Month: February 2019

Mid-February (Feb. 17 – 23) was a hot week for data and cloud migration companies with two big acquisitions.

Google announced on Tuesday, Feb. 19 the acquisition of Alooma to assist with cloud data migration issues. This acquisition aligns well with the 2018 acquisition of Velostrata to support cloud workload migration. This acquisition reflects Google’s continued interest in the Israeli cloud tech ecosystem to acquire technology solutions. It also shows that Google is pushing for enterprise cloud data and workloads and will be better positioned to migrate storage and compute from other clouds, such as Amazon Web Services and Microsoft Azure. As Google Cloud Platform continues to grow, this allows Google to be better positioned to

• become a primary enterprise solution for more enterprises as Google is better positioned to acquire structured data en masse from public and private competitors
• be a credible backup or business continuity solution for enterprises that may want a multi-cloud or hybrid cloud solution
• be a competitive provider to help enterprises with cost reduction either through being a foil in contract negotiations or to simply optimize cost in areas where Google resources and services end up being either more cost-effective or easier to manage than similar services from other cloud vendors

This acquisition will allow enterprises looking at cloud as a primary or significant compute and storage tool to consider Google Cloud Platform to replace, replicate, and/or backup existing cloud environments and provides a step forward for Google Cloud Platform to continue growing in the Infrastructure-as-a-Service market that Amalgam Insights estimates is currently growing at over 50% year-over-year driven by AWS growing 45%, Microsoft growing 76%, Google Cloud Platform’s assumed growth of 30-40%, and Alibaba Cloud growth of 84% based on the last quarter, the last of which shows Alibaba’s potential to leapfrog Google Cloud Platform in the next even as it has not significantly expanded past the Chinese market.

And there’s more!

On Thursday, February 21, Qlik announced its intention to acquire Attunity for $560 million. Attunity is based in the Boston area and has been a market leader in change data capture and data duplication with a considerable enterprise and cloud provider customer list. The scale of Attunity’s data transfer needs has made Attunity a proven solution for managing, transfer, backup, and recovery of data for on-prem, private cloud, and public cloud.

When I first covering Attunity in 2012, the stock was trading at around $5 per share after having survived both the dot-com and 2008 recessions. At the time, the company was repositioning itself as a cloud enabler and I had the opportunity to speak at their 2013 Analyst Day. At that time, I told the investing crowd that Attunity was aligned to a massive cloud opportunity because of its unique role in data replication and in supporting Cloud-based Big Data.

In 2018, Attunity’s stock finally reaped the benefits of this approach as the stock tripled based on rapidly growing customer counts and revenue driven by the need to manage data across multi-region cloud environments, multiple cloud vendors, and hybrid cloud environments. In light of this rapid growth, it is no surprise that Attunity was a strong acquisition target in early 2019’s cash-rich, data-rich, and cloud-dependent world. Looking at Attunity’s income statements, it is easy to see why Qlik made this acquisition from a pure financial perspective as Attunity has crossed the line into profitability and developed a scalable and projectable business that now needs additional sales and marketing resources to fully execute.

Amalgam Insights believes that Attunity provides Qlik with a strong data partner to go with last year’s acquisition of Podium Data (also a Boston-area startup) as a data catalog. With this acquisition, Qlik continues to build itself out as a broad enterprise data solution post-Thoma Bravo acquisition.

With this acquisition, Qlik users are in a comfortable position of being provided with a next-generation data ecosystem to support their move to the cloud and to support a broad range of data sources, formats, and use cases. Qlik is taking a step forward to support mature enterprise needs at a time when a number of its Business Intelligence competitors are focusing on incremental changes in usability, data preparation, or performance.

Amalgam Insights sees the acquisition of Attunity as a competitive advantage for Qlik in net-new deals and believes that this acquisition provides companies considering investments in cloud data or broad cloud migrations to immediately add Qlik to the list of vendors that need to be added to the enterprise toolkit to fully manage these projects.

The big picture for enterprises is that cloud data migration is a core capability to support BCDR (Business Continuity and Disaster Recovery), hybrid cloud, and multi-cloud environments. Google Cloud Platform is now more enterprise-ready and competitive with its larger competitors, Amazon Web Services and Microsoft Azure, at a time when Thomas Kurian is taking the reins. Qlik is establishing itself as a powerful Big Data and Cloud Data vendor at a time when Big Data continues to triple year after year. The enterprise data world is changing quickly and both Google and Qlik made moves to respond to burgeoning market demand.

DevOps Research Fellow Tom Petrocelli has just published a new report describing the roles that Cloud Foundry Application Runtime and Kubernetes play in supporting microservices. This report explores when each solution is appropriate and provides a set of vendors that provide resources and solutions to support the development of these open source projects.

Organizations and Vendors mentioned include: Cloud Foundry Foundation, Cloud Native Computing Foundation, Pivotal, IBM, Suse, Atos, Red Hat, Canonical, Rancher, Mesosphere, Heptio, Google, Amazon, Oracle, and Microsoft

To download this report, which has been made available at no cost until the end of February, go to https://www.amalgaminsights.com/product/analyst-insight-cloud-foundry-and-kubernetes-different-paths-to-microservices

Today, Todd Maddox published the report “Why Augmented Reality is Effective in Product Lifecycle Management: A Brain Science Analysis.” This report provides best practices for implementing augmented reality across product development, supply chain management, equipment operation, troubleshooting, and field service.

Recommendations are based on Maddox’ research, which has been cited over 10,000 by his academic peers.

This report is available at no cost through the end of February based on the generosity of our clients. To download this report at no cost for the rest of February, please go to https://www.amalgaminsights.com/product/analyst-insight-why-augmented-reality-is-effective-in-product-lifecycle-management.

Last week at H2O World San Francisco, H2O.ai announced a number of improvements to Driverless AI, H2O, Sparkling Water, and AutoML, as well as several new partnerships for Driverless AI. The improvements provide incremental improvements across the platform, while the partnerships reflect H2O.ai expanding their audience and capabilities. This piece is intended to provide guidance to data analysts, data scientists, and analytic professionals working on including machine learning in their workflows.

Announcements

H2O.ai has integrated H2O Driverless AI with Alteryx Designer; the connector is available for download in the Alteryx Analytics Gallery. This will permit Alteryx users to implement more advanced and automatic machine learning algorithms into analytic workflows in Designer, as well as doing automatic feature engineering for their machine learning models. In addition, Driverless AI models can be deployed to Alteryx Promote for model management and monitoring, reducing time to deployment. Both of these new capabilities provide Alteryx-using business analysts and citizen data scientists more direct and expanded access to machine learning via H2O.ai.

H2O.ai is integrating Kx’s time-series database, kdb+, into Driverless AI. This will extend Driverless AI’s ability to process large datasets, resulting in faster identification of more performant predictive capabilities and machine learning models. Kx users will be able to perform feature engineering for machine learning models on their time series datasets within Driverless AI, and create time-series specific queries.

H2O.ai also announced a collaboration with Intel that will focus on accelerating H2O.ai technology on Intel platforms, including the Intel Xeon Scalable processor and H2O.ai’s implementation of XGBoost. Driverless AI on Intel, globally.  Accelerating H2O on Intel will help establish Intel’s credibility in machine learning and artificial intelligence for heavy compute loads. Other aspects of this collaboration will include expanding the reach of data science and machine learning by supporting efforts to integrate AI into analytics workflows and using Intel’s AI Academy to teach relevant skills. The details of the technical projects will remain under wraps until spring.

Finally, H2O.ai announced numerous improvements to both Driverless AI and their open-source H2O, Sparkling Water, and AutoML, mostly focused on expanding support for more algorithms and heavier workloads among their product suite. Among the improvements that caught my eye was the new ability to inspect trees thoroughly for all of the tree-based algorithms that the open-source H2O platform supports. With concern about “black-box” models and lack of insight around how a given model performs its analysis and why it yields the results it does for any given experiment, providing an API for tree inspection is a practical step towards making the logic behind model performance and output more transparent for at least some machine learning models.

Recommendations

Alteryx users seeking to implement machine learning models into analytic workflows should take advantage of increased access to H2O Driverless AI. Providing more machine learning capabilities to business analysts and citizen data scientists enhances the capabilities available to their data analytics workflows; Driverless AI’s existing AutoDoc capability will be particularly useful for ensuring Alteryx users understand the results of the more advanced techniques they now have access to.

If your organization collects time-series data but has not yet pursued analytics of this data with machine learning yet, consider trialing KX’s kdb+ and H2O’s Driverless AI. With this integration, Driverless AI will be able to quickly and automatically process time series data stored in kdb+, allowing swift identification of performant models and predictive capabilities.

If your organization is considering making significant investments in heavy-duty computing assets for heavy machine learning loads in the medium-term future, keep an eye on the work Intel will be doing to design chips for specific types of machine learning workloads. NVIDIA has its GPUs and Google its TPUs; by partnering with H2O, Intel is declaring its intentions to remain relevant in this market.

If your organization is concerned about the effects of “black box” machine learning models, the ability to inspect tree-based models in H2O, along with the AutoDoc functionality in Driverless AI, are starting to make the logic behind machine learning models in H2O more transparent. This new ability to inspect tree-based algorithms is a key step towards more thorough governance surrounding the results of machine learning endeavors.

Amalgam Insights’ Learning Science Research Fellow Todd Maddox has recently published an Analyst Insight focused on exploring how psychology and brain science can inform learning practitioners and provide tools that optimize information retention and behavior change. The workplace is changing rapidly and the modern employee needs continuous learning of hard skills, people (aka soft) skills and situational awareness. Neuroscience reveals that each of these skill sets is mediated by a distinct learning system in the brain, each of which has its own unique operating characteristics. The modern employee expects learning in the flow of work, available 24/7 on any device, with engaging content and experience.

Maddox’ key finding was that Qstream’s mobile microlearning solution meets these challenges by delivering content in a way that engages the cognitive skills learning system in the brain during hard skills training, the behavioral skills learning in the brain during people skills training, and the emotional skills learning system in the brain during situational awareness training. The user experience engages employees through scenario-based challenges which stimulate critical thinking, gives real-time feedback, explains answers, supports personalized coaching, and delivers learning in minutes per day.

For a complementary copy of the complete report, vist the Qstream website at: https://info.qstream.com/leveraging-learning-science-how-qstreams-mobile-microlearning-solution-changes-behavior.

On a monthly basis, I will be rounding up key news associated with the Data Science Platforms space for Amalgam Insights. Companies covered will include: Alteryx, Amazon, Anaconda, Cambridge Semantics, Cloudera, Databricks, Dataiku, DataRobot, Datawatch, Domino, Elastic, Google, H2O.ai, IBM, Immuta, Informatica, KNIME, MathWorks, Microsoft, Oracle, Paxata, RapidMiner, SAP, SAS, Tableau, Talend, Teradata, TIBCO, Trifacta, TROVE.

Cloudera and Hortonworks Complete Planned Merger

In early January, Cloudera and Hortonworks completed their planned merger. With this, Cloudera becomes the default machine learning ecosystem for Hadoop-based data, while providing an easy pathway for expanding into  machine learning and analytics capabilities for Hortonworks customers.

Study: 89 Percent of Finance Teams Yet to Embrace Artificial Intelligence

A study conducted by the Association of International Certified Professional Accountants (AICPA) and Oracle revealed that 89% of organizations have not deployed AI to their finance groups. Although a correlation exists between companies with revenue growth and companies that are using AI, the key takeaway is that artificial intelligence is still in the early adopter phase for most organizations.

Gartner Magic Quadrant for Data Science and Machine Learning Platforms

In late January, Gartner released its Magic Quadrant for Data Science and Machine Learning Platforms. New to the Data Science and Machine Learning MQ this year are both DataRobot and Google – two machine learning offerings with completely different audiences and scope. DataRobot offers an automated machine learning service targeted towards “citizen data scientists,” while Google’s machine learning tools, though part of Google Cloud Platform, are more of a DIY data pipeline targeted towards developers. By contrast, I find it curious that Amazon’s SageMaker machine learning platform – and its own collection of task-specific machine learning tools, despite their similarity to Google’s – failed to make the quadrant, given this quadrant’s large umbrella.

While data science and machine learning are still emerging markets, the contrasting demands of these technologies made by citizen data scientists and by cutting-edge developers warrants splitting the next Data Science and Machine Learning Magic Quadrant into separate reports targeted to the considerations of each of these audiences. In particular, the continued growth of automated machine learning technologies will likely drive such a split, as citizen data scientists pursue a “good enough” solution that provides quick results.