How Startups Can Leverage Data Virtualisation To Accelerate Machine Learning

Data is a priceless resource that empowers businesses all over the world. The complexity of the data ecosystem grows as more data flows through an organisation. Most organisations have a highly distributed data ecosystem and attempt to consolidate data in a single system such as a data lake or a data lakehouse. This is done to support data initiatives such as advanced analytics and machine learning (ML).

Machine Learning can provide significant benefits to an organisation by accelerating business growth, improving operational efficiencies, lowering costs, and reducing business risk. These data science initiatives heavily rely on data. 

Storing all required data, both structured and unstructured, in a single central repository, such as a data lake or lakehouse, can facilitate data discovery, potentially reduce time spent on data integration, and provide tremendous processing power.

However, data science practitioners continue to spend a significant amount of time wrangling and curing data. This is before working on the actual machine learning algorithms, modelling, and training of these models to gain insights that drive business change and improvement.

No Such Thing As  ‘Silver Bullet’ Repository

Managing multiple systems that use different technologies is complex and costly from many perspectives. So a single platform that handles all of our data analytics needs makes intuitive sense.

Having all of your data in one place, however, does not guarantee that discovery will be simple; it frequently resembles looking for a needle in a haystack. Not all data will be stored in the data lake or lakehouse, owing to the time-consuming and expensive methods for copying data from its original systems. Businesses may have hundreds of repositories spread across multiple cloud service providers and on-premise databases, further muddying the waters.

As Gartner said recently, “a single data persistence tier and type of processing is inadequate when trying to meet the full scope of modern data and analytics demands.” If you examine the reference architectures of cloud providers closely, you will notice that even if you can move all of your data to the cloud (a big “if”), each cloud provider provides different processing engines for different tasks and data types.

Furthermore, data may be unusable even when stored in its original raw form. Before using machine learning methods, data may still need to be modified, transformed, or prepared. This is typically the responsibility of data scientists, who frequently lack the necessary data engineering and data integration skills. Data preparation can be a difficult and very time-consuming task. 

New methods, such as data virtualisation, are required to solve these problems, reduce data science workloads, and enable organisations to fully capitalise on a data lake or data lakehouse and existing technology investments.

Data Does Not Need a Destination

Data virtualisation allows data scientists to access more data in the format that is most appropriate for their needs. It is not necessary to replicate or move the data into a single repository. When serving the various data needs across the business, data can remain at the source, reducing the need to materialise data into a target repository. 

It provides a single point of access to all data, regardless of where it is stored or how it is formatted. For data scientists, data virtualisation reduces the need for additional data replicas. It can provide various logical combinations and perspectives of the same physical data while still applying complex data transformation and required functions to the physical data to achieve the desired output.

Utilising data virtualisation provides a quick and cost-effective method of utilising data to meet the specific needs of various users and applications. It can be extremely beneficial in resolving some of the major issues that data science practitioners face. By leaving data at its source, data can be accessed in real-time.

A Logical Approach

Data virtualisation makes data integration more transparent and accessible. It presents all company data in a single system, but in a logical order. Using a logical first approach and virtualisation, you can reduce delivery times, data preparation efforts, and time to value significantly.

According to a recent Forrester research study titled ‘The Total Economic Impact of Using Data Virtualisation,’ data preparation efforts can be reduced by up to 67% by building and maintaining the logic for preparing data in one place, within the logical layer.

This logical approach also allows for a clear and efficient division of labour between data scientists and data engineers.  Data engineers can create “reusable logical data sets” that expose information in ways that are suitable for many niche applications by utilising data virtualisation.

It reduces the complexity of navigating the data ecosystem while ensuring data security.  Data, particularly personally identifiable information (PII), is a resource that must be handled ethically and responsibly.

The virtual layer’s unique combination with the data catalogue creates a powerful combination for empowering end users in self-service initiatives and accelerating ML initiatives.

It Makes Sense

As cloud adoption grows and data lakes and lakehouses become more common, data virtualisation will become increasingly important in boosting the output of ML initiatives. 

Data science practitioners can alleviate the burden of data administration by leveraging data virtualisation to increase data access, take advantage of catalogue-based data discovery, and streamline data integration and data preparation efforts. Using a logical approach to data management and integration will enable the organisation to fully benefit from the abundance of data available!