 Hi, this is Yoho Sabli Bhartiya and welcome to the 2023 predictions series. And today we have with us once again, Lee Kang, VP of strategy at seller data. Lee, it's great to have you back on the show. Thank you for having me as well. Yeah, of course I'm going to ask you to grab your crystal ball and share some of the predictions that you have for us. But before we go there, tell us quickly about the company. What is seller data all about? Yeah, seller data provides a unified analytics platform that delivers timely insight to all the stakeholders inside and outside of enterprises. Seller data product is built on the open source database called Starrocks. And the seller data provides a performance of three to five times faster than other solutions on the market. And it reduces operating costs by up to 80% based on our customers' findings. Over 300 enterprise customers have chosen the seller data as their analytics platform. And hundreds of developers worldwide are actively working on the Starrocks project, which is led by seller data. Thanks for talking about the company. Now talk a bit about to go grab your crystal ball and tell us what predictions you have for us. My first prediction is in 2023, enterprise will prioritize unified analytics platform. We have seen a lot of momentum about real-time analytics in the past couple of years. And obviously it has a lot to offer to today's enterprises. But building an RTA real-time analytics environment has not been cheap. And a lot of companies today, they have to build and maintain two separate analytics platforms. One is for your traditional batch-based analytics and another platform and the pipeline for your real-time analytics platform. So in 2023, companies will pay more attention to cost saving, improve efficiency. And so with this trend, we'll see more and more enterprises will prioritize the adoption of a unified analytics platform. My second prediction is cloud costs will be under more scrutiny. The economy headwinds in the coming year will definitely affect our more and more businesses. And business leaders will put their cloud cost consumption models and ROI under the microscope as they look for ways to control spending. So using and the provisioning of cloud services and the resources, as well as existing SaaS subscriptions, will be examined for potential cost reduction and maybe even cancellations. Since people will be asking questions like, do we really need the service? So we should expect more creativity and even flexibility from cloud vendors when it comes to product development, service offerings, and even pricing models in response to this trend. And this could be a great opportunity for businesses to negotiate for better terms or even time to look at for new vendors. My third prediction is real-time analytics will continue to gain momentum. I touched up on this topic in my first prediction. We've seen real-time analytics becoming more popular and widely discussed in more and more enterprises. In 2023, it will be a year of real-time analytics. More businesses will be chasing a few dollars in the market and will be looking for every advantage they can get to stay ahead of their competitors. So this means doing more with less and making most efficient and timely decisions. So RTA real-time analytics offers enterprises this fastest path to generating new insights that allow them to act on opportunities before the competition. So resources like data warehouses, data lakes, analytics services in the cloud becoming more and more almost commoditized. So new technologies are helping analytics becoming more democratized. And RTA will really give these companies a new competitive advantage here. My next prediction is data products will become strategic assets. We will see the end of organizations treating their data products as disposable, one-time use goods. Designing for reusability is not only wasteful, but also with today is a smart data management of processes and technologies. It can help businesses extract more value from their data. Enterprise will begin to regard their data products as tangible, valuable assets. So in 2023, the market will reward those companies that have a better preparation, better strategy for their enterprise products. My next prediction, fifth one, is the beginning of the end of data engineering as we know it today. That's a little bit mouthful. What that means is today's data engineers spend a lot of time maintaining legacy data pipelines. Or they build complex data pipelines, piecemeal different products together, because the underlying platform has limited functionality. So a lot of the data engineering works, although it's challenging. It's quite exciting for a lot of data engineers. From the business standpoint, a lot of these efforts are wasteful, inefficient allocation of talent and the resources. So in 2023, we can expect with the latest development in cloud architecture, in artificial intelligence, all these technology advancements will fundamentally change how data is prepared and results in a more efficient data engineering. So these developments will bring about new products, new architectures, and new methodologies that improve the data profiling, data acquisition, and data movement. So all these activities will be powered by AI and will greatly reduce the complexity and the cost of today's data engineering processes. My sixth prediction is web three and analytics or joint forces, right? Although no one knows what the third generation of internet will look like, right? One thing for sure is it won't be what we think it will be. So some of the use cases being taught today will likely never happen, while some others that unexpected use cases will emerge and play a critical role in the coming years. The sheer amount of data generated and used by web three infrastructure will push the envelope of today's capabilities, demanding the adoption of real-time analytics and the more AI-driven analytics to keep this progress moving. We also see web three being used to enable RTA applications, right? So this will create exciting opportunities for innovations in industries like financial services, entertainment and media, and logistics and supply chain management. So my final prediction is analytics at the edge will go mainstream. It's not a new concept having analytics at the edge, but in 2023, we'll see more happening at the edge than ever before. Today's terminal devices are almost as powerful as a small low-end server, right? And increasing availability of 5G networks has enabled the transmission of more data at higher speeds. So as more data is pushed and generated to the edge, it will enable real-time decision-making in the field, shorten the response time, and reduce the compute storage and network costs of cloud infrastructure. So for these reasons, and along with the lower technical barriers, we're sure to see a greater adoption of analytics at the edge in 2023. Excellent. Thanks for sharing these predictions. Now, tell us a bit about what is going to be the focus of seller data in 2023? First and foremost, we'll continue to improve the core capability of the product, right? A lot of great features are being planned and are being actively developed at seller data. The main focus is to make the product more cloud friendly, like we just talked about the cloud trends, right? Things like better manage the cloud resources, leveraging the latest development in the hardware resources to improve our compute efficiency and to further reduce cost, cloud resource cost for our customers. That improving our cloud offering is our main focus in 2023. Can you also talk about what are the challenges that you see will be there for the industry in 2023? And how seller data is going to help customers, users kind of navigate through some of these challenges? I see. What we're seeing is in the last couple of years, right? More and more companies have spent a lot of time trying to build, whether you call it to modern data stack or new architecture, right? People have been trying to figure out how to get data from different sources in their cloud environment. And for some companies, it's a mix of cloud and on-premise environments, right? And make data available in the data platform, right? Whether it's a lake house architecture or some company still choose a data warehouse architecture, you know, data landed in that repository. Then customers start realizing you can even call it a last mile of data servicing, right? How do I make this data available to my end consumers, data consumers, right? This can be analysts, can be frontline workers, can be even your external partners, right? This last mile of data service or analytics, you know, people naturally thought, well, if I land my data in my data lake or a lake house or data warehouse, then I'm good. But the last mile of services has not been as easy as people thought, right? Query can be slow or it's not a scalable, right? When you have, once you are trying to make this insights available to your external partners or your frontline workers, suddenly you're facing a concurrency issue, right? The data is not fresh, right? You're getting all kinds of real-time feeds, but you have to batch them together and then analyze them, right? So this last mile of data delivery to, in terms of insights and analytics has not been as easy as people thought. So how do we solve this problem? You know, we need to look at the data platform and figure out how can I, you know, once I collected all the data, transform them, right, build a data model, and how do I deliver, right? Make that information available at any moment and sooner to the, when the event happens, right? When the business transaction happens. So I think that's the struggle we're seeing and that's the issues we're trying to resolve for our customers. Lee, thank you so much for taking time out today, share this prediction, focus on the company, the challenges that I had there, and also share your insights there. And I would love to have you back on the show once again. Thank you. Of course. Thank you, Swap.