person using MacBook Pro

Finding new growth opportunities in a data-driven business landscape can be challenging, even for the most well-organized businesses. Thankfully, big data holds all the solutions a modern-day business organization would need to provide customers with a seamless user experience, outperform their competitors, and expand their operations.

However, the only way to unleash the full potential of big data and ensure prosperity in an overwhelmingly competitive digital business realm is to build a top-grade data lake that can provide your enterprise with all the perks of digital transformation.

You can build one yourself, or you could buy it from data lake providers. Whatever is more convenient for you, one can’t deny the fantastic advantages a data lake can offer to a modern-day business.

Advantages of using data lakes

You should look at a data lake as a central repository of information in charge of managing, organizing, structuring, and cleansing your data. This central location stores all pieces of business-relevant information and keeps it in its raw and native format.

It can organize and diversify huge quantities of unprocessed data promptly and structure it according to your specific needs. Whether you keep semi-structured, unstructured, or structured data in a data lake, it helps you to load and store it in a preferred condition, format, or structure.

A data lake is a more advanced solution than a data warehouse. Instead of saving data in folders or files, data lakes rely on an advanced architecture to load and keep data as-is. Because of its advanced architecture, a data lake offers several advantages for businesses, such as:

● An opportunity to gather both structured and unstructured data and keep it in a preferred format, safely stored in one central location;

● More flexibility for easier data management;

● Data scalability;

● Ability to gather, process, and analyze raw data;

● Refining all data formats to improve your insights and decision-making;

● Multiple ways to query the data;

● Access to an array of tools for advanced data analysis;

● Turning any type of data into actionable insights;

● Eliminating the need for using data silos;

● Getting a unified data view across your organization.

Now, let’s discuss the easiest ways to build a data lake and develop a robust, flexible, and scalable data architecture.

Set a clear data goal

The initial step toward building an agile data lake architecture is to clarify the type of data your business needs to acquire to accomplish its goals. Aside from avoiding data swaps, setting a clear data goal allows you to develop a data-driven company culture and avoid outdated data storage solutions.

It also allows you to achieve objectives such as ensuring scalability, coping with cloud-based environments, and maintaining customer-centricity. However, although your data pipeline needs massive volumes of data daily, that should not be your main objective.

Modern data architecture implementation

Traditional data architecture models are outdated and may not be enough to match your modern-day data management needs. Nowadays, digital businesses have new data requirements requiring a new, more efficient, affordable data architecture.

Here are the essentials for implementing a modern data architecture for your business:

● Data profiling – one of the main priorities for businesses is to use the potential of unstructured data to their advantage. Data profiling allows them to accomplish that goal by lending them a profound understanding of their data assets and how to best use them.

● Data cataloging – a data catalog is the best way for organizations to turn data assets in their possession into actionable insights and extract maximum value from their current KPI glossary.

● Data archives and backup – you should always have a backup of your data assets safely stored in the cloud. Data archives, on the other hand, are the best solution for continuous access to the data you have to use daily for extended periods.

Invest in data security, privacy, and governance

Metadata management and data security are essential to developing and maintaining an effective and robust data lake architecture. Both help you curate your data lake to ensure your data is secure, accurate, accessible, and clean.

You can leverage the power of AI and automation to automate processes such as data gathering, harvesting, and transformation. AI lends you the power of advanced analytical, data storage, and

next-gen data integration techniques to analyze and classify your data assets to turn them into the most accurate and relevant actionable insights.

Conclusion

Data is crucial for improving the process of decision-making and accomplishing your business goals. However, harnessing the true potential of your data assets requires a matching architecture that allows you to manipulate the data in your possession according to your short-term and long-term business objectives.

The advanced method of data management requires you to complete multiple processes such as data aggregation, processing, analysis, and transformation. All these processes depend on your data architecture.

Each stage of data processing is vulnerable to cyber threats, which is why you should ensure the highest levels of data security, privacy, and governance. As your business operations grow, your data grows with them.

Since scalability can impose additional complexity and increase costs, data lakes help to decrease that complexity and make your database processing as cost-efficient as possible.