Thursday, September 29, 2022
Tech ObserverNewsOpinionSimplifying data complexity with data virtualization

Simplifying data complexity with data virtualization

No matter where the data is located or how it is formatted, data virtualization offers a single access point to all data.

No matter where the data is located or how it is formatted, data virtualization offers a single access point to all data.

The Association of Southeast Asian Nations (ASEAN) will have the fourth-largest economy in the world by 2030. The region’s quick embrace of technology and the rapidly expanding digital economy are vital elements fuelling this progress. is becoming more prevalent and is being heavily relied upon due to this digitally driven expansion. Businesses will do better than their rivals if they can extract value from their data successfully. However, they would first need to be able to efficiently harness and manage their data.

Finding, maintaining, and using data will only get more difficult as the volume of data increases and cutting-edge data-centric technologies like artificial intelligence (AI) and predictive or stream analytics come into play.

Stepping into the Data Lake

Since a significant amount and considerable variety of data flows through today’s commercial organisations, data lakes have quickly been incorporated into many data management architectures. This is because data lakes enable the long-term storage of enormous amounts of organised, semi-structured, and unstructured data in their original format.

Data lakes are valuable because they provide a thin data-management layer within a company’s technology stack and may be implemented with little disruption to existing designs. According to a recent study by Aberdeen Group, companies using data lakes beat their peers in organic revenue growth by 9%. The management of data lakes is necessary to prevent them from becoming “data swamps,” reducing the utility of stored data.

Data lakes are not as query-friendly as data warehouses, and this is their biggest obstacle. Although data lakes can store practically any form of data, they are not designed for quick data retrieval. If firms are creating data lakes to quickly analyse and generate insights, this difficulty in accessing the data is a significant problem.

How Can Data Virtualization Resolve These Issues?

No matter where the data is located or how it is formatted, offers a single access point to all data. It aggregates data from multiple underlying sources and distributes it in real-time, making it possible to access various sorts of data more quickly and affordably.

Data virtualization facilitates data discovery by increasing data accessibility. It also eliminates data replication, making integrated content delivery faster and more cost-effective than older approaches like extract, transform, and load (ETL) processes.

A few user-friendly data virtualization technologies provide easy-to-read catalogues of all the available data sets. The process of finding data is facilitated by the necessary metadata in these catalogues that describe the background and lineage of the data sets and their connections to other data sets.

Platforms for data virtualization also assist in combining and organising data into a standardised representation and query paradigm. Regardless of the format in which the data was first saved, businesses can now visualise all of their data in a common format. Additionally, data virtualization solutions enable end users to access this data through analytical, reporting, or operational applications by using SQL constructs or APIs.

Information can be exposed in practical ways for various reasons by data architects using data virtualization to build reusable logical data sets. It requires a lot less work to develop and maintain these logical data sets than it would with conventional techniques because there is no requirement to copy the data physically. With this method, an application can retrieve and change data without knowing any technical information, such as how the data was formatted at the source, or where it was stored.

With data virtualization, data scientists can concentrate on creating a variety of logical models for quickly answering essential questions, and this offloads complex activities such as data transformation and performance optimisation.

A Virtualized Future

According to a recent survey, data-ready businesses in the Asia Pacific region achieved 90% greater commercial results than those who could not claim that they were data-ready. Enterprises that do not adapt to the changing nature of data risk becoming obsolete.

Organisations need to be able to effectively manage their data and exploit that data properly. Businesses that embrace data virtualization can use advanced query optimisation techniques to get results faster than ever before.

With faster access to data, executives can act more quickly to reduce risks, boost operations, and seize unrealised or approaching possibilities. In the digital age, improved business knowledge can mean the difference between remaining competitive and dropping out of the game.

The author is Chief Technical Officer, . Views are personal.

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