In today’s data-driven world, organizations that lack a well-defined data and analytics strategy risk falling behind their competition. But what does a strong strategy look like, and how can companies develop one that drives business value? This session will explore the critical components of a successful data and analytics strategy, why it is essential for modern enterprises, and how organizations can approach its development. We will break down key elements, including data governance, integration, analytics enablement, cloud strategy, and AI/ML adoption. Additionally, we’ll discuss common pitfalls, best practices, and actionable steps to align data initiatives with business goals. Whether you are starting from scratch or refining an existing strategy, this session will equip you with the tools to drive data-driven transformation in your organization. In this session, you will: Gain insights into real-world implementation approaches.  Learn how to measure the success of their strategy. Leave with a roadmap to build a data and analytics foundation that delivers tangible business outcomes.

Moving enterprise data to the cloud presents a transformative shift in the way data is managed, accessed, and leveraged. However, there are a number of challenges to understand and overcome. This session will move beyond the hype to explore the top five tangible opportunities and associated challenges that data professionals must navigate to ensure a successful cloud data strategy. We’ll dig into the opportunities, like how the cloud unlocks scalability and flexibility and enables real-time analytics, and then dive into the critical challenges that come with this evolution including security and compliance, cost, governance, and more.  Key takeaways: Expert advice on navigating the key challenges managing data in the Cloud. Insight into the other side of the coin - the unique opportunities that the Cloud presents. Answers to your toughest data in the Cloud questions.

Faced with the challenge of modernizing a fragmented data landscape, Caleres built a scalable, cloud-native architecture that unified SAP and non-SAP data sources for a future-ready data platform. In this session, we’ll share how we achieved seamless integration across SAP S/4HANA, legacy ECC, and non-SAP systems using SAP Datasphere and Microsoft Fabric. You’ll learn how SAP Datasphere’s ELT capabilities accelerated the creation of our business models, how native data lineage and impact analysis supported governance and transparency, and how maintaining Microsoft Power BI as the reporting layer enabled unified analytics without disrupting our existing workflows. Join us to discover how the strategic architectural choices we made, and the synergy between SAP Datasphere and Microsoft Fabric, delivers near real-time insights, ensures governance, and optimizes cloud spend while minimizing disruption. You’ll Learn: How we integrated SAP S/4HANA, ECC, and non-SAP data into Microsoft Fabric for unified analytics. Techniques for accelerating time to value using ELT modeling and real-time data availability. Ways to leverage SAP Datasphere’s advanced features to enhance data quality and usability. Strategies we deployed for ensuring reliability and cost efficiency in daily operations and cloud architecture.

Norfolk Southern knew from the start that clean, reliable data would be the cornerstone of a successful SAP S/4HANA transformation. This session shares how the organization made data cleansing a strategic priority, from implementing scalable data profiling, cleansing, and validation processes to establishing sustainable governance models to ensure lasting data integrity. Understand the business impact of clean data in SAP S/4HANA and get tips to resolve common data quality issues in the ECC environment pre-migration. Plus, discuss key metrics the company used to measure data quality improvements and support continuous monitoring. Key takeaways: An understanding of how to make data cleansing a strategic priority. Real-world insight into our data cleansing project and processes. Key metrics that you can use to monitor and measure your data quality.

The reality of managing data across disparate systems often results in a fragmented, siloed data landscape that hinders agility and decision-making. This session presents a detailed case study of Dodge Industrial’s comprehensive data integration journey, offering practical insights into how we transitioned from significant data disconnection to a harmonized, integrated environment with Datasphere at the center. We’ll discuss our scenario before the integration project, detailing pain points, data quality issues, and inefficiencies we faced. Then we’ll walk through the project and share with you how we arrived at our current state and lessons learned along the way. You’ll get: A transparent review of the critical choices we made along the way. Key takeaways from our experience connecting various non-SAP data sources (Snowflake, Salesforce, MS SQL Server, SharePoint, etc.). How we stitched it all together - the approach we used to create a unified data fabric. Actionable insight into enabling bi-directional data feeds. Insight into the specific integration platforms, ETL tools, and middleware that enabled our solution.

Metrie—the largest manufacturer and supplier of solid wood and composite molding in North America—has been transforming homes since 1926. In this candid case study session, we’ll walk through how the team modernized sales and procurement reporting by migrating a legacy SAP HANA solution to SAP Business Data Cloud, using SAP Datasphere and SAP Analytics Cloud (SAC). You’ll see the end-to-end journey: why Metrie chose to modernize, the target data architecture, how semantic models and data products were designed in Datasphere, and how business-ready dashboards in SAC now drive faster decisions across sales and sourcing.  Key takeaways: Lessons learned from our analytics modernization journey. Performance tips and the operating model that keeps IT and business aligned. A preview of what’s next: evaluating SAP Databricks to power forecasting and broader ML scenarios—all while preserving governance and trust.