Key Stakeholders: CIO, CFO, Controllers, Comptrollers, Accounting Directors and Managers, IT Finance Directors and Managers, IT Expense Management Directors and Managers, Telecom Expense Management Directors and Managers, Enterprise Mobility Management Directors and Managers, Digital Transformation Managers, Internet of Things Directors and Managers On May 21st and 22nd, Amalgam Insights attended and presented at Tangoe LIVE…
For release 830am May 28, 2018
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|Hyoun Park||Steve Friedberg|
|Amalgam Insights||MMI Communications|
AMALGAM INSIGHTS MEDIA ALERT: Starbucks is closing its stores and doing awareness training for all of its employees this week. Learning researcher asks, “Is it enough?”
WHAT: Starbucks says it’s closing its 8,000 stores tomorrow, May 29, for what it calls “a conversation and learning session on race, bias and the building of a diverse welcoming company.”
Todd Maddox, Ph.D., an Amalgam Insights Learning Scientist/Research Fellow, applauds the company’s commitment to ongoing training, saying that approach may work, but warns that unless the training is continuous, Starbucks runs the risk of backsliding:
“My hope is that the company utilizes training content that focuses on true behavior change, as opposed to simply teaching people to identify inappropriate behavior. I also hope that Starbucks goes beyond training during the onboarding process, and incorporates it as a regular, ongoing part of employee training. The brain is hardwired to forget and requires refreshers to consolidate information in long-term memory.
“Just as sexual harassment prevention and many other people skills are about behavior, so is unconscious (racial) bias and all other aspects of appropriate interaction. People skills matter in all facets of society and corporate life. It is time to embrace the science of learning and work to address these shortcomings with effective training.”
WHO: Todd Maddox, Ph.D. has more than 200 published articles, 10,000 citings, and $10 million in external research funding in his 25+ years researching the brain basis of behavior. He’s been quoted in Forbes, CBS Radio, Training Journal, Chief Learning Officer, and other publications on topics such as the use of virtual reality in workplace sexual harassment avoidance training.
Todd’s available for comment on this or other topics; if you’d like to speak with him, please contact email@example.com.
It’s not news that there is a lot of buzz around containers. As companies begin to widely deploy microservices architectures, containers are the obvious choice with which to implement them. As companies deploy container clusters into production, however, an issue has to be dealt with immediately: container architectures have a lot of moving parts. The…
Today, I provided a quick presentation on the BrightTALK channel on Outsourcing Core IT when you ARE Core IT. It turns out that one of the takeways from this webinar is about your risk of being outsourced based on your IT model. First, your fear and approach to outsourcing really depend on whether you are…
This blog is the first of a multi-blog series explaining the challenges of Enterprise Performance Management aka Financial Performance Management, Business Performance Management, Corporate Performance Management, Financial Planning and Analysis, and Planning, Budgeting, and Forecasting. Frankly, this list of names alone really helps explain a lot of the confusion. But one of the strangest aspects…
Industry: Data Science Platforms
Key Stakeholders: IT managers, data scientists, data analysts, database administrators, application developers, enterprise statisticians, machine learning directors and managers, current DataScience.com customers, current Oracle customers
Why It Matters: Oracle released a number of AI tools in Q4 2017, but until now, it lacked a data science platform to support complete data science workflows. With this acquisition, Oracle now has an end-to-end platform to manage these workflows and support collaboration among teams of data scientists and business users, and it joins other major enterprise software companies in being able to operationalize data science.
Top Takeaways: Oracle acquired DataScience.com to retain customers with data science needs in-house rather than risk losing their data science-based business to competitors. However, Oracle has not yet not defined a timeline for rolling out the unified data science platform, or its future availability on the Oracle Cloud.
Oracle Acquires DataScience.com
On May 16, 2018, Oracle announced that it had agreed to acquire DataScience.com, an enterprise data science platform that Oracle expects to add to the Oracle Cloud environment. With Oracle’s debut of a number of AI tools last fall, this latest acquisition telegraphs Oracle’s intent to expedite its entrance into the data science platform market by buying its way in.
Oracle is reviewing DataScience.com’s existing product roadmap and will supply guidance in the future, but they mean to provide a single unified data science platform in concert with Oracle Cloud Infrastructure and its existing SaaS and PaaS offerings, empowering customers with a broader suite of machine learning tools and a complete workflow.
This week, everybody is talking about Google Duplex, announced earlier this week at Google I/O. Based on previous interactions with IVRs from calling vendors for customer support, Duplex is an impressive leap forward in natural language AI, and offers future hope at making some clerical tasks easier to complete. Duplex will be tested further by a limited number of users in Google Assistant this summer, refining its ability to complete specific tasks: getting holiday hours for a business, making restaurant reservations, and scheduling appointments specifically at a hair salon.
So what does this mean for most businesses?
Key Stakeholders: IT Managers, IT Directors, Chief Information Officers, Chief Technology Officers, Chief Digital Officers, IT Governance Managers, and IT Project and Portfolio Managers. Top Takeaways: Information technology is innovating at an amazing pace. These technologies hold the promise of increased effectiveness, efficiency and profits. Unfortunately, the training tools developed to onboard users are often…
From April 18-20, Amalgam Insights attended Cloud Foundry Summit 2018 in our hometown of Boston, MA. Both Research Fellow Tom Petrocelli and Founder Hyoun Park attended as we explored the current positioning of Cloud Foundry as an application development platform in light of the ever-changing world of technology. The timing of Cloud Foundry Summit this…
My name is Lynne Baer, and I’ll be covering the world of data science software for Amalgam Insights. I’ll investigate data science platforms and apps to solve the puzzle of getting the right tools to the right people and organizations.
“Data science” is on the tip of every executive’s tongue right now. The idea that new business initiatives (and improvements to existing ones) can be found in the data a company is already collecting is compelling. Perhaps your organization has already dipped its toes in the data discovery and analysis waters – your employees may be managing your company’s data in Informatica, or performing statistical analysis in Statistica, or experimenting with Tableau to transform data into visualizations.
But what is a Data Science Platform? Right now, if you’re looking to buy software for your company to do data science-related tasks, it’s difficult to know which applications will actually suit your needs. Do you already have a data workflow you’d like to build on, or are you looking to the structure of an end-to-end platform to set your data science initiative up for success? How do you coordinate a team of data scientists to take better advantages of existing resources they’ve already created? Do you have coders in-house already who can work with a platform designed for people writing in Python, R, Scala, Julia? Are there more user-friendly tools out there your company can use if you don’t? What do you do if some of your data requires tighter security protocols around it? Or if some of your data models themselves are proprietary and/or confidential?
All of these questions are part and parcel of the big one: How can companies tell what makes a good data science platform for their needs before investing time and money? Are traditional enterprise software vendors like IBM, Microsoft, SAP, SAS dependable in this space? What about companies like Alteryx, H2O.ai, KNIME, RapidMiner? Other popular platforms under consideration should also include Anaconda, Angoss (recently acquired by Datawatch), Domino, Databricks, Dataiku, MapR, Mathworks, Teradata, TIBCO. And then there’s new startups like Sentenai, focused on streaming sensor data, and slightly more established companies like Cloudera looking to expand from their existing offerings.
Over the next several months, I’ll be digging deeply to answer these questions, speaking with vendors, users, and investors in the data science market. I would love to speak with you, and I look forward to continuing this discussion. And if you’ll be at Alteryx Inspire in June, I’ll see you there.