The storage methods for data have evolved through data streams, data ponds, data warehouses and such. Of all the methods used for making sense of data in today’s data-driven world, a data lake is the most recent and widely popular across industries.  

There are many reasons why a data lake is the most preferred choice for data storage and management. Considering the whooping amount of data, a typical retail enterprise deals with, there is little to no question why data lake is the most popular choice of top retailers such as Walmart, Amazon and Costco. This blog explores why data lake is gaining traction in the realm of retail and the various benefits it brings to the world of retail. 

Why use Data Lakes in the retail sector? 

Unlike other forms of data storage like data warehouses, data lakes support all kinds of structured and unstructured data. They also provide users with a deeper understanding of the business situation with a completely new set of contexts through advanced and real-time analytics. Gaining a new understanding allows users to indulge in analytical experiments with the provided data. Since data lakes are primarily created to handle big data, organizations can move raw data in batches or streams in and out of the data lakes without having to make changes to them.   

These are just some positives of using data lake that attract a lot of organizations. Here is a pie chart that depicts the usage of data lakes in various industries by Grand View Research. 

According to the report, Data lakes are more apt for usage in the retail industry since they would aid the companies in providing an in-depth customer understanding with proper classification of potential buyers. Several studies say that data lakes are expected to be the next game-changer in the realm of data management and storage, especially when businesses want industry-specific insights. 

Major Challenges to Leveraging Data Lakes in the Retail Industry

Since data lake is gaining more traction in today’s market, the power to make an informed decision while simultaneously making use of the data lake can turn out to be difficult. Using data lakes in retail is not a task that can be done in a simple manner. In a corporate environment, the process of implementing a data lake is quite complex and involves a lot of technicalities. Here are some of the major challenges companies usually deal with when implementing a data lake: 

The quality and integrity of data 

Since data lakes are well known for their vast storage capacity, ensuring the quality of the data stored can be challenging. There are cases when inconsistencies, duplications, and inaccuracies can occur due to large amounts of unstructured and raw data. 

Data privacy 

Working in retail entails working with sensitive customer data, where it is crucial to protect these data from security breaches, compliance violations, and unauthorized access. Therefore, taking extreme security measures is a necessity while using data lakes.  

The data integration  

Retailers tend to work with multiple sources of data such as CRM platforms, social media platforms, e-commerce platforms, and much more. Integrating all the diverse data sources into one centralized data lake can become complex.  

The tools and skillsets required

In the lack of proper tools, Data lakes will be prone to reliability issues. These issues may turn out to cause more work for data analysts and data scientists. Extracting meaningful insights by leveraging data lakes requires expertise and a certain skill set. 

Benefits of Using Data Lakes in the Retail Industry 

Even though using data lakes is a challenging task, the benefits of using data lakes overcompensate the hurdles to advanced performance. Here are some of the benefits of using data lakes, that organically solve the issues mentioned prior. Here are some of the advantages of utilizing data lakes in the retail sector: 

The 3C’s of data quality 

When a data lake is built with a golden record of master data, it increases the potential of the three C’s of data. Correctness, Completeness and Consistency. When companies ensure their data lake implementation is in tune with the 3 Cs of data quality, the level of intricacies involved in the process can be reduced. 

Data Security and Governance 

Some enterprise data lake services come with a data security and data governance feature which assigns the rights and responsibilities in accordance. They also come with features that enable restrictions on who can load data, who can read data, etc. 

Smart data collaboration 

Since data lakes act as a centralized repository which integrates data from various platforms, they make it easier to understand customer behavior and personalize customer experience.  They offer various services like customer sentiment analysis, market basket analysis and much more. These benefits make the tedious effort of entering the data and the workforce used for building the data lake totally worth it. 

It Is Time We Embrace the Data Lake Revolution and Redefine the Retail Operations 

In the ever-evolving retail landscape where data-driven insights and customized customer experience have become paramount, data lakes have emerged to be the new game-changer. Data lakes have improved the industry by providing retailers with a platform to drive accurate customer insights and personalize their experience. As the retail industry transforms, the leveraging of data lakes can no longer be considered an option but a necessity. Retailers can harness the complete potential of their data and deliver competent personalized experiences to their customers, by harnessing enterprise data lake services. Data lakes are predicted to remain at the forefront of the retail industry’s transformation, revolutionizing the engagement and the services provided to customers. 

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