Mainframe data, often referred to as the lifeblood of many organizations, holds a vast amount of critical information. From financial transactions to customer records, mainframe systems have been reliably processing and storing valuable data for decades. However, in today’s data-driven world, simply having access to this treasure trove of information is not enough. Organizations need to make the most of their mainframe data to stay competitive and drive innovation.

In this article, we will explore the challenges of mainframe data utilization, discuss strategies to harness its power, highlight best practices for optimization, and delve into real-world examples of successful implementation. Let’s dive in and discover the untapped potential of mainframe data.

Table of Contents

• Challenges in Managing Mainframe Data
  • Perplexity and burstiness of mainframe data
  • Limited access
• Leveraging Mainframe Data for Business Insights
  • Integration with modern technologies
  • Legacy means software that works
  • Mainframe data is big data
• Overcoming common misconceptions about mainframe data
  • Perception of the mainframe as outdated
  • Cost-effectiveness and ROI considerations
• Future trends and advancements in mainframe data utilisation
  • Cloud integration and hybrid architectures
  • Automation and AI-driven insights
• Conclusion
• FAQ

Introduction

Mainframe data refers to the data stored and processed on mainframe computers. These powerful machines have been the workhorses of large enterprises for years, handling mission-critical tasks and storing vast amounts of valuable information. Despite their importance, mainframe data often remains untapped, leaving organizations unaware of the hidden potential and business value it holds.

Challenges in Managing Mainframe Data

One of the primary challenges in utilizing mainframe data is the reliance on legacy systems and outdated technology. Mainframes have a long lifespan, and many organizations continue to use them due to their reliability and stability. However, this also means that the technology and tools used to manage mainframe data may be outdated and not compatible with modern analytics platforms.

Perplexity and burstiness of mainframe data

Mainframe data is known for its perplexity, which refers to the complexity and intricacy of the data structures and formats. Due to legacy systems and outdated data models, extracting valuable insights from mainframe data can be a daunting task. Moreover, mainframe data often exhibits burstiness, where sudden spikes in data volume occur during peak usage periods. This requires organizations to handle the surge in data processing and storage requirements efficiently.

Limited access

Another challenge is the limited access and integration capabilities of mainframe data. Mainframes are often isolated from other systems, making it difficult to extract and integrate the data with modern applications and analytics tools. This lack of integration restricts the ability to combine mainframe data with other sources, hindering comprehensive analysis and insights.

The understanding of how to optimally move vast amounts of legacy data – is brought to Precisely’s line of data integration tools – particularly for moving both logs and data from the IBM mainframe and the IBMi series to advanced analytics platforms like Hadoop, Databricks, Snowflake, Splunk, ServiceNow, and more.

These data integration and change data capture solutions are complemented by the data quality stack, meaning that we don’t just move data efficiently, we ensure its quality as well.

Leveraging Mainframe Data for Business Insights

Unlocking the potential of mainframe data is crucial for gaining valuable business insights. Organisations can leverage this data to understand customer behaviour, identify market trends, and make informed strategic decisions. By combining mainframe data with other sources, businesses can gain a holistic view of their operations and uncover valuable patterns and correlations.

Integration with modern technologies

Integration with modern analytics tools is essential for the effective utilization of mainframe data. By connecting mainframe systems with advanced analytics platforms, organizations can extract, transform, and analyze data more efficiently. This integration allows for real-time monitoring, predictive modelling, and data-driven decision-making.

Legacy means software that works

When most people think of legacy software, we think of software that is outdated and due for replacement. Yet, an alternative definition of legacy, particularly when it comes to mainframe applications is, simply, software that works. This is a definition that our partner, Precisely, is proud of. Precisely Connect has been helping customers to reduce the cost of running their mainframe for decades.

Mainframe data is big data

Data on the mainframe is characterized by all three V’s of big data.

1. Volume: A typical mainframe will generate terabytes of log and application data daily
2. Velocity: A typical mainframe will process millions of transactions a day, with peak loads that may run into many thousands of records a second.
3. Variety: Mainframe data, and mainframe log file,  are complex data. In many cases, records are self describing and may be of variable length and structure.

Overcoming common misconceptions about mainframe data

Despite its proven value, mainframe data is often perceived as outdated or cumbersome to work with. However, several misconceptions need to be addressed:

Perception of the mainframe as outdated

While mainframe systems have been around for decades, they continue to evolve and adapt to modern technology trends. Mainframe platforms now support cloud integration, APIs, and microservices, enabling seamless connectivity with other systems. By embracing these advancements, organizations can bridge the gap between legacy mainframe systems and modern technologies.

Cost-effectiveness and ROI considerations

Contrary to popular belief, optimizing mainframe data utilization can result in significant cost savings. By streamlining processes, identifying inefficiencies, and making data-driven decisions, organizations can achieve higher returns on their mainframe investments. Furthermore, the actionable insights gained from mainframe data can lead to improved business outcomes and revenue generation.

Future trends and advancements in mainframe data utilisation

The future of mainframe data utilization holds immense potential for organizations seeking to innovate and remain competitive:

Cloud integration and hybrid architectures

As organizations increasingly adopt cloud technologies, the integration of mainframe data with cloud platforms becomes crucial. Hybrid architectures that combine on-premises mainframe systems with cloud-based services offer scalability, flexibility, and cost-effectiveness. This integration enables seamless data sharing, real-time analytics, and improved accessibility to mainframe data.

Automation and AI-driven insights

Automation and artificial intelligence (AI) will play a pivotal role in extracting actionable insights from mainframe data. AI-powered algorithms can automate data processing, anomaly detection, and predictive modelling, allowing organizations to derive valuable insights at scale. This automation frees up resources and enables data professionals to focus on strategic initiatives.

Conclusion

In conclusion, organizations can unlock a wealth of opportunities by making the most of their mainframe data. By understanding the challenges, harnessing the power of mainframe data through analytics and integration, and following best practices for optimization, organizations can drive innovation, improve operational efficiency, and gain a competitive edge.

Through our partnership with Precisely we provide both the deep mainframe subject matter expertise and the technology to move both applications and log data from the mainframe into advanced analytics platforms like Databricks, Snowflake, Splunk, ServiceNow, and more.

Despite common misconceptions, mainframe data remains a valuable asset that, when properly utilized, can fuel growth and success.

FAQ

How can organizations overcome the complexities of mainframe data?

By implementing modern data management strategies, such as advanced analytics, integration with modern technologies, and robust security measures.

What are the benefits of harnessing mainframe data?

Organizations can achieve improved operational efficiency, enhanced customer experience, cost savings, and increased revenue through valuable insights gained from mainframe data.

Is mainframe data considered outdated?

No, mainframe systems continue to evolve and adapt to modern technology trends, supporting cloud integration, APIs, and microservices.

How can organizations optimize the utilization of mainframe data cost-effectively?

By streamlining processes, identifying inefficiencies, and making data-driven decisions, organizations can achieve higher returns on their mainframe investments.

What are the future trends in mainframe data utilization?

Cloud integration and hybrid architectures, along with automation and AI-driven insights, will shape the future of mainframe data utilization.