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Gartner Top 10 Trends in Data and Analytics for 2020

In response to the COVID-19 emergency, over 500 clinical trials of potential COVID-19 treatments and interventions began worldwide. The trials use a living database that compiles and curates data from trial registries and other sources. This helps medical and public health experts predict disease spread, find new treatments and plan for clinical management of the pandemic.

Explore the latest: Gartner Top 10 Data and Analytics Trends for 2021

Data and analytics combined with artificial intelligence (AI) technologies will be paramount in the effort to predict, prepare and respond in a proactive and accelerated manner to a global crisis and its aftermath.

Download roadmap: IT Roadmap for Data and Analytics

“In the face of unprecedented market shifts, data and analytics leaders require an ever-increasing velocity and scale of analysis in terms of processing and access to accelerate innovation and forge new paths  to a post-COVID-19 world,” said Rita Sallam, Distinguished VP Analyst, during her presentation at virtual Gartner IT Symposium/Xpo™ 2020 .

Here are the top 10 technology trends that data and analytics leaders should focus on as they look to make essential investments to prepare for a reset.

Trend 1: Smarter, faster, more responsible AI

By the end of 2024, 75% of enterprises will shift from piloting to operationalizing AI, driving a 5X increase in streaming data and analytics infrastructures.

Within the current pandemic context, AI techniques such as machine learning (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 machine learning are critical realigning supply and the supply chain to new demand patterns.

“ Pre-COVID models based on historical data may no longer be valid ”

AI techniques such as reinforcement learning and distributed learning are creating more adaptable and flexible systems to handle complex business situations; for example, agent-based systems can model and stimulate complex systems - particularly now when pre-COVID models based on historical data may no longer be valid. 

Significant investments made in new chip architectures such as neuromorphic hardware that can be deployed on edge devices are accelerating AI and ML computations and workloads and reducing reliance on centralized systems that require high bandwidths. Eventually, this could lead to more scalable AI solutions that have higher business impact.

Responsible AI that enables model transparency is essential to protect against poor decisions. It results in better human-machine collaboration and trust for greater adoption and alignment of decisions throughout the organization.

Trend 2: Decline of the dashboard

Dynamic data stories with more automated and consumerized experiences will replace visual, point-and-click authoring and exploration. As a result, the amount of time users spend using predefined dashboards will decline. The shift to in-context data stories means that the most relevant insights will stream to each user based on their context, role or use. These dynamic insights leverage technologies such as augmented analytics, NLP, streaming anomaly detection and collaboration.

Data and analytics leaders need to regularly evaluate their existing analytics and business intelligence (BI) tools and innovative startups offering new augmented and NLP-driven user experiences beyond the predefined dashboard.

Trend 3: Decision intelligence

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 bring together 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 semi-automated, or must be documented and audited.

Trend 4: X analytics

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.

Data and analytics leaders use 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 (another top trend) will play a key role in identifying, predicting and planning for natural disasters and other business crises and opportunities in the future.

Data and analytics leaders should explore X analytics capabilities available from their existing vendors, such as cloud vendors for image, video and voice analytics, but recognize that innovation will likely come from small disruptive startups and cloud providers.

Источник: https://www.gartner.com/smarterwithgartner/gartner-top-10-trends-in-data-and-analytics-for-2020
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