Enterprise data lake services are becoming increasingly common across various verticals and the world of retail hasn’t been immune to this phenomenon. Dividing data lakes into zones can allow retail businesses to make the most of the data at hand and come up with transformative insights that further their bottom line and lead to customer delight. Read on for useful insights on how retailers can leverage data lake zones in their everyday operations.

A Word About Data Lake Zones 

Data lake zones are structured divisions within a data lake that organize the storage and processing of data according to its stage in the lifecycle or its intended use. 

There are four common data lake zones: 

  1. Raw Zone: This is where data is stored in its original, unprocessed form. It is where the data is first ingested into the data lake. 
  2. Enriched Zone: This is where the data is initially processed and cleansed after ingestion. The data is made more valuable for analysis here. 
  3. Curated Zone: This is where data is further refined and made ready for consumption by end-users or for advanced analytics. 
  4. Development Zone: This zone is a sandbox environment that allows data scientists and analysts to experiment and develop new data models. 

By segregating data into these zones, organizations can better manage data quality, compliance, and usage. 

Examples of Data Lake Zones in Retail 

A retail data lake with zones, especially one offered as a Data Lake as a Service for Enterprises, can be used in a retail business in the following ways: 

Raw Zone: A retail company might use this zone to store raw transaction records from different sources, such as online sales platforms and physical store purchases. 

Enriched Zone: This zone could include data that has been cleansed and enriched with additional information, like customer demographics or product categories, to provide more context for analysis. 

Curated Zone: Here, the data will be further refined and could include aggregated sales reports, customer behavior analytics, and inventory levels that are ready for business intelligence tools. 

Development Zone: Retailers can use this zone for predictive analytics, such as forecasting sales trends based on historical data and market research.

What are the Benefits of Data Lake Zones in Retail? 

Data lake zones offer many benefits to retail providers and help them improve their bottom line. 

  1. Data lakes with zones can be trained with machine learning to offer transformative business insights that can reduce churn, improve cross selling, etc. 
  2. Data lake zones improve data governance by providing a structured framework within a data lake. 
  3. Data lake zones improve data quality as each zone serves a specific purpose, ensuring that data is appropriately processed and refined. 
  4. Data lake zones allow retailers to perform advanced analytics, customer segmentation, and trend analysis. 
  5. Data lake zones enable retailers to easily locate and access relevant user or business data. 
  6. Data lake zones help retail businesses personalize customer experiences through understanding user behavior.

When Should Retailers Implement Data Lake Zones?

Retailers of all sizes can benefit from using data lake services divided into data lake zones. Starting with data lake zone implementation early on can help a retail business leverage its data correctly right from the outset. Having a data lake platform divided into zones can also help as the business scales its operations by providing transformative insights into the direction and offerings the business must focus on. 

For larger retail businesses, a data lake platform divided into zones is non-negotiable as there would be a large volume of data to handle. Without data lake zones, making sense of all this information, both structured and unstructured, would be a significant challenge. This is the reason why the largest retail providers like Amazon and Walmart make use of data lake zones as part of their business operations.

What Types of Retail Data Can Data Lake Zones Handle?

Data Lake Zones, depending on the zone concerned, can handle a large variety of retail data. 

The raw zone can handle raw sales data from point-of-sale (POS) systems, inventory records like stock levels, product availability, and supply chain data, customer profiles, feedback and communication history, information from IoT devices (eg. temperature sensors in stores), and data pertaining to user behavior on e-commerce websites. 

The enriched zone can handle transformed and standardized data including aggregate sales by time, region, or product, consistent customer attributes, calculated metrics like customer lifetime value or average order value, and standardized product categories. 

The curated zone handles data that is further refined like customer recommendations, preferences, and segmentation, historical sales trends, and information on the impact of marketing campaigns on sales. 

The development zone allows analysts and data scientists to freely explore data both raw and enriched and train machine learning models. 

Final Word

Using an organized data lake platform with well-defined zones is increasingly becoming non-negotiable in retail. And the trend has been to use cloud-based enterprise data lake engineering services. Making use of the right data lake service providers can aid retail businesses in better managing large volumes of data. 

And the changes they offer can be transformational, right from the level of everyday operations to improving customer experience and upselling and cross-selling potential. 

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