Top 3 Technological Trends Shaping the 2024 Data Landscape
Three pivotal technologies -- generative AI, data fabric, and MLOps -- are poised to redefine the data landscape. Here are the best practices for leveraging them to optimize your business operations and decision-making.
- By Rajan Nagina
- January 2, 2024
In an era when data is king and the digital realm is evolving with lightning speed, data and analytics professionals must periodically review their data landscape. They need to ensure that their data is accessible for consumption by stakeholders while ensuring top-notch compliance and data integrity, along with being ready for future scalability. For this, they need to stay updated with the latest trends in data and analytics, which is not just smart but imperative.
As we inch closer to 2024, three pivotal technologies -- generative AI, data fabric, and MLOps -- are poised to redefine the data landscape. Here's a closer look at these trends and the best practices for leveraging them to optimize business operations and decision-making:
Generative AI: Beyond Model Development to Model Interpretation
Generative AI, famed for its ability to create realistic data, is stepping into a crucial role of not just developing but interpreting models. Generative AI goes a step beyond analytical AI. It can create content by mimicking art styles, drafting content, and producing unique compositions. It can hypothesize and create simulations that aid research and development. It can interact in real-time for interactive simulations, ensuring a seamless user experience. This evolution is significant because it simplifies the complexity of machine learning (ML) and artificial intelligence (AI), making them more accessible and understandable for daily business decision-making.
For instance, in the banking sector, generative AI can transcend traditional boundaries by automating pre-approval processes as well as explaining the risk factors in a business-friendly language. This transparency aids in better decision-making and instills a higher level of trust between the institution and its clientele.
Recommended best practice: Engage generative AI in both model development and interpretation phases to drive more precise insights and foster an environment where data-driven decisions become second nature.
Data Fabric: Bridging Technology with Business Acumen
Data complexity, heterogeneity, and its spread across applications necessitate that organizations rethink data management, and the data fabric can help. It has evolved from the concepts of a data warehouse and data lake and brings in an architecture for seamless data utilization across the enterprise. At its core, a data fabric is about integrating diverse data sources (SQL, NoSQL, file systems, etc.) into a unified platform.
However, its true potential is unleashed when it is coupled with a feature store -- a repository where reusable feature sets are curated using data analytics, business insights, and generative AI. This concoction translates the business logic into data, which can then be reused across various use cases.
Consider a “Banking 360” scenario where a feature store built on a data fabric can be a game-changer. It allows for creating repeatable feature sets that encapsulate a holistic view of customer interactions and transactions, thereby enabling personalized services and better compliance management.
Recommended best practice: A data fabric can support unstructured data from multiple ingestion points, process it, and deliver insights with minimal complexity. Pre-built models and algorithms for analytical and cognitive processing in the data fabric architecture help speed up data to insights processing at scale. Invest in creating a robust feature store on top of your data fabric. The amalgamation of technical integration and business understanding will drive more value from your data assets.
MLOps: The Beacon of Continuous Improvement
MLOps has become a fundamental building block for scaling AI in organizations. Mature MLOps capability is a must for organizations looking to build AI decision-making at scale. It stands as a cornerstone for ensuring the continuous development, monitoring, and enhancement of ML models. It embodies the spirit of perpetual evolution through rigorous testing of models at scale (A/B/C...X testing) and deploying the best-performing model at any given time. Visual model documentation and versioning will become more mainstream as they provide better traceability and governance, and low-code studios will power intuitive and better governability of MLOps process.
The practice streamlines the ML life cycle, ensuring that the models in production are not just accurate but are the best versions at that point in time. The iterative nature of MLOps fosters a culture of continuous improvement and adaptation to the changing business environment.
Recommended best practice: Adopt a culture of continuous testing and improvement. The right MLOps practices adapting to new data trends and consumer behaviors will enable your organization to stay ahead of the curve.
Key Takeaway
Your organization's data landscape is the foundation that defines the successful outcome of your data strategy. You need a razor-sharp focus on data management and be on top of trends for successful business outcomes. The confluence of generative AI, data fabric, and MLOps is setting the stage for a new era of data management and analytics. By understanding and adopting the best practices associated with these technologies, data and analytics professionals can significantly enhance their decision-making prowess, operational efficiency, and ultimately their competitive edge in the market.
About the Author
Rajan Nagina leads AI practice and is responsible for the AI business at Newgen Software. He has 20 years of experience in product management, business development, and sales. He is co-founder of Number Theory, a low-code data science platform recently acquired by Newgen. He is passionate about democratizing Enterprise AI as he believes all leading companies will be AI-first companies in the coming time.