Leveraging data lake architecture to simplify information archival is great; the trick is to overcome that simplicity to handle complexity.

By 2030, the Association of Southeast Asian Nations (ASEAN) is projected to become the fourth-largest economy in the world. Key factors driving this growth are the region’s rapid adoption of technology and fast-growing digital economy.

With this digitally driven growth comes the proliferation of, and heavy reliance, on big data. Business organizations that can successfully generate value from their data will outperform their competition. To do that, businesses will need to first be able to effectively harness and manage their data.

As the volume of data continues to increase, and with advanced data-centric technologies such as artificial intelligence (AI) and predictive or stream analytics coming into play—the complexities of discovering, managing and utilizing data will only become more demanding.

Drowning in the data lake

The volume and variety of data that flows through today’s business organizations are so vast that data lakes have fast become part of many organizations’ main data management architecture. Reason being that data lakes allow for storing huge volumes of structured, semi-structured, and unstructured data in their native format until they are needed.

The value of data lakes is that they offer a thin data-management layer within an organization’s technology stack, and they can be deployed with minimal impact on existing architectures. A recent report revealed that organizations employing a data lake tend to outperform their peers by 9% in organic revenue growth. Data lakes will need to be managed to ensure they do not turn into ‘data swamps’, which will compromise the usefulness of stored data. 

However, the main challenge for many organizations adopting data lakes is that they are not query-friendly like data warehouses. While data lakes can store almost any type of data, they are not built for easy retrieval of specific data. This lack of easy access is a major issue if organizations are deploying data lakes with the objective of analyzing and gaining insights quickly. Accessing, querying and preparing data for analysis may take more time than acceptable to these organizations.

The case for data virtualization

Is there a silver bullet to solve this problem? The answer is a resounding yes, and it starts with data virtualization.

Data virtualization provides single access point to any data, regardless of its location or format. It combines data from various underlying sources and delivers it in real time, enabling a faster and more cost-effective way to access different types of data.

For a start, data virtualization helps organizations to discover data by making it more accessible. Unlike traditional data integration techniques like Extract, Transform and Load (ETL), data virtualization removes the need for data to be replicated, delivering integrated content faster and cheaper than the traditional methods.

Some data virtualization platforms are also user-friendly, offering easy-to-read catalogues of all available data sets. These catalogues contain extensive metadata on the history and lineage of the data sets, and their relationship with other data sets, simplifying the process of discovery.

Additionally, data virtualization platforms help integrate and organize data into a consistent representation and query model. This means that organizations can now view all of their data in a standard format, regardless of the format that it was originally stored in. Also, data virtualization platforms allow end users to use SQL constructs or Application Programming Interfaces (APIs) to access this data within analytical, reporting or operational applications.

Data architects can use virtualization to create “reusable logical data sets”, exposing information in useful ways for different purposes. Since there is no longer a need to physically replicate the data, it takes considerably less effort to create and maintain these logical data sets than with traditional methods. This approach allows an application to retrieve and manipulate data without requiring technical details about the data, such as how it is formatted at source, or where it is physically located.

As data virtualization offloads complex tasks such as data transformation and performance optimization, data scientists can now focus on developing multiple logical models for answering critical questions quickly.

Virtualizing the future ahead  

A recent study revealed that data-ready organizations in the Asia Pacific region contributed to 90% of better business outcomes than laggards. Business organizations that fail to keep pace with the evolution of data may risk becoming redundant.

The key is for businesses to be able to effectively harness and manage their data. By embracing data virtualization, business can leverage powerful query optimization to extract more meaningful results and insights from their data than ever before.

In other words, business leaders can make more timely decisions to better mitigate risks, drive business performance, and take advantage of untapped or imminent opportunities. In the digital age, this heightened business intelligence can make all the difference between success and failure.