Adopting conceptual artificial intelligence (AI) and foundational designs emerged as the primary focus in 2023. However, as enterprises rushed to incorporate advanced AI into their operations, they realized the critical importance of streamlining their data management practices.
Although the significance of high-quality data for business success has long been acknowledged, the rise of general AI has elevated its centrality, capturing widespread attention across industries. Esteemed professionals and vendors are now sharing their insights on the anticipated advancements in the data landscape as we approach 2024, poised to witness even greater strides in general AI innovation.
1. Transition Towards SQL-Free Relational Models
In 2024, businesses are poised to embark on ambitious initiatives leveraging cutting-edge technologies such as the Internet of Things (IoT) and conceptual AI to drive organizational growth. These initiatives hinge on seamless access to business data. However, many enterprises still grapple with legacy operational databases tailored to meet the needs of outdated systems.
While SQL remains embedded within software servers connected to SQL databases via recurring asynchronous sessions for most applications, it lacks a standardized approach to legal logic. This legacy SQL design, though viable decades ago, poses significant challenges for contemporary HTTP cloud services. The insistence on co-locating software code and databases within the same data center region severely hampers Linux or regionally distributed applications crucial for modern businesses like IoT and edge applications.
Businesses are poised to adopt more agile database infrastructures capable of supporting the agility, consistency, scalability, and adaptability required for cutting-edge AI-driven applications. As legacy databases increasingly constrain business developers and impede the pace of innovation, the hurdles associated with outdated database systems are set to become more pronounced and costly.
– Bob Muglia, former Snowflake CEO and current Executive President of Fauna
2. Emergence of Vector Data as a Premier Technology
“In 2024, vector data is slated to become the most coveted technology.” With the burgeoning emphasis on data-driven insights propelling technological advancements, vector databases have garnered significant attention owing to their proficiency in managing high-dimensional data and facilitating complex similarity searches. Mastery of leading vector databases is poised to become indispensable for software development across diverse sectors, spanning recommendation systems, image recognition, natural language processing, economic forecasting, and other AI-driven ventures.
As organizations forge ahead in developing AI-enabled products imbued with novel deep learning-powered capabilities, the demand for flexible, user-friendly, and technically adept vector data storage solutions will intensify.
– Avthar Sewrathan, AI and Vectors General Manager at Timescale, and Ratnesh Singh Parihar, Lead Designer at Talentica Software
3. Harnessing Conceptual AI for Unearthing Data Treasures in Business Data Lakes
In 2024, large corporations grapple with copious amounts of data, ranging from hundreds to petabytes. Despite this data abundance, many enterprises acknowledge that they harness less than half of this information, predominantly structured data, for actionable insights. The advent of conceptual AI in 2024 is set to revolutionize data utilization by enabling organizations to craft and tailor Large Language Models (LLMs) to extract value from this untapped data reservoir. By leveraging AI-powered supercomputing, businesses are primed to mine unstructured data such as chats, videos, and code to propel their conceptual AI initiatives towards training bidirectional models. This transformative approach will empower companies to furnish precise responses, unearth novel opportunities, and elevate operational efficiency beyond the realm of structured data, encompassing realms like identifying financial trends, aiding in health scan anomaly detection, and optimizing business processes.
– Charlie Boyle, Vice President of Nvidia’s DGX Systems
4. Imperative of Robust Automation for AI-Driven Enterprises
Enterprises lacking robust automation capabilities essential for AI-driven operations risk facing significant setbacks. As organizations deploy AI to fortify their competitive edge, the ramifications of subpar data infrastructure will be acutely felt. Transitioning from merely disseminating data insights on dashboards to potentially automating decisions and actions based on this data elevates the stakes. The repercussions of erroneous information or data inadequacies are magnified when AI is leveraged in critical operational contexts. The convergence of poor governance and data infrastructure with generative AI applications can precipitate accuracy compromises, underscoring the urgency for enterprises to fortify their automation frameworks.
Sean Knapp, CEO of Ascend dai
5. Enhancing Information Pipelines Through Fog FinOps
In 2024, organizations are poised to enhance their information pipelines by embracing Fog FinOps practices to pinpoint superfluous expenditures. Against the backdrop of escalating cloud computing costs and the imperative for swift cost optimization measures, realigning data pipelines emerges as a strategic imperative. Ascend’s annual survey reveals that 48% of respondents intend to optimize their data pipelines to curtail cloud computing expenses, with 89% anticipating a surge in pipeline development over the ensuing year. Leveraging platforms capable of identifying surplus expenditures in data pipelines and swiftly orchestrating cost-saving interventions will be pivotal in circumventing misdirected directives and fostering fiscal prudence.
Sean Knapp, CEO of Ascend dai
6. The Ascendancy of Objective Data for Go-To-Market Teams
“In 2024, objective data will transition from a ‘nice-to-have’ to a business imperative for go-to-market teams.” The ability to anticipate customer needs through behavioral data analysis grounded in objective data will assume heightened significance as enterprises strive to synchronize their sales and marketing endeavors. The evolution towards proactive customer engagement facilitated by advanced AI technologies is poised to catalyze a paradigm shift from reactive to proactive customer relationship management, amplifying conversion rates and nurturing enduring customer loyalty.
Henry Schuck, CEO of ZoomInfo
7. Navigating the Data Terrain: Balancing AI Adoption and Data Governance
Despite burgeoning demand from business users for AI tools like ChatGPT, data teams are poised to uphold stringent data access protocols, underscoring the imperative of robust data governance frameworks. As AI technologies gain traction and demonstrate reliability and security, the impetus for accelerated AI adoption will intensify. This dynamic interplay between data teams and business users navigating the AI adoption landscape underscores the need for striking a delicate balance. Additionally, prioritizing fresh datasets to fuel AI-driven research initiatives will be pivotal in harnessing the transformative potential of AI technologies, enabling enterprises to glean actionable insights and sustain competitive relevance.
– Arina Curtis, CEO and Co-founder of DataGPT
8. Empowering Business Agility through Real-Time AI-Powered Analytics
“AI-driven real-time data analytics in 2024 will empower application engineers to expedite organizational processes, delivering unprecedented cost savings and competitive intelligence.” Traditionally, vast troves of data, such as those housed in insurance databases, necessitated painstaking manual coding to extract actionable insights. However, the advent of real-time AI capabilities enables businesses to swiftly mine data repositories, extracting valuable insights without the need for bespoke model coding. Real-time AI heralds a paradigm shift, enabling enterprises to unlock latent value from data repositories with agility, potentially yielding substantial cost efficiencies.
– Dhruba Borthakur, Co-founder and CTO of Rockset
9. Dissolving Information Silos with Information Graphs
“As enterprises accumulate myriad data silos in their cloud environments, information graphs emerge as a potent tool for dismantling silos and enabling comprehensive data exploration.” Leveraging the interconnections between diverse data reservoirs, knowledge graphs facilitate seamless traversal of disparate information silos by language models. This symbiotic relationship between information graphs and cutting-edge AI techniques heralds a new era of intelligent applications empowered by sophisticated data graph analytics.
– Molham Aref, CEO and Founder of RelationalAI
10. AI’s Disruptive Influence on Data Management Paradigms
Enterprises are increasingly cognizant of AI’s transformative potential in enhancing their value propositions and competitive advantages. To leverage AI effectively, organizations must grapple with diverse data types, encompassing publicly available data alongside proprietary client information and intellectual property. Striking a delicate balance between safeguarding data underpinning AI models and harnessing it to drive strategic decisions will be paramount. Against the backdrop of evolving regulatory landscapes, innovative data management solutions will continue to evolve to cater to the burgeoning demands of AI-driven enterprises.
— Osmar Olivo, VP of Product Management at Inrupt
11. The Rise of Chief Data Officers in the C-Suite
“In 2024, CIO aspirants may find themselves compelled to embrace the role of Chief Data Officer (CDO) as a strategic imperative.” The evolution of the CDO role from a peripheral advisory function to a pivotal enabler of data-driven decision-making underscores the growing indispensability of data stewardship in contemporary enterprises. As organizations pivot towards AI and data-driven technologies to streamline their operations and fuel innovation, the role of CDOs assumes paramount importance. CDOs, equipped with profound insights into data flows and organizational dynamics, are poised to spearhead data-centric initiatives, shaping the strategic trajectory of enterprises and cementing their pivotal role in driving technological innovation.
In conclusion, the year 2024 is poised to witness a seismic shift in the data landscape, propelled by the convergence of AI technologies, data governance frameworks, and organizational agility. Enterprises that adeptly navigate this transformative terrain are primed to harness the full potential of AI-driven innovations, fostering operational efficiency, strategic agility, and sustained competitive relevance.
