Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Gartner has released its latest Hype Cycle for emerging technologies, giving a glimpse at 25 embryonic and early-stage technological advances that can help enterprises differentiate and drive growth in the coming years. Modern organizations are racing to innovate to stay ahead of the competition and drive technology-enabled business transformation. They are watching market trends, but with hundreds of advances happening worldwide and the surrounding publicity, it becomes difficult to separate hype from reality.

Gartner has identified three key areas that it believes data and analytics (D&A) companies should move to implement into their strategy in 2022. "This year's top D&A trends represent business, market and technology dynamics that will help organisations anticipate change and transform uncertainty into opportunity, both of which have come under the purview of the D&A leader," Gartner distinguished research vice president Rita Sallam says. Gartner says the rise of adaptive AI systems, such as AI engineering, drives growth and innovation while coping with fluctuations in global markets, adding that innovations in this area are needed to best utilise D&A. In particular, data management for AI, automated, active metadata-driven approaches and data-sharing competencies that are all founded on data fabrics. Citing the'always share data' trend, Gartner says this proves the value of data sharing as a business-facing key performance indicator by demonstrating that an organisation is achieving effective stakeholder engagement and increasing access to the right data to generate public value.

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.

For years we've been told that data science is the future; that artificial intelligence (AI) and machine learning (ML) will enable us to automate everything. And yet most (85%) data science projects fail, according to a 2019 report, though such scare statistics might not reflect reality. Still, there are plenty of reasons why a data science project might not work as advertised, but one reason stands out: Talent. Or, rather, the lack thereof, as Gartner has highlighted. If you're thinking, "Well, I'll just send my recruiters to LinkedIn to scour for talent," I have news for you: It's not going to work.

Gartner has unveiled its biggest predictions for IT organisations and users for 2020 and beyond. These predictions analyse how technology is changing society and the expectations of users. "Technology is changing the notion of what it means to be human," said Daryl Plummer, VP and Fellow at Gartner. "CIOs in end-user organizations must understand the effects of the change and reset expectations for what technology means." "This year's predictions help us move beyond thinking about mere notions of technology adoption and draw us more deeply into issues surrounding what it means to be human in the digital world," said Plummer.

As a recent article in the Wall Street Journal points out, artificial intelligence (AI) is becoming one of the most important technological advances of our era. It uses statistical methods and very large datasets to identify patterns and predict outcomes, but it still has a ways to go before it can identify cause-and-effect relationships. Being able to do this, however, just may represent the next frontier in AI. According to the Wall Street Journal article, determining causal relationships requires tried and true scientific, empirical and measurable methods that can "detect faint signals within large and/or noisy data sets -- the proverbial needle in a haystack." It's one thing to use statistical methods and very large data sets to find patterns that, for example, can identify the presence of a mass on an Xray, but it's another thing entirely to identify how a specific treatment will affect the outcome.

IT and business leaders are often confused about what artificial intelligence (AI) can do for their organisations and are challenged by several AI misconceptions. Gartner has said IT and business leaders developing AI projects must separate reality from myths to devise their future strategies. "With AI technology making its way into the organisation, it is crucial that business and IT leaders fully understand how AI can create value for their business and where its limitations lie. AI technologies can only deliver value if they are part of the organisation's strategy and used in the right way," said Alexander Linden, Research Vice President at Gartner. Gartner has identified five common myths and misconceptions about AI.