Introduction to Applied Information Technologies in Agronomy

The integration of information technology into agricultural practices is revolutionising how we grow food, manage resources, and ensure sustainability. This presentation explores how digital innovation is transforming traditional farming into a data-driven, precision-based science that addresses global challenges while improving productivity and environmental stewardship.

1. II. Promising Directions for the Development of Big Data in 2025 This presentation explores the cutting-edge trends and technologies that will shape the big data landscape in 2025 and beyond, providing insights into how organisations can leverage these developments for strategic advantage.

2. Chapter 1: The Big Data Landscape Today Before exploring tomorrow's innovations, we must understand today's big data ecosystem. This foundation will help us appreciate the significance of emerging trends and their potential impact on various industries. The big data landscape has evolved dramatically over the past decade, from basic data collection to sophisticated analytics powering mission-critical decisions across every sector. What was once the domain of tech giants is now accessible to organisations of all sizes, democratising data-driven insights and creating new competitive opportunities. This transformation has been driven by advances in computing power, storage capabilities, and analytical tools, alongside the proliferation of data sources from mobile devices, IoT sensors, social media, and digital transactions.

3. The Data Explosion We are witnessing an unprecedented explosion in data generation that shows no signs of slowing down. This exponential growth is fundamentally reshaping how we think about data collection, storage, processing, and analysis. The sheer volume is staggering—over 2.5 quintillion bytes of data are being generated daily in 2025. To put this in perspective, that's equivalent to the content of 250 million DVDs created every day. This acceleration means that approximately 90% of the world's data has been created in just the past two years , representing the most dramatic data growth in human history. 2.5Q Daily Data Generated Quintillion bytes of data created every day in 2025, requiring increasingly sophisticated storage and processing solutions. 181ZB Annual Global Data Zettabytes of data produced globally this year, expanding the digital universe at an unprecedented rate. 90% Recent Creation Of the world's data created in just the past two years, highlighting the acceleration of digital transformation across all sectors. This explosive growth presents both challenges and opportunities. Organisations must develop strategies to effectively manage this deluge while extracting valuable insights that drive innovation and competitive advantage.

4. Big Data Market Growth The economic significance of big data cannot be overstated. As organisations increasingly recognise the value of data-driven decision making, investment in big data technologies and services continues to surge at remarkable rates. The global big data market is expected to reach $103 billion by 2027 , reflecting strong confidence in the transformative potential of data analytics across industries. Meanwhile, the more specific big data analytics market is projected to hit an astounding $655 billion by 2029 . This growth is being driven by several factors: Increased adoption of cloud-based analytics solutions Growing enterprise recognition of data as a strategic asset Emergence of powerful, accessible analytics tools Rising demand for real-time insights to drive business decisions Regulatory requirements necessitating better data management With a compound annual growth rate (CAGR) exceeding 30% through 2026 , the data analytics market is outpacing many other technology sectors. This growth reflects the critical role that big data plays in driving digital transformation initiatives across industries.

5. Chapter 2: AI and Machine Learning – The Heart of Big Data Innovation Artificial intelligence and machine learning have emerged as the primary engines driving big data innovation. These technologies are transforming how we extract value from massive datasets, enabling unprecedented levels of automation, prediction, and insight generation. The symbiotic relationship between big data and AI/ML is reshaping industries, with each technology amplifying the capabilities of the other. Big data provides the fuel that AI systems need to learn and improve, while AI delivers the sophisticated analysis and pattern recognition needed to make sense of complex datasets. This chapter explores how this powerful combination is creating new possibilities for organisations across sectors, from healthcare and finance to manufacturing and retail. We'll examine specific implementations, emerging trends, and the transformative potential of AI-powered big data analytics.

6. AI & ML Integration Transforming Big Data The integration of artificial intelligence and machine learning into big data workflows is fundamentally changing how organisations extract value from their data assets. This convergence represents one of the most significant technological developments of our time. AI systems now automate the entire data pipeline —from cleaning and structuring raw data to validating results and generating insights. This automation dramatically accelerates workflows that previously required extensive manual intervention, enabling faster time-to-insight and reducing human error. Perhaps most importantly, machine learning models adapt continuously based on new data , creating a virtuous cycle of improvement. As more data flows through these systems, their predictive accuracy increases, enabling increasingly sophisticated analysis and forecasting capabilities. Data Pipeline Automation AI-powered systems handle data collection, cleaning, processing, and analysis with minimal human intervention, reducing manual effort by up to 70%. Continuous Model Improvement ML algorithms become more accurate over time as they process additional data, creating self-improving analytics systems. Recommendation Engine Success Netflix drives 80% of content consumption through AI-powered recommendations, demonstrating the business impact of intelligent data analysis. These capabilities are already delivering measurable business results across industries. For example, Netflix drives 80% of its content consumption through AI-powered recommendations , translating to billions in value from improved customer engagement and retention. Similar success stories are emerging in finance, healthcare, manufacturing, and virtually every other sector.

7. Generative AI Expanding Horizons Generative AI represents one of the most exciting frontiers in big data development, creating entirely new possibilities for how we work with and derive value from data. These systems can create new content, generate synthetic datasets, and solve complex problems in ways that were previously impossible. Pharmaceutical Innovation The generative AI market in drug discovery is expected to reach $1.4 billion by 2025 , with systems capable of designing novel molecular structures and predicting their properties with unprecedented speed and accuracy. Software Development AI-driven code development tools are reducing software delivery times by up to 50% , generating functional code from requirements and automating testing processes, fundamentally changing how applications are built. Personalised Education AI tutors could impact 50 million students annually worldwide , creating individualised learning experiences based on each student's learning style, pace, and knowledge gaps. The potential applications extend far beyond these examples. From generating marketing content to designing new materials with specific properties, generative AI is transforming how we approach innovation across disciplines. This technology allows organisations to explore design spaces and solution possibilities at scales impossible for human teams working alone. The key to success with generative AI lies in effectively combining human expertise with AI capabilities. The most powerful implementations maintain humans in supervisory roles, leveraging AI to amplify human creativity and problem-solving rather than replace it entirely.

8. Chapter 3: Real-Time and Edge Computing – Speed and Proximity As data volumes continue to grow exponentially, the traditional model of sending all information to centralized data centers for processing is becoming increasingly impractical. Two critical developments are addressing this challenge: real-time data processing and edge computing. These approaches fundamentally change where and when data analysis occurs, moving processing closer to data sources and delivering insights with unprecedented speed. This shift is particularly important for time-sensitive applications like autonomous vehicles, industrial automation, and financial trading systems where milliseconds matter. This chapter explores how these technologies are evolving, their implications for system architecture, and the new capabilities they enable across industries. We'll examine how organisations are leveraging these approaches to gain competitive advantages through faster decision-making and reduced data transmission costs.

9. Real-Time Data Processing: Instant Insights Real-time data processing represents a fundamental shift from traditional batch processing approaches. Rather than collecting data for periodic analysis, these systems process information as it's generated, enabling immediate action based on the latest insights. The adoption of real-time analytics is accelerating rapidly, with 33% of organisations already implementing these capabilities . This growth is driven by use cases where timely decisions deliver significant business value. One prominent example is Uber, which matches drivers and optimises routes instantly using real-time data from millions of mobile devices. This capability enables dynamic pricing based on supply and demand conditions and efficient allocation of transportation resources across urban environments. The technological foundation for real-time processing includes stream processing platforms like Apache Kafka, Apache Flink, and Apache Spark Streaming . These tools provide the infrastructure needed to handle continuous data flows at scale, enabling organisations to process millions of events per second with minimal latency. Key benefits include: Immediate detection of anomalies and threats Dynamic optimisation of resources and pricing Continuous monitoring of critical systems Enhanced customer experiences through contextual interactions As 5G networks expand and IoT device deployments accelerate, the volume of real-time data will continue to grow exponentially. Organisations that develop capabilities to process and act on this information immediately will gain significant advantages over competitors relying on older, batch-oriented approaches.

10. Edge Computing: Processing at the Source Edge computing represents a paradigm shift in data processing architecture, moving computation closer to data sources rather than transmitting everything to centralised data centres. This approach is particularly valuable for IoT deployments, autonomous systems, and applications requiring minimal latency. 50%+ Data Processed at Edge Over half of all data will be processed in edge environments by 2025, marking a significant shift away from centralised processing models. 90% Bandwidth Reduction Edge computing can reduce network bandwidth requirements by up to 90% for certain applications by processing data locally and sending only results. ≤10ms Latency Reduction Edge computing achieves response times under 10 milliseconds for critical applications, compared to 50-100ms for cloud-based processing. Beyond performance benefits, edge computing also enhances privacy and security by limiting the transmission of sensitive data. Personal information can be processed locally, with only aggregated or anonymised results sent to cloud systems for further analysis. Key use cases for edge computing include: Autonomous vehicles processing sensor data in real-time Smart manufacturing with local control systems Remote healthcare monitoring with immediate anomaly detection Retail environments with computer vision for inventory management Smart city infrastructure optimising resource utilisation The combination of edge computing with 5G connectivity is creating particularly powerful capabilities, enabling complex processing at the network edge with reliable, high-bandwidth connections to cloud resources when needed.

11. Chapter 4: Data Governance, Privacy, and Synthetic Data As big data systems grow in scope and impact, the importance of robust governance frameworks, privacy protections, and innovative approaches to sensitive data has never been greater. These elements are no longer merely compliance considerations but strategic imperatives that directly impact an organisation's ability to derive value from data assets. The regulatory landscape continues to evolve rapidly, with frameworks like GDPR in Europe (CCPA in California), and similar regulations emerging globally. These developments are driving organisations to implement more sophisticated approaches to data management, including advanced metadata systems, access controls, and privacy-enhancing technologies. This chapter explores how leading organisations are addressing these challenges, with particular focus on governance frameworks, privacy-preserving analytics, and the growing role of synthetic data in enabling innovation while protecting sensitive information.

12. Data Quality and Governance as Strategic Imperatives Data governance has evolved from a compliance checkbox to a strategic imperative that directly impacts business performance. Organisations now recognise that the value of their data assets depends directly on their quality, consistency, and trustworthiness. Robust governance frameworks ensure data accuracy and consistency across complex enterprise environments, building trust in analytics outputs and enabling confident decision-making. These frameworks incorporate policies, procedures, roles, and technologies that collectively manage data throughout its lifecycle. Regulatory requirements like GDPR, CCPA, and industry-specific regulations further drive the adoption of governance practices. These requirements impose significant penalties for non-compliance, making governance a financial as well as operational necessity. Modern governance approaches increasingly leverage advanced technologies: Metadata management systems track data lineage, quality metrics, and usage patterns AI-powered data quality tools automatically detect anomalies and inconsistencies Data catalogs provide searchable inventories of available data assets Multimodal data fabrics create unified views across disparate data sources Leading organisations are establishing dedicated data governance teams with clear executive sponsorship, recognising that effective governance requires both technological solutions and organisational commitment. These teams typically include representatives from business units, IT, legal, and compliance functions to ensure a balanced approach that enables innovation while managing risk.

13. Synthetic Data: Privacy and Innovation Combined Synthetic data has emerged as one of the most promising solutions to the fundamental tension between data utility and privacy protection. This approach involves generating artificial datasets that preserve the statistical properties and relationships of original data without exposing actual records. Interest in synthetic data has surged dramatically, with searches related to synthetic data up 600% in recent years . This growth reflects the increasing recognition of its potential across industries. The applications of synthetic data are diverse and expanding: Filling gaps in sparse or incomplete datasets Protecting privacy whilst enabling analytics on sensitive information Accelerating AI and ML model training with expanded data volumes Creating test data that mimics production environments without risk Sharing "realistic" data with partners without exposing actual customer information Healthcare Innovation UC Davis Health uses synthetic patient data to develop and test new clinical algorithms without exposing actual patient records, accelerating research while maintaining privacy compliance. Urban Development The city of Vienna uses synthetic datasets to simulate urban environments and test smart city applications, allowing software developers to work with realistic data without accessing sensitive citizen information. Financial Services Major banks leverage synthetic transaction data to train fraud detection systems and test new financial products without risking exposure of actual customer financial records. The quality of synthetic data continues to improve as generative AI techniques advance. The most sophisticated approaches now produce synthetic datasets that are virtually indistinguishable from real data in terms of statistical properties while providing strong privacy guarantees through differential privacy techniques.

14. Chapter 5: Emerging Technologies and Trends Shaping Big Data Beyond the core developments in AI, edge computing, and data governance, several emerging technologies and trends are poised to reshape the big data landscape in the coming years. These innovations promise to fundamentally change how we collect, process, analyse, and derive value from data. From agentic AI systems that operate with greater autonomy to quantum computing's potential to solve previously intractable problems, these developments represent both opportunities and challenges for organisations across sectors. Similarly, the democratisation of data access and the rise of composite AI approaches are changing who can leverage data and how they can apply it. This chapter explores these cutting-edge developments, examining their current state, projected evolution, and potential impact on big data practices. We'll consider real-world applications already emerging and highlight how forward-thinking organisations are preparing to leverage these capabilities.

15. Agentic AI and AI Agents Agentic AI represents a significant evolution in artificial intelligence, moving beyond passive analytical tools to systems that can independently pursue goals, make decisions, and take actions with limited human oversight. These autonomous AI programs are designed to perform complex tasks independently, often interacting with multiple systems to achieve their objectives. The adoption of agentic AI is accelerating rapidly, with 37% of IT leaders reporting early adoption and an additional 68% expecting to implement these capabilities within 6 months . This rapid uptake reflects the significant operational benefits these systems can deliver. Early applications focus on areas with well-defined processes and clear success metrics: IT Support & Maintenance AI agents that monitor systems, detect anomalies, diagnose issues, and initiate remediation steps without human intervention, reducing downtime and support costs. HR Process Automation Agents handling employee onboarding, benefits administration, and routine inquiries, freeing human HR staff for more complex and interpersonal tasks. Customer Service Sophisticated AI agents that handle complex customer interactions across multiple channels, with the ability to access various systems to resolve issues completely. While agentic AI promises significant benefits, organisations are implementing these systems with appropriate safeguards. Most deployments maintain human oversight through approval workflows for significant actions and continuous monitoring of agent performance. This "human in the loop" approach balances automation benefits with necessary governance.

16. Quantum Computing Meets Big Data Quantum computing represents one of the most profound technological developments on the horizon, with potentially transformative implications for big data analytics. These systems leverage quantum mechanical principles to perform certain types of calculations exponentially faster than classical computers, opening new frontiers in data analysis. $20B Global Investment Expected investment in quantum technologies by 2025, reflecting confidence in its commercial potential despite technical challenges. 1000x Speed Improvement Potential acceleration for specific algorithms relevant to big data problems, including optimisation and simulation tasks. 5-10yrs Mainstream Timeline Expected timeframe for quantum computing to become practical for commercial big data applications beyond specialized use cases. While fully fault-tolerant quantum computers remain years away, early quantum systems are already demonstrating value in specific applications: Molecular Simulation Quantum algorithms modeling chemical reactions and drug interactions at unprecedented scales, accelerating pharmaceutical development. Supply Chain Optimisation Volkswagen has piloted quantum-powered traffic management in Lisbon, optimising vehicle routes in real-time to reduce congestion. Financial Modeling JPMorgan Chase and other financial institutions exploring quantum algorithms for risk assessment and portfolio optimisation. Organisations seeking to prepare for the quantum era should identify high-value problems within their domains that align with quantum computing's strengths, particularly in optimisation, simulation, and machine learning acceleration. Building expertise through partnerships with quantum technology providers can position companies to leverage these capabilities as they mature.

17. Data Democratization and Data-as-a-Service (DaaS) Data democratisation—making data accessible to non-technical users throughout an organisation—represents a fundamental shift in how companies leverage their information assets. This trend is transforming data from a resource controlled by specialists to a broadly available tool for decision-making at all levels. Self-service analytics platforms have emerged as the primary vehicles for this democratisation, providing intuitive interfaces that allow users without programming or statistical expertise to explore data, create visualisations, and generate insights independently. This trend aligns with broader changes in technology creation and consumption. Gartner predicts that 80% of technology products and services will be built by non-technology professionals by 2025 , highlighting the growing technical capabilities of business users. Complementing internal democratisation, Data-as-a-Service (DaaS) is extending access to valuable datasets beyond organisational boundaries. The DaaS market is projected to grow by $56 billion by 2027 , providing: Ready-to-use datasets for enhancing analytics API access to live data streams from various sources Pre-processed information tailored to specific use cases Tools for integrating external data with internal systems These developments are particularly significant for smaller organisations, which can now access sophisticated data resources without building extensive internal capabilities. This democratisation is leveling the playing field, allowing nimble competitors to challenge established players through data-driven innovation. Successful implementation requires balancing accessibility with appropriate governance, ensuring that democratised data access doesn't compromise quality or security. Leading organisations are addressing this challenge through automated data quality checks, clear metadata, and role-based access controls that maintain protection while enabling broad utilisation.

18. Composite AI and Explainable AI (XAI) Composite AI: Strength Through Diversity Composite AI refers to the strategic combination of multiple AI techniques to solve complex problems that no single approach can address effectively. This integration creates systems with greater versatility, resilience, and performance across diverse scenarios. Examples of composite AI implementations include: Combining computer vision with natural language processing to enable multimodal understanding Integrating symbolic reasoning with neural networks to handle both structured rules and unstructured data Pairing generative AI with traditional analytics for both creative outputs and rigorous validation These hybrid approaches are particularly valuable for complex real-world applications where data may be incomplete, context is critical, and multiple types of reasoning are required. Explainable AI: Transparency and Trust As AI systems take on increasingly significant roles in decision-making, the need for transparency and accountability has become paramount. Explainable AI (XAI) addresses this need by creating models whose decisions can be understood and interpreted by humans. XAI approaches are essential for: Detecting and mitigating bias in automated systems Building trust with stakeholders and end-users Meeting regulatory requirements for algorithmic transparency Enabling effective human oversight of AI systems Techniques range from inherently interpretable models to post-hoc explanation methods that help users understand complex "black box" systems. Together, composite AI and explainable AI represent complementary trends that are making artificial intelligence both more powerful and more accessible. By combining diverse capabilities while maintaining transparency, these approaches are addressing many of the limitations that have previously constrained AI adoption in sensitive domains like healthcare, finance, and public sector applications.

19. Chapter 6: The Road Ahead – Strategic Opportunities As we've explored throughout this presentation, the big data landscape is evolving rapidly, presenting both opportunities and challenges for organisations across sectors. This final chapter synthesizes these developments into a cohesive view of the road ahead, highlighting strategic considerations for leaders navigating this dynamic environment. The convergence of AI, edge computing, quantum technologies, and enhanced governance frameworks is creating unprecedented possibilities for data-driven innovation. Organisations that successfully integrate these capabilities can achieve transformative outcomes—from radically improved operational efficiency to entirely new business models built on data-derived insights. However, realizing these benefits requires thoughtful strategy, appropriate investment, and careful attention to the human and ethical dimensions of data utilization. This chapter explores these considerations and provides guidance for organizations seeking to lead in the data-driven future.

20. Big Data's Future: A Smarter, More Connected World The next phase of big data development will be characterized by the seamless integration of advanced technologies we've explored, creating systems that are simultaneously more powerful, more accessible, and more trustworthy than today's solutions. The integration of AI, edge computing, and robust governance frameworks will unlock new value across sectors, enabling organizations to derive unprecedented insights from their data assets while maintaining appropriate controls. The business impact of these developments will be substantial. Research indicates that organizations fully embracing these trends can boost productivity by over 130% compared to laggards, creating significant competitive advantages in virtually every industry. Over the next decade, we anticipate breakthroughs in several key areas: Healthcare Precision medicine tailored to individual genetic profiles, dramatically improving treatment efficacy whilst reducing adverse effects. Finance Hyper-personalised financial services that adapt in real-time to changing customer circumstances and market conditions. Retail Seamless omnichannel experiences that blur the line between digital and physical shopping through predictive analytics. The path forward requires strategic investment in both technology and people. Organizations must build technical capabilities around these emerging trends while simultaneously developing the data literacy and analytical skills of their workforce. Our call to action is clear: Invest strategically in these promising directions to lead the data-driven future. The organizations that move decisively now—experimenting with emerging technologies, building necessary capabilities, and cultivating a culture of data-driven decision making—will be best positioned to thrive in the rapidly evolving big data landscape.