Conference Updates

May 6, 2021

Gartner Data & Analytics Summit Americas: Day 3 Highlights

We are bringing you news and highlights from the Gartner Data & Analytics Summit, taking place this week virtually in the Americas. Below is a collection of the key announcements, and insights coming out of the conference. You can read highlights from Day 1 here and Day 2 here.

On Day 3 from the conference, we are highlighting how to optimize data and analytics value, the myths and pitfalls of artificial intelligence (AI), and how to recruit AI talent. Be sure to check this page throughout the day for updates.

Key Announcements

How to Optimize Data and Analytics Value: The New Strategic Imperative

Presented by Rita Sallam, Distinguished VP Analyst, Gartner

Data and analytics is a strategic business function that fuels digital acceleration. Yet, most organizations don’t have a systematic way to assess, optimize and articulate data and analytics value. In this session, Rita Sallam, Distinguished VP Analyst at Gartner, highlighted how CDOs, CIOs, and data and analytics leaders across the organization can optimize business impact and align to key business initiatives.

Key Takeaways

  • “We know from our chief data officer research and surveys that data and analytics leaders that are involved in strategy development are able to deliver far more business value than those who aren’t.”
  • “Data and analytics leaders struggle to optimize business value, and there’s often a lack of alignment to mission-critical priorities.”
  • “You as data and analytics leaders need to function as your organization’s ‘chief value officers.’”
  • “The ROAR (Risk, Opportunity, Appetite, and Return) Model gives you a systematic way to score both benefits and risks for each initiative and then think about the combination of those initiatives in terms of an optimal portfolio.”
  • “While we have models that can help you think through and prioritize, it’s what goes behind those models that’s important. Thinking trumps doing.”
  • “Optimizing value from data and analytics is a continuous process. It’s not just a once a year activity, it’s not just a quarterly activity — you need to think about it as central to everything else that you manage.”

Learn how to use data & analytics to re-engineer decision making in the free Gartner e-book “The Future of Decisions.”

Myths and Pitfalls of Artificial Intelligence and How to Navigate Them

Presented by Alexander Linden, VP Analyst, Gartner

Even as enterprise artificial intelligence (AI) maturity grows, many myths persist about this technology. In this session, Alexander Linden, VP Analyst at Gartner, discussed the most common myths and pitfalls facing AI and machine learning experts.

Key Takeaways

  • Myth #1: AI capabilities surpass human capabilities. “The deception is that they allowed the computer top five accuracy metrics. This means that the computer was allowed to make five guesses, and only one of those guesses has to be correct for the system to score ‘correct.’”
  • Myth #2: AI is disrupting industries. “When you apply AI it has so many use cases, which all will result in better cost savings, customer satisfaction, downtime reduction, risk reduction and more, but all these things that we’re going to see be better don’t necessarily translate into huge disruption.”
  • Myth #3: AI is about intelligence. “The systems that we create don’t understand much, they only react.”
  • Myth #4: AI can do anything. “The spectrum of problems that AI can tackle is surprising, but we have to say that AI is a point solution. A solution for fraud detection is not going to drive cars.”
  • Myth #5: AI will replace human intelligence. “With our flexibility to learn things, to understand things, and to be super fast and adaptive, it will take a long while before AI will replace human intelligence.”
  • Myth #6: AI can learn on its own. “If you look at the whole lifecycle of AI, only the advanced analytics is fully automated.”

AI Talent: Recruiting, Hiring, Organizing, Training and Retaining

Presented by Peter Krensky, Director Analyst, Gartner

What are the best practices for attracting and retaining successful artificial intelligence (AI) talent? What are the best options for upskilling, reskilling and education in data science and machine learning (ML)? In this session, Peter Krensky, Director Analyst at Gartner, covered trends around managing and developing not only data scientists, but the entire skills mix necessary to build data science teams.

Key Takeaways

  • “Acquisition, retention and development of AI talent is a challenge for most organizations.”
  • “According to Gartner survey research, 1 in 5 organizations believe they have the AI talent they need. The talent gap is narrowing, but procuring the right skills and experience will still be an ongoing challenge.” 
  • “Poor hiring strategies, inflated expectations and mismanagement are among the pitfalls of acquiring AI talent.”
  • “The four main buckets of roles within an AI group: 1) domain expert, 2) data scientist, 3) developer or ML specialist, 4) data engineers.”
  • “Create a culture and team structure that leverages and values supporting AI roles. Expect most data science teams to be small and distributed in the business.”
  • “It’s important to be realistic about the turnover that will come with good AI talent. Competitive salaries alleviate this as money is the #1 reason AI talent leaves.”
  • “Skill development and career building are vital to AI talent retention and optimal collaboration.”

About Gartner

Gartner, Inc. (NYSE: IT) delivers actionable, objective insight to executives and their teams. Our expert guidance and tools enable faster, smarter decisions and stronger performance on an organization’s mission-critical priorities. To learn more, visit


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