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The year 2023 was all about adopting generative AI and foundation models. However, as organizations raced to bring gen AI front and center in their workflows, they realized how important it was to get their data affairs in order.
While companies always understood the role of high-quality data in business success, the rise of gen AI reinforced its value, making sure it was the point of focus for everyone. Now, as we head into 2024, which is set to bring even bigger gen AI stories, leading industry experts and vendors share their predictions on how they expect to see different sides of the data ecosystem evolve in the coming months.
1. Relational will break free of SQL
“Whether harnessing modern edge, IoT intelligent applications, or generative AI to grow the business, there is no shortage of bold plans for enterprises in 2024. All of these plans rely on secure access to enterprise data.
Database infrastructure will evolve to support this new generation of applications. For operational applications, while SQL database usage will remain popular, the dynamic nature of modern applications will encourage developers to look for alternatives. Document-relational databases that conform to modern developer CICD workflow and support strictly serialized transactions with relational joins across collections will emerge as a superior solution for many projects.
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On the analytic front, it will become clear that large language models require detailed context in order to operate with high accuracy. Retrieval Augmented Generation (RAG) built on vector databases will go mainstream in 2024. Further, business concepts and complex documents will be structured as knowledge graphs to provide the context required for AI solutions. In 2024, the relational knowledge graph will arrive as a new database architecture to support this.”
– Bob Muglia, executive chairman of Fauna and former CEO of Snowflake
2. Vector databases will become the most sought-after technology
“In 2024, vector databases will become the most sought-after technology to acquire. In an era where data-driven insights fuel innovation, vector databases have swiftly gained prominence due to their prowess in handling high-dimensional data and facilitating complex similarity searches. Whether for recommendation systems, image recognition, natural language processing, financial forecasting, or other AI-driven ventures, understanding the top vector databases will be critical for software development across industries.”
“As new applications get built from the ground up with AI …, vector databases will play an increasingly important role in the tech stack, just as application databases have in the past. Teams will need scalable, easy-to-use and operationally simple vector data storage as they seek to create AI-enabled products with new LLM-powered capabilities.”
– Ratnesh Singh Parihar, principal architect at Talentica Software, and Avthar Sewrathan, GM for AI and vector at Timescale
3. Fishing for LLM gold in enterprise data lakes
“There’s no shortage of statistics on how much information the average enterprise stores — it can be anywhere in the high hundreds of petabytes for large corporations. Yet many companies report that they’re mining less than half that information (largely structured data) for actionable insights. In 2024, businesses will begin using generative AI to make use of that untamed data by putting it to work building and customizing LLMs. With AI-powered supercomputing, businesses will begin mining their unstructured data — including chats, videos and code — to expand their generative AI development into training multimodal models. This leap beyond the ability to mine tables and other structured data will let companies deliver more specific answers to questions and find new opportunities. That includes helping detect anomalies on health scans, uncovering emerging trends in retail and making business operations safer.”
– Charlie Boyle, vice president of DGX Systems, Nvidia
4. Companies without sophisticated enough automation to power AI will feel the burn
“As businesses implement AI to maintain their competitive edge, many will feel the effects of their disorganized data infrastructure more acutely. The effects of bad data (or not enough data) will be compounded when the stakes are raised from simply serving up bad information on a dashboard to potentially automating the wrong decisions and behaviors based on that data. It’s only a matter of time before someone without strong data infrastructure and governance puts generative AI in a mission-critical context and suffers from a loss in accuracy.”
– Sean Knapp, CEO of Ascend.io
5. Cloud FinOps teams will optimize their data pipelines
“Confronted with the reality of run-away spending in the cloud this year, in 2024, true cross-organization partnerships will be required to identify unnecessary spending, with both finance and engineering teams playing critical roles. In Ascend’s annual research, 48% of respondents cited plans to optimize their data pipelines to reduce cloud computing costs, with 89% of those respondents expecting the number of pipelines to grow in the next 12 months. It will be imperative next year to leverage platforms that pinpoint where extra spending is occurring in data pipelines and push back with rapid demonstrations of cost optimizations to avoid misguided mandates from above.”
– Sean Knapp, CEO of Ascend.io
6. Intent data will become a must-have for go-to-market teams
“In 2024, intent data will no longer be a ‘nice-to-have’ for go-to-market teams. As companies strive to align sales and marketing efforts, the ability to anticipate customer needs through behavioral data analysis from intent data will be increasingly vital. With AI becoming more sophisticated every year, we anticipate seeing a continued shift from reactive to proactive customer engagement, boosting conversions and fostering long-term customer loyalty.”
– Henry Schuck, CEO of ZoomInfo
7. Data and business teams will lock horns over onboarding AI products
“While business users’ demand for AI products like ChatGPT has already taken off, data teams will still impose a huge checklist before allowing access to corporate data. This tail-wagging-the-dog scenario may be a forcing function to strike a balance, and adoption could come sooner rather than later as AI proves itself as reliable and secure.
Moreover, businesses will prioritize clean datasets to jump on the bandwagon of AI-driven analysis. Clean datasets will serve as the foundation for successful AI implementation, enabling businesses to derive valuable insights and stay competitive.”
– Arina Curtis, CEO and co-founder of DataGPT
8. Enterprises will get a double whammy from real-time and AI
“AI-powered real-time data analytics will give enterprises far greater cost savings and competitive intelligence than before by way of automation, and enable software engineers to move faster within the organization. Insurance companies, for example, have terabytes and terabytes of data stored in their databases. With AI, in 2024, we will be able to process these documents in real-time and also get good intelligence from this dataset without having to code custom models.
Until now, a software engineer was needed to write code to parse these documents, then write more code to extract out the keywords or the values, and then put it into a database and query to generate actionable insights. The cost savings to enterprises will be huge because thanks to real-time AI, companies won’t have to employ a lot of staff to get competitive value out of data.”
– Dhruba Borthakur, CTO and co-founder of Rockset
9. Knowledge graphs will help users eliminate data silos
“As enterprises continue to move more data into a data cloud, they are collecting hundreds, thousands, and sometimes even tens of thousands, of data silos in their clouds. Knowledge graphs will easily drive language models to navigate all of the data silos present by leveraging the relationships between various data sources. With this, in the new year, we will see a variety of established and novel knowledge graph-based AI techniques that support the development of intelligent applications emerge.”
– Molham Aref, CEO and founder of RelationalAI
10. AI will change the current approach to data management
“Businesses are realizing AI’s potential to contribute to their overall value proposition and competitive advantage. To achieve this, AI needs to be trained on and process different kinds of data. Some data is public, but a lot of it is personal consumer information or intellectual property specific to an organization. Companies will find they need to strike a balance to protect data that is being used by AI models, while still using that data to support valuable decision-making. These innovative data management solutions will continue to evolve alongside regulatory compliance and emerging legislation.”
— Osmar Olivo, VP of product management, Inrupt
11. The role of Chief Data Officer will become a prerequisite for CIO hopefuls
“In 2024, there will be a new, surefire career path carved out for CIO hopefuls – becoming and excelling as a Chief Data Officer. Over the last couple of years, the CDO has evolved from a low-budget advisory role to a critical asset helping businesses get the most out of their data. As more organizations invest in AI and the cloud to democratize their data and spur innovation, CDOs are in the driver’s seat – and closer to the CIO as well as the success of the business than ever. Organizations looking for great CIOs will choose the ones who truly understand how data moves, flows through and influences organizations, meaning that CDOs will have a natural advantage in pursuing that career path and continue to exert tremendous influence in the enterprise.”
– Heath Thompson, president & GM, Quest Software
