{"id":3880,"date":"2018-06-15T14:30:39","date_gmt":"2018-06-15T18:30:39","guid":{"rendered":"https:\/\/solutionsreview.com\/business-intelligence\/?p=3880"},"modified":"2019-02-06T13:17:39","modified_gmt":"2019-02-06T18:17:39","slug":"comparing-top-data-science-and-machine-learning-platforms-companies-and-tools","status":"publish","type":"post","link":"https:\/\/solutionsreview.com\/business-intelligence\/comparing-top-data-science-and-machine-learning-platforms-companies-and-tools\/","title":{"rendered":"Comparing Top Data Science and Machine Learning Platforms, Companies and Tools"},"content":{"rendered":"

\"Comparing<\/p>\n

The lines used to define solution categories in the enterprise are beginning to blur. Data analytics technologies once considered advanced in the grand scheme of business intelligence are now mainstream. As a result, the solution providers have had to shift their focus and develop tools that meet the needs of organizations dealing with increasingly large data volumes.<\/p>\n

Data science is an umbrella term used to describe how the scientific method can be applied to data in a business setting. In this way, organizations use mathematics, statistics, predictive analytics, and artificial intelligence (including machine learning) to dig into cumbersome data sets in order to reveal trends. Data science is a product of big data through and through, and can be seen as a direct result of increasingly complex data environments.<\/p>\n

Cue the process of seeking out, evaluating, choosing, purchasing, and deploying data science platforms. There’s no such thing as a one-size-fits-all approach when it comes to doing analytics. Solutions come in a variety of flavors\u2014ranging from predictive analytics and advanced visualization to AI-enabled and machine learning. Each features a particular set of capabilities, strengths, and drawbacks. Choosing the right vendor and solution is a complicated process\u2014one that requires in-depth research and often comes down to more than just the solution and its technical capabilities.<\/p>\n

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Here at Solutions Review, it’s our job to help simplify that process. To that end, we’ve created a variety of buyer resources and articles<\/a> to speed the evaluation of enterprise technology solutions. In our Business Intelligence and Data Analytics Buyer’s Guide<\/a>, we offer readers a full market overview. This includes company and product profiles and best use cases for the top-28 providers, ten questions for prospective buyers, and our bottom line analysis.<\/p>\n

Pair this with Solutions Review’s\u00a0online directory<\/a> of BI and data analytics vendors, platforms and software solutions. It includes an abbreviated overview, contact information and links to each company’s social media handles for the top-28 providers. If you would like a printed version of this page including complete solutions profiles and a list of the top questions to ask in an RFP, click here for a free PDF<\/a>.<\/p>\n

Solutions Review also offers a Business Intelligence and Data Analytics Buyer’s Matrix<\/a>, a graphical side-by-side comparison of the top-28 tools. It’s our most popular resource to date, and offers five feature and benefit category views so buyers can gain a better understand of their options before sitting down to a formal meeting with a prospective vendor.<\/p>\n

Our coverage of this marketplace is rounded out by our Business Intelligence and Data Analytics Factbook<\/a>. Our newest resource provides buyers with a high-altitude snapshot of the space, featuring a directory listing of the best tools with vital data on each company’s maturity, longevity, business model, size, geographic reach, user base, notable clients, and financials. Readers can toggle between each vendor to gain a simplistic view if just beginning their search, or help them decide between a final shortlist of potential tools.<\/p>\n

Data science and machine learning vendors covered in our suite of buyer’s resources include KNIME, Alteryx, SAS, RapidMiner, H2O.ai, TIBCO Software, MathWorks, Looker, TIBCO Software IBM, Domino Data Lab, Microsoft, Databricks, SAP, Angoss, Anaconda, Teradata and Dataiku.<\/p>\n

If you\u2019re in the beginning process of buying your first data science solution, or if you’re looking for something a little different than what you already have, we hope this is the perfect set of resources to get you started along the decision-making process.<\/p>\n

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The lines used to define solution categories in the enterprise are beginning to blur. Data analytics technologies once considered advanced in the grand scheme of business intelligence are now mainstream. 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