Amazon Aurora Serverless vs. Oracle Autonomous Database: A Microcosm for The Future of IT

On November 29th, Amazon Web Services announced a variety of interesting database announcements at Amazon re:invent. Amazon Neptune, DynamoDB enhancements, and Aurora Serverless. Amalgam found both Neptune and DynamoDB announcements to be valuable but believes Aurora Serverless was the most interesting of these events both in its direct competition with Oracle and its personification of…

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Why Did TIBCO Acquire Alpine Data?

On November 15, 2017, TIBCO announced the acquisition of Alpine Data, a data science platform long known for its goals of democratizing data science and simplifying access to data, analytic workflows, parallel compute, and tools. With this acquisition, TIBCO makes its second major foray into the machine learning space after June 5th acquisition of Statistica….

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How Does myTrailhead Excel and How Can Science Make myTrailhead Even Better

Astro, Einstein, and other Salesforce Trailhead characters
Salesforce’s Trailhead branding is both on-point and adorable.

Key Takeaways:

  • myTrailhead allows customized training content and incorporates useful motivational and performance testing tools.
  • myTrialhead could be enhanced by incorporating scientifically-validated best practices in training, which suggest that hard skills are best trained by a cognitive skill learning system in the brain and soft skills are best trained by a behavioral skill learning system in the brain
  • In its current implementation, myTrailhead is more nearly optimized for hard skill training, but is sub-optimal for soft skills training

Technology is progressing at an accelerating rate. Jobs are constantly being updated or redefined by, and with the help of technology. Employees are constantly being asked to learn new skills whether in the same job or in a new position. Constant training is the rule, not the exception, and training platforms must be built with this in mind.

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Why Computer Based Sexual Harassment Awareness Training is Ineffective and Why Virtual Reality (VR) Offers a Better Solution

What do Bill O’Reilly, Harvey Weinstein, Roy Moore, Louis C.K., Michael Oreskes, Kevin Spacey and many others have in common (other than all being male)? Certainly not political beliefs or professional expertise. Whether left or right leaning in the political arena, or focused on entertainment, journalism or government service, all have been accused of, and…

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With myEinstein, Salesforce Embraces that “AI is the New UI”

Astro, Einstein, and other Salesforce Trailhead characters
Salesforce Einstein Airplane - Courtesy of Salesforce
Salesforce Einstein Airplane – Courtesy of Salesforce

Key Takeaway: Amalgam believes that the go-live date of myEinstein will be the most important date for Enterprise AI in 2018 as it represents the day that AI will become practical and available to a broad business audience across industries, verticals, company sizes, and geographies.

On November 6, 2017, Salesforce [NYSE:CRM] announced the launch of myEinstein: services based on Salesforce’s Einstein machine learning platform to support point-and-click-based and codeless AI app development. This announcement was one of several new services that Salesforce built across platform (mySalesforce and myIoT), training (myTrailhead), and user interface development (myLightning).

myEinstein consists of two services:

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4 Key Developer Responsibilities Where Machine Learning Can Help

Note: A version of this post was published to Tom’s Tech Take II

As the fall season of tech conferences starts to wind down, something is quite clear – machine learning (ML) is going to be everywhere. Box is putting ML in content management, Microsoft in office and CRM, and Oracle is infusing it into, well, everything. While not a great leap forward on the order of the Internet, smartphones, or PCs, the inclusion of ML technology into so many applications will make a lot of mundane tasks easier. This trend promises to be a boon for developers. The strength of machining learning is finding and exploiting patterns and anomalies. What could be more useful to developers?

Here are some examples:

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Amalgam Insights Analyzes Sage Intacct and Pacioli AI

Amalgam Insights recently attended Sage Intacct Advantage. In the past, Intacct got AI’s attention for its strong technology foundation that positions it well for a future of predictive analytics, ease of integration, and machine learning while maintaining the core financial responsibilities associated with being a nominative mid-market ERP solution. Sage has traditionally been known as…

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With Oracle Universal Credits, the Cloud Wars Are Truly On

In late September, prior to Oracle Open World, Oracle (NYSE: ORCL) held an event to announce its consumption pricing model of Universal Credits and the ability to reuse existing software licenses across Oracle’s Platform as a Service (PaaS) middleware, analytics, and database offerings. The Universal Credits represent a fundamental change in cloud pricing as they will allow Oracle Cloud customers to switch between Oracle’s IaaS and PaaS services. In addition, Larry Ellison also unveiled a “self-driving” database that would greatly reduce the cost of administration.
Continue reading “With Oracle Universal Credits, the Cloud Wars Are Truly On”

28 Hours as an Industry Analyst at Strata Data 2017

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Companies Mentioned: Aberdeen Group, Actian, Alation, Arcadia Data, Attunity, BMC, Cambridge Semantics, Cloudera, Databricks, Dataiku, DataKitchen, Datameer, Datarobot, Domino Data Lab, EMA, HPE, Hurwitz and Associates, IBM, Informatica, Kogentix, LogTrust, Looker, < MesoSphere, Micro Focus, Microstrategy, Ovum, Paxata, Podium Data, Qubole, SAP, Snowflake, Strata Data, Tableau, Tamr, Tellius, Trifacta.

Last week, I attended Strata Data Conference at the Javitz Center in New York City to catch up with a wide variety of data science and machine learning users, enablers, and thought leaders. In the process, I had the opportunity to listen to some fantastic keynotes and to chat with 30+ companies looking for solutions, 30+ vendors presenting at the show, and attend with a number of luminary industry analysts and thought leaders including Ovum’s Tony Baer, EMA’s John Myers, Aberdeen Group’s Mike Lock, and Hurwitz & Associates’ Judith Hurwitz.

From this whirwind tour of executives, I took a lot of takeaways from the keynotes and vendors that I can share and from end users that I unfortunately have to keep confidential. To give you an idea of what an industry analyst notes, following are a short summary of takeaways I took from the keynotes and from each vendor that I spoke to:

Keynotes: The key themes that really got my attention is the idea that AI requires ethics, brought up by Joanna Bryson, and that all data is biased, which danah boyd discussed. This idea that data and machine learning have their own weaknesses that require human intervention, training, and guidance is incredibly important. Over the past decade, technologists have put their trust in Big Data and the idea that data will provide answers, only to find that a naive and “unbiased” analysis of data has its own biases. Context and human perspective are inherent to translating data into value: this does not change just because our analytic and data training tools are increasingly nuanced and intelligent in nature.

Behind the hype of data science, Big Data, analytic modeling, robotic process automation, DevOps, DataOps, and artifical intelligence is this fundamental need to understand that data, algorithms, and technology all have inherent biases as the following tweet shows:

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Microsoft: The New Player in Quantum Computing

Doppelspalt

On the week of September 25th, 2017, Microsoft made a huge announcement at its annual Ignite and Envision conference. Microsoft has become one of a small number of companies that is demonstrating quantum computing. IBM is another company that is also pursuing this rather futuristic computing model. For those who are not up-to-date on quantum…

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