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Mainframe Data Is Critical for Cloud Analytics Success—But Getting to It Isn’t Easy

Modern Data Integration Technology Is Helping

With the drive to modernize data infrastructure on cloud technologies, one would be forgiven for thinking mainframe systems are destined to go the way of the dodo. The truth is, far from extinction, mainframes are making a comeback. Seventy-four percent of respondents in a 2021 Forrester survey said they see the mainframe as a long-term strategic platform. This is unsurprising given the mainframe’s security, reliability, and incredible processing power. 

Mainframes also hold a treasure trove of data. Nearly 75 percent of Fortune 500 companies still use mainframe systems for almost all credit card transactions and over 65 percent of production IT workloads, according to a September 2022 report by Bloor Research. Organizations have decades of vital customer information, sales data, logistics, and more housed in mainframes. 

Great news, right? Since we now have the computing power and scale to run analytics on enormous data sets, adding mainframe data to the mix will give data scientists and analysts a richer data set for better analytics and reporting. Following that to the logical conclusion, line of business users across the enterprise will be able to make better data-driven decisions that lead to more innovation, revenue gains, and happier customers.

More specifically, unlocking mainframe data can help enterprises:

Refine regulatory reporting: Many mainframe-oriented industries, like banking and insurance, have extensive, constantly changing regulatory reporting requirements. With a continual push toward more transparency in business transactions and relationships, the teams that provide these reports need agility and speed.  
Conduct deeper market analysis: With access to every customer interaction/transaction, data analysts can aggregate as much data about customers as possible to understand buying trends, behaviors, and much more. This helps companies identify opportunities to sell more, manage better, and predict and prevent churn.
Improve manufacturing/supply operations: Mainframes in retail ore-commerce are often at the core of managing and executing supply chain processes, assets, and supplier interactions. Data analysts looking to improve manufacturing output, speed production, and consolidate or optimize their supply chain need ongoing access to mainframe data for their efforts.
Enhance customer interactions and experience: Customer-facing teams need customer business transaction data housed in mainframes to strengthen support, address issues around orders, and more. 

Just one problem: Accessing data from mainframes is currently a slow and tedious process—if you can access it at all. 

The State of Mainframe Data Access

Central IT, mainframe teams, and CISO offices carefully manage and govern mainframe systems, and for good reason. Since these systems are usually responsible for mission-critical business operations, they require the highest level of security. They’re also extremely expensive, so companies must be cautious about what data they put on the mainframe and how they access it lest they expose themselves to unexpected increases in compute that require them to pay substantially more to the mainframe vendor.

Because of these concerns, data requests are typically managed ad hoc and include only specific data sets. These requests result in data ‘dump and loads’ into the data warehouse that are costly and bespoke. Since that data is then not refreshed, it becomes out of date quickly. 

More often than not, mainframe data is excluded entirely from analytics and reporting because it’s just too hard to access. 

Enter StreamSets Mainframe Collector

StreamSets’ new Mainframe Collector lets enterprises unlock mainframe data without ceding control. 

Mainframe Collector has a lightweight listener that securely and cost-effectively captures mainframe data and delivers it to end-users in an analytics-ready format. It enables real-time data access, data virtualization, and data movement (ETL, ELT, and CDC), and has connectivity to all the common cloud data stores and modern analytics platforms.

Mainframe Collector fits the needs of every department involved. 

Data engineers: Easily and securely connect the mainframe (and other enterprise data sources) to any cloud data platform. Create a trusted partnership with mainframe data owners where they continue to operate as they want, and you can service line of business data requests. 

Information security professionals: Allow access to mainframe data without security risk. It fits into and extends existing security policies to maintain security and governance—without changes.

Mainframe managers: Distribute the computing and avoid high usage peaks. StreamSets does the heavy lifting outside the mainframe (converting EBCDIC to ASCI, translating SQL queries to the correct mainframe data access request, and building result tables). There’s no installation required to access data in DB2 or IMS DB, and only a small access component installation to access data in VSAM or QSAM files. 

Line of business analysts: Get access to your company’s mainframe data in a familiar format (SQL in a relational format). StreamSets data pipelines securely move the data you want and need into the cloud data platforms and analytics and reporting tools you use consistently. 

StreamSets Mainframe Collector helps your team work together effortlessly, efficiently, and cost-effectively to deliver and use valuable mainframe data.

Unlock Mainframe Data for More Robust Cloud Analytics

Mainframes are a strategic platform for the future of the enterprise. With decades of valuable information inside, organizations must find a way to unlock access to that data for better cloud analytics and reporting. StreamSets Mainframe Collector offers a secure, easy, and cost-effective solution. 

Learn More About Mainframe Connector

 

The post Mainframe Data Is Critical for Cloud Analytics Success—But Getting to It Isn’t Easy appeared first on StreamSets.

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