I had the privilege of attending the Open Data Science Conference (ODSC) in London and it was an incredibly enriching experience. Immersed in a gathering of like-minded professionals and experts, ODSC provided a captivating platform to delve into the latest advancements in the field of data science.

From exploring the transformative power of cloud technologies to unlocking the creative potential of generative AI and the boundless world of open source tools and frameworks, this was an immersive experience. ODSC delivered on its promise to ignite inspiration and expand horizons in the ever-evolving landscape of data-driven innovation.

Let’s explore my top three takeaways from the conference.

  1. The relevance of on-premises deployments for organizations

One key learning was the continued importance of on-premises deployments for many organizations. Factors such as security, compliance and cost play a significant role in this preference. Certain industries have stringent regulatory guidelines, making it necessary for companies to keep their data and infrastructure on-premises to ensure greater control and security. Additionally, on-premise deployments offer more predictable costs, especially when a cloud optimization process is not in place. Organizations with substantial existing infrastructure investments may find it more cost-effective to continue utilizing their current resources rather than undergo a complete migration to the cloud. While the cloud offers undeniable benefits in terms of scalability and flexibility, on-premise deployments still hold relevance for organizations seeking enhanced control, security, compliance, cost predictability and specialized infrastructure requirements.

  1. The challenges of democratizing analytics and AI

The democratization of analytics and AI, which aims to empower employees at all levels with data-driven insights and tools, is something that organizations still approach with reticence. A significant challenge lies in the lack of trust in the data literacy of employees While providing access to analytics tools is crucial, it assumes that employees possess the necessary skills to interpret and leverage data effectively. However, many individuals may lack the required knowledge of statistics, data analysis, and data visualization techniques. More advanced users are skeptical about the ability of analysts at all levels could provide reliable data driven insights. To address this, organizations must invest in structured training programs as well as collaborate with advanced users to ensure that employees are equipped with the skills needed to navigate the analytics arena and derive value from their tools while earning trust at the same time.

  1. The significance of speed and scalability in the cloud

In the cloud, time is of the essence. Fast and scalable analytics are crucial for accelerating the time to value and supporting data-driven innovation while keeping operational costs in check. Organizations need to respond swiftly to changes in the market, customer demands and competitive pressures. Time-sensitive scenarios, such as predicting trends or identifying emerging opportunities require fast analytics to maximize their value. Moreover, as data continues to grow exponentially, inefficient tools and algorithms may struggle to handle the sheer magnitude of information. Scalable analytics, on the other hand, can efficiently process and analyze massive datasets, enabling organizations to uncover meaningful patterns, correlations and insights that can drive strategic decision-making. By harnessing the power of fast and scalable analytics, organizations can avoid bottlenecks, improve operational efficiency, and discover news avenues for growth.

Attending ODSC was truly a transformative experience for me. The conference shed light on the importance of on-premises deployments, the challenges of democratizing analytics and AI and the significant of speed and scalability in the cloud. These insights have provided me with a deeper understanding of the evolving landscape of data science. I left the event feeling more equipped with valuable knowledge to drive innovation in my own work.

Learn more about how SAS Viya can help organizations navigate the complexities of the cloud.


About Author

Spiros Potamitis

Senior Data Scientist and Global Product Marketing Manager, SAS Forecasting and Optimization

Spiros Potamitis is a Senior Data Scientist and a Global Product Marketing Manager of Forecasting and Optimization at SAS. He has extensive experience in the development and implementation of advanced analytics solutions across different industries and provides subject matter expertise in the areas of Forecasting, Machine Learning and AI. Prior of joining SAS, Spiros has worked and led advanced analytics teams in various sectors such as Credit Risk, Customer Insights and CRM.”

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