Welcome!

@CloudExpo Authors: Liz McMillan, Pat Romanski, Yeshim Deniz, Elizabeth White, Maria C. Horton

Related Topics: Containers Expo Blog, Microservices Expo

Containers Expo Blog: Article

How Data Virtualization Improves Business Agility – Part 2

Accelerate value with a streamlined, iterative approach that evolves easily

Business Agility Requires Multiple Approaches
Agile businesses create business agility through a combination of business decision agility, time-to-solution agility and resource agility.

This article addresses how data virtualization delivers time-to-solution agility. Part 1 addressed business decision agility and Part 3 will address resource agility.

Time-To-Solution Agility = Business Value
When responding to new information needs, rapid time-to-solution is critically important and often results in significant bottom-line benefits.

Proven, time and again across multiple industries, substantial time-to-solution improvements can be seen in the ten case studies described in the recently published Data Virtualization: Going Beyond Traditional Data Integration to Achieve Business Agility.

Consider This Example: If the business wants to enter a new market, it must first financially justify the investment, including any new IT requirements. Thus, only the highest ROI projects are approved and funded. Once the effort is approved, accelerating delivery of the IT solution also accelerates realization of the business benefits and ROI.

Therefore, if incremental revenues from the new market are $2 million per month, then the business will gain an additional $2 million for every month IT can save in time needed to deliver the solution.

Streamlined Approach to Data Integration
Data virtualization is significantly more agile and responsive than traditional data consolidation and ETL-based integration approaches because it uses a highly streamlined architecture and development process to build and deploy data integration solutions.

This approach greatly reduces complexity and reduces or eliminates the need for data replication and data movement. As numerous data virtualization case studies demonstrate, this elegance of design and architecture makes it far easier and faster to develop and deploy data integration solutions using a data virtualization platform. The ultimate result is faster realization of business benefits.

To better understand the difference, let's contrast these methods. In both the traditional data warehouse/ETL approach and data virtualization, understanding the information requirements and reporting schema is the common first step.

Traditional Data Integration Has Many Moving Parts
Using the traditional approach IT then models and implements the data warehouse schema. ETL development follows to create the links between the sources and the warehouse. Finally the ETL scripts are run to populate the warehouse. The metadata, data models/schemas and development tools used within each activity are unique to each activity.

This diverse environment of different metadata, data models/schemas and development tools is not only complex but also results in the need to coordinate and synchronize efforts and objects across them.

Experienced BI and data integration users will readily acknowledge the long development times that result from this complexity, including Forrester Research in its 2011 report Data Virtualization Reaches Critical Mass.

"Extract, transform, and load (ETL) approaches require one or more copies of data staged along the physical integration process flow. Creating, storing, and manipulating these copies can be complex and error prone."

Data Virtualization Has Fewer Moving Parts
Data virtualization uses a more streamlined architecture that simplifies development. Once the information requirements and reporting schema are understood, the next step is to develop the objects (views and data services) used to both model and query the required data.

These virtual equivalents of the warehouse schema and ETL routines and scripts are created within a single view or data service object using a unified data virtualization development environment. This approach leverages the same metadata, data models/schemas and tools.

Not only is it easier to build the data integration layer using data virtualization, but there are also fewer "moving parts," which reduces the need for coordination and synchronization activities. With data virtualization, there is no need to physically migrate data from the sources to a warehouse. The only data that is moved is the data delivered directly from the source to the consumer on-demand. These result sets persist in the data virtualization server's memory for only a short interval.

Avoiding data warehouse loads, reloads and updates further simplifies and streamlines solution deployment and thereby improves time-to-solution agility.

Iterative Development Process Is Better for Business Users
Another way data virtualization improves time-to-solution agility is through support for a fast, iterative development approach. Here, business users and IT collaborate to quickly define the initial solution requirements followed by an iterative "develop, get feedback and refine" process until the solution meets the user need.

Most users prefer this type of development process. Because building views of existing data is simple and fast, IT can provide business users with prospective versions of new data sets in just a few hours. The user doesn't have to wait months for results while IT develops detailed solution requirements. Then business users can react to these data sets and refine their requirements based on the tangible insights. IT can then change the views and show the refined data sets to the business users.

This iterative development approach enables the business and IT to hone in on and deliver the needed information much faster than traditional integration methods.

Even in cases where a data warehouse solution is mandated by specific analytic needs, data virtualization can be used to support rapid prototyping of the solution. The initial solution is built using data virtualization's iterative development approach, with migration to the data warehouse approach once the business is fully satisfied with the information delivered.

In contrast, developing a new information solution using traditional data integration architecture is inherently more complex. Typically, business users must fully and accurately specify their information requirements prior to any development, with little change tolerated. Not only does the development process take longer, but there is a real risk that the resulting solution will not be what the users actually need and want.

Data virtualization offers significant value, and the opportunity to reduce risk and cost, by enabling IT to quickly deliver iterative results that enable users to truly understand what their real information needs are and get a solution that meets those needs.

Ease of Data Virtualization Change Keeps Pace with Business Change
The third way data virtualization improves time-to-solution agility is ease of change. Information needs evolve. So do the associated source systems and consuming applications. Data virtualization allows a more loosely coupled architecture between sources, consumers and the data virtualization objects and middleware that integrate them.

This level of independence makes it significantly easier to extend and adapt existing data virtualization solutions as business requirements or associated source and consumer system implementations change. In fact, changing an existing view, adding a new source or migrating from one source to another is often completed in hours or days, versus weeks or months in the traditional approach.

Conclusion
Data virtualization reduces complexity, data replication and data movement. Business users and IT collaborate to quickly define the initial solution requirements followed by an iterative "develop, get feedback and refine" delivery process. Further independent layers make it significantly easier to extend and adapt existing data virtualization solutions as business requirements or associated source and consumer system implementations change.

These time-to-solution accelerators, as numerous data virtualization case studies demonstrate, make it far easier and faster to develop and deploy data integration solutions using a data virtualization platform than other approaches. The result is faster realization of business benefits.

Editor's Note: Robert Eve is the co-author, along with Judith R. Davis, of Data Virtualization: Going Beyond Traditional Data Integration to Achieve Business Agility, the first book published on the topic of data virtualization. This series of three articles on How Data Virtualization Delivers Business Agility includes excerpts from the book.

More Stories By Robert Eve

Robert Eve is the EVP of Marketing at Composite Software, the data virtualization gold standard and co-author of Data Virtualization: Going Beyond Traditional Data Integration to Achieve Business Agility. Bob's experience includes executive level roles at leading enterprise software companies such as Mercury Interactive, PeopleSoft, and Oracle. Bob holds a Masters of Science from the Massachusetts Institute of Technology and a Bachelor of Science from the University of California at Berkeley.

Comments (0)

Share your thoughts on this story.

Add your comment
You must be signed in to add a comment. Sign-in | Register

In accordance with our Comment Policy, we encourage comments that are on topic, relevant and to-the-point. We will remove comments that include profanity, personal attacks, racial slurs, threats of violence, or other inappropriate material that violates our Terms and Conditions, and will block users who make repeated violations. We ask all readers to expect diversity of opinion and to treat one another with dignity and respect.


@CloudExpo Stories
HyperConvergence came to market with the objective of being simple, flexible and to help drive down operating expenses. It reduced the footprint by bundling the compute/storage/network into one box. This brought a new set of challenges as the HyperConverged vendors are very focused on their own proprietary building blocks. If you want to scale in a certain way, let's say you identified a need for more storage and want to add a device that is not sold by the HyperConverged vendor, forget about it...
Enterprises are moving to the cloud faster than most of us in security expected. CIOs are going from 0 to 100 in cloud adoption and leaving security teams in the dust. Once cloud is part of an enterprise stack, it’s unclear who has responsibility for the protection of applications, services, and data. When cloud breaches occur, whether active compromise or a publicly accessible database, the blame must fall on both service providers and users. In his session at 21st Cloud Expo, Ben Johnson, C...
It is of utmost importance for the future success of WebRTC to ensure that interoperability is operational between web browsers and any WebRTC-compliant client. To be guaranteed as operational and effective, interoperability must be tested extensively by establishing WebRTC data and media connections between different web browsers running on different devices and operating systems. In his session at WebRTC Summit at @ThingsExpo, Dr. Alex Gouaillard, CEO and Founder of CoSMo Software, presented ...
In this presentation, you will learn first hand what works and what doesn't while architecting and deploying OpenStack. Some of the topics will include:- best practices for creating repeatable deployments of OpenStack- multi-site considerations- how to customize OpenStack to integrate with your existing systems and security best practices.
As you move to the cloud, your network should be efficient, secure, and easy to manage. An enterprise adopting a hybrid or public cloud needs systems and tools that provide: Agility: ability to deliver applications and services faster, even in complex hybrid environments Easier manageability: enable reliable connectivity with complete oversight as the data center network evolves Greater efficiency: eliminate wasted effort while reducing errors and optimize asset utilization Security: implemen...
Your homes and cars can be automated and self-serviced. Why can't your storage? From simply asking questions to analyze and troubleshoot your infrastructure, to provisioning storage with snapshots, recovery and replication, your wildest sci-fi dream has come true. In his session at @DevOpsSummit at 20th Cloud Expo, Dan Florea, Director of Product Management at Tintri, provided a ChatOps demo where you can talk to your storage and manage it from anywhere, through Slack and similar services with...
Most people haven’t heard the word, “gamification,” even though they probably, and perhaps unwittingly, participate in it every day. Gamification is “the process of adding games or game-like elements to something (as a task) so as to encourage participation.” Further, gamification is about bringing game mechanics – rules, constructs, processes, and methods – into the real world in an effort to engage people. In his session at @ThingsExpo, Robert Endo, owner and engagement manager of Intrepid D...
Recently, WebRTC has a lot of eyes from market. The use cases of WebRTC are expanding - video chat, online education, online health care etc. Not only for human-to-human communication, but also IoT use cases such as machine to human use cases can be seen recently. One of the typical use-case is remote camera monitoring. With WebRTC, people can have interoperability and flexibility for deploying monitoring service. However, the benefit of WebRTC for IoT is not only its convenience and interopera...
Evan Kirstel is an internationally recognized thought leader and social media influencer in IoT (#1 in 2017), Cloud, Data Security (2016), Health Tech (#9 in 2017), Digital Health (#6 in 2016), B2B Marketing (#5 in 2015), AI, Smart Home, Digital (2017), IIoT (#1 in 2017) and Telecom/Wireless/5G. His connections are a "Who's Who" in these technologies, He is in the top 10 most mentioned/re-tweeted by CMOs and CIOs (2016) and have been recently named 5th most influential B2B marketeer in the US. H...
Michael Maximilien, better known as max or Dr. Max, is a computer scientist with IBM. At IBM Research Triangle Park, he was a principal engineer for the worldwide industry point-of-sale standard: JavaPOS. At IBM Research, some highlights include pioneering research on semantic Web services, mashups, and cloud computing, and platform-as-a-service. He joined the IBM Cloud Labs in 2014 and works closely with Pivotal Inc., to help make the Cloud Found the best PaaS.
Companies are harnessing data in ways we once associated with science fiction. Analysts have access to a plethora of visualization and reporting tools, but considering the vast amount of data businesses collect and limitations of CPUs, end users are forced to design their structures and systems with limitations. Until now. As the cloud toolkit to analyze data has evolved, GPUs have stepped in to massively parallel SQL, visualization and machine learning.
"With Digital Experience Monitoring what used to be a simple visit to a web page has exploded into app on phones, data from social media feeds, competitive benchmarking - these are all components that are only available because of some type of digital asset," explained Leo Vasiliou, Director of Web Performance Engineering at Catchpoint Systems, in this SYS-CON.tv interview at DevOps Summit at 20th Cloud Expo, held June 6-8, 2017, at the Javits Center in New York City, NY.
"Venafi has a platform that allows you to manage, centralize and automate the complete life cycle of keys and certificates within the organization," explained Gina Osmond, Sr. Field Marketing Manager at Venafi, in this SYS-CON.tv interview at DevOps at 19th Cloud Expo, held November 1-3, 2016, at the Santa Clara Convention Center in Santa Clara, CA.
"This week we're really focusing on scalability, asset preservation and how do you back up to the cloud and in the cloud with object storage, which is really a new way of attacking dealing with your file, your blocked data, where you put it and how you access it," stated Jeff Greenwald, Senior Director of Market Development at HGST, in this SYS-CON.tv interview at 18th Cloud Expo, held June 7-9, 2016, at the Javits Center in New York City, NY.
Creating replica copies to tolerate a certain number of failures is easy, but very expensive at cloud-scale. Conventional RAID has lower overhead, but it is limited in the number of failures it can tolerate. And the management is like herding cats (overseeing capacity, rebuilds, migrations, and degraded performance). In his general session at 18th Cloud Expo, Scott Cleland, Senior Director of Product Marketing for the HGST Cloud Infrastructure Business Unit, discussed how a new approach is neces...
Cloud-enabled transformation has evolved from cost saving measure to business innovation strategy -- one that combines the cloud with cognitive capabilities to drive market disruption. Learn how you can achieve the insight and agility you need to gain a competitive advantage. Industry-acclaimed CTO and cloud expert, Shankar Kalyana presents. Only the most exceptional IBMers are appointed with the rare distinction of IBM Fellow, the highest technical honor in the company. Shankar has also receive...
"We're developing a software that is based on the cloud environment and we are providing those services to corporations and the general public," explained Seungmin Kim, CEO/CTO of SM Systems Inc., in this SYS-CON.tv interview at 21st Cloud Expo, held Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA.
Business professionals no longer wonder if they'll migrate to the cloud; it's now a matter of when. The cloud environment has proved to be a major force in transitioning to an agile business model that enables quick decisions and fast implementation that solidify customer relationships. And when the cloud is combined with the power of cognitive computing, it drives innovation and transformation that achieves astounding competitive advantage.
Leading companies, from the Global Fortune 500 to the smallest companies, are adopting hybrid cloud as the path to business advantage. Hybrid cloud depends on cloud services and on-premises infrastructure working in unison. Successful implementations require new levels of data mobility, enabled by an automated and seamless flow across on-premises and cloud resources. In his general session at 21st Cloud Expo, Greg Tevis, an IBM Storage Software Technical Strategist and Customer Solution Architec...
In his session at Cloud Expo, Alan Winters, U.S. Head of Business Development at MobiDev, presented a success story of an entrepreneur who has both suffered through and benefited from offshore development across multiple businesses: The smart choice, or how to select the right offshore development partner Warning signs, or how to minimize chances of making the wrong choice Collaboration, or how to establish the most effective work processes Budget control, or how to maximize project result...
To get the most out of their data, successful companies are not focusing on queries and data lakes, they are actively integrating analytics into their operations with a data-first application development approach. Real-time adjustments to improve revenues, reduce costs, or mitigate risk rely on applications that minimize latency on a variety of data sources. In his session at @BigDataExpo, Jack Norris, Senior Vice President, Data and Applications at MapR Technologies, reviewed best practices to ...