How do you build a data fabric strategy without mainframe data, the company’s most valuable data? It’s like throwing a dinner party without the entree.
Data management consistently ranks among the top challenges facing enterprise organizations. This reality should come as no surprise, as IDC estimates that data volumes will grow exponentially at an average annual rate of 23% through 2025.1
The explosion of data, coupled with the growing need to generate insights to gain a competitive edge, presents enormous opportunities for organizations with the right technology stack and architecture. Add the fact that data is becoming more diverse, complex, and distributed across different mediums, and it becomes clear that traditional data management techniques are inadequate.
Against this backdrop, the Data Fabric Architecture promises to manage, secure, and organize data through a metadata layer that enables self-service capabilities for end users across various domains and organizational boundaries. increase.Benefits of Adopting a Data Fabric Architecture data management Nothing new — they include improved visibility and insight, access and control, and security. The “data fabric” itself is a new term for what companies have been doing for a long time. That focus is gaining momentum as computing power and storage become cheaper and data growth continues to surge unabated.
A successful data fabric strategy requires a fundamental change in how data is organized and presented. According to Gartner, a holistic architecture with the goal of achieving “frictionless access and sharing of data in a distributed network environment” is required.2 Under this unified data management framework, existing data sources residing in traditional data warehouses, data lakes, cloud data stores, or mainframes can be maintained as is, eliminating the need to have multiple copies of the same data. reduce the risks and unnecessary costs associated with .
More than just a data source
No data fabric strategy is complete without incorporating the unique characteristics of the mainframe as a system of record for the valuable data generated from transactions. For many companies main frame is the foundation of our business, supporting 72% of the world’s mission-critical transaction processing with unparalleled speed and scale. It’s a treasure trove of actionable data insights.
Although it is possible to incorporate mainframe data as another data source, the most compelling data fabric architectures leverage the tightly coupled nature of mainframe data and the native applications that generate it. Most data fabric architectures lean towards propagating raw mainframe data out of the platform and combining it with other sources for information generation and analysis. But we are left with decades of application knowledge and business context that help define the meaning of the data. Context about core applications helps discover and define data, a fundamental characteristic of high-performance data fabric architectures.
For example, mainframes handle much of the transaction processing for large banks and insurance institutions, generating valuable data about claims, purchases, loans, and more. A large insurance customer with a mission to provide best-in-class member service uses Data Fabric to improve response times during periods of expected severe weather. Billing is handled by a mainframe system, so the customer needed immediate access to this data to combine with weather forecasts for the area of interest. They now have better insight into the potential risks associated with a significant increase in claims and are able to proactively respond to members.
Despite the fundamental role data plays in any data fabric strategy, applications built on data provide insights and interact with consumers. The architecture must take into account the unique integration between data and applications on mainframe systems. Without that in mind, understanding where the application resides and how to expose data with applied application logic becomes a non-scalable challenge.
To democratize data for secure access across the business (the hallmark of any data fabric’s success), it’s essential to first understand the underlying meaning of mainframe data within the context of an application. Applications and data evolve together, and data is “interpreted” by applications in non-standard ways. Fortunately, advances in technology have found ways to enable access to this valuable information.input Application programming interface (API).
API and mainframe access
You can’t mention data and mainframes in the same sentence these days without talking about APIs. The API is Unlock real-time access To mainframe data as part of the data fabric architecture.
Some APIs allow a context-agnostic form of data access, allowing you to view, understand what it is, and use it, but there are pitfalls. Some use cases present all that data without context, undermining its usefulness and value.
Other APIs let you access mainframe data through well-defined business logic. For example, a CICS transaction carries both application logic and data.
The best-case scenario is to combine APIs in a data fabric. In this case, accessing data includes metadata describing how the API works. This allows you to identify and describe the services (including mainframes) provided by a particular data source. Data fabric architecture uses metadata to categorize and make sense of data collected from myriad sources. For z/OS-generated data, metadata is not available until the application accesses the data because it is so tightly coupled to the application logic.
APIs also play a key role in enterprise modernization. It provides access to mainframe application logic and data that developers can use in conjunction with their preferred integrated development environment (IDE). Developers can use her IDE of choice to build new applications or enhance existing applications and focus on innovation with APIs that integrate deployment pipelines. This modernization enables organizations to leverage existing business data to build new her web, mobile, or custom applications to transform the user experience for internal and external customers.
from safety By opening access to your data the right way, you can minimize your risk. It’s true that the further your data is from its original source, the less control you have over it. However, many APIs provide a way to access mainframe data without compromising security. These APIs allow customers to selectively pull only the data they need for specific applications when they need it. Data is held in mainframe security-rich systems of record, greatly reducing the risk of potential compromise. For additional security measures, APIs can have an additional layer of protection through user access validation, restricting access to only the data required for a particular application.
The future of data fabric architecture
Data fabric deployments are expected to quadruple by 2024, increasing data utilization efficiency and reducing human data management tasks by 50%.3
Organizations in this competitive market are looking to gain a competitive advantage through data-driven decision-making. Data stored in systems of record is arguably the most important component for informing these business and operational decisions. For example, by making additional data assets available through the Data Fabric, supply chain leaders can more quickly and fully understand the relationship between supplier delays and production challenges. This information gives the organization time to formulate an appropriate response. Essentially, a data fabric strategy helps power decision-making with the right information, giving supply chain leaders time to source new suppliers and customers.Four
Another powerful tool is incorporating machine learning (ML) algorithms into the data fabric architecture. These algorithms can quickly comb through operational data and provide recommendations for action, or even take action automatically. For example, automating manual tasks for remediation frees employees up significant time to focus on other projects that provide more value to the business. Additionally, ML can be used to enforce rules or standards for information shared across the data fabric. This benefit is advantageous in compliance situations where governance rules apply to information across the data fabric, regardless of location.
Advantages of mainframes
Even if data is not ruled by you, in a data-driven competitive world, focusing your data fabric strategy on mainframe data and platform quality of service can yield high rewards.
As data volumes continue to grow and the need for high-quality data increases, how companies manage their data directly impacts customer experience and ultimately improves business outcomes.
so work together broadcom See how business insights turn into a growing feast of value by making mainframe data the main course of your data fabric strategy.
- IDC. Data creation and replication will grow faster than installed storage capacity, according to IDC Global DataSphere and StorageSphere Forecasts. https://www.businesswire.com/news/home/20210324005175/en/Data-Creation-and-Replication-Will-Grow-at-a-Faster-Rate-Than-Installed-Storage-Capacity-According-to- the-IDC-Global-DataSphere-and-StorageSphere-Forecasts
- Gartner. Gartner identifies the top 10 trends in data and analytics technology for 2019. accessed https://www.gartner.com/en/newsroom/press-releases/2019-02-18-gartner-identifies-top-10-data-and-analytics-technolo
- Gartner. Looking to become a data-driven organization? Start with five key D&A initiatives. https://www.gartner.com/en/information-technology/insights/data-and-analytics-essential-guides
- Gartner. A data fabric architecture is the key to modernizing data management and integration. https://www.gartner.com/smarterwithgartner/data-fabric-architecture-is-key-to-modernizing-data-management-and-integration
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