- February 21, 2019
- Posted by: admin
- Category: Uncategorized
Technology Trends 2019- Augmented analytics, continuous intelligence and explainable artificial intelligence (AI) are among the top trends in data and analytics technology that have significant disruptive potential over the next three to five years, according to Gartner, Inc.
Speaking at the Gartner Data & Analytics Summit in Sydney today, Rita Sallam, research vice president at Gartner, said data and analytics leaders must examine the potential business impact of these trends and adjust business models and operations accordingly, or risk losing competitive advantage to those who do.
“The story of data and analytics keeps evolving, from supporting internal decision making to continuous intelligence, information products and appointing chief data officers,” she said. “It’s critical to gain a deeper understanding of the technology trends fueling that evolving story and prioritize them based on business value.”
According to Donald Feinberg, vice president and distinguished analyst at Gartner, the very challenge created by digital disruption — too much data — has also created an unprecedented opportunity. The vast amount of data, together with increasingly powerful processing capabilities enabled by the cloud, means it is now possible to train and execute algorithms at the large scale necessary to finally realize the full potential of AI.
“The size, complexity, distributed nature of data, speed of action and the continuous intelligence required by digital business means that rigid and centralized architectures and tools break down,” Mr. Feinberg said. “The continued survival of any business will depend upon an agile, data-centric architecture that responds to the constant rate of change.”
Gartner recommends that data and analytics leaders talk with senior business leaders about their critical business priorities and explore how the following top trends can enable them.
Trend No. 1: Augmented Analytics
Augmented analytics is the next wave of disruption in the data and analytics market. It uses machine learning (ML) and AI techniques to transform how analytics content is developed, consumed and shared.
By 2020, augmented analytics will be a dominant driver of new purchases of analytics and BI, as well as data science and machine learning platforms, and of embedded analytics. Data and analytics leaders should plan to adopt augmented analytics as platform capabilities mature.
Trend No. 2: Augmented Data Management
Augmented data management converts metadata from being used for audit, lineage and reporting only, to powering dynamic systems. Metadata is changing from passive to active and is becoming the primary driver for all AI/ML.
Augmented data management leverages machine learning capabilities and AI engines to make enterprise information management categories including data quality, metadata management, master data management, data integration as well as database management systems (DBMSs) self-configuring and self-tuning. It is automating many of the manual tasks and allows less technically skilled users to be more autonomous using data. It also allows highly skilled technical resources to focus on higher value tasks.
Through to the end of 2022, data management manual tasks will be reduced by 45 percent through the addition of ML and automated service-level management.
Trend No. 3: Continuous Intelligence
By 2022, more than half of major new business systems will incorporate continuous intelligence that uses real-time context data to improve decisions.
Continuous intelligence is a design pattern in which real-time analytics are integrated within a business operation, processing current and historical data to prescribe actions in response to events. It provides decision automation or decision support. Continuous intelligence leverages multiple technologies such as augmented analytics, event stream processing, optimization, business rule management and ML.
“Continuous intelligence represents a major change in the job of the data and analytics team,” said Ms. Sallam. “It’s a grand challenge — and a grand opportunity — for analytics and BI (business intelligence) teams to help businesses make smarter real-time decisions in 2019. It could be seen as the ultimate in operational BI.”
Trend No. 4: Explainable AI
AI models are increasingly deployed to augment and replace human decision making. However, in some scenarios, businesses must justify how these models arrive at their decisions. To build trust with users and stakeholders, application leaders must make these models more interpretable and explainable.
Unfortunately, most of these advanced AI models are complex black boxes that are not able to explain why they reached a specific recommendation or a decision. Explainable AI in data science and ML platforms, for example, auto-generates an explanation of models in terms of accuracy, attributes, model statistics and features in natural language.
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Article Credit: CIOL
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