This episode of Techsplainers explores Enterprise Performance Management (EPM), the business practices and tools organizations use to plan, forecast, and improve overall performance. We examine how EPM differs from ERP systems, trace its evolution from manual spreadsheets to AI-powered platforms, and break down key features like unified dashboards, predictive analytics, and scenario modeling. The episode also covers the tangible benefits of EPM implementation—from cost insights to streamlined account reconciliation—along with best practices for successful deployment. Finally, we look at the AI-driven future of EPM through IBM's own transformation story, which achieved 95% fewer tools and 40% productivity gains through an integrated approach powered by IBM Cognos Analytics and watsonx.&nbsp; Find more information at <a href="https://www.ibm.com/think/topics/enterprise-performance-management" rel="noopener noreferrer" target="_blank">https://www.ibm.com/think/topics/enterprise-performance-management</a>Find more episodes at <a href="https://www.ibm.biz/techsplainers-podcast" rel="noopener noreferrer" target="_blank">https://www.ibm.biz/techsplainers-podcast</a> Narrated by Teaganne Finn

This episode of Techsplainers explores Enterprise Performance Management (EPM) and how it helps finance teams transform from number-crunchers into strategic advisors through AI-powered planning and analytics. 

What is enterprise performance management (EPM)?

This episode of Techsplainers explores scenario planning, a strategic approach where organizations create multiple potential future scenarios to prepare for uncertainties. Building on previous episodes about enterprise performance management, extended planning and analysis, and AI in financial planning, we examine how scenario planning helps businesses proactively manage risk through a five-step process: defining objectives, analyzing outcomes, recognizing influential factors, evaluating scenarios, and developing indicators. The episode highlights Shell's pioneering use of scenario planning, explains different methodologies (quantitative, normative, exploratory, operational, and strategic), and showcases applications across finance, supply chain, sustainability, and sales. We conclude with best practices including team building, data management, simplification, and leveraging advanced technology to gain competitive advantage in an uncertain business environment.&nbsp; Find more information at <a href="https://www.ibm.com/think/topics/scenario-planning" rel="noopener noreferrer" target="_blank">https://www.ibm.com/think/topics/scenario-planning</a>Find more episodes at <a href="https://www.ibm.biz/techsplainers-podcast" rel="noopener noreferrer" target="_blank">https://www.ibm.biz/techsplainers-podcast</a> Narrated by Teaganne Finn

This episode of Techsplainers explores scenario planning—how organizations create multiple possible futures to manage uncertainty, mitigate risks, and make better strategic decisions across finance, supply chain, and beyond. 

What is scenario planning?

This episode of Techsplainers explores how artificial intelligence is transforming Financial Planning and Analysis (FP&amp;A), building on our previous discussions of Enterprise Performance Management and Extended Planning. We examine how AI-powered tools—particularly generative AI applications—are automating routine tasks while providing deeper financial insights through machine learning, natural language processing, and predictive analytics. The episode details four key applications: predictive forecasting that adapts to market changes, multi-variable scenario planning for risk management, anomaly detection for financial integrity, and productivity enhancements like automated report generation. We also discuss how AI is reshaping finance roles, requiring new skill sets, and supporting sustainable practices. The conversation concludes with five implementation steps and a vision of how AI enables the integrated business planning approach needed for finance teams to evolve from reactive reporters to proactive strategic partners.&nbsp; Find more information at <a href="https://www.ibm.com/think/topics/ai-in-financial-planning-and-analysis" rel="noopener noreferrer" target="_blank">https://www.ibm.com/think/topics/ai-in-financial-planning-and-analysis</a>Find more episodes at <a href="https://www.ibm.biz/techsplainers-podcast" rel="noopener noreferrer" target="_blank">https://www.ibm.biz/techsplainers-podcast</a> Narrated by Teaganne Finn

This episode of Techsplainers explores AI in Financial Planning &amp; Analysis—how machine learning and generative AI are transforming finance from number-crunchers into strategic advisors. 

AI in financial planning & analysis

This episode of Techsplainers explores Extended Planning and Analysis (xP&amp;A), building on our previous discussion of Enterprise Performance Management. We examine how xP&amp;A expands traditional financial planning across the entire organization, breaking down data silos by connecting financial and operational planning. The episode covers six key features of effective xP&amp;A solutions—from automation and AI-driven insights to scenario planning and scalability—and outlines a seven-step implementation process. We also highlight the benefits of this approach, including centralized data, enhanced forecasting, better collaboration, and increased visibility. The discussion includes a real-world success story from Landmark Retail, which achieved a 75% reduction in budgeting time, before concluding with five emerging trends shaping the future of xP&amp;A, from AI advancements to increased focus on sustainability and compliance.&nbsp; Find more information at <a href="https://www.ibm.com/think/topics/extended-planning-analysis" rel="noopener noreferrer" target="_blank">https://www.ibm.com/think/topics/extended-planning-analysis</a>Find more episodes at <a href="https://www.ibm.biz/techsplainers-podcast" rel="noopener noreferrer" target="_blank">https://www.ibm.biz/techsplainers-podcast</a> Narrated by Teaganne Finn

This episode of Techsplainers explores Extended Planning &amp; Analysis (xP&amp;A)—how organizations can evolve beyond traditional financial planning to create a unified, AI-powered approach across all departments. 

What is extended planning analysis (xP&A)

This episode of Techsplainers explores modern ETL (Extract, Transform, Load), examining how traditional data integration has evolved into a more agile, cloud-native approach. Building on our previous discussion of ETL versus ELT, we detail six key characteristics that define modern ETL: cloud-based architecture, real-time ingestion capabilities, unified data sources, automated orchestration, scalability, and AI-ready pipelines. The podcast covers popular platforms powering these solutions including Snowflake, BigQuery, and Apache Kafka, along with implementation strategies and emerging trends like low-code tools and serverless integration. Through a retail flash sale example, we demonstrate how modern ETL enables businesses to process data in real-time, transforming potential operational challenges into competitive advantages by ensuring information flows instantly to where it's needed most.&nbsp; Find more information at <a href="https://www.ibm.com/think/insights/modern-etl" rel="noopener noreferrer" target="_blank">https://www.ibm.com/think/insights/modern-etl</a>Find more episodes at <a href="https://www.ibm.biz/techsplainers-podcast" rel="noopener noreferrer" target="_blank">https://www.ibm.biz/techsplainers-podcast</a> Narrated by Matt Finio&nbsp;

This episode of Techsplainers explores how modern ETL has evolved into an agile, cloud-native approach that processes data in real-time to support today's fast-paced business needs.

Modern ETL: The brainstem of enterprise AI

This episode of Techsplainers explores Change Data Capture (CDC), a powerful technique for identifying and synchronizing database changes across multiple systems in real-time. Host Matt Finio explains how CDC works by monitoring databases and transferring only the modified data—insertions, updates, or deletions—to target systems like data warehouses, data lakes, or streaming platforms. The discussion covers three primary CDC methods: log-based (monitoring transaction logs), timestamp-based (using modification timestamps), and trigger-based (executing stored procedures when changes occur). The episode highlights CDC's significant benefits, including enabling real-time analytics, facilitating cloud migrations, optimizing ETL processes, and improving AI performance through continuously updated data. Practical applications span various industries—from detecting fraudulent financial transactions and processing IoT device data to managing inventory and ensuring regulatory compliance.&nbsp;&nbsp; Find more information at <a href="https://www.ibm.com/think/topics/change-data-capture" rel="noopener noreferrer" target="_blank">https://www.ibm.com/think/topics/change-data-capture</a>Find more episodes at <a href="https://www.ibm.biz/techsplainers-podcast" rel="noopener noreferrer" target="_blank">https://www.ibm.biz/techsplainers-podcast</a> Narrated by Matt Finio&nbsp;

This episode of Techsplainers explores Change Data Capture (CDC)—the technique that keeps systems synchronized by tracking and transferring only data changes rather than entire databases.

What is change data capture?

This episode of Techsplainers explores real-time data integration—the process of capturing and processing data from multiple sources the instant it becomes available. Host Matt Finio explains why millisecond-level data access has become critical in today's business landscape, where the global datasphere will reach 394 zettabytes by 2028 and generative AI demands fresh, high-quality information. The episode distinguishes between streaming data (continuous flows from IoT devices or financial markets) and event data (specific changes like transactions or threshold triggers), while examining four key integration methods: Stream Data Integration (SDI), Change Data Capture (CDC), Application Integration, and Data Virtualization. Listeners will discover practical applications in operational intelligence, customer hyper-personalization, fraud detection, and artificial intelligence—all requiring the immediate insights that only real-time integration can provide.&nbsp; Find more information at <a href="https://www.ibm.com/think/topics/real-time-data-integration" rel="noopener noreferrer" target="_blank">https://www.ibm.com/think/topics/real-time-data-integration</a>Find more episodes at <a href="https://www.ibm.biz/techsplainers-podcast" rel="noopener noreferrer" target="_blank">https://www.ibm.biz/techsplainers-podcast</a> Narrated by Matt Finio&nbsp;

This episode of Techsplainers explores real-time data integration—how organizations capture and process data in milliseconds to power instant decision-making and AI applications.

What is real-time data integration?

Introducing the Techsplainers by IBM podcast, your new podcast for quick, powerful takes on today’s most important AI and tech topics. Each episode brings you bite-sized learning designed to fit your day, whether you’re driving, exercising, or just curious for something new. This is just the beginning. Tune in every weekday at 6 AM ET for fresh insights, new voices, and smarter learning.

What is enterprise performance management (EPM)?

DESCRIPTION

Recent Episodes

What is enterprise performance management (EPM)?

DESCRIPTION

Related Resources

Recent Episodes

What is scenario planning?

AI in financial planning & analysis

What is extended planning analysis (xP&A)

Modern ETL: The brainstem of enterprise AI

What is change data capture?

What is real-time data integration?