In this modern era, data is the lifeblood of any successful business operation. Given how businesses have multiple data collection points, collating data can be time-consuming and overwhelming. Thanks to technology, we now have new techniques for collating, combining, and curating big data.
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Notable among these techniques is data virtualization, which involves collecting and integrating data from different data sources and formats into a single, reliable data source. As data continues upward, data virtualization can fuel data analytics for critical decision-making, cost optimization, and performance management. Is your organization planning to integrate data virtualization software? If yes, this article is a must-read to help you understand what the concept is all about.
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Data Virtualization Defined
The obvious question that resonates in the minds of less tech-savvy entrepreneurs who come across this term is what is data virtualization? Data virtualization technology provides a modern virtual data layer that allows users access to multiple datasets with increased speed and reduced costs. More so, it refers to a data management technique that enables an application to collect and manipulate data without requiring its technical details.
This technology applies various abstraction and transformation techniques to settle disputes between the source database and consumer formats and semantics. Unlike the ETL (Extract, Transform, Load) process, the data remains in place, and the source system is given real-time access to the data. This process assures business users of data integrity as it reduces the risk of errors and imposition of a single data model on the heterogeneous data stores.
It goes without saying that some businesses contain disparate data sources such as multiple data marts, data warehouses, and data silos. Ideally, a data warehouse or data mart is expected to serve as a single source of truth. With data virtualization, users can link data across data marts, data silos, and data warehouses without creating a new integrated platform. Moreover, data virtualization is a subset of data integration.
Data virtualization is commonly used by BI developers that provide service-oriented architecture data services. A notable example of a data virtualization platform is Denodo which virtualizes big data stored on an SQL server and any other database.
Vital components of a data virtualization system?
The ultimate goal of data virtualization is to give business users fast access to integrated data from multiple sources without any hassle. Below are a set of components that ensure the efficiency of a data virtualization system.
Abstraction tier: Data abstraction requires a layer of technology that serves as an abstraction layer between the business user and databases. This abstraction layer usually takes the form of an end-user tool that allows the user to explore the data without understanding its technical details. Moreover, users only have access to schematic data models.
Dynamic data catalog: Dynamic data catalog involves classification and tagging, descriptions, and data lineage. It’s integral for keyword-based search and discovery. Most data analysts integrate a translation tier for mapping data labels to terms less tech-savvy businesspeople can understand.
Model governance: Data virtualization focuses on virtual data models or schemas. That said, model governance plays an integral role in a successful data virtualization. Ideally, model governance must include data inputs and corresponding outputs, reports, calculations, and machine learning logic. Analysts use definition reasoning, solid security measures, and proper documentation to govern the data model.
Metadata and semantics mediation: The metadata layer is unarguably the most critical component of a data virtualization system. It gathers the syntax and semantics of source models and observes model changes that inform decision-making. An integrated data virtualization system that stores metadata that allows users to reuse data with less risk.
Benefits of data virtualization.
Data virtualization enables business owners to make timely, informed decisions that improve their efficiency and performance without breaking the bank. What’s more, with data virtualization, risk managers can effectively analyze and mitigate risks.
Additionally, data virtualization helps improve marketing campaign performance and allows business users to accelerate business outcomes at a reduced cost. Lastly, business leaders can leverage data virtualization to stay ahead of the competition and meet rapidly changing market trends with agility.
