Latest Real-Time Analytics News: Key Updates for March 8 Week Ending

The Gartner Data & Analytics Summit addressed the critical steps organizations must take to expand and operationalize their use of advanced analytics and AI. A key theme was that persistent poor data quality remains a significant hurdle for advanced analytics and AI deployment through 2025.

Navigating AI Deployment: Gartner’s Three Interdependent Journeys

Gartner emphasized that data and analytics (D&A) leaders must focus on three interdependent journeys to advance enterprise AI initiatives: business outcomes, D&A capabilities, and behavioral change.

Journey to Business Outcomes: Prioritizing Value and Trust

To maximize business outcomes, Gartner advises D&A leaders to prioritize demonstrable value. Key actions include:

• Establish trust models: Focus on trusted,high-quality data to overcome AI initiative failures due to poor data quality. Trust models should assess data’s value and risk, providing a trust rating based on lineage and curation.
• Monetize productivity improvements: Evaluate the value and competitive impact in relation to total cost, complexity, and risk.
• Communicate value of D&A: Account for all costs, including data management, governance, and change management.

Journey to D&A Capabilities: Building a Flexible Technology Stack

D&A leaders must leverage a range of tools and technologies to build a robust AI technology stack. This adaptability requires:

• Create a modular and open ecosystem: Update or replace architecture components to meet new requirements and adapt to rapidly changing technologies.
• Make data AI-ready and reusable: Integrate trust into FinOps, DataOps, and PlatformOps to transition from a tech stack to a trust stack.
• Explore AI Agents: Utilize dynamic agents that adapt to changes using an AI-ready data ecosystem powered by active metadata.

Journey to Behavioral Change: Addressing the Human element

Addressing the human aspect is critical for AI success. To foster a culture that supports AI adoption and utilization, D&A leaders should:

• Establish repeatable habits: Prioritize training and education, emphasizing data and AI literacy.
• Embrace new roles and skills: develop roles that facilitate adaptation to GenAI’s change management requirements.
• Collaborate with others: Work with diverse teams, including security and software engineering, for seamless integration.

Real-Time Analytics News in Brief

• Alation announced the launch of its Agentic Platform, designed to reinvent the data catalog for the AI era, empowering large-scale AI deployments with features like single sign-on (SSO), cloud role-based access control (RBAC), granular database API keys for granular RBAC, advanced monitoring and observability with Prometheus/OpenMetrics, and a cloud API for seamless automation.
• R Systems International launched its iot Smart C2C Connector, built on Amazon Web Services (AWS), to address challenges in managing and integrating diverse smart home devices. The connector enables secure bidirectional communication between smart home devices and OEM clouds.
• SIOS Technology announced that SIOS LifeKeeper and SIOS datakeeper clustering software have been validated for use with Cimcor’s cybersecurity solution, the CimTrak Integrity suite, ensuring continuous protection against cyber threats and minimizing downtime.
• Teradata announced Teradata Enterprise Vector Store, an in-database solution that brings the speed, power, and multi-dimensional scale of Teradata’s hybrid cloud platform to vector data management, crucial for Trusted AI.
• VDURA announced the launch of its V5000 All-Flash Appliance, engineered to address the escalating demands as AI pipelines and generative AI models move into production, setting a new benchmark for AI infrastructure scalability and reliability.

The Gartner Data & Analytics Summit highlighted the importance of addressing data quality, building a flexible technology stack, and fostering a supportive culture for prosperous AI deployment. By focusing on these three interdependent journeys, organizations can maximize the value of their AI initiatives and drive meaningful business outcomes. Now is the time to assess your organization’s readiness and begin implementing these strategies to stay ahead in the rapidly evolving landscape of AI and analytics.

Are we truly building an AI strategy that is not just innovative, but also ethically sound, responsible, and enduring for the long term?

Unlocking AI Potential: An Interview with Data strategy Expert, Anya Sharma

Understanding the roadmap for successful AI deployment is crucial for organizations today. We spoke with anya sharma, Chief Data strategist at Innovate Solutions, about key takeaways from recent industry insights and how businesses can navigate the complexities of AI adoption.

Anya, welcome! Recent reports highlight persistent data quality issues hindering AI deployment. What’s the biggest mistake companies are making?

The biggest mistake is underestimating the importance of data quality from the outset. Many organizations jump directly into AI projects without establishing robust data governance and quality frameworks. This leads to “garbage in,garbage out” scenarios,ultimately undermining the value of their AI initiatives. They need to focus on building trusted, high-quality data foundations.

Gartner emphasized three interdependent journeys: business outcomes, D&A capabilities, and behavioral change. Which of these is frequently enough overlooked, and why is it so critical?

I’d say behavioral change is frequently overlooked. Many companies invest heavily in technology and data infrastructure but neglect the human element. Successfully deploying AI requires fostering a data-driven culture, investing in data literacy training, and establishing new roles to manage the impact of AI. Without this cultural shift, even the most advanced AI models will fall flat.

Building a flexible AI technology stack seems daunting.What practical steps can companies take to ensure their architecture is adaptable and future-proof?

Start with a modular and open ecosystem. Avoid vendor lock-in and choose tools that integrate well with each other. Focus on creating reusable data assets and incorporating trust into every layer – from FinOps to DataOps. Actively explore and experiment with emerging technologies like AI agents,which can dynamically adapt to changes using an AI-ready data ecosystem.

Several companies, like Alation with its Agentic Platform and Teradata with its Enterprise Vector Store, are launching AI-focused solutions. How can organizations evaluate the value proposition of these new offerings?

Organizations should carefully assess how these solutions align with their specific business needs and existing infrastructure. Prioritize offerings that improve data quality, enhance data governance, and facilitate the creation of trusted AI models. Evaluate the total cost of ownership, including implementation, maintenance, and training. Don’t be afraid to pilot these solutions before making a full-scale investment.

Establishing trust models is highlighted as essential. Can you elaborate on what these models should encompass?

Trust models should assess the data’s value and risk. This involves evaluating data lineage, curation processes, and compliance with relevant regulations. The model should assign a trust rating to each data asset, providing users with the confidence to leverage it in their AI workflows.It’s about transparency and accountability.

With the rise of generative AI, what new roles and skills are becoming essential for data and analytics teams?

Roles focusing on AI ethics and responsible AI growth are crucial. We also need more data storytellers who can effectively communicate the insights generated by AI to business stakeholders. moreover,skills in prompt engineering and model evaluation are becoming increasingly critically important.

Data integration is always a challenge. How can companies address the complexities of integrating diverse data sources for AI?

A modern data catalog is essential for discovering, understanding, and governing diverse data sources. Leverage data virtualization and data federation technologies to access data without physically moving it. Invest in robust data quality tools to cleanse and transform data before it’s used in AI models.

Change management is mentioned as key in the report. What is one thing that companies can implement today to drive change?

Start with small, impactful projects that demonstrate the value of AI. This will help to build momentum and generate buy-in from stakeholders. Celebrate early wins and communicate the results widely.

Anya, thank you for sharing your insights. considering all the challenges and opportunities, what’s one question you think every data and analytics leader should be asking themselves right now about their AI strategy?

That’s a great question. I think every leader should be asking: “Are we truly building an AI strategy that is not just innovative, but also ethically sound, responsible, and sustainable for the long term?” I am curious to know, what are your thoughts?