Now that data is the most transformative asset in business, it's essential to prepare for what lies ahead, and to adjust strategies accordingly in order to successfully face the business landscape of tomorrow. We have identified 10 trends happening in 2020 that will be catalysts and enablers for change, and they will drive companies to enhance capabilities to stay at the forefront of innovation. They will allow data to be consumed dynamically and in different ways, causing people to search and think of new ways to use data. Given Trends These trends are a must, and they require action now. It's apparent that legacy on-premises platforms have failed to make data accessible to all users.

Traditionally, banks targeted older customers for wealth management services, assuming that this age group would be the most interested. Using augmented analytics, banks found that younger clients (aged 20 to 35) are actually more likely to transition into wealth management -- a clear example of how relying on business users to find patterns, and on data scientists to build models manually, may result in bias and incorrect conclusions. Augmented analytics is just one of the top 10 technologies Gartner has identified with the potential to address these and other major data and analytics challenges in the next three to five years. Digital transformation has put data at the center of every organization. Businesses are awash with data.

Artificial intelligence uses vast amounts of data and sophisticated probabilistic algorithms to offer "the intimacy of a small town in a big city scale," Gartner VP Svetlana Sicular said at the company's annual IT Symposium last week. But she said, the growth of AI applications in deployment was actually less this year than last year, with the total percentage of CIOs saying their company has deployed AI now at 19 percent, up from 14 percent last year. That's a nice increase, but it's far lower than the 23 percent of companies that thought they would newly roll out AI in 2019. She said, "something is stalling AI adoption." She noted that when asked what challenges they faced in adopting AI, the top concerns are the lack of skills on staff, the quality of the data they have available, and also understanding the real benefits and uses of AI.

The recent AI revolution was based on a few important pillars: abundance of data, lower costs in storing it, faster computing speed and distributed computing. To date, however, there still are some critical steps that are missing to achieve true continuous intelligence. Continuous intelligence refers to a design in which real-time analytics is seamlessly integrated within a business operation to support decision automation. Data processing can therefore be used to respond real-time to events. In order to achieve true continuous intelligence we are still missing a couple of key elements, in particular explainable AI and better graph analytics. Deep Learning uses multi-layers neural nets that have been extremely popular in recent years, in particular for computer vision.

Augmented analytics and artificial intelligence (AI) are among the top data and analytics technology trends that have the potential to significantly change business operations in the next three to five years, according to a presentation at the Gartner Data and Analytics Summit in Sydney this week. Data and analytics leaders must examine the potential business impact of these technology trends, and adjust business models accordingly--or risk losing competitive advantage to companies that do, Rita Sallam, research vice president at Gartner, said at the event and in a press release. "The story of data and analytics keeps evolving, from supporting internal decision making to continuous intelligence, information products and appointing chief data officers," Sallam said in the release. "It's critical to gain a deeper understanding of the technology trends fueling that evolving story and prioritize them based on business value." With digital transformation efforts underway at most organizations, businesses are collecting more data than ever before, creating challenges but also major opportunities, Donald Feinberg, vice president and distinguished analyst at Gartner, said in the release.

What do technology leaders and professionals need to do to help their organizations achieve the holy grail of continuous intelligence? Look to artificial intelligence and machine learning to pave the way. However, achieving a state of continuous intelligence isn't an overnight sprint by any means -- many organizations aren't quite ready to bring together the adroit data management, automation, processes and skills needed to make things happen. That's the word from a three-part series published by ThoughtWorks, which advocates an approach it calls Continuous Delivery for Machine Learning (CD4ML), "a software engineering approach in which a cross-functional team produces machine learning applications based on code, data, and models in small and safe increments that can be reproduced and reliably released at any time, in short adaptation cycles." Employing data "to produce tangible outcomes for business is the real value driver and for that, we are seeing the world moving more towards intelligence," write Ken Collier, Mark Brand and Pramod N, all with ThoughtWorks.

Business today is more than simply matching traditional competitors, it's about exploiting digital technologies to create new opportunities, and being able to repeat this. The economy is quickly going digital and Australian businesses must evolve into Modern Digital Businesses (MDBs) which strategically use intelligence assets to improve operations and deploy new products and services, in order to stay competitive and create value for their customers. A group of digital business leaders recently gathered at ThoughtWorks Live in Sydney and Melbourne, to share their insights into how organisations can take advantage of data to adapt and thrive in the digital economy. This report includes strategic and practical advice taken from the event for any business leader – regardless of their organisation's digital maturity – on best practices for taking advantage of data and driving change. A Continuous Intelligence (CI) framework starts with the process of acquiring data and, with the help of analytics and machine learning, derive insights from it to be able to make confident decisions and actions – which are in turn reviewed and validated, to ensure the organisation continuously improves its decision-making capabilities. Steps organisations can take to apply CI to building an MDB, which is agile and technology-driven are also covered.

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.

As data continues to pile up, enterprises that maintain flexible approaches to managing and mining that data are the ones most likely to achieve competitive success, according to Gartner, which recently released its top 10 analytics technologies and trends for 2019. The Global Datashere currently measures 33 zettabytes, according to a recent IDC report, and is predicted to grow to 175 zettabytes by 2025. Navigating this data deluge is no simple matter, as the volume and velocity exceeds the capabilities of existing data analytics rigs running atop legacy architectures. "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," explains Donald Feinberg, vice president and distinguished analyst at Gartner. "The continued survival of any business will depend upon an agile, data-centric architecture that responds to the constant rate of change."

Augmented analytics and artificial intelligence (AI) are among the top data and analytics technology trends that have the potential to significantly change business operations in the next three to five years, according to a presentation at the Gartner Data and Analytics Summit in Sydney this week. Data and analytics leaders must examine the potential business impact of these technology trends, and adjust business models accordingly--or risk losing competitive advantage to companies that do, Rita Sallam, research vice president at Gartner, said at the event and in a press release. "The story of data and analytics keeps evolving, from supporting internal decision making to continuous intelligence, information products and appointing chief data officers," Sallam said in the release. "It's critical to gain a deeper understanding of the technology trends fueling that evolving story and prioritize them based on business value." With digital transformation efforts underway at most organizations, businesses are collecting more data than ever before, creating challenges but also major opportunities, Donald Feinberg, vice president and distinguished analyst at Gartner, said in the release.