Cookies help us display personalized product recommendations and ensure you have great shopping experience.

By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData CollectiveSmartData Collective
  • Analytics
    AnalyticsShow More
    image fx (67)
    Improving LinkedIn Ad Strategies with Data Analytics
    9 Min Read
    big data and remote work
    Data Helps Speech-Language Pathologists Deliver Better Results
    6 Min Read
    data driven insights
    How Data-Driven Insights Are Addressing Gaps in Patient Communication and Equity
    8 Min Read
    pexels pavel danilyuk 8112119
    Data Analytics Is Revolutionizing Medical Credentialing
    8 Min Read
    data and seo
    Maximize SEO Success with Powerful Data Analytics Insights
    8 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-25 SmartData Collective. All Rights Reserved.
Reading: Data Virtualization: 6 Best Practices to Help the Business ‘get it’
Share
Notification
Font ResizerAa
SmartData CollectiveSmartData Collective
Font ResizerAa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Big Data > Data Visualization > Data Virtualization: 6 Best Practices to Help the Business ‘get it’
AnalyticsBusiness IntelligenceData Visualization

Data Virtualization: 6 Best Practices to Help the Business ‘get it’

JoeMcKendrick
JoeMcKendrick
3 Min Read
SHARE

Something that doesn’t get talked about enough in the service orientation world is data virtualization. That is, it’s handy to be able to pull data from various sources into an abstracted service layer, versus having services or applications tapping live production databases. This helps cut down the need for physical storage, and provides a common interface for all applications using the data, especially BI, analytics, and transaction systems.

Something that doesn’t get talked about enough in the service orientation world is data virtualization. That is, it’s handy to be able to pull data from various sources into an abstracted service layer, versus having services or applications tapping live production databases. This helps cut down the need for physical storage, and provides a common interface for all applications using the data, especially BI, analytics, and transaction systems.

The whys and hows of data virtualization are explored by Judith Davis and Robert Eve in a new book, Going Beyond Traditional Data Integration to Achieve Business Agility. As with any service technology engagement, data virtualization involves a lot of players across the enterprise, so challenges tend to be more organizational and cultural than technical.

Davis and Eve outline 6 key best practices anyone undertaking a data virtualization effort needs to consider:

1) Centralize responsibility for data virtualization. “The key benefit here is the ability advance the effort quickly and to take on bigger concepts, such as defining common canonicals and implementing an intelligent storage component,” the authors say.

2) Agree on and implement a common data model. “This will ensure consistent, high quality data, make business users more confident in the data and make IT staff more agile and productive.”

3) Establish a governance approach. “This needs to include how to manage the data virtualization environment. Key issues are who is responsible for the shared infrastructure and for shared services.”

4) Educate the business side on the benefits of data virtualization. “Allocate time to consult with business users and make sure they understand the data,” Davis and Eve advise. “Establish an ongoing effort to make data virtualization acceptable to other areas of the organization.”

5) Pay attention to performance tuning and scalability. “Tune performance and test solution scalability early in the development process. Consider bringing in massively parallel processing capability to handle query performance on high-volume data. Accommodate the fact that users are unpredictable on ad hoc analysis and reporting.”

6) Take a phased approach to implementing data virtualization. “First abstract the data sources, then layer the BI applications on top and gradually implement the more advanced federation capabilities of data virtualization.”

 

Share This Article
Facebook Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

image fx (2)
Monitoring Data Without Turning into Big Brother
Big Data Exclusive
image fx (71)
The Power of AI for Personalization in Email
Artificial Intelligence Exclusive Marketing
image fx (67)
Improving LinkedIn Ad Strategies with Data Analytics
Analytics Big Data Exclusive Software
big data and remote work
Data Helps Speech-Language Pathologists Deliver Better Results
Analytics Big Data Exclusive

Stay Connected

1.2kFollowersLike
33.7kFollowersFollow
222FollowersPin

You Might also Like

The Top Challenges with Sentiment Analysis

4 Min Read

With physicists across the country pushing for universities to…

1 Min Read

SAP Spends Big on SuccessFactors for Cloud Computing and Talent Management

12 Min Read

The Internet Needs a Bill of Rights Before It’s Too Late

8 Min Read

SmartData Collective is one of the largest & trusted community covering technical content about Big Data, BI, Cloud, Analytics, Artificial Intelligence, IoT & more.

ai chatbot
The Art of Conversation: Enhancing Chatbots with Advanced AI Prompts
Chatbots
ai is improving the safety of cars
From Bolts to Bots: How AI Is Fortifying the Automotive Industry
Artificial Intelligence

Quick Link

  • About
  • Contact
  • Privacy
Follow US
© 2008-25 SmartData Collective. All Rights Reserved.
Go to mobile version
Welcome Back!

Sign in to your account

Username or Email Address
Password

Lost your password?