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The 'Big Bang' of Data Science and ML Tools

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The tools used for data science are rapidly changing at the moment, according to Gartner, which said we're in the midst of a "big bang" in its latest report on data science and machine learning platforms. "The data science and ML market is healthy and vibrant, with a broad mix of vendors offering a range of capabilities," Gartner says in its Magic Quadrant for Data Science and Machine Learning Platforms published January 28. "The market is experiencing a'big bang' that is redefining not only who does data science and ML, but how it is done." The analyst group defines a data science platform as an integrated place where data scientists, citizen data scientists, and developers can get all of the core capabilities that they need to not only build data science application, but to embed them into existing business processes and manage and maintain them over time. Integration and cohesion are keys, in Gartner's view, and applications that simply bundle various packages and libraries – especially open source offerings -- are not considered true platforms.


Who's Winning the Cloud Database War

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Three-quarters of all databases will be deployed or migrated to the cloud within two years, Gartner said today in its much-anticipated report on cloud database management systems. The big cloud companies are winning their share of battles, but there's plenty of market share available for smaller and nimbler database providers too. Gartner turned heads in June 2019 when it declared that the cloud had become the default deployment mechanism for databases. "The message in our research is simple–on-premises is the new legacy," wrote Gartner analysts Adam Ronthal, Merv Adrian, and Donald Feinberg. Fast-forward 17 months, through the start of the COVID-19 pandemic and the ensuring lock-down on in-person work, and shift to the cloud has accelerated. Now those Gartner analysts (in addition to Rick Greenwald and Henry Cook) have teamed up for a reprisal of that 2019 report.


Winners and Losers from Gartner's Data Science and ML Platform Report

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Gartner published its latest Magic Quadrant for data science and machine learning platforms last week. Sixteen vendors made cut for Gartner's report this year, the same number as last year. However, there were some important changes, including some vendors who made big jumps and some who lost ground. The biggest difference arguably was the addition of "machine learning" to the name of Gartner's report. "Although data science and machine learning are slightly different," the Gartner analysts write, "they are usually considered together and often thought to be synonymous."


Gartner's 2020 Magic Quadrant For Data Science And Machine Learning Platforms Has Many Surprises

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Enterprise decision-makers look up to Gartner for its recommendations on enterprise software stack. The magic quadrant report is one of the most credible, genuine, and authoritative research from Gartner. Since it influences the buying decision of enterprises, vendors strive to get a place in the report. Gartner recently published its magic quadrant report on data science and machine learning (DSML) platforms. The market landscape for DS, ML and AI is extremely fragmented, competitive, and complex to understand.


Gartner's 2021 Magic Quadrant cites 'glut of innovation' in data science and ML

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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.