3-3-3 data virtualization use pattern.

Winners Recognition

  • Industry recognition of the Organization, Project and Project Leader
  • Participation in the award ceremony, held at the 2014 Data Virtualization Day in New York City
  • Engraved glass sculpture commemorating the achievement
  • Opportunities to speak at a Data Virtualization Day or other events

Rules of Entry

  • You must submit your nominee via e-mail to dvla_nominee@cisco.com by July 9, 2014.
  • Your email must include name, title, company, award, and a paragraph justifying your nomination.
  • Individual companies may nominate themselves in up to two categories. However, it’s best to create tailored versions for each category submitted.
  • Prior year’s winners may apply if they submit a new application for a new or significantly enhanced project.

Learn More

To learn more about Cisco Data Virtualization, check out our Data Virtualization Video Portal.

Active Archiving with Big Data

Historical data is now an essential tool for businesses as they struggle to meet increasingly stringent regulatory requirements, manage risk and perform predictive analytics that help improve business outcomes. While recent data is readily accessible in operational systems and some summarized historical data available in the data warehouse, the traditional practice of archiving older, detail-level data on tape makes analysis of that data challenging, if not impossible.

Active Archiving Uses Hadoop Instead of Tape

What if the historical data on tape was loaded into a similar low cost, yet accessible, storage option, such as Hadoop?  And then data virtualization applied to access and combine this data along with the operational and data warehouse data, in essence intelligently partitioning data access across hot, warm and cold storage options.  Would it work?

Yes it would!  And in fact does every day at one of our largest global banking customers.  Here’s how:

Adding Historical Data Reduces Risk

The bank uses complex analytics to measure risk exposure in their fixed income trading business by industry, region, credit rating and other parameters.  To reduce risk, while making more profitable credit and bond derivative trading decisions, the bank wanted to identify risk trends using five years of fixed income market data rather than the one month (400 million records) they currently stored on line.  This longer time frame would allow them to better evaluate trends, and use that information to build a solid foundation for smarter, lower-risk trading decisions.

As a first step, the bank installed Hadoop and loaded five years of historical data that had previously been archived using tape.  Next they installed Cisco Data Virtualization to integrate the data sets, providing a common SQL access approach that made it easy for the analysts to integrate the data.  Third the analysts extended their risk management analytics to cover five years.   Up and running in just a few months, the bank was able to use this long term data to better manage fixed income trading risk.

Archiving with Big Data_BankTo learn more about Cisco Data Virtualization, check out our Data Virtualization Video Portal.

Save Big Money with Big Data

Data in data warehouses doubles every 2.5 years. For users, this means more data to analyze, leading to better business outcomes. That’s the good news. The bad news is that this extra storage capacity and computing power comes at a cost. A high cost it turns out.

So what is an enterprise to do?

Keep writing bigger and bigger checks to the data warehouse vendor? At least the business can take advantage of the extra data?

Or should they move some of the lesser-used data to tape? That will save money. But it will also limit business access to this now “off-line” data which may mean missed business opportunities.

What if there was a third option that would preserve the on-line access for the business analysts and control these escalating costs for IT?

Cisco’s new Big Data Warehouse Expansion solution announced this week at Cisco Live provides this third option.

Log in here to access the presentations at Cisco Live on Cisco’s new Big Data Warehouse Expansion.

Cisco Big Data Warehouse Expansion is a new offering that combines hardware, software and services to help customers control the costs of their ever-expanding data warehouses by offloading infrequently used data to low-cost big data stores. Analytics are enriched as more data is retained and all data remains accessible.

Components in the solution include:

  • Cisco UCS optimized for big data stores.
  • Cisco Data Virtualization for federating multiple data sources.
  • Appfluent VisibilityTM to deliver analytics on business activity and data usage across Teradata, Oracle / Exadata, IBM DB2, IBM Netezza, IBM® PureData™ for Analytics and Hadoop.
  • Cisco Services methodology for assessing, migrating, virtualizing and operating a logically expanded warehouse.

If you are looking for a solution to your rising enterprise data warehouse costs, look no further than Cisco.

Follow us @CiscoDataVirt to stay up to date on the latest news!

Data Virtualization: Live at Cisco Live! San Francisco

It has been a great year for Data Virtualization at Cisco Live!   Milan, Melbourne, and Toronto were fantastic opportunities to introduce Data Virtualization to Cisco customer and partner audiences.  And we have saved the best for last with multiple activities at Cisco Live! San Francisco.

We kick things off on Monday May 18 with a by-invitation program for Cisco Data Virtualization customers and prospects.  We start the day at 3:00 with a special pass to John Chambers’ keynote address.  This is followed by a reception, data virtualization demo and tour in the World of Solutions hall.  And we close the evening with a dinner at one of San Francisco’s finest restaurants. Participants in this program return on Wednesday night for a special performance by Lenny Kravitz.   If you would like to join us, please contact Paul Torrento at ptorrent@cisco.com.

For those of you attending the full event, Data Virtualization is also featured in two sessions both entitled, Driving Business Outcomes for Big Data Environment.  I will lead a quick summary session on Thursday at 11:15am, with Jim Green providing a deeper-dive technical session from 11:30-12:30 that day.   In these sessions we will address one of the major issues organizations are facing as a consequence of exponential data growth – that is the huge expenses required to upgrade capacity in their enterprise data warehouses. To avoid this spend, customers are looking for lower cost alternatives such as offloading infrequently used data to Hadoop.  In these sessions you will find out about Cisco’s complete solution with Unified Computing System hardware and Data Virtualization software and Services methodology.

Please also stop by the Data Virtualization booth in the Cisco Services pavilion where we can chat about your business outcome objectives and how data virtualization can help.

And if you can’t make it to Cisco Live! San Francisco, then no worries.  Just check out the recording of my colleague Peter Tran’s session, Utilizing Data Virtualization to Create More Business Agility and Better Decision-Making, from Cisco Live! Milan.  It’s a great crash course intro to data virtualization.

How Data Virtualization Helps Data Scientists

By now it is clear that big data analytics opens the door to unprecedented analytic opportunities for business innovation, customer retention and profit growth. However, a shortage of data scientists is creating a bottleneck as organizations move from early big data experiments into larger scale adoption. This constraint limits big data analytics and the positive business outcomes that could be achieved.

Jason Hull

Click on the photo to hear from Comcast’s Jason Hull, Data Integration Specialist about how his team uses data virtualization to get what they need done, faster

It’s All About the Data

As every data scientist will tell you, the key to analytics is data. The more data the better, including big data as well as the myriad other data sources both in the enterprise and across the cloud. But accessing and massaging this data, in advance of data modeling and statistical analysis, typically consumes 50% or more of any new analytic development effort.

• What would happen if we could simplify the data aspect of the work?
• Would that free up data scientists to spend more time on analysis?
• Would it open the door for non-data scientists to contribute to analytic projects?

SQL is the key. Because of its ease and power, it has been the predominant method for accessing and massaging data for the past 30 years. Nearly all non-data scientists in IT can use SQL to access and massage data, but very few know MapReduce, the traditional language used to access data from Hadoop sources.

How Data Virtualization Helps

“We have a multitude of users…from BI to operational reporting, they are constantly coming to us requesting access to one server or another…we now have that one central place to say ‘you already have access to it’ and they immediately have access rather than having to grant access outside of the tool” -Jason Hull, Comcast

Data virtualization offerings, like Cisco’s, can help organizations bridge this gap and accelerate their big data analytics efforts. Cisco was the first data virtualization vendor to support Hadoop integration with its June 2011 release. This standardized SQL approach augments specialized MapReduce coding of Hadoop queries. By simplifying access to Hadoop data, organizations could for the first time use SQL to include big data sources, as well as enterprise, cloud and other data sources, in their analytics.

In February 2012, Cisco became the first data virtualization vendor to enable MapReduce programs to easily query virtualized data sources, on-demand with high performance. This allowed enterprises to extend MapReduce analyses beyond Hadoop stores to include diverse enterprise data previously integrated by the Cisco Information Server.

In 2013, Cisco maintained its big data integration leadership with updates of its support for Hive access to the leading Hadoop distributions including Apache Hadoop, Cloudera Distribution (CDH) and Hortonworks (HDP). In addition, Cisco now also supports access to Hadoop through HiveServer2 and Cloudera CDH through Impala.

Others, beyond Cisco, recognize this beneficial trend. In fact, Rick van der Lans, noted Data Virtualization expert and author, recently blogged on future developments in this area in Convergence of Data Virtualization and SQL-on-Hadoop Engines.

So if your organization’s big data efforts are slowed by a shortage of data scientists, consider data virtualization as a way to break the bottleneck.

Data Virtualization: Achieve Better Business Outcomes, Faster

Bob Eve, Director, Product Management
View Bob Eve’s original post on Cisco Data Center’s Blog

Data, Data Everywhere!

The challenge of making business decisions in a networked world isn’t a lack of data. It’s having data residing in multiple systems, global locations, locked away in spreadsheets, and in people’s heads.

Almost every enterprise faces this data silos challenge to a greater or lesser degree. But how businesses address it makes the difference between becoming a market leader or an “also-ran.” The fact is, better information leads to better decisions and better business outcomes. The Harvard Business Review (Big Data’s Management Revolution, October 2012) stated that data-driven companies are 5 percent more productive and 6 percent more profitable than their competitors.

Being able to easily access and use vast data stores has always been difficult. But in just the past few years, the problem has become 10 times worse. If it was just more data, then more compute and database horsepower could fix it. The bigger issues for businesses are proliferating data silos and ever-expanding distribution.

Data Virtualization to the Rescue

Industry-leading businesses are addressing the challenge with data virtualization. Data virtualization is an agile data integration approach that organizations use to:

  • Gain more insight from their data
  • Respond faster to accelerating analytics and business intelligence requirements
  • Reduce costs by 50 to 75 percent compared to data replication and consolidation approaches
  • Data virtualization abstracts data from multiple sources and transparently brings it together to give users a unified, friendly view of the data that they need.

Data Virtualization Presents a Unified View

Armed with quick and easy access to critical data, users can analyze it with their favorite business intelligence and analytic tools to drive a wide range of business outcomes. For example, they can increase customer profitability. Bring products to market faster. Reduce costs. And lower risk.

To read more about what Data Virtualization might mean to your enterprise, check out our new white paper Data Virtualization: Achieve Better Business Outcomes, Faster.

The Fourth V in Big Data

Bob Eve, Director, Product Management

View Bob Eve’s original post on Cisco Data Center’s Blog

At Cisco Live! Melbourne, I was invited to speak at the Executive Symposium to nearly 100 of Cisco’s top customers in the Australia and New Zealand region. In mytalk, Gaining Insight from the Big Data Avalanche, I covered big data business opportunities and technology challenges.

To level set at the start, I opened with a definition of big data, including the typical velocity, volume, and variety seem to be the three V’s everyone hears when it comes to big data. But then I challenged the audience to consider the fourth and in fact most important V, holding back on identifying it so the audience could consider what was missing.

After an appropriate pause, I told them the most important V was value. Value is the only reason to work on big data. This value must be seen in better business outcomes such as:

  • Higher Customer Profitability
  • Faster Time to Market
  • Reduced Cost
  • Improved Risk Management
  • Better Compliance
  • Greater Business & IT Agility

It is interesting how people get knocked off guard by the big data buzzwords. So go back to the basics. Start by getting your business case in order. Once the value to the business is understood, juggling higher data velocity, volume and/or variety becomes an engineering problem. Certainly, a new class of engineering problem, requiring new technologies and skills, but it is a fully solvable engineering problem nonetheless.

For IT, big data is as much an organizational change challenge, as a technology challenge. Practical first steps that seem to work well include:

  • Experiment with a smaller, “SWOT” team on a selected set of projects. This is a great way to introduce something new.
  • Go for some quick and easy wins, rather than boiling the ocean with large-scale initiatives. That is a proven technique for gaining momentum.
  • Implement a solution with revenue impact, such a next-best offer analytic to improve upsell performance or a predictive churn analytic that helps reduce customer defection. These high visibility projects will ease business funding challenges and improve executive visibility / sponsorship.

 

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