Archive for category: News

Many organizations often overlook cybersecurity and data protection, focusing more on integrating the latest SAP technology rather than maintaining strong cyber hygiene. A recent SAPinsider report highlights the importance of empowering, equipping, and educating employees to ensure robust data protection. ERP Today spoke with Marty Menard, CIO of Pacific Coast Companies, to gain insights from his 20+ years of experience on the key cybersecurity challenges and solutions tech leaders are facing today. In an article written by Rizal Ahmed (CIO Leader, WIS) and Charles Whitmore (Cybersecurity Editor, ERP Today), they highlight Menards message of the importance of data protection in the context of cybersecurity, focusing on the CTO’s perspective. It stresses that data protection should be a primary concern for businesses, especially in light of growing cyber threats. Key points include:

1. Data protection is the top priority in cybersecurity, with 57% of experts emphasizing its critical role in securing valuable SAP data amidst rising cyber threats.
2. Human error is a major cybersecurity risk, as 96% of breaches start with phishing attacks targeting employees, highlighting the need for comprehensive security awareness and training.
3. Improving data security requires strong alignment between cybersecurity efforts and company leadership, utilizing external audits for impartial evaluations, and prioritizing ongoing employee education to prevent cyber incidents.

Overall, businesses must recognize that human-engineered cyber attacks, like phishing, remain a significant threat and that educating employees is key to enhancing data security.

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Thomas Gavaghan, Vice President at Kyriba, shares an article on ERP today that discusses the rush from “Big Data” to Artificial Intelligence (AI) and how companies might be overlooking the foundational work required to leverage these technologies effectively. He stresses the importance of strategic data management in finance and treasury for smarter decision-making, not just adopting AI for its own sake. The five essential dimensions of data management—Streaming, Searching, Data Lakes, Machine Learning, and Generative AI—are crucial for optimizing liquidity performance.

1. Streaming: Real-Time Data Flow. Real-time data streaming breaks down silos and enables quicker, smarter liquidity decisions. APIs help link systems and enhance flexibility, making it crucial for dynamic decision-making in volatile markets.
2. Searching: Efficient Data Discovery. As data centralization grows, the ability to search large datasets quickly is essential. Modern, cloud-native search tools enable finance teams to access and query data efficiently, driving proactive intelligence and better liquidity management.
3. Data Lakes: Centralized and Scalable Data. Data lakes centralize both structured and unstructured data, providing treasury teams with a single source of truth for analysis. This infrastructure supports better liquidity forecasting and decision-making, especially when integrated with APIs and business intelligence tools.
4. Machine Learning: Leveraging Patterns for Insights. Machine learning identifies patterns in data to improve areas like fraud detection and cash forecasting. The quality of ML insights depends on well-organized, accessible data, which is provided by strong data lakes.
5. Generative AI: Transforming Decision-Making. Generative AI automates decision-making by generating insights from complex data. It can predict and execute liquidity decisions, but its effectiveness relies on robust data foundations.

To fully realize AI’s potential, Gavaghan stresses that companies must focus on solid data management. Prioritizing technologies like streaming, searching, and data lakes enables AI-driven decision-making and helps finance teams unlock long-term strategic value from data.

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With millions of emails, texts, and searches occurring every minute, organizations must manage their data to avoid issues related to storage limits, software updates, and system re-platforming. Tech expert Martin Visser shares an article that discusses the critical importance of planning data migrations effectively, especially as the volume of data generated by businesses continues to rise significantly. To ensure successful data migration, the first step is to take inventory of existing systems and their dependencies, identifying all components that need to be supported during the migration. This process helps uncover overlooked instances or necessary updates before the migration begins. Additionally, evaluating the current performance of systems—tracking metrics such as throughput and latency—provides a baseline for comparison post-migration. It’s essential to define a clear migration target and communicate this to all stakeholders. The implications of a migration can vary widely; for instance, an infrastructure refresh may only result in temporary downtime, while a complete database overhaul may require significant retraining and adjustments in applications. Visser supplies a step-by-step approach to migrations rather than a “big bang” cutover, recommending full backups and the use of canary deployments to test migrations incrementally. This method allows for identifying potential issues before migrating all systems. Additionally, automated alerts can help manage performance tracking during the transition, facilitating timely rollbacks if necessary. Finally, a comprehensive communication plan is vital to keep all involved parties informed about the migration process, ensuring that both technical teams and business stakeholders understand the implications of changes. By considering data migration as part of a broader operational framework, organizations can enhance their chances of a successful transition.

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