All Things Data

Key Takeaway:

"All Things Data" represents a broad, integral shift in data management strategies driven by the need for greater efficiency, strategic insight, and technological integration. The trend encompasses various facets of data management, from application-specific optimizations to advanced GenAI integrations, reflecting the evolving demands and opportunities within modern business environments. In 2024, this shift will not merely be about managing data but transforming it into a pivotal strategic asset. Furthermore, in a recent BCG survey, 68% of companies enquired plan to implement next-generation data architectures within the next three years, highlighting the broad-based commitment to evolving data strategies to meet contemporary and future demands better.

Trend Type: Technology

Sub-trends: Application performance as measure of data management, Management visibility into database operations, GenAI implications on data management, Organizations adopt autonomic data man-agement, Customer Data Revolution, GenAI need for data accessibility and mobility, DevOps de-mand for automated data control, Next-Gen Data Architecture, GenAI Enterprise Data, Synthetic data bonanza, Machine Customers

May, 2024 • Less than 4 min read

The increasing complexity and scale of data within modern enterprises lead into a shift in data management strategies, catalyzing a trend towards more dynamic, integrated, and technologically advanced approaches. This trend is reshaping how organizations manage, utilize, and leverage data for competitive advantage. The emphasis on optimizing data specifically for application performance, enhancing management visibility into operations, and integrating advanced technologies like Generative AI (GenAI) are at the forefront of this movement. Each element of this trend is not just a response to technological opportunities but also a strategic imperative to enhance operational efficiency and decision-making.
Enhancing application performance
At the core of this trend is the focus on enhancing application performance through targeted data management strategies. Traditional data management practices often involved scheduled reorganizations that did not necessarily align with the dynamic needs of applications. Now, organizations are prioritizing the optimization of data for application performance using SQL (Structured Query Language) performance tools and database optimization techniques. By tailoring data management to the applications’ specific needs, companies can significantly reduce resource wastage and address system lock-ups effectively. This approach is facilitated by tools that optimize IMS (Information Management System) checkpoints and Db2 (Database 2) commits, ensuring that data management is both proactive and responsive to application requirements. For instance, e-commerce product catalog operations can be scheduled automatically by these systems to reduce impact on customers traffic requests. As a result, high traffic is handled more smoothly, enhancing customer experience and potentially increasing sales.
Increasing visibility into database operations
Another aspect of this trend is the heightened visibility into database operations. Enhanced management-level dashboards allow for real-time insights into database usage, which is critical for prompt decision-making and operational efficiency. These dashboards, integrated with analytical tools, enable managers and DBAs (Database Administrators) to quickly assess and improve the quality, velocity, and efficiency of enterprise infrastructure operations. This visibility is not merely about monitoring; it’s about leveraging data to drive strategic improvements and optimize resource allocation across the board.
Integrating Generative AI in Data Management
In addition, the integration of GenAI into data management marks a significant evolution in the field. GenAI’s ability to handle a vast array of data types—both structured and unstructured—without compromising data integrity is revolutionizing data management. It facilitates an exponential increase in the volume and complexity of data managed, necessitating robust automated systems to maintain efficiency and reliability. Furthermore, GenAI is being used to refine data analytics in sectors like media or advertising, uncovering hidden patterns and offering new strategic insights that were previously unattainable. The recent surge in GenAI applications, particularly in the retail sector, illustrates the critical role of AI in filling the gaps left by the decline of third-party cookies. AI-driven chatbots, for example, are now crucial for gathering first-party data, providing personalized customer interactions that enhance data collection and customer engagement.
Emerging challenges and developments
However, challenges remain, as indicated by an eTail Connect research showing that 52% of leaders from 100 European ecommerce businesses struggle with data consolidation across platforms, and 50% find it difficult to derive actionable insights from the data they collect. On top of that, autonomic data management, where AI is used to automate traditionally manual reorganization processes, highlights a shift towards more intelligent, self-regulating systems. By analyzing past data and performance trends, AI can predict optimal reorganization times and methods, minimizing manual intervention and optimizing both performance and costs.
Non human economic actors and their impact
A groundbreaking development within the “All Things Data” trend is the emergence of nonhuman economic actors—machine customers—that purchase goods and services autonomously. By 2028, according to Gartner, it is projected that 20% of human-readable digital storefronts will become obsolete due to these machine customers. This shift marks the first time in human history that companies can literally create their own customers. Furthermore, there will be 15 billion connected products with the potential to act as customers by 2028, with billions more anticipated in subsequent years. These nonhuman actors are expected to influence trillions of dollars in transactions by 2030, potentially surpassing the significance of the digital commerce revolution.
This all leads to a diversity in data handling practices that also reflects this trend, emphasizing the need for a variety of data management techniques that enhance data utility and impact. This focus on diversity extends to the adoption of different data sources and types, thereby maximizing their strategic value more effectively.
Sub-Trend Sources
Application performance as measure of data management: BMC Blogs IT Trends
Management visibility into database operations: BMC Blogs IT Trends
GenAI implications on data management: BMC Blogs IT Trends, Kyndryl, Kantar's Media Trends, Finance & Development
Organizations adopt autonomic data management: BMC Blogs IT Trends
Customer Data Revolution: eTail Connect European Playbook, Thoughtworks Tech Radar
GenAI need for data accessibility and mobility: BMC Blogs IT Trends
DevOps demand for automated data control: BMC Blogs IT Trends
Next-Gen Data Architecture: BCG The Next Wave
GenAI Enterprise Data: Delloite TMT Predictions
Synthetic data bonanza: CBInsights Emerging Tech Trends
Machine Customers: Gartner Strategic Trends

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