Gartner Data & Analytics Summit 2024 Sydney: Day 1 Highlights

We are bringing you news and highlights from the Gartner Data & Analytics Summit 2024, taking place this week in Sydney, Australia. Below is a collection of the key announcements and insights coming out of the conference.

On Day 1 from the conference, we are highlighting sessions on how to make data AI-ready; AI risk and governance becoming a societal concern; and how to measure and quantify the cost, risk and value of AI initiatives. 

• Gartner Predicts 30% of Generative AI Projects Will Be Abandoned After Proof of Concept By End of 2025

Presented by Nina Showell, Principal Analyst, Gartner

Chief data and analytics officers (CDAOs) and heads of data management are being asked to make their data AI-ready as their companies are eager to invest in AI and machine learning (ML). In this session, Nina Showell, Principal Analyst at Gartner, discussed what AI-ready is and how data management should evolve to support these requirements.

• “AI-ready data means that your data must be representative of the use case, of every pattern, error, or outlier that is needed to train the AI model."
• “Having data ready for AI drives greater business outcomes by 20%.”
• “Formal data management practices are not what you need. You need to know all the formal and informal ways data is managed.”
• “To prove data is AI-ready, D&A teams will need to be capable of quickly iterating and converging to identify data that is fit for use.”
• “Make data management an AI strategic priority. Put in place cross-functional teams to support your AI-ready data requirements.”

Presented by Svetlana Sicular, VP Analyst, Gartner

The hype around generative AI (GenAI) and the concentration of pretrained AI models in the hands of a few providers make it a societal concern. Organizations must develop AI governance to get ready for new AI regulations. In this session, Svetlana Sicular, VP Analyst at Gartner, discussed how AI governance can balance AI value and risk. 

• “AI is not just a technology, it’s a leadership responsibility.”
  
• “By 2027, the productivity value of AI will be recognized as a primary economic indicator of national power.” 
• “AI is the only industry ever that asked to be regulated because there are so many gray areas.”
• “Become familiar with the AI policy and regulation efforts underway in all jurisdictions where you operate. Use them as a baseline for your own policies, but don’t rely on them alone to prepare your organization.”
• “Determine criticality of use cases to assess how much risk is appropriate. Differentiate your governance depending on criticality.” 

Presented by Luke Ellery, VP Analyst, Gartner

Cost is one of the greatest near-term threats to AI success. Calculating that cost represents a significant challenge for leaders. In this session, Luke Ellery, VP Analyst at Gartner, discussed how to identify the cost drivers for AI, as well as the ensuing risks that need to be estimated, managed and mitigated.

• “Effectively estimate cost, risk and value by categorizing opportunities into three initiative types - ‘defend’ for marginal gains and micro-innovations; ‘extend’ to grow market size, reach, revenue or profitability; and ‘upend’ to bring new products to market quickly.”
• “Build a cost estimate by focusing on the cost factors relevant to your initiative type. Get your assumptions correct, otherwise costs can blow out 5 to 10 times their estimates.”
• “Mitigate risk by allocating budget, resources, training and tools to ensure a safe, secure, ethical and reliable AI solution. Don’t under invest, as it could be detrimental to your organization’s brand and reputation.”
• “Predict your GenAI cost and cost risk by modeling several scenarios incorporating over 100 different cost line items.”
• “While productivity is the leading benefit most organizations expect from AI initiatives, maximize value realization by incorporating multiple benefit measures of GenAI.”