Key Highlights from the Gartner Data & Analytics Summit 2023: The Gartner Data & Analytics Summit is one of the most anticipated events in the field of data and analytics. The summit brings together thought leaders, industry experts, and technology innovators to discuss the latest trends and best practices in data management, analytics, and business intelligence. As we approach the 2023 edition of the summit, there is growing excitement about the new ideas and innovations that will be presented. In this article, we will explore the key highlights from the Gartner Data & Analytics Summit 2023 and provide insights into the emerging trends and technologies that are shaping the future of data and analytics.
Orlando: Day 1 Highlights
AI Maturity Recipes: Lessons from Known Successes and Failures
Artificial intelligence (AI) technology has an influence on many business challenges. To help data and analytics leaders accelerate and improve their AI strategy and implementations and to make the most of AI technology, Gartner has created an AI maturity model. In this webinar, Svetlana Sicular, VP Analyst at Gartner, discussed how data and analytics professionals may use Gartner’s AI maturity model to enable innovative business models.
Create a complete AI mechanism to spread AI across society. By accomplishing the following, you may raise the maturity of AI use cases:
initial planning and creation of use cases experimenting in a pilot with use cases that have shown commercial value AI strategy stabilisation with a plan for multiple use cases Democratizing use cases while industrialising more Utilize case transformation to support new operational and business models “Align your AI capabilities with your AI ambitions by matching the maturity stages to your own activities for AI-powered applications and projects.”
Create a framework for AI that can support all roles. “Develop artificial intelligence technology, put it into operation, and scale it.” “Invest in technical and human frameworks that ease stage changes.”
The Enterprise Implications of ChatGPT and Generative AI
The one trait that people have always believed robots would possess is creativity, yet ChatGPT and its family of models are the first publicly acknowledged AI technologies to contradict this assumption. “Foundation models, despite being a significant advance, still require careful training and, due to their black-box nature, can deliver unacceptable results,” according to the study. “Deep fakes that closely resemble the original are simpler to produce because foundation models lower the cost of content creation.” This includes anything from forgeries and targeted attacks to voice and video imitation.
Allow for smooth human-machine connection by experimenting with various cues for the current issue. Even though it may appear identical to humans, differing formulations of the same stimulus might produce radically different generations. “The raw potential of generative AI grows as more robust and powerful models emerge, but its safety and veracity remain in question.”
Gartner Data & Analytics Summit 2023 Orlando: Day 2 Highlights
How D&A and AI Drive Better Sustainability for your Organization
“A critical mission priority is sustainability.” 80% of boards anticipate increasing their expenditure on sustainability efforts over the next two years, according to Gartner research. Environmental, social, and governance are the three main facets of sustainability that D&A has the capacity to impact. Aerial and satellite photos, meteorological data, vegetation, and soil data may all be analysed using artificial intelligence (AI) in agriculture. These data can then be integrated with simulation and reinforcement approaches to examine wildfire risk, prevention, spreading, and mitigation.
From a societal perspective, “AI can help with workplace safety, diversity, and financial equity.” AI may be applied to governance to harmonise regulations and promote compliance. To keep businesses compliant and quickly adjust their practices, artificial intelligence techniques are being used to monitor specific regulation changes in local and federal rules. “While D&A and AI can be beneficial, we also need to take the environment into account.” AI has a significant carbon effect because of growing data quantities and more complex machine learning models. Focus on energy use by adopting more energy-efficient technology, better cooling, residual heat utilization, and renewable energy choices to address D&A and AI concerns.
Gartner Data & Analytics Summit 2023 Orlando: Day 3 Highlights
Top Data and Analytics Predictions, 2023
By 2026, 5% of workers will regularly use AI to do tasks against the will of their employers. AI will change how all work is done in the future. The range of worker skills will have increased by 800% by the year 2026 as 20% of the top data science teams will have changed their names to cognitive science or scientific consultancies. “Data science will develop and broaden to include more disciplines as a result of its pervasiveness.” By 2026, 50% of business intelligence systems will interact with the metadata of their users, delivering data stories, insights, and contextualised experiences. “D&A teams must move away from dashboards and towards stories while using metadata to personalise the user experience.”
By 2027, 80% of corporate marketers will have a dedicated content authenticity department to combat misinformation and false material. “The ramifications and capacities of AI technology are developing far more quickly than humans or companies can grasp them.Prioritizing responsible artificial intelligence is necessary. By 2026, at least one data and analytics-based product will be managed by more than a quarter of Fortune 500 CDAOs and become a top earner. “CDAOs can enhance organisational performance while also bolstering their own position by collaborating with the Chief Digital Officer to drive digital product revenue growth.”
By 2026, more than 60% of data management operations will take environmental sustainability into account critically, supported by financial governance practises. “Within a cloud ecosystem, cost management outcomes are inextricably linked to environmental sustainability initiatives.”
What You Need to Do About New AI Risks
“Regulatory compliance is the main factor causing privacy, security, and/or risk to be implementation barriers for AI.” “AI breaches and hacks are widespread and have many flavours.” Organizations must actively consider compromises from both inside and outside parties. “Collective organisational management of AI privacy, security, and risk results in improved AI business outcomes.” “Data and analytics leaders must organise and prioritise AI trust, risk, and security management (AI TRiSM) in order to address new AI risks.”
For AI TRiSM, the top five priorities are as follows:
1. AI Inventory: By taking an inventory of AI within the company and ensuring the right degree of explainability, you may ascertain the exposure’s breadth.
2. AI Risk Awareness: Run a structured AI risk education plan to increase awareness among personnel in the firm.
3. Data protection and privacy: Adopt data protection and privacy initiatives to prevent the disclosure of shared and internal AI data.
4. Strong ModelOps: By including risk management into model operations, you may increase model dependability, reliability, and security. Adopt specific AI security measures to provide resistance and resilience against adversarial attacks.
5. AI Security and Resilience: By 2026, adoption, business objectives, and user acceptance will all have improved 50% for firms that operationalize AI transparency, trust, and security. To handle AI privacy, security, and risk, organisational peers with an interest are required to work together.
Articulating the Value of a D&A Initiative in Four Easy Steps
Four phases make up the process of expressing D&A value:
1. Tying data challenges and opportunities to corporate goals
2. Estimating a data improvement’s potential value
3. Calculating a D&A investment’s value
4. Visualizing and explaining the benefit to business leaders “Demonstrating improvements to decisions that will drive strategic goals should be at the heart of making the case for data investments,” says the author.
“Building a data-driven culture” entails more than just transforming the information architecture on a broad scale; it also entails a number of subtle, over time, shifting staff decision-making practises. The four tiers of success that D&A teams should monitor in terms of how their business uses data to create value are results, choices, D&A initiatives, and data measures. Data, data management, and data literacy programmes should be focused on enhancing the inputs and results of improved analysis and decision-making.
The 8th and 9th of June 2021 saw a virtual version of the Gartner Data & Analytics Summit in India. The summit’s topic, “Leading with Resilience: Analytics and AI for Accelerating Digital,” centred on how data and analytics can support organisations in thriving in the quickly evolving corporate landscape of today.
Among the important subjects covered at the summit were:
Creating a culture that is data-driven Including AI and analytics in business operations constructing a flexible platform for data and analytics driving digital transformation using data and analytics By using data and analytics, the customer experience may be improved. addressing moral issues with analytics and data.
The conference included networking opportunities, interactive workshops, and keynote addresses from prominent members of the business. Senior leaders from top companies, industry experts, and Gartner analysts were among the keynote speakers at the occasion.
Anyone interested in the most recent developments in data and analytics should attend the Gartner Data & Analytics Summit in 2023. The summit will offer invaluable insights and tactics for organizations looking to use data and analytics to promote business growth and resilience, from developing end-to-end AI processes and raising the maturity of use cases to aligning AI capabilities with ambitions and evolving AI frameworks. Businesses can expedite the adoption of AI-powered products and projects and realize the full potential of data and analytics by investing in human and technological frameworks that make the transition between phases easier.