The Center for Data Innovation spoke with Eamonn O’Neill, co-founder and chief technology officer (CTO) of Lemongrass, a U.K.-based cloud and data consulting company that helps large enterprises move their SAP applications to the cloud. Many businesses use the suite of enterprise resource planning applications produced by SAP, a German software company, to manage their operations and customer relations. O’Neill discussed how challenges with managing the SAP landscape inspired him to co-found the company and how Lemongrass is planning to use generative AI to make the most of their data.
Martin Makaryan: What is the main mission of Lemongrass?
Eamonn O’Neill: Lemongrass, which started 15 years ago, helps SAP customers migrate to the cloud and manage their cloud platforms effectively. Our mission is to help enterprises leverage cloud capabilities to organize, manage, and make the best use of their data. Moving data to the cloud provides several key benefits. First, it increases scalability and storage flexibility since we can scale up or down based on customer needs. Second, it can make data use and analytics faster by, for example, offloading outdated data. These are a few of the benefits in general, and each client’s needs will vary, but our goal is to guide them through this process to ensure companies can make data-driven decisions and make the most of their data.
We partner with SAP, Amazon Web Services (AWS), Azure, Google Cloud, and others, and we operate globally in all five continents. We offer advisory services as a first step to help customers understand their options, as well as the benefits and the process of migrating to the cloud. The core of our mission is to help customers maximize value from their cloud investments.
Makaryan: What was the inspiration behind the company, and why the name “Lemongrass”?
O’Neill: I have been in this industry for a long time, and seeing how many SAP customers struggle with managing their SAP environments with enough agility pushed me to start Lemongrass. What I mean by that is that while the SAP software has a robust architecture, many customers are hesitant to make changes to optimize their data management. For example, a retail company may not want to update its SAP configurations because a minor error could potentially disrupt inventory management across hundreds of stores, resulting in millions in lost sales. Because SAP offers on-premises data storage and management, it can sometimes limit opportunities for optimization. And for many clients, moving to the cloud can provide tangible benefits. This is where I saw an opportunity—advising enterprises on this potential and helping them migrate their data to the cloud if doing so makes sense based on their business model, data flow, and needs.
In terms of the name, the story is actually quite amusing. My co-founder and I once went to a Thai restaurant. We were brainstorming name ideas for our future company, and at this time, color names were really popular, like “Red Squares.” At some point, one of us looked at the menu and we saw “lemongrass.” It was unique and easy to remember, and we immediately grabbed it. If not for that Thai restaurant, I think we would have chosen “Crackers Consulting,” which in my opinion is not as catchy as “Lemongrass.”
Makaryan: What are some of the challenges in large-scale SAP data migrations to the cloud?
O’Neill: Scale is a major challenge. Some customers have databases with over 100 terabytes of raw data, such as transfer orders or customer receipts, which requires a lot of effort to successfully migrate and manage. Transferring this data can also require significant downtime—up to a week for the largest systems. Moving such large amounts of data across to the cloud requires us to temporarily halt the entire system for a while, and if you are a large clothing brand, for example, that is inconvenient and sometimes even impossible depending on business needs. We also must ensure that the cloud can handle massive amounts of data that we are helping our clients migrate so generally, we go through several transitory stages to mitigate the negative business impact on enterprises.
Makaryan: What emerging trends in the cloud space or the SAP environments are likely to shape Lemongrass’ future trajectory?
O’Neill: Businesses in many sectors are interested in moving their data to the cloud. I would highlight two ongoing trends on the SAP side, which I think will be interesting to watch unfold—data integration and AI. Besides helping customers migrate their data to the cloud, we also consult clients who want to optimize their data management using their existing SAP environments. The core of our business is digital transformation to optimize business operations for a variety of different enterprises—whether by migrating their on-premises SAP data to the cloud or analyzing their current data management with SAP to make better use of their data. What SAP does directly affects our services and our business decisions to help our clients. In this context, SAP’s recent strategy includes launching new products for data analysis and AI that understand its data model and can extend to non-SAP data as well. Given that a large portion of global business transactions occur through SAP systems, consolidating this data into one model could unlock tremendous value.
Makaryan: How do you harness AI in your business operations?
O’Neill: As a technology company, we could not afford to overlook the growing importance of and interest in AI, especially large language models (LLMs). From a purely business perspective, we needed to have an answer when our customers asked us how we are planning to integrate generative AI into our services and products. Initially, this started as a research project for us to understand what would be the most optimal way for us to use AI: even though AI is generally a powerful technology, its benefits vary for companies based on their sector and business models. Since then, we have realized that using generative AI was existential for us, which meant that we must build it into what we do.
We created a structured source of metadata, which included data on things like how much space we have on backup drives or how much memory a certain SAP system may be using. These are questions that managers routinely ask their subordinates, and creating this structured dataset and using it to build a simple LLM that allows staff to save time and resources in daily tasks will be an important step forward for us. Customers ask these questions about their data systems as well. So, our goal in the near future is to roll out this AI-powered tool to our customers once we have trained the model enough to satisfy customer needs.
