Among the many drivers of the tech ecosystem's rapid growth, artificial intelligence (AI) and its subdomains are at the fore. Described by Gartner as the application of "advanced analysis and logic-based techniques" to simulate human intelligence, AI is an all-inclusive system with numerous use cases for individuals and enterprises across industries. There are many ways of leveraging AI to support, automate and augment human tasks, as seen by the range of solutions available today. These offerings promise to simplify complex tasks with speed and accuracy, and to spur new applications that were impractical or possible previously. Some question whether the technology will be used for good or perhaps become more effective than humans for certain business use cases, but its prevalence and popularity cannot be doubted.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Among the many drivers of the tech ecosystem's rapid growth, artificial intelligence (AI) and its subdomains are at the fore. Described by Gartner as the application of "advanced analysis and logic-based techniques" to simulate human intelligence, AI is an all-inclusive system with numerous use cases for individuals and enterprises across industries. There are many ways of leveraging AI to support, automate and augment human tasks, as seen by the range of solutions available today.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Among the many drivers of the tech ecosystem's rapid growth, artificial intelligence (AI) and its subdomains are at the fore. Described by Gartner as the application of "advanced analysis and logic-based techniques" to simulate human intelligence, AI is an all-inclusive system with numerous use cases for individuals and enterprises across industries. There are many ways of leveraging AI to support, automate and augment human tasks, as seen by the range of solutions available today.

Fivetran took a big step into the world of enterprise data integration today when it announced an Andreessen Horowitz-led $565 million round of financing and plans to acquire change data capture (CDC) vendor HVR for $700 million. The move positions the up-and-coming ETL company to further access exabytes of data stored in on-prem databases and ERP systems on behalf of its customers. The Series C round and acquisition position Fivetran to be at the forefront of the next generation of data integration and extract, transform, and load (ETL) capabilities. The nine-year-old, Oakland, California company has made its mark by simplifying the process of setting up pipelines that extract data from source systems–primarily SaaS applications running on clouds–and load it into cloud-based data warehouses. Today's news will help to expand Fivetran's footprint with on-prem systems, including the ERP applications at the heart of established enterprises.

It's human nature to choose the path of least resistance, as we're most comfortable with what we already know. Take the example of, "No one ever got fired for buying Cisco." But there comes a point in time when inaction becomes the greatest risk of all. Market transitions are real, and every product has a life cycle. Understanding these transitions and a vendor's product strategy is just as important--or sometimes more important--than selecting a specific company.

The data science and machine learning market is more vibrant than ever before, according to Gartner's new 2021 Magic Quadrant for Data Science and Machine Learning Platforms. "Movement in this market is rapid and multidirectional," said the IT research firm in its new report ranking the 20 world-leading companies in the market. The global COVID-19 pandemic hasn't slowed down the rapid pace of data science and machine learning (DSML) innovation or the bullish growth strategies from vendors. Many organizations are starting DSML initiatives using free or low-cost open-source and public cloud offerings to build up their expertise and explore new opportunities. CRN breaks down the 20 market-leading vendors that made Gartner's 2021 Magic Quadrant for Data Science and Machine Learning Platforms, as well as each company's weaknesses and strengths in the market according to Gartner.

These are heady days in data science and machine learning (DSML) according to Gartner, which identified a "glut" of innovation occurring in the market for DSML platforms. From established companies chasing AutoML or model governance to startups focusing on MLops or explainable AI, a plethora of vendors are simultaneously moving in all directions with their products as they seek to differentiate themselves amid a very diverse audience. "The DSML market is simultaneously more vibrant and messier than ever," a gaggle of Gartner analysts led by Peter Krensky wrote in the Magic Quadrant for DSML Platforms, which was published earlier this month. "The definitions and parameters of data science and data scientists continue to evolve, and the market is dramatically different from how it was in 2014, when we published the first Magic Quadrant on it." The 2021 Magic Quadrant for DSML is heavily represented by companies to the right of the axis, which anybody who's familiar with Gartner's quadrant-based assessment method knows represents the "completeness of vision."

Gartner's Magic Quadrant report on data science and machine learning (DSLM) platform companies assesses what it says are the top 20 vendors in this fast-growing industry segment. Data scientists and other technical users rely on these platforms to source data, build models, and use machine learning at a time when building machine learning applications is increasingly becoming a way for companies to differentiate themselves. Gartner says AI is still "overhyped" but notes that the COVID-19 pandemic has made investments in DSLM more practical. Companies should focus on developing new use cases and applications for DSML -- the ones that are visible and deliver business value, Gartner said in the report released last week. Smart companies should build on successful early projects and scale them.

If you're a buyer of technology stocks and solutions, you are most likely familiar with Gartner, Inc. (NYSE: IT) and the power it holds in the space. The firm, founded in 1979 by Gideon Gartner, now has a market capitalization of $10B and it employs a team of 1,900 analysts who glean insights from more than 380,000 client interactions each year. When it comes to industry research, Gartner is the 800-pound gorilla. And when Gartner publishes market research, people pay attention. Take for example the last pieces the firm released over the last two weeks: Gartner's February 2018 Magic Quadrant for Data Science and Machine-Learning Platforms and the Magic Quadrant for Analytics and Business Intelligence Platforms.

WorkFusion, a top provider of Intelligent Automation technology, announced it has been placed by Gartner in the Leaders quadrant of the 2020 "Magic Quadrant for Robotic Process Automation."1 Gartner recognized WorkFusion based on its completeness of vision and ability to execute. "We believe Gartner's recognition of WorkFusion in the Leaders quadrant validates our customer-first approach -- with pre-built automation solutions designed to lower costs dramatically, and in record time, for areas with higher levels of document-related manual and cognitive work across some of the most critical processes in Banking, Financial Services, Insurance, Healthcare and other sectors, " said WorkFusion CEO Alex Lyashok. "The right automation approach is to be able to deliver meaningful and measurable benefits to businesses in weeks or months, not years, and without disrupting core infrastructure or systems. As an example, for organizations in regulated-service industries such as Banking, Financial Services and Insurance (BFSI), the WorkFusion Intelligent Automation Cloud platform can quickly and easily manage document-heavy processes -- and in some cases can cut costs by 50% within 3 months," Lyashok said.