2025: A Transformative Year for Data and Analytics
2025 is poised to be a transformative year for data and analytics, characterized by increased automation, deeper integration with business processes, and an enhanced focus on AI and privacy. The data and analytics landscape will feature more intelligent, accessible, and efficient systems. To stay ahead, businesses must be agile and invest in these emerging technologies to harness the full potential of their data assets.
Revolutionizing Data Management with Autonomous Data Warehouses:
Autonomous Data Warehouses (ADWs) will offer enhanced support for multi-cloud environments, enabling organizations to seamlessly manage data across different cloud platforms. There will be a greater emphasis on data governance and compliance at the platform level, leveraging AI and automation to minimize human intervention in data management tasks. Additionally, the AI-driven self-managing modules in these platforms will continuously learn and optimize performance, security, and cost at the user level, automatically.
Embracing Multi- and Hybrid-Cloud Architectures for Resilience and Flexibility:
The multi- and hybrid-cloud architectures will not only provide resilience against cloud failures, as witnessed in 2024, but also support instantaneous failover from one cloud to another. Based on my client interactions, this approach will also avoid vendor lock-in and the associated technical debt incurred during transitions.
Lakehouse Architecture: The go-to for Analytical Workloads:
Lakehouse architecture will become the preferred choice for all analytical workloads. This shift is driven by the cost savings and flexibility offered by these systems, with large organizations reporting savings of over 50% on data processing needs.
The Rise of Small Data: Targeted, High-Quality Data Sets:
Organizations are increasingly recognizing the importance of focusing on targeted, high-quality data sets rather than overwhelming volumes of information. The rise of combining small language models (SLMs), through model merging or model ensembling, will be particularly notable in customer support and healthcare. This approach will expedite data analysis, increase data utilization, and drive data monetization.
Domain-Specific LLMs: Enhancing Data Understanding with Semantic Layers:
Domain-specific large language models (LLMs) for BI will emerge to better understand the data they process through the semantic layer. AI will drive the creation of this semantic layer, delivering insights with actionable recommendations by understanding context, source, lineage, calculation, persona, grain, and meaning. For example, the LLM will understand the type of data (e.g., transactions, customer) in tables, the requested KPIs/metrics, the requesting persona, the required aggregation level for the use case, the source of truth for the data, the formula to be applied, the appropriate visualization, and so on.
Empowering Users with Conversational BI and Self-Service Analytics:
There will be a significant reduction in the number of canned reports and an increase in self-service capabilities across the board. Employees across all roles will be empowered and expected to leverage data without relying on IT or other teams. Data storytelling will evolve with conversational BI, enabling non-technical users to generate impactful, interactive dashboards and narratives. Additionally, conversational BI will help employees confirm that the decisions they will make based on the data are correct and that no critical factors have been overlooked.
Driving Data Transparency and Interoperability with Data Catalogs:
As hybrid and multi-cloud ecosystems grow, organizations will demand seamless interoperability to drive data transparency, literacy, democratization, data product identification, and monetization initiatives. This will spur innovation in governance, lineage capture, and easier maintenance from catalog tool vendors as they now have to integrate better with the semantic layer.
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Integrating Observability into Development: A Paradigm Shift:
Engineers will incorporate observability as part of the design process, rather than treating it as a siloed tool or retrofitting observability post-development. This approach is similar to test-driven development, where tests are written before the actual code, ensuring the code meets the desired requirements and behaves as expected.
Ensuring Fairness and Accountability with Ethical AI Governance:
Ethical considerations will remain paramount. We can expect a sharp rise in the formalization of AI ethics boards and the development of robust frameworks for responsible AI development and deployment—for example, continuous AI impact scoring. Additionally, there will be enhancements to governance and privacy frameworks that make them more flexible in adapting to evolving privacy regulations while enabling innovation. A possible move towards Data Responsibility Platforms (DRPs) driven by growing government regulation, increased public concern about AI bias, surveillance, and misuse.
Combating AI-Generated Fakes: Advanced Detection Tools and Techniques:
With the rise of hyper-realistic deepfakes, widespread misinformation, and social manipulation, the development of sophisticated AI-powered tools for detecting and identifying AI-generated content will become crucial. Incorporating modules at the model/foundational level to prevent or catch deepfakes will be a key focus.
Building Hybrid Skill Sets: The Future of AI/ML Talent Development:
Organizations will invest in developing hybrid skill sets that combine domain expertise with technology literacy. The demand for skilled AI/ML professionals will continue to surge, requiring organizations to invest in talent development and upskilling initiatives.
Some key risks to monitor include:
Your insights and thoughts are welcome as we navigate these exciting changes together! Feel free to comment on the post.
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