Gartner Data & Analytics Summit Sydney: Day 2 Highlights
We are bringing you news and highlights from the Gartner Data & Analytics Summit 2023, taking place this week in Sydney, Australia. Below is a collection of the key announcements and insights coming out of the conference. You can read the highlights from Day 1 here.
On Day 2 of the conference, we are highlighting sessions on the foundations of AI; the top data and analytics trends; and the future of data science and machine learning.
Foundations of AI — Still Confused? A Minimal Viable Knowledge Set to Get Started With AI
Presented by Erick Brethenoux, Distinguished VP Analyst, Gartner
Erick Brethenoux, Distinguished VP Analyst at Gartner, provided a high-level introduction to AI and its practical impact on organizations at the Gartner Data & Analytics Summit in Sydney. In his presentation, Erick outlined 10 myths about AI.
New AI techniques and vendors continue to enter the market quickly and can create confusion for enterprises trying to determine how to implement AI in their organizations. In this session, Erick Brethenoux, Distinguished VP Analyst at Gartner, outlined how to pragmatically introduce AI techniques to solve complex business problems; steps to take (and avoid) while implementing these techniques; and best practices of organizations that are applying AI strategically.
Key Takeaways
“Educate, inform, evangelize, enlighten and demystify. It is better to light a small educational candle than curse the technology darkness. This helps manage executives, employees, business and SME expectations.”
“Demystify AI, remove technology jargon, and focus conversations on real business problems and achievable use cases.”
“Your first five projects should address and improve challenges in business now. Transformative projects should wait.”
“Take an inventory of current AI initiatives and build a community. Start syndicating AI talents and engaging champions.”
“AI is evolving from tools to teammates, but don’t worry, it won’t be your CEO any time soon.”
“Scout the organization for current challenges and business cases. Consider problems that can be objectively measured.”
Top Trends for Data and Analytics in 2023
Presented by Gareth Herschel, VP Analyst, Gartner
At the Gartner Data & Analytics Summit in Sydney, Gartner analyst Gareth Herschel highlighted the importance of agility in transforming siloed and disconnected data and analytics platforms into integrated D&A ecosystems.
Top trends in data and analytics (D&A) technology and practices can help anticipate change and transform uncertainty into opportunity. In this session, Gareth Herschel, VP Analyst at Gartner, shared how organizations can leverage these trends to drive new business growth, efficiency, resilience and innovation.
D&A leaders must focus on adopting a proactive approach to delivering value while also accepting the responsibility of owning the implications of the D&A activities.
Key Takeaways
“Top trends in D&A technology and practices create new sources of value by helping organizations anticipate change and transform extreme uncertainty into new opportunities.”
The top 10 trends for D&A in 2023 are:
Value optimization
Managing your artificial intelligence (AI) risk
Data sharing is essential
D&A sustainability
Observability
Practical data fabric
Emergent AI
Converged and composable ecosystem
Consumers become creators
Humans remain the decision makers
“Data observability provides broad visibility into an organization’s data landscape and multilayer data dependencies at all times, helping identify, control, prevent, escalate and remediate data outages rapidly within expectable SLAs.”
“D&A and AI are increasingly being used to monitor and benchmark environment, social and governance (ESG) goals and, more importantly, to improve sustainability.”
“Emergent AI enables organizations to apply AI in situations where it is not feasible today, making AI ever more pervasive and valuable.”
“Not every decision can or should be automated. Efforts to drive decision automation without considering the human role in decisions will result in a data-driven organization without conscience or consistent purpose.”
The Future of Data Science and Machine Learning: Critical Trends You Can't Ignore
Presented by Peter Krensky, Director Analyst, Gartner
Emerging machine learning (ML) capabilities that seemed unfamiliar a few years ago, are now shaping the future of artificial intelligence (AI). In this session, Peter Krensky, Director Analyst at Gartner, discussed innovative AI initiatives and how organizations can improve their current AI solutions.
Gartner analyst Peter Krensky pointed out to the audience that generative AI is an emerging technology that has only begun to be exploited commercially.
Gartner analysts said that leaders in technology innovation must take a risk-proportional approach to deliver AI value.
Key Takeaways
By 2025, more than 55% of all data analysis by deep neural networks will occur at the point of capture in an edge system. “Identify the applications, AI training and inferencing required to move to edge environments near IoT endpoints.”
By 2025, the concentration of pretrained AI models among 1% of AI vendors will make responsible AI a societal concern. “Adopt a risk-proportional approach to deliver AI value, protecting your organization from potential financial loss, legal action and reputational damage.”
By 2025, 55% of IT departments will adopt data ecosystems, which consolidates the vendor landscape by 40%, reducing cost and choice. “Evaluate data ecosystems based on your ability to resolve distributed data challenges, as well as to access and integrate with data sources outside of your immediate environment.”
By 2024, 60% of data for AI will be synthetic to simulate reality, future scenarios and derisk AI, up from 1% in 2021. “Secure data sharing via cleanrooms and federated learning.”
By 2027, 50% of developers will use ML-powered coding tools, up from less than 5% today. “Master prompt engineering, using a combination of natural language and coding practices to optimize code generation.”
By 2027, data science organizations will cut AI technical debt by 70% by using simulation platforms and technologies to manage complexity of AI systems. “Use adaptive AI to optimize applications to adapt to, resist or absorb disruptions.”
