We are bringing you news and highlights from the Gartner Data & Analytics Summit, taking place this week in Orlando, Florida. Below is a collection of the key announcements and insights coming out of the conference.
On Day 1 from the conference, we are highlighting the Gartner opening keynote presentation on leading for purpose and making an impact, as well as sessions on enterprise AI maturity and the future of generative AI. Be sure to check this page throughout the day for updates.
Presented by Rita Sallam, Distinguished VP Analyst and Kurt Schlegel, VP Analyst, Gartner
Data and analytics (D&A) leaders can deliver increasing value to their organizations. In this session, Rita Sallam, Distinguished VP Analyst at Gartner and Kurt Schlegel, VP Analyst at Gartner highlighted new ways for D&A leaders to connect with stakeholders, address skills shortages and lead with purpose to ensure the data-driven transformation required for success.
“The most common roadblocks to the success of D&A initiatives are all human-related challenges, such as skills shortages, lack of business engagement, difficulty accepting change and poor data literacy throughout the organization.”
“D&A leaders are still not speaking the language of business, but we’re under intense pressure to demonstrate the value of our initiatives to other business peers and executives.”
“Much of what we do in D&A is strategy enablement, which by definition plays out over a longer period of time. We need a new way to talk about investments in D&A to show future impact and how they meet the needs of multiple stakeholders.”
“Think about speaking the language of business. Now more than ever, D&A leaders must have a translation layer between their D&A initiatives and the organization’s mission-critical priorities.”
“Make business stakeholders the hero in your business value story by meeting their KPIs and enabling their objectives by connecting them with data and analytics insights.”
“Culture is the reason we say we fail. Even the highest ranking executives in your organization can’t change the whole culture, but you can change the culture around you to a data-driven one.”
Presented by Svetlana Sicular, VP Analyst, Gartner
Artificial intelligence (AI) technologies affect many business challenges. Gartner has crafted an AI maturity model to accelerate and optimize data and analytics leaders’ AI strategy and implementations to achieve the best value from AI technologies. In this session, Gartner VP Analyst Svetlana Sicular discussed how data and analytics leaders can use Gartner’s AI maturity model to enable new business models.
“Figure out an end-to-end AI process in order to make AI pervasive.”
Increase the maturity of AI use cases by:
Planning and framing initial use cases
Experimenting with proven business values of uses cases in a pilot
Stabilizing AI strategy that includes a roadmap for a variety of use cases
Expanding industrialization and democratization of use cases
Transforming use cases to support new business models and processes
“Align your AI capabilities with your AI ambitions by matching the maturity stages to your own activities for AI-powered applications and projects.”
“Evolve AI framework to accommodate all roles.”
“Engineer, operationalize and scale AI technology.”
“Invest in human and technical frameworks that ease the transition between stages.”
Presented by Arun Chandrasekaran, Distinguished VP Analyst, Gartner
Due to their massive scale and broad use-case applicability, foundation models such as ChatGPT are a significant AI advancement, but the major risks they pose aren’t fully understood. In this session, Arun Chandrasekaran, Distinguished VP Analyst at Gartner, evaluated the benefits, risks and opportunities of these models to generate business value.
“ChatGPT and its family of models are the first widely known AI technology that challenges the one trait humans always thought they would have over machines: creativity.”
“Although a significant advance, foundation models still require careful training, and they can deliver unacceptable results due to their black-box nature.”
“Foundation models lower the cost of content creation, which means it becomes easier to create deep fakes that closely resemble the original. This includes everything from voice and video impersonation to fake art, as well as targeted attacks.”
“Foundation models are capable of generating original and coherent text in a highly contextualized way, which lends itself to use cases such as writing headlines and paragraphs, creating product descriptions, generating conversational responses and completing text.”
“It is important to instill responsible AI practices when using AI foundation models. This includes publishing usage guidelines and terms of use, proactively mitigating harmful model behavior and collaborating with stakeholders to seek diversity and publish lessons learned.”
“Facilitate seamless human-machine interaction by trying a range of different prompts for the problem you are trying to solve. Different formulations of the same prompt which might sound similar to humans can lead to generations that are quite different from each other.”
“The raw potential of generative AI is enormous as more powerful and versatile models emerge, but its safety and veracity remain questionable.”
