Retail Data Management - Locus IT Services

Retail Data Management

Retail Data Management

Capturing data is only a portion of Retail Data Management. Sticking together disparate data for analysis requires proper handling of data across an organization thus improving a retailer’s ability to make impact decisions about running their business. Adequate data usage can activate the customer to buy by helping the retailer delivering a more relevant experience.

Data is the main foundation for developing and delivering better retail experiences. Data is found in each and every retail organization and can be used to extract insights across the value chain into operational performance and customer behavior, as well as leveraged to power improved service experiences.

The action is to offer a promotion is informed by a combination of data points and triggered by the customer entering the store. The ability to make these connections and the resulting actions are based on the Retail Data Management model. When bringing data into Azure, consider the 3P’s of data sources and their applicability to the scenarios the retailer wants to enable.

3P’s of Data Sources

  • Purchased – Purchased data typically augments and enhances the organization’s existing data most often with market and demographic data that supplements the organization’s data capture reach
  • Public – Public data is freely available and may be harvested from social media, government resources and other online sources.
  • Proprietary – Proprietary data resides within the organization.

These different data types are used for various insights coming from the Retail data management pipeline.

Ingest in Retail Data Management

At first, data is loaded into Azure in its native format, and is stored accordingly. Managing and receiving disparate data sources can be daunting, but Microsoft Azure offers services to load data into the cloud quickly and easily, making it available for processing in the Retail Data Management pipeline.

Azure has several helpful services for migrating data. The choice depends on the kind of data being migrated. Azure Data Migration Services for SQL Server and the Azure Import or Export are the services that help data get into Azure. Other data services to consider include Azure Data Factory and Azure Logic Apps connectors.


Before analysis begins, the data must be prepared. This forming of data is important to ensure quality of predictive models, reporting KPIs and relevancy of data. There are two types of data to address when preparing data for analysis that is structured and unstructured.
Structured data is very easier to deal with since it is already formed and formatted. It may require just a simple transformation to go from structured data in resource format to which structured data is ready for analysis jobs.

Unstructured data consistently provides more challenges. Unstructured data is not stored in a fixed record length format. Examples include documents, social media feeds, digital images and videos. These data must be managed differently than structured data and often require a dedicated process to ensure these data end up in the right data store, in a usable way.


Storing data before processing requires consideration. Data can come in unstructured or structured formats and the shape of the data often determines its storage destination. For instance, highly structured data may be suitable for Azure SQL. Less structured data might be held in blob storage, file storage, or table storage.

Likewise, Both structured and unstructured data can be stored in Azure Data Lake and queried using U-SQL, a query language specific to Azure Data Lake.

Structured Data

  • CRM data and other line of business applications
  • POS transaction data
  • Sensor data
  • Relational data
  • eCommerce transaction data

Unstructured Data

  • Social feeds
  • Video
  • Digital images
  • Website clickstream analysis


For problems like reducing cost of inventory, retailers can use analysis performed by a Machine Learning process. Data analysis assembles data for processing through a Machine Learning engine to gain deeper insights into the customer experience. This process delivers a model that “learns” and may be applied to future data to predict outcomes.

Models characterize the data that will be examined and how the data will be analyzed through various algorithms. Utilizing the output data from the analysis with data visualization is what could trigger an insight such as offering an in-store coupon for an item from the customer’s wish list in the retailers e-Commerce platform.


Data in retail moves consequently, and systems that handle it must do so in a timely manner. For instance, eCommerce shopper data needs to be processed quickly. This items in a buyer’s cart can be used to offer additional services, or add-on items during the checkout process.

This form of handling data and analysis must occur almost immediately and is typically carried out by systems performing “micro-batch” transactions. That is, analyzed data in a system which has access to already processed data and is run through a model.
Use the following steps to get started.

  • Create data ingestion plan for data stores providing value to the analysis which to be performed. With detailed data synchronization or data migration plan in place, get the data into Azure in its original format.
  • Define the actionable insights needed and choose a data processing pipeline to accommodate the data processing activities.
  • With these data features, we can create a data processing pipeline using the appropriate algorithms to gain the insights being sought.
  • We can use a common data model for output into a data warehouse, if possible; this can expose the most interesting data features. This generally implies reading data in the original Azure storage systems and writing the cleaned version to another data store.
  • Process the data through the ML (Machine Learning) pipelines provided by Spark or Hadoop. Then feed the output to a data warehouse. There are so many default algorithms to process the data, or retailers can implement on their own.

Microsoft Dynamics Retail Data management is complex. In any case, it offers the valuable ability to deliver relevance and an improved customer experience.

Using these techniques in this article, insights may be gained to improve the customer experience, drive profitable business outcomes and uncover trends that may drive operational improvements. We at Locus IT provide such services like implementation, support and training. For more information please contact us.

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