This article has been adapted from the “Extract Data from Mainframe Reports in Retail Operations” flyer.

Many large-scale retailers rely on mainframe computers to manage their financial, supply chain, inventory, purchasing, marketing, and fulfillment processes. Retailers must have efficient methods for integrating data from mainframe reports with other systems, including business intelligence (BI), customer relationship management (CRM), and marketing software. Many retailers apply automated or semi-automated workflows built using the Altair® RapidMiner® data analytics and artificial intelligence (Al) platform to extract, normalize, and cleanse data from so-called “green bar reports” generated by mainframes and prepare the data from mainframe reports for use in other systems. 

Why Mainframes?

Retailers must support a variety of high-volume applications, point-of-sale (POS) transaction processing, inventory management, supply chain management (SCM), and enterprise resource planning (ERP) systems. Mainframes are a perfect fit for major online, brick-and-mortar, and hybrid retailers thanks to their reliability, high security, and ability to handle millions of daily transactions. Their virtues include:

• Reliability and Uptime: Downtime in a retail environment can lead to significant revenue loss and customer dissatisfaction. Mainframes are designed to run continuously without failure, ensuring critical business functions stay up and running.
• Scalability: Retailers must contend with fluctuating workloads; for example, system loads can rise by orders of magnitude during peak shopping periods like the holidays. Mainframes can scale efficiently to support increased numbers of simultaneous transactions and users without sacrificing performance.
• Security: Mainframes offer advanced encryption, access controls, and auditing functions to ensure retailers comply with regulations like Payment Card Industry Data Security Standard (PCI DSS).
• Cost Effectiveness: Mainframes may appear expensive to casual observers. However, their very high transaction throughput – alongside a myriad of other capabilities – provides retailers with lower total cost of ownership (TCO) than alternative architectures.
• Disaster Recovery: Resilience is vital to retail businesses, and mainframes can recover data and resume operations quickly in the event of a system failure or natural disaster.
• Energy Efficiency: Mainframes consume less power compared to many distributed servers, which helps retailers reduce their carbon footprint and operating costs.

Data Extraction from Any File Type

Mainframe-deployed retail systems generate green bar reports as very large PDF or text files that may contain thousands of pages. Parsing and cleansing this data reliably, accurately, and repeatedly is a formidable challenge. Altair RapidMiner gives retail businesses the ability to build auditable automation workflows than manage this task and direct the resulting clean data to other systems.

Altair RapidMiner also enables retailers to automate data extraction workflows for virtually any type of data source, including PDFs, complex spreadsheets, XML files, SQL databases, NoSQL databases, and cloud services. This flexibility is crucial for organizations that utilize many different types of enterprise software systems produced by multiple vendors. The platform empowers users to:

• Enable no-code, pattern-driven data extraction of reports and other transactional business documents stored in virtually any format, including PDF, text, Microsoft® Excel®, as well as tabular sources; connect to business applications including Salesforce, Netsuite, HubSpot, and ServiceNow; database and analytical sources including SOL Server, Oracle, Teradata, Redshift, BigQuery, Cassandra, MongoDB, and others.
• Save parsing and transformation rules, including filters, sorts, joins, and exports into reusable automated workflows.
• Link disparate datasets quickly through a friendly, visual interface and eliminate the need to create code-intensive SQL joins.
• Handle any type of text encoding.
• Connect to local and cloud data sources. 
• Automate and streamline data processing workflows, improve data quality, and centralize storage, making data more readily available to end users and enabling more informed decision-making.

Variety of Approaches

Altair RapidMiner provides several ways to build and manage data extraction workflows. And it offers tools that work at the desktop, workgroup, and enterprise levels.

Businesses with substantial technical resources may elect to use Altair RapidMiner to deploy custom coded extract-transform-load (ETL) applications built in their choice of the SAS, Python, R, and SQL languages – or in a combination of those languages. Companies with fewer IT professionals may choose tools that allow business users without coding skills to build, deploy, and manage ETL workflows using a completely visual user interface.

To learn more about Altair RapidMiner, visit https://altair.com/altair-rapidminer. To read the full flyer, visit https://altair.com/resource/extract-data-from-mainframe-reports-in-retail-operations.