Data and Analytics Conferences

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. AI techniques such as ML, optimization and natural language processing (NLP) are providing vital insights and predictions for the future of business. 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 was 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.