Data and Analytics Conferences

Create a resilient data analytics strategy that aligns to business outcomes

Data and analytics strategies are shifting from “how to create a successful initiative” to “how do we use our capabilities to make the organization successful.” As a result, data and analytics leaders, including chief data officers, must include stakeholder needs analysis as part of their strategy. 

As organizations recognize the need to accelerate the use of technologies such as artificial intelligence (AI) and machine learning (ML) for maximum business impact, they also need to promote data literacy and ensure the quality of the data through data governance programs.

Join us to learn how to create and execute a data and analytics strategy that meets the ever-increasing demands of digital business. 

Gartner Data & Analytics Summit
4 – 6 May 2021  | Americas | Virtual 
18 – 20 May 2021 | EMEA | Virtual
8 – 9 June 2021 | APAC | Virtual
23 – 24 June 2021 | INDIA | Virtual

Challenges data and analytics leaders face in 2021

From accelerating digital business to cultural changes, data and analytics leaders will face serious challenges in 2021. Explore the challenges and solutions here!

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Gartner Data & Analytics Summit

Build and execute a data and analytics strategy as you drive business innovation. Join a community of trailblazers and industry experts pushing the bounds of data and analytics.
What’s covered

Discover what matters most now and how to prepare for what’s ahead:

  • Data and analytics everywhere
  • Leadership, skills, culture and value management
  • Composable data and analytics 
  • Accelerating AI and machine learning
  • Augmented data management
  • Privacy and ethics
Who should attend

Explore how we help you address your top challenges:

Heads of data and analytics, chief data officers (CDOs), and chief analytics officers (CAOs)
Leverage data as a key asset to construct frameworks to build and execute on a successful strategy.

Data and analytics program lead (VP, director, manager) 
Define and execute on strategy by aligning to business value and closing the data, technology, organization and skills capabilities gaps necessary for success.

Data governance and master data management (MDM)
Learn best practices for building an effective data governance strategy and how to communicate the value of MDM to the organization.

Architects
Build the right information strategy and architecture to help the business exploit data and analytics to improve business outcomes.

First-time attendee, and very impressed. It’s been inspiring and educational. Well done.
IT Manager, Services Australia

Gartner data and analytics conferences give you the unbiased advice to create an agile data and analytics strategy

Our team of Gartner experts conducts thousands of interactions every year with data and analytics leaders like you. They use this knowledge to help you address your biggest challenges in areas such as culture, digital trust, data literacy, augmented analytics and more. Take a deep dive into the technologies and best practices that will help you build a future-proof data and analytics strategy.

Join us for thought-leading guidance and practical tools on how to:

  • Empower everyone and everything in the organization to leverage data and analytics to optimize every decision, every process and every action
  • Overcome data and analytics complexity and ambiguity to drive business value
  • Drive innovation through leading technologies: AI, ML, virtual/augmented reality (AV/AR), the Internet of Things (IoT) and digital twins
  • Lead by building a foundation of trust, accountability, governance, security and privacy
  • Accelerate the adoption of AI and cut through the hype to develop a strategy that impacts the business
  • Develop a clear line of sight to concrete objectives and results
  • Foster new roles, (such as CDO), new ways of working and new data-driven thinking
  • Build a portfolio of innovative and relevant business, and industry-specific analytic applications
  • Exploit diverse data, thinking and teams to spark creativity and innovation
  • Harness the right organization, skills, strategy and technologies to build a data-centric culture to support digital business 

Find content aligned to your roles below

Communicate value to business leaders

Data and analytics leaders have to build an effective data and analytics strategy that will lead to significant business value. Gartner helps you understand the fundamentals for customizing a data and analytics vision and strategy for your enterprise that will engage key stakeholders.

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Scale the Value of Analytics

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The Best Ways to Organize Your Data Structures

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Take a look inside Gartner Virtual Conferences

Explore the latest trends in business and technology with the added interactive benefits of virtual. Get actionable insight, connect with peers, find inspiration and discover game-changing tech to lead your organization into the future. Gain an immersive virtual experience with the greatest minds in IT and business.

The Value of Gartner Event Sponsorship

Gartner top trends in data and analytics

Gartner top data and analytics trends help accelerate renewal, drive innovation and rebuild organizations and society over the next three to five years. Leverage these trends into must-have investments to enable recovery.

By the end of 2024, 75% of enterprises will shift from piloting to operationalizing AI, driving a fivefold increase in streaming data and analytics infrastructures.

What does smarter, faster and more responsible AI look like? First, AI systems will grow more adaptable to complex business situations — particularly important to businesses during COVID-19. AI techniques such as ML, optimization and natural language processing (NLP) are providing vital insights and predictions about the spread of the virus, and the effectiveness and impact of countermeasures. AI and ML are critical for realigning supply and the supply chain to new demand patterns.

As organizations increase investments in new chip architectures such as neuromorphic hardware, they’re reducing reliance on centralized systems that require high bandwidths. Eventually, this could lead to more scalable AI solutions that have greater business impact.

Finally, AI security and explainability is a concern, with AI data pipelines at risk. Responsible AI that enables model transparency is essential to protect against poor decisions, and to enable better human-machine collaboration and trust.

By 2025, data stories will be the most widespread way of consuming analytics, and 75% of stories will be automatically generated using augmented analytics techniques.

As a result of the shift to more dynamic, in-context data stories for insight monitoring and analysis, the percentage of time users spend in predefined dashboards will decline. With the amount of data increasing exponentially all the time, it can be difficult to identify and separate the valuable insights. Typical analysis currently requires a person with a specialized skill set, which limits the business impact of data.

However, over the next few years, augmented analytics will enable ML and AI techniques to automate certain data and analytics tasks. This, combined with NLP and business monitoring capabilities, will allow personalized data insights to be delivered to relevant business partners, increasing the business impact and decreasing the amount of predefined data dashboards in use.

By 2023, more than 33% of large organizations will have analysts practicing decision intelligence, including decision modeling.

Decision intelligence brings together a number of disciplines, including decision management and decision support. It encompasses applications in the field of complex adaptive systems that combine multiple traditional and advanced disciplines.

It provides a framework to help data and analytics leaders design, compose, model, align, execute, monitor and tune decision models and processes in the context of business outcomes and behavior.

Explore using decision management and modeling technology when decisions need multiple logical and mathematical techniques, must be automated or semiautomated, or must be documented and audited.

By 2025, AI for video, audio, vibration, text, emotion and other content analytics will trigger major innovations and transformations in 75% of Fortune 500 global enterprises.

Gartner coined the term “X analytics” to be an umbrella term, where “X”  is the data variable for a range of different structured and unstructured content such as text analytics, video analytics, audio analytics, etc.

X analytics enable organizations to combine content types and create a richer situation awareness beyond the insights that can be derived from only highly structured/transactional data. These new insights can then lead to a transformation or other innovation.

Data and analytics leaders are using X analytics to solve society’s toughest challenges, including climate change, disease prevention and wildlife protection.

During the pandemic, AI has been critical in combing through thousands of research papers, news sources, social media posts and clinical trials data to help medical and public-health experts predict disease spread, capacity-plan, find new treatments and identify vulnerable populations. X analytics, combined with AI and other techniques such as graph analytics, will play a key role in identifying, predicting and planning for natural disasters and other business crises and opportunities in the future.

By 2023, organizations utilizing active metadata, ML and data fabrics to dynamically connect, optimize and automate data management processes will reduce time to integrated data delivery by 30%.

Augmented data management uses ML and AI techniques to optimize and improve operations. It also converts metadata from use in auditing, lineage and reporting to powering dynamic systems.

Augmented data management products can automatically discover metadata from operational data, including actual queries, performance data and schemas. Using the existing usage and workload data, an augmented engine can tune operations and optimize configuration, security and performance.

Data and analytics leaders should look for augmented data management enabling active metadata to simplify and consolidate their architectures and connect data silos, and also increase automation in their redundant data management tasks.