Traditional data management operations are expected to be disrupted as sophisticated analytics such as machine learning and artificial intelligence gain traction. Gartner has highlighted a number of data and analytics themes for this year. While effective data management has long been a core practise for business intelligence and analytics, enterprises will need to alter their practises in the coming years to meet the demands of advanced analytics technologies such as machine learning and other artificial intelligence. Gartner's recent Data and Analytics Trends study for 2022 came to this conclusion. "Building AI without the correct data can be problematic, if not dangerous," argues Gartner VP Rita Sallam.

While strong data management has long been a foundational practice for business intelligence and analytics, enterprise organizations will need to update what they do to meet the needs of growing advanced analytics implementations such as machine learning and other artificial intelligence in the years ahead. That was one of the conclusions of Gartner's recent Data and Analytics Trends report for 2022. "Without the right data, building AI can be risky and even dangerous," says Gartner VP Rita Sallam. "Most organizations, from a data management perspective, address very important AI-specific considerations for data management like data bias, diversity, labeling. They often address those things haphazardly."

The analytics firm has released its top 10 data and analytics technology trends for 2020 that it says can help organisations prepare for a post-pandemic reset. "To innovate their way beyond COVID-19, data and analytics leaders require an ever-increasing speed and scale of analysis in terms of both processing and access to succeed," explains Rita Sallam, research vice president at Gartner. By the end of 2024, 75% of organisations will shift from piloting to operationalising artificial intelligence (AI), driving a 5x increase in streaming data and analytics infrastructures. "Within the current pandemic context, AI techniques such as machine learning (ML), optimisation and natural language processing (NLP) are providing vital insights and predictions about the spread of the virus and the effectiveness and impact of countermeasures," Sallam. "Other smarter 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 that model and stimulate complex systems."

Businesses that trust AI to operate will leverage different kinds of data input and infuse automation into how they extract insights. The year began with an ambitious data mandate for organizations: leverage data analytics and AI techniques to keep up with the competition and increase efficiency. Pressed by the challenges of a redrawn business landscape, leaders searched for guidance in their data and analytics toolkit. In the pivot to distributed work, AI helped field rising help desk requests from a mobile workforce. Data analytics informed leaders in near-real time how consumption patterns shifted, helping manage supply chain constraints.

Gartner has released a report with its top 10 data and analytics (D&A) technology trends for 2021. Business insights are rapidly becoming an integral part of any organisation's operations. A holistic look at these trends shows that the market is now moving toward refining the use of technologies and behaviours to improve efficiencies and gain higher-value results. Distinguished research vice president Rita Sallam said, "The speed at which the COVID-19 pandemic disrupted organisations has forced D&A leaders to have tools and processes in place to identify key technology trends and prioritise those with the biggest potential impact on their competitive advantage." Machine learning has become a ubiquitous technology in almost every area of IT.

Gartner, Inc. identified the top 10 data and analytics (D&A) technology trends for 2021 that can help organisations respond to change, uncertainty and the opportunities they bring in the next year. "The speed at which the COVID-19 pandemic disrupted organisations has forced D&A leaders to have tools and processes in place to identify key technology trends and prioritise those with the biggest potential impact on their competitive advantage," said Rita Sallam, distinguished research vice president at Gartner. D&A leaders should use the following 10 trends to determine investments that accelerate their capabilities to anticipate, shift and respond. The greater impact of artificial intelligence (AI) and machine learning (ML) requires businesses to apply new techniques for smarter, less data-hungry, ethically responsible and more resilient AI solutions. By deploying smarter, more responsible, scalable AI, organisations will leverage learning algorithms and interpretable systems into shorter time to value and higher business impact.

Artificial intelligence and machine learning are key factors. Businesses must apply new techniques for smarter, less data-hungry, ethically responsible and more resilient AI solutions. When smarter, more responsible, scalable AI is applied, organizations will be able to "leverage learning algorithms and interpretable systems into shorter time to value and higher business impact," Gartner's report said. Composable data and analytics leverages components from multiple data, analytics and AI solutions to quickly build flexible and user-friendly intelligent applications to help D&A leaders make the correlation between the discovered insights to actions they must execute. Open, containerized analytics architectures make analytics capabilities more composable.

Business intelligence and analytics platform vendors are now providing augmented analytics capabilities that empower citizen data scientists. Specifically, they use natural language understanding capabilities to enable natural language searches and deliver the results using natural language generation. The result is a "conversation" between the user and the system. Augmented analytics tools also come with pre-built machine learning models to empower any user to do single click forecasts, identify trends and trend reversals, anomalies, outliers -- tasks that in the past required involvement from professional data scientists. In short, the opportunities are many, but many enterprises have a way to go before their businesses are truly "insight-driven."

Augmented analytics is entering the mainstream in 2021, which means more enterprise organizations will be able to take advantage of its benefits to accelerate business intelligence, machine learning, and other forms of artificial intelligence in their organizations, whether that means more production projects or faster insights for decision makers. But just what is augmented analytics? But it is the idea of leveraging technologies such as machine learning and analytics to help automate the entire data management pipeline from data preparation to generating insights to assisting with building models and operationalizing them. That's crucial because data science and machine learning are complex and difficult. That's why just a few years ago so many organizations were struggling to hire "unicorn" data scientists who were experienced in three different areas: statistics, coding, and a specific business domain.

Enterprises struggling to get their data management and machine learning practices up to speed in an era of more and more data may be in for a nice surprise. After years of bending under the weight of more data, more need for insights, and a shortage of data science talent, augmented analytics is coming to the rescue. What's more, it could also help with putting machine learning into production, something that has been an issue for many enterprises. Identified as a major trend by Gartner at its Symposium event last year, augmented analytics has been around for several years already, according to Rita Sallam, distinguished research VP and Gartner fellow. But in recent years the concept has expanded to encompass automation of many of the processes that are required by the entire data pipeline.