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In today’s technology landscape, mainframe systems continue to play a pivotal role in large organizations across various sectors, including finance, healthcare, manufacturing, retail, and government. These robust systems are chosen for their unwavering reliability, scalability, and exceptional performance. Notably, over 70% of Fortune 500 companies still rely on mainframes to manage their most critical workloads.

Mainframes house an extensive repository of valuable data, encompassing credit card transactions, stock trading records, customer information, billing processes, product catalogs, and inventory details. The ability to generate accurate and trustworthy reports from this treasure trove of data is paramount for informed decision-making and compliance with regulatory requirements. However, achieving this goal within the mainframe environment presents formidable challenges.

Mainframe Data Challenges

One primary challenge arises from the inherent complexity of mainframe data. Data within these systems is stored in diverse and intricate formats, including hierarchical and relational structures. The presence of legacy technologies further compounds this complexity. Given the decades-long existence of mainframes, many of their applications and data structures are constructed using very old programming languages and technologies. Extracting meaningful insights from these structures using traditional methods can prove arduous, if not impossible.

Another hurdle is the need for seamless inter-system connectivity. Mainframes often contain data that must be integrated with information from other sources to provide comprehensive reports. The prerequisite for integrating mainframe data with data from other systems, databases, data warehouses, or data lakes is obtaining visibility into all these technologies and their respective transformation processes. This task is far from trivial.

Discovering and accessing mainframe data is a further challenge. While enterprises may maintain a data catalog, these catalogs often lack detailed and well-classified information about mainframe data, making it difficult to generate reliable reports.

Data quality and integrity represent additional roadblocks to accurate and trustworthy reporting. It is essential to employ tools that monitor data quality and ensure data consistency throughout the reporting process.

Moreover, complying with data privacy regulations while generating reports is of paramount importance and can significantly add to the complexity of the process. Enterprises must be capable of automatically identifying sensitive data, such as personally identifiable information (PII), and implementing data governance policies to ensure data security and privacy compliance.

Lastly, traditional approaches to reporting from mainframe data frequently require extensive manual effort. This reliance on manual processes often leads to inaccuracies, inefficiencies, and a lack of trust in the reporting outcomes.

EIIG Resolves Mainframe Data Challenges

To surmount these challenges, enterprises can adopt a self-defined data fabric provided by Orion Governance’s Enterprise Information Intelligence Graph (EIIG). EIIG represents the next generation of metadata management and data governance platforms, offering unparalleled support for the mainframe environment.

EIIG automatically ingests metadata from critical mainframe technologies, including databases like IMS and Db2, as well as legacy programming languages such as PL1, JCL, and COBOL, thereby establishing intra-system connectivity. By scanning the code of these technologies, EIIG provides sub-table level granularity and a comprehensive understanding of dataset structures.

Furthermore, EIIG covers over 70 technologies across the entire enterprise data landscape (from DBMS, ETL and reporting tools, to modern programming languages like Python and Java), building inter-system connectivity and visualizing it within a knowledge graph. With EIIG, enterprises gain complete transparency of their enterprise information flow, from source to report, laying the foundation for comprehensive, accurate, and trustworthy reporting.

This transparency is further enhanced through the propagation of key metrics, including data quality, code quality, defects, and business requirements, across all data sources, including the mainframe. For instance, EIIG incorporates data quality metrics and displays data quality scores at each step of the data pipeline, both before and after transformation, within the data lineage. Users can conveniently access key metrics like quality score, trust score, and user rating directly within their reports. If desired, users can seamlessly dive into EIIG from their reports to explore additional details, such as data origins and ownership.

Beyond enhancing data quality through metadata activation, EIIG offers metadata analytics, including similarity analysis, which enables enterprises to automatically identify duplicate tables, ETL jobs, and reports. This minimizes data chaos, improves data integrity, and ultimately ensures the accuracy and trustworthiness of reporting.

All-In-One Platform

EIIG is an all-in-one platform that natively integrates key metadata management and data governance capabilities, encompassing data cataloging, data lineage, impact analysis, active metadata, and metadata analytics.

With EIIG’s dynamic data catalog, inclusive of comprehensive mainframe data coverage, users can effortlessly discover and access data from the mainframe and other sources for their reporting needs. Rules and policies are seamlessly mapped to technical data assets to implement security measures, such as data access control. Additionally, EIIG automates the identification of sensitive data, facilitating the tagging of all sensitive data, including PII, to ensure compliance with data privacy regulations.

Another distinct advantage of EIIG is its scalability. Mainframes are renowned for their capacity to process and store massive amounts of data. For instance, a global financial services firm with a substantial mainframe footprint utilizes EIIG to meticulously trace millions of data points as they traverse various systems, including the mainframe, Oracle, MS SQL, Greenplum, Java, IBM DataStage, Qlik, and Tableau.

Remarkably, EIIG accomplishes these feats with minimal human intervention. Its AI/ML-powered automation empowers enterprises to instill trust in reporting from the mainframe and other data sources while optimizing human resources and reducing costs.

About the Author: Niu Bai, Ph.D. is the Head of Global Business Development at Orion Governance, Inc. Connect with Niu on LinkedIn.

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