
By Anne Buff | Article Rating: |
|
October 31, 2014 01:00 PM EDT | Reads: |
6,206 |

Five Reasons for IT Leaders to Consider Data Virtualization
It is no doubt that the latest and greatest technologies get your juices flowing. Researching the details, comparing the benchmarks, imagining the differences you could make within your organization if you could just put the right technology solutions in the right place. If only budgets and time were endless. If only you could start again from scratch. How different things would be. Insert screeching record and your return to reality. You are not only tasked with integrating legacy systems the business has relied on for years and numerous data sources scattered throughout the organization but now the expectation (and excitement) is to integrate Big Data. What the what? As you shake (or bang) your head in disbelief, stop for a moment and consider data virtualization.
Data virtualization is the process of providing access to a comprehensive, consistent view of enterprise data by means of abstraction and integration of data from disparate sources. Sounds great, but what does that really offer IT? Here are a few technical advantages data virtualization brings to the table:
Optimize hardware capabilities. A virtualized data environment does not require data to move or be replicated throughout the enterprise. Data resides in the originating sources (or in defined data marts, stores, etc.) with the central virtualization server accessing data as needed at the time of query. No new data storage requirements are necessary. The query optimization engine in the data virtualization server significantly reduces processing times by not only applying algorithms for the most efficient access and integration but also employs query push down capabilities to take advantage of the available commodity hardware resources from the contributing data sources. The data virtualization server can also cache commonly run queries for even further efficiency.
The reduction in storage requirements and optimized processing capabilities of data virtualization allows organizations to enhance, not rebuild or replace their existing data infrastructures.
Reduce complexity. Scalability. The complexity of integrating data in your existing infrastructure is wildly maddening and only increases when considering the need to integrate external data. Data virtualization provides the ability to integrate data of any type (structured, semi structured and unstructured) from almost any source - internal or external - bringing the data into a consistent format that is consumable for the business at large. The scalability of a virtualized data environment is exceptional as new data sources can be rapidly added and made available to the business with all data management tasks, business rules and security requirements in effect immediately. The virtualized environment also allows changes to be made and applied in parallel with the ever-changing needs of the business.
Increase staff productivity. The virtualization server provides the business logic for data access and integration insulating data consuming applications from the changes made at data sources (which are often unknown until something breaks) such as a change in data type or from the addition of an entirely new data source. This eliminates hours, if not days, of coding and scripting changes that traditional ETL methods and application data access required. By centralizing data access and simplifying complex data environments, data virtualization reduces the IT workload required for implementing new data requests and changes. Niche skill sets are not required to write scripts for accessing every unique data source. IT resources are freed up to work on strategic initiatives and are able to support a more agile development environment that works hand and hand with the business.
Centralize control and security. Administrators have the ability to create and enforce business rules, data requirements and security policies at the centralized virtualization server rather than having to develop, implement, manage and monitor them at every source system ̶ which ensures the consistency necessary for strict regulatory and compliance mandates (and allows IT better sleep at night). This centralized control allows data sources to be added and removed essentially on the fly and critical changes can be made as soon as the need arises. Data provisioned through the reusable business views of data virtualization, ensures business users are getting the most up-to-date comprehensive view of the enterprise data, which provides a warmly welcomed new level of confidence in enterprise decision making.
Faster time to solution. Rapid Prototyping. With the more comprehensive view of data and the ability to create new business views of the data on demand, business and IT can work together to implement new, innovative ways at looking at the data to solve problems and implement new business ideas. Extensive, time-consuming coding changes and/or expensive structural changes are not required when the business has new applications to deploy, questions to answer or problems to solve allowing for a much quicker time to solution. This is one of the easiest places to justify ROI for data virtualization.
Consider this scenario...
The business wants to implement a new sales and marketing management application that could generate an estimated $300k a month in additional revenue. Based on the current technology and data environment IT has estimated it will take four months to have the application fully in production. An environment with data virtualization would eliminate many of the time and technology obstacles and cut implementation time down to four weeks. With a much more rapid time to solution, data virtualization would bring the organization an additional $900k in revenue before the traditional methods would even have a solution available.
The simplified, faster access to largely varying data types and sources and the enhanced scalability that data virtualization provides allows organizations to incorporate valuable data into their enterprise environment previously considered infeasible or impossible. This streamlined, efficient data access not only makes for a less complex environment but also allows executives to make more accurate, real-time decisions and opens opportunities for strategic new business insights, innovation and revenue generation.
There are plenty of things to bang your head about. Data integration does not have to be one of them.
Published October 31, 2014 Reads 6,206
Copyright © 2014 SYS-CON Media, Inc. — All Rights Reserved.
Syndicated stories and blog feeds, all rights reserved by the author.
More Stories By Anne Buff
Anne Buff is a SAS Best Practices Thought Leader specializing in data integration, master data management, and team building. In this role, she leverages her 15 years of training experience to lead best-practices workshops and facilitate intra-team dialogues in a fun and engaging way.
![]() Dec. 14, 2017 05:45 PM EST Reads: 1,876 |
By Elizabeth White ![]() Dec. 14, 2017 04:00 PM EST Reads: 1,215 |
By Liz McMillan ![]() Dec. 14, 2017 03:15 PM EST Reads: 898 |
By Elizabeth White ![]() Dec. 14, 2017 03:15 PM EST Reads: 481 |
By Pat Romanski ![]() Dec. 14, 2017 03:00 PM EST Reads: 958 |
By Elizabeth White ![]() Dec. 14, 2017 01:00 PM EST Reads: 492 |
By Elizabeth White ![]() Dec. 14, 2017 12:45 PM EST Reads: 960 |
By Liz McMillan ![]() Dec. 14, 2017 11:45 AM EST Reads: 1,262 |
By Elizabeth White ![]() Dec. 14, 2017 11:30 AM EST Reads: 1,819 |
By Elizabeth White ![]() Dec. 14, 2017 11:00 AM EST Reads: 1,277 |
By Elizabeth White ![]() Dec. 14, 2017 07:15 AM EST Reads: 678 |
By Liz McMillan ![]() Dec. 14, 2017 06:00 AM EST Reads: 1,971 |
By Pat Romanski ![]() Dec. 13, 2017 02:00 PM EST Reads: 1,087 |
By Elizabeth White ![]() Dec. 13, 2017 11:00 AM EST Reads: 1,193 |
By Liz McMillan ![]() Dec. 11, 2017 11:15 PM EST Reads: 10,028 |
By Liz McMillan ![]() Dec. 10, 2017 07:45 AM EST Reads: 3,884 |
By Elizabeth White ![]() Dec. 8, 2017 07:30 AM EST Reads: 1,178 |
By Pat Romanski ![]() Dec. 7, 2017 08:00 PM EST Reads: 2,699 |
By Liz McMillan ![]() Dec. 7, 2017 02:00 PM EST Reads: 2,025 |
By Pat Romanski ![]() Dec. 6, 2017 05:00 PM EST Reads: 1,311 |