Innovation Distinguishes Between a Leader and a Follower

Steve Jobs is arguably the most amazing innovator of our times.  I recently read some of his thoughts on innovation. His statement “Innovation distinguishes between a leader and a follower,” caused me to reflect upon my eight-year association with data virtualization, and consider who in the IT analyst community have been the innovative leaders.

Since 2006, I have worked with over one hundred IT analysts to define and advance the data virtualization market.  I even teamed up with one, Judith Davis, to co-author the first book on data virtualization, Data Virtualization: Going Beyond Traditional Data Integration to Achieve Business Agility.

Others such as Rick van der Lans, author of data virtualization’s second book, Data Virtualization for Business Intelligence Systems: Revolutionizing Data Integration for Data Warehouses and the seminal article, The Network is the Database, have contributed mightily to the market’s understanding of data virtualization’s capabilities, advantages and benefits.

The role call of top analysts doing innovative work continues with Noel Yuhanna of Forrester who wrote the analyst community’s first research paper on data virtualization in January 2006, Information Fabric: Enterprise Data Virtualization.

Gartner’s Ted Friedman and Mark A. Beyer, and more recently Merv Adrian, Roxane Edjlali, Mei Selvage, Svetlana Sicular and Eric Thoo, have been both descriptive and proscriptive about the use of data virtualization as a data integration delivery method, a data service enabler and a key component in what Gartner calls the Logical Data Warehouse.

Dave Wells, author of TDWI’s Data Virtualization Course, Data Virtualization: Solving Complex Data Integration Challenges, helped bring data virtualization into the mainstream.    As did Boulder BI Brain Trust members Claudia Imhoff, Colin White, John O’Brien, Ralph Hughes, John Myers and more who I recently wrote about in Rocky Mountains High On Data Virtualization.

Further, there have been myriad analysts who have amazing contributions.

  • The learned trio of Dr. Barry Devlin, Dr. Robin Bloor, and Dr. Richard Hackathorn have pushed the art of the possible.
  • While analyst / practitioners such as Jill Dyche, Mike Ferguson, Rick Sherman, Steve Dine, Evan Levy, David Loshin and William McKnight, via their hands-on client work, have “kept data virtualization grounded on reality street,” to quote Mike Ferguson.
  • And let’s not forget the Massachusetts’ Waynes — Wayne Eckerson formerly of TDWI and Wayne Kernochan, author of the eponymous Thoughts From a Software IT Analyst blog.  Their voices and insights have proven invaluable.

To quote Gene Rodenberry, “It isn’t all over; everything has not been invented; the human adventure is just beginning.”  The same is true for data virtualization.  So I look forward to more great insights from these innovators, as well as a new generation led by Puni Rajah of Canalys and Vernon Turner of IDC.

To see Rick van der Lans and Barry Devlin on stage and gain even more insights from the 2014 Data Virtualization Leadership Award winners, join us at Data Virtualization Day 2014 on October 1 in New York City.

Watch for a sneak peek of Data Virtualization Day 2014.

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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.

 

Announcing the 2013 Data Virtualization Innovative Solutions Award Winner

Making insightful usage of the growing plethora of internal data is difficult enough. But what if the business thrives and grows by developing data external to the firm, using it to sell insights to customers and to shape new products as well?  Associated challenges are even greater since each customer has different business contexts and needs. Innovation in such an environment would certainly take time, and new projects would be fraught with uncertainty, risk, and high cost.

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Announcing the 2013 Data Virtualization Champion

There is an old saying: “If you want something done, give it to the busiest person you know who is qualified to do that thing.”  When I consider my experience, it seems to be true that the most reliable candidate for a task is typically the one most people go to with their requests.  Thus, that person is very busy indeed!

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The Biggest Day in Data Virtualization

Data Virtualization Day 2013 was the largest gathering of data virtualization professionals in history. With 350 attendees from over 130 organizations, this year’s attendance was 65% higher than 2012.

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When Data Virtualization Meets the Network

Most followers of data virtualization have a data management background. This is why many did not immediately understand why “a networking company” like Cisco would be interested in acquiring data virtualization market leader Composite Software.

Data Virtualization Meets the Network,” a report from analyst firm EMA does a great job exploring several of the factors behind this powerful new combination.

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Archiving with Big Data = Better Business Results

Business Value from Mixing Current and Historical 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 decisions.And while recent data may be available in from operational systems and some summarized historical data available in the data warehouse, the traditional practice of archiving older, detail-level data offsite on tape makes business analytics challenging, if not impossible, because the historical information needed is simply unavailable.

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Forrester Information Fabric 3.0 – A Fresh Take on Data Virtualization

On the Data Virtualization Vanguard

Forrester’s Mike Gilpin and Noel Yuhanna have been on the vanguard of data virtualization since their January 9, 2006 report trends report, “Information Fabric: Enterprise Data Virtualization.”

Their new report, “Forrester Information Fabric 3.0, Forrester’s Reference Architecture For Enterprise Data Virtualization” was published on August 8, 2013.

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How Data Virtualization Addresses the Big Data Integration Skills Shortage

Big data opens the door to unprecedented analytic opportunities for business innovation, customer retention and profit growth. However, the big data skills shortage is creating a bottleneck at every organization today as they move from early big data experiments into enterprise scale adoption. This constraint limits big data analytics success.

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Data Challenges in the Analytics Pipeline

Data is the lifeblood of analytics — the more diverse the better.

In their best-selling book, Big Data: A Revolution That Will Transform How We Live, Work, and Think, Mayer-Schonberger and Cukier describe the synergy that occurs when previously unrelated and disparate data is brought together to uncover hidden insights. But these advanced analytics data requirements are a double-edged sword as these more diverse sources complicate data integration and constrain progress.

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