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Cloudera Analyst Conference Makes The Case for Analytic & AI Insights at Scale

On April 9th and 10th, Amalgam Insights attended the fifth Cloudera’s Industry Analyst and Influencer Conference (which I’ll self-servingly refer to as the Analyst Conference since I attended as an industry analyst) in Santa Monica. Cloudera sought to make the case that it was evolving beyond the market offerings that it is currently best known for as a Hadoop distribution and commercial data lake in becoming a machine learning and analytics platform. In doing so, Cloudera was extremely self-aware of its need to progress beyond the role of multi-petabyte storage at scale to a machine learning solution.
Cloudera’s Challenges in Enterprise Machine Learning 
Cloudera’s conference came in the face of multiple challenges. First, Cloudera’s stock had just plunged about 40% after lowering its revenue guidance over the rest of the year. As an industry analyst, this market activity tends to only be important if it fundamentally affects the company’s operations or ability to maintain product and service roadmap progress. Frankly, it is hard to truly understand how, in a rational market, a $20 million change in annual revenue could realistically lead to a billion dollar drop in market cap. Amalgam is not a financial analysis firm, but from my non-financial perspective, it seems extremely odd to make that kind of valuation change on a cloud company that is in transition: either you believe in the evolution from data provider to analytic provider or you have already decided that Cloudera’s future is solely as a data provider. But changing your mind based on $20 million in 2018 business is an extremely short-sighted reason to decide one way or the other.
But enough about that; the market is what it is. The more interesting set of challenges from Amalgam’s perspective is that Cloudera seeks to gain traction as a machine learning workbench and analytic store. These are the value-added use cases that will drive Cloudera’s long-term growth and evolution. The Cloudera Data Science Experience, in particular, is interesting because this framework directly competes against a number of well-funded startups seeking to also manage the future of data science, including Domino Data, Databricks, Dataiku, H20.ai, MapR, and Rapidminer as well as established companies such as Alteryx (once a startup darling, but now a publicly traded company), IBM, Mathworks, Microsoft, SAS, Teradata, and TIBCO. In short, there are a number of companies competing for the mindshare of data scientists seeking to either code or model predictive analytics for enterprise use cases. And this is the case that Cloudera sought to make at this Analyst Conference.
OK, so did Cloudera make its case?
Over the past calendar year, Cloudera has made several key moves towards increased machine learning and analytic support.
  • Cloudera has launched its Altus Analytic DB to support BI and SQL based data in the cloud based on Impala
  • Altus Data Engineering supports scale-out data sets both for Amazon and Microsoft Azure.
  • Apache Kudu provides a fast analytics capability for IoT and log data. Cloudera’s Data Science Workbench provides a straightforward capability for data scientists to bring R, Python, Scala, and other data science tools into a compliant and secure environment.
  • Cloudera started its Data Science Experience to support complex data applications based on a shared data catalog.
  • Cloudera has also acquired Fast Forward Labs, Hilary Mason’s applied research firm focused on best practices for data science.
The competitive stance would be to say that these capabilities, by and large, are not new or unique to Cloudera and, for the most part, that would be a fair statement. The biggest differentiation, though, is in incorporating Cloudera’s analytics and machine learning with its existing base of 400+ Global 2000 companies as part of approximately 700 large global enterprises and a variety of data-devouring startups requiring a combination of strong data governance, shared data collaboration, and relevant guidance and services.
This stance comes in context of Cloudera’s continued focus of targeting companies that are focused on data monetization and the investments needed to treat data as a protected asset that is always available, massively scalable, and can be delivered based on customer demand. Amalgam notes that this messaging was consistent across all of Cloudera’s executive presenters. In particular, Amalgam notes that Amy O’Connor’s presentation as Cloudera’s Chief Data and Information Officer was especially helpful in showing how Cloudera executes on delivering its products and how Cloudera uses its own technologies to support sales, marketing, support, and security use cases.
Cloudera’s Analytics and Machine Learning Businesses
The big picture perspective is that Cloudera has executed well on the analytic side and that the machine learning story is still a work in progress based both on Cloudera’s existing products and the state of this market. On the analytics side, Cloudera has an analytic database business of over $100 million in revenue built on data optimization, discovery-based data marts for text and advance analytics, and operational data marts for log data, web data, and IoT-based data. This is important because analytic data is an important foundation for developing reporting, analytics, and machine learning on a shared and consistent set of data.
Based on the metrics Cloudera shared, the analytic database business is both mature and has significant growth potential as Cloudera continues to evolve the database with automated workload management and metadata management capabilities to both reduce the total cost of ownership and increase the business context associated with this data. It is hard to argue that Cloudera is not executing on this front or that Cloudera is not well positioned to provide analytics at massive scale both for standalone reporting and for managing workloads and applications.
Cloudera’s Machine Learning story was relatively comprehensive between its support of a wide variety of data and cloud environments through the Shared Data Experience, Cloudera’s Data Science Workbench, and the integration of Fast Forward Labs to provide primary education across topics such as natural language generation, image analytics, probabilistic programming, semantic recommendations, real-time streams, and other key topics. Cloudera’s combination of products, services, and applied research is a model that more vendors should seek to emulate in emerging technology areas to educate business communities on how to support practical application.
But Amalgam’s doubts come from the inherent dynamics of an emerging market. Although Cloudera has a significant percentage of large analytic workloads and a working toolkit, Cloudera’s buying audience has traditionally been focused on those responsible for data warehouses, data marts, and enterprise analytics environments. To cross into the machine learning world, Cloudera will need to bridge the gap between analytic workloads and the worlds of data science and machine learning development. From a practical perspective, this means shifting brand from operational database administration teams to the agile and experimental lone wolves of devs, coding, and massive experimentation. Because data science is still a relatively new enterprise practice with little to no formal governance practices, large organizations are currently not driven to manage data science with the same rigor and structure that core enterprise data is held to. In addition, early adopter enterprises that have built out data science teams will likely have developers who are set in their own approaches and toolkits, which can make standardization and internal adoption of any tool difficult.
Amalgam believes that the need for enterprise-grade data science exists and will grow over time. But the honest truth is that it will take time for this market to mature to the point that a majority of large global enterprises will have a team of distributed data scientists collaborating with each other on key business challenges in the same way that businesses have developed application development teams. This evolution is inevitable in a global business environment where analytics and automation are key drivers for improvement, but this change will realistically take another two-three years as global enterprises increase their pool of data scientists and the need for data science management, lineage, and collaboration increases over time. The growth of Cloudera’s data science business will track the enterprise adoption of data science teams that expand beyond two or three data scientists and beyond a single location. This is where Cloudera will both excel and be able to take advantage of the “data gravity” associated with its existing data.
Overall, Cloudera’s combined data science offering is headed towards “where the puck is headed,” to use the often-cited Wayne Gretzky quote. But this approach is slightly ahead of the current data science market, which means that the short-term prospects for Cloudera data science offerings will be based on the uptake of the Data Science Workbench by individual data scientists in the Cloudera ecosystem and research components based on the demand for Fast Forward Labs. Expect 2018 to be a consolidation year for Cloudera on this front where its data science and machine learning offerings will continue to merge together into converged packages of education, services, deployment, and workload management to provide a scalable approach for enterprise machine learning.
Recommendations based on Cloudera Analyst Conference
Cloudera is shifting from a data company to an analytics company. Enterprises that understand that their data archives and operational data are potential assets and not just archived liabilities should consider Cloudera’s capabilities as an analytic store both as an on-prem and a cloud-based solution.
Cloudera is now a starting point to consolidate data science teams as enterprise data science initiatives scale and operationalize over the next few years. Cloudera Data Science Workbench is a capable data science tool in its own right, but realistically, small data science teams will likely make independent decisions regarding their initial toolkits and portfolios.
Amalgam believes that Cloudera’s data science offerings provide their greatest value when data science becomes operationalized and enterprises seek to gain insight on all of their trusted data. As data science teams grow and need to consolidate their R, Python, Scala, and other code in a consistent and collaborative environment, Cloudera will be one of the few options available for developing a DevOps-like rigor around data science and it will likely be augmented with Fast Forward-based best practices and comprehensive tools for ongoing workload management.
Conclusion
At Cloudera’s Analyst Conference, Cloudera made its case as an analytics and machine learning provider based on its DNA as an enterprise data provider. Amalgam’s biggest takeaway is that Cloudera is taking a long-term approach to its product development with the assumption that both Big Data analytics and Machine Learning will become core capabilities in enterprise IT that require both a well-governed platform and enterprise-grade support. Cloudera is not positioning itself to compete directly with any particular machine learning startup, but rather as a comprehensive enterprise solution that could potentially partner with niche partners along the machine learning value chain. In this regard, Amalgam believes that Cloudera successfully presented its vision for the future and provided realistic guidance for what to expect from Cloudera in the near future.
If you would like more detail on Cloudera’s machine learning efforts and how particular aspects of Cloudera’s Data Science Workbench and Data Science Experience match up against enterprise competitors, please feel free to set up a free initial inquiry with Amalgam Insights at info@amalgaminsights.com
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Anaplan States Planning Is Dead, Focuses on the Era of Real-Time Decision


Recommended Reading for: Finance, Sales Operations, Supply Chain Management, IT Management, and Enterprise Strategy Personnel
Companies Mentioned: Anaplan, IBM, SAP, Oracle, Microstrategy, Tableau, DataRobot, TROVE Data, Louis Vuitton, Premji Invest, Salesforce Ventures, Top Tier Capital Partners, Baillie Gifford, Granite Ventures, Industry Ventures, Meritech Capital, Constellation Research, Ventana Research, IDC, Mint Jutras, ISG, Gartner, Apps Run the World, TechVentive

On March 6th and 7th, 2018, Amalgam Insights attended Anaplan Hub 18. Anaplan has been on Amalgam analysts’ radar for several years, as we consider Anaplan’s Hyperblock foundation and ability to serve multi-departmental planning in enterprises without a year or more of setup to be fundamental advantages. As we have covered this company, we have been waiting for Anaplan to reach its breakthrough moment where it takes its place as one of the true market leaders in enterprise applications. It is in this context that we attended Anaplan Hub and judged our interactions with Anaplan executives, customers, and partners.

This report provides updates on Anaplan’s key business metrics, executive insights from an analyst-only panel, keynote and product announcements, a 2018 perspective on customer success stories with Anaplan, and Amalgam’s expectations for Anaplan in 2018 and beyond as both a real-time planning application and a Platform as a Service.

Anaplan Key Business Updates
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Blockchain! What is it Good For?

Diamond - Immutable and Hardened
Tom Petrocelli, Amalgam Insights Contributing Analyst

Blockchain looks to be one of those up and coming technologies that is constantly being talked about. Many of the largest IT companies – IBM, Microsoft, and Oracle to name few – plus a not-for-profit or two are heavily promoting blockchain. Clearly, there is intense interest, much of it fueled by exotic-sounding cryptocurrencies such as Bitcoin and Ethereum. The big question I get asked – and analysts are supposed to be able to answer the big questions – is “What can I use blockchain for?”
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Market Milestone: Red Hat Acquires CoreOS Changing the Container Landscape

Red Hat Acquires CoreOS

We have just published a new document from Tom Petrocelli analyzing Red Hat’s $250 million acquisition of CoreOS and why it matters for DevOps and Systems Architecture managers.

This report is recommended for CIOs, System Architects, IT Managers, System Administrators, and Operations Managers who are evaluating CoreOS and Red Hat as container solutions to support their private and hybrid cloud solutions. In this document, Tom provides both the upside and concerns that your organization needs to consider in evaluating CoreOS.

This document includes:
A summary of Red Hat’s Acquisition of CoreOS
Why It Matters
Top Takeaways
Contextualizing CoreOS within Red Hat’s private and hybrid cloud portfolio
Alternatives to Red Hat CoreOS
Positive and negative aspects fcr current Red Hat and CoreOS customers

To download this report, please go to our Research section.

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Data and Analytic Strategies for Developing Ethical IT: a BrightTALK webinar

Recommended Audience: CIOs, Enterprise Architects, Data Managers, Analytics Managers, Data Scientists, IT Managers Vendors Mentioned: Trifacta, Paxata, Datameer, Datawatch, Lavastorm, Alation, Tamr, Unifi, 1010Data, Podium Data, IBM, Domo, Microsoft, Information Builders, Board, Microstrategy, Cloudera, H20.ai, RapidMiner, Domino Data Lab, Dataiku, TIBCO, SAS, Amazon Web Services, Google, DataRobot. In case you missed it, I just finished…

Please register or log into your Free Amalgam Insights Community account to read more.
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Calero Purchases European TEM Leader A&B Groep: What to Expect?


Note: This blog contains excerpts from Amalgam’s Due Diligence Dossier on Calero. To get the full report, click here.

In January 2018, Calero announced two acquisitions, Comview and A&B Groep. These acquisitions have increased Calero’s headcount by over 70 employees, added geographic footprint, demonstrated a specific profile for acquisition, and demonstrates the willingness for new owners, Riverside Partners, to quickly take action within 120 days of acquiring Calero. This combination of acquisition, execution, and stated focus result in the need to re-evaluate Calero in context of these significant changes. Continue reading “Calero Purchases European TEM Leader A&B Groep: What to Expect?”

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The Brain Science of Effective Corporate Soft Skills Training

Cognitive and Behavior Systems of Learning

Companies Mentioned: Deloitte, Salesforce, SAP, Cornerstone, Saba, Skillsoft, Fivel, PageUp, PeopleFluent, Talentsoft, Oracle, SilkRoad, IBM, Lumesse, Litmos, D2L, LearnCore, and Lessonly

Soft skills are “people skills”, and they are extremely important in the commercial sector. They involve showing and feeling empathy, embracing diversity, and understanding that we all have biases that we need to be aware of and keep in check. They involve effective interpersonal interactions and real-time communication skills and are relevant at all corporate levels. Whether office staff who interface with clients, office managers who interface with employees and their superiors, or the C-suite who provide the leadership and vision for the company, effective soft skills matter. An individual with strong soft skills can be an effective collaborator, leader, and “good” citizen. They not only know “what” behaviors are appropriate and inappropriate, but they know “how” to generate those behaviors and do so in a highly effective manner.

As suggested by Deloitte, the movement toward increased automation and artificial intelligence in the workplace has led many in the C-suite to suggest that soft skills are going to become increasingly important in the workplace. The #metoo movement makes glaringly clear that effective soft skills training is lacking in many workplace environments, and in society in general.

Corporate approaches to soft skills training do a good job of teaching employees how to identify and define appropriate and inappropriate behavior, and even offering suggestions for how to behave appropriately, but they are lacking in the use of tools for effective behavior change.

Brain science suggests specific solutions to this problem and can drive innovation and success in the commercial sector. As applied to soft skills training the science is highly suggestive. Training tools targeted directly at behavior change and the systems in the brain that drive behavior are required.

Brain Science of Soft Skills Learning

The human brain has evolved in such a way that there exist two distinct learning systems. One system focuses on learning the “what”, and is referred to as the cognitive skills learning system in the brain. The cognitive skills learning system in the brain learns through passive observation, studying and mental repetition, and recruits the prefrontal cortex and medial temporal lobes. The second system focuses on learning the “how”, and is referred to as the behavioral skills learning system in the brain. Learning in the behavioral skills system in the brain is active, it involves learning by doing, and physical repetitions, and recruits the basal ganglia. A more detailed discussion of these two systems can be found here.

The most common approach to corporate soft skills training involves having learners read text, view slideshows, and watch videos of appropriate and inappropriate soft skills behaviors. Learners are trained on definitions, how to identify appropriate and inappropriate behaviors, and are given strategies for incorporating appropriate soft skills behavior into their repertoire. This is focused on the “what” and is an important first step in soft skills training.

Unfortunately, most soft skills training programs stop here. It is assumed that the learner will somehow take this “what” information and will convert this into behavior (the “how”). The brain science makes clear that this is highly unlikely, and our own experience reinforces this claim. As anyone who has ever tried to change their own behavior knows, it is much easier to know “what” to do, than it is to learn behaviorally “how” to do it.

Once the learner is well versed on definitions and has strategies in place for effective soft skills, the next step is to train behavior (the “how”). Behavior change occurs when the learner’s behavior is following in real-time, literally within 100s of milliseconds, by feedback that rewards correct behaviors and punishes incorrect behaviors. Thus, the training scenarios must be interactive. Although the detailed neurochemistry is beyond the scope of this article, suffice it to say that behavioral skills are learned gradually and incrementally via dopamine-mediated, error-correction learning in the basal ganglia of the brain. When rewarded, behaviors are more likely to occur in the future, and when punished behaviors are less likely to occur in the future.

Interactive Training Platforms

Traditional approaches train a cognitive understanding of soft skills. These need to be supplemented with interactive approaches that train behavior. Interactive computer-based training platforms, as well as immersive virtual reality (VR) platforms, are available and should be used to complement traditional soft skills training procedures.

The learner can be placed in situations in which they must interact with an individual or avatar who is poor in soft skills so that they learn how to deal with situations like those, or they can be placed in situations in which their behavior is responded to in a negative manner. In other words, they can learn how to affect change in another, or affect change in themselves. Regardless of the platform, whether VR or computer-based, the key is to target the behavioral skills learning system with realistic interpersonal interactions and real-time communication. Behavior change will follow.

Buyers Beware

Corporate training professions need to beware of platforms that claim interactivity that is not truly interactive. From a brain science of learning perspective, behavior change will only be effective if interactivity involves real-time feedback—that is, rewards and punishments that occur within a few hundred milliseconds of the behavior in question. Providing feedback even several seconds following the behavior will not lead to effective behavior change. Interactivity must occur in real time or it won’t be effective.

Corporate Training Platforms

A number of vendors offer corporate training including Salesforce, SAP, Cornerstone, Saba, Skillsoft, PageUp, PeopleFluent, Talentsoft, Oracle, SilkRoad, IBM, Lumesse, Fivel, Litmos, D2L, LearnCore, and Lessonly, to name a few.

All of these vendors offer soft skills training focused on the cognitive skills learning system in the brain. These vendors are urged to complement their current training platform with real-time interactive training that targets the behavioral skills learning system in the brain. In the end, effective soft skills are effective behaviors that are only learned by engaging the behavioral skills learning system in the brain.

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Dual Learning Systems in the Brain: Implications for Corporate Training

Effective training is critical in all business sectors. In 2017, over $360 billion was spent on training worldwide, with over $160 billion spent in the U.S. alone. Given the ever-changing nature of the corporate landscape, as new technologies are introduced (e.g., AI) or upgraded (e.g., constant software upgrades), and as new challenges arise (e.g., sexual harassment in the workplace) corporate training must evolve to meet the growing need.

Corporate training can be loosely classified into two categories: hard skills and soft skills. An extensive body of scientific and neuroscientific research (much of it from the author’s research laboratory) suggests that the human brain contains at least two distinct systems that are recruited during learning. One system is referred to as the cognitive skills learning system, which is optimized for hard skills learning. The other is referred to as the behavioral skills learning system, which is optimized for soft skills learning.

Cognitive Skills Learning System

The cognitive skills learning system in the brain is comprised of the prefrontal cortex and the medial temporal lobes. This system is optimally tuned to learn hard skills such as learning new software, learning a company’s rules and regulations, or memorizing the set of steps to take to complete a task. The neural architecture of this system determines the set of training procedures that optimize learning. The scientifically-validated best practices for optimized learning of hard skills are many, and will be outlined in subsequent articles, but suffice it to say that learning in this system is passive, it involves observing and learning by watching, and repeating information mentally. For example, suppose you are learning a new CRM. You might read the manual, watch a few slide shows, or watch a video of someone performing specific functions within the CRM. You might study this information multiple times and practice the tasks in your head. Eventually, you will launch the CRM software to try out some of the things that you learned.

Behavioral Skills Learning System

The behavioral skills learning system in the brain resides in the basal ganglia. This system is optimally tuned to learn soft skills (also called people skills, 21st Century skills or socio-emotional skills). Soft skills include showing empathy, embracing diversity, and minimizing unconscious biases. These are all reflected in one’s behavior such as active listening, making eye contact, praising employees and co-workers when appropriate, avoiding overt punishment, and showing respect. Soft skills are relevant in all aspects of the corporate world including management, collaborative communication, and customer service, to name a few. Soft skills have received significant attention in 2017 and likely will receive even more in 2018 as corporations work to reduce the incidence of sexual harassment. I have written extensively on this topic.

Like the cognitive skills learning system, the neural architecture of the behavioral skills learning system determines the set of training procedures that optimize learning. The scientifically-validated best practices for optimized learning of soft skills are many, and will be outlined in subsequent articles, but suffice it to say that learning in this system is active, it involves learning by doing, and physical repetition.

Without going into the detailed neuroanatomy, soft skill learning relies critically on interactivity in the form of real-time immediate corrective feedback. You generate a behavior and receive feedback (literally within a few hundred milliseconds, no more). If the behavior is rewarded with a smile or nod, then that behavior will be more likely to occur next time you are in the same situation. If the behavior is punished with a frown or head shake, then that behavior will be less likely to occur next time you are in the same situation. This interactive back-and-forth in real-time is what leads to behavior change.

Optimal Delivery System for Hard and Soft Skills Training

With a few exceptions, the most common delivery system for corporate training is computer-based. Whether hard or soft skills, the training content generally comes in the form of written text, slide shows, or perhaps video. Notice that all of these content media involve passive observation on the part of the learner. This suggests a strong bias in corporate training toward optimized hard skills training, but sub-optimal soft skills training. Given the growing recognition of the importance of soft skills training in the workplace (e.g., the #metoo phenomena), this is unacceptable. Corporate training must expand to include interactivity of the form outlined above, in order to optimize soft skills training.

Most Tasks Require a Mixture of Cognitive and Behavioral Skills Learning

Although I have described hard skill learning as being mediated by the cognitive skills learning system in the brain, and soft skill learning as being mediated by the behavioral skills learning system in the brain, the reality is that nearly all tasks involve a mixture of cognitive and behavioral skills. For example, learning a new CRM might begin with reading the manual, watching some slide shows, or watching videos of someone performing specific functions within the CRM, but ultimately you need to use your mouse, touchpad, tablet or arrow keys to navigate the CRM and the keyboard to enter data. These are all behaviors and with enough practice these behaviors will become habitized through behavioral skills learning in the brain. Analogously, when training to behave as an effective manager, it makes sense to begin by reading descriptions of appropriate and inappropriate leadership behaviors, and even passively observing video interactions that portray an array of appropriate and inappropriate behaviors. This will set the stage, and likely facilitate the subsequent training targeted directly at increasing effective leadership behaviors and decreasing ineffective leadership behaviors.

A number of vendors offer corporate training, including Salesforce, SAP, Cornerstone, Saba, Skillsoft, PageUp, Fivel, Halogen, PeopleFluent, Talentsoft, Haufe, Oracle, SilkRoad, Deltek, IBM, Lumesse, Cegid, and many more. All of these vendors offer hard and soft skills training within a single platform. In most cases, the same training procedures are used for hard and soft skills training, with only the content changing. As outlined above, this approach is disadvantageous for soft skills training.

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As API Management Problem Grows, Informatica Jumps into the Market

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Tom Petrocelli, Amalgam Insights Contributing Analyst

API management is a necessary but boring practice. As developers make use of a mix of public cloud, purchased or open source libraries, and homegrown services, the number of APIs used by developers quickly renders pouring through documentation impractical.

Microservices, usually accessed via RESTFul APIs, cause API calls to rapidly proliferate. Even modest-sized microservices-based systems experience API overload quickly. Agile development can exacerbate the problem of understanding and using APIs. The rapid pace of Agile, especially Scrum, leaves little time for proper documentation of APIs. Documentation often takes a back seat to continuous deployment.
Continue reading “As API Management Problem Grows, Informatica Jumps into the Market”

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The Enterprise Opportunity in Apple’s Slowdown

From Pixabay


On December 28th, Apple released a statement regarding the fact that it was planning to lower the battery replacement costs for Apple 6s and 7s from $79 to $29 as a followup to a December 20th statement made to Techcrunch in which Apple described how it was slowing down iPhone 6s and 7s. This piece provides guidance to enterprise IT, mobility, and procurement managers on what this issue is and how to take full advantage of this situation to improve your enterprise mobility environment.

Recommended Audience: CIO, Chief Procurement Officer, IT Procurement Directors and Managers, Mobility Directors and Managers, Telecom Directors and Managers, IT Service Desk Directors and Managers

Vendors Mentioned: Apple, Samsung, Huawei, Calero, DMI, DXC, Ezwim, G Squared Wireless, Honeywell, IBM, Intratem, MDSL, MOBI, Mobichord, MobilSense, Mobile Solutions, NetPlus, Network Control, One Source Communications, Peak-Ryzex, RadiusPoint, Stratix, Tangoe, vCom Solutions, Visage, Vox Mobile, Wireless Analytics
Continue reading “The Enterprise Opportunity in Apple’s Slowdown”