Zoho Is Moving to Austin!

I recently attended Zoholics 2019 in Austin, Texas. It was quite an event. The conference opened with the big news that was Zoho is moving its headquarters to Austin! This made headline news on the front page of the Austin American Statesman, and Austin mayor, Steve Adler offered words of excitement and encouragement during his Keynote address.

Zoho also announced two new product offerings. Zoho Commerce Plus offers a comprehensive E-commerce platform that provides an end-to-end solution for the commerce vertical. Zoho MarketingHub allows businesses to coordinate marketing with sales by integrating with a number of Zoho apps (e.g., Zoho CRM, Zoho Campaigns, etc), as well as other customer applications such as Facebook, Twitter, and LinkedIn.

Given my focus on talent management and learning and development, two Zoholics topics were of particular interest to me. One was an update on the success of Zoho University, and the second was the announcement that Zoho has a Learning Management System (LMS) currently in beta.

Zoho University

Rajendran Dandapani, Evangelist and Raconteur at Zoho gave an enthusiastic presentation on the success of Zoho University. Zoho University offers a “crusade against academic credentialism”. It was built on the philosophy that the majority of new Zoho employees did not find their 4-year degree useful in their job, the necessity for good employees, and the realization that Product Managers were frustrated by how little new employees appeared to learn in college. As a former University Professor, the word “ouch” comes to mind, but when I take a step back and think about it, there is merit in this crusade, especially in the software development industry.

One of the main advantages of Zoho University, is that students (and their families) do not incur debt during the education process. Instead, students are paid to attend Zoho University, not the other way around.

There are also a number of learning science—the marriage of psychology and neuroscience—advantages of the Zoho University approach to teaching software development. First, the emphasis is on “learning by doing”. Students spend the majority of their time in labs working on real-world software problems, and very little time in lectures. In the end, developing software solutions is more about trial and error and behavioral learning than it is about learning facts and figures. Learning by doing targets these behavioral learning centers in the brain directly. Second, the learning is in teams, is highly collaborative, and centers around solving specific, current, real-world problems. The software development industry is becoming more cross-functional and collaborative by the day. Given this fact, it is highly efficient to instill this way of thinking and approach to problem solving directly into the educational process from Day 1. Finally, when lectures are necessary a “flipped classroom” approach is utilized. The lecture material is provided using videotaped content and the classroom setting is reserved for discussion and hands on practice. This integration of knowledge acquisition and behavioral training is ideal for software development.

Rajendran also mentioned that Zoho University plans to expand its curriculum to include Technology, Design and Marketing. Finally, the new Austin Headquarters will double as a new Zoho University campus. As an Austin local, I believe that Zoho University will be highly coveted by students in the Austin Metro area.

Zoho LMS

In my individual meeting with Chandrashekar L S P (Zoho Evangelist) and Raja Ramasamy (Head of Product Management for Zoho People Plus) I was delighted to hear that Zoho is currently developing an LMS. This is exciting news and is one product that is currently lacking from the Zoho One suite. I hope to obtain a detailed briefing and to learn more about the LMS in the coming months. Stay tuned.

  1. Final Thoughts

I was impressed by the enthusiasm and loyalty of the Zoho users to the Zoho product line. Whether from organized customer and partner panels, or one-off happenstance conversations, the message that I heard was clear: Zoho users like the products, feel “heard” when they have a problem, and find the overall customer service experience to be outstanding.

I will continue to follow Zoho, with particular interest in Zoho University and Zoho’s upcoming LMS. It will be exciting to have Zoho’s headquarters “right down the block” so to speak.


Enterprise Data World 2019: Data Science Will Take Over The World! … Eventually.

Amalgam Insights attended Enterprise Data World, a conference focused on data management, in late March. Though the conference tracks covered a wide variety of data practices, our primary interest was in the sessions on the AI and Machine Learning track. We came away with the impression that the data management world is starting to understand and support some of the challenges that organizations face when trying to get complex data initiatives off the ground, but that the learning process will continue to have growing pains.

Data Strategy Bootcamp

I began my time at Enterprise Data World with the Data Strategy Bootcamp on Monday. Often, organizations focus on getting smaller data projects done quickly in a tactical fashion at the expense of consciously developing their broader data strategy. The bootcamp addressed how to incorporate these “quick wins” into the bigger picture, and delved into the details of what a data strategy should include, and what does the process of building one look like. For people in data analytics and data scientist roles, understanding and contributing to your organization’s data strategy is important because well-documented and properly-managed data means data analysts and data scientists can spend more of their time doing analytics and building machine learning models. The “data scientists spend 80% of their time cleaning and preparing data” number continues to circulate without measurable improvement. To build a successful data strategy, organizations will need to identify business goals that are data-centric to align the organization’s data strategy with its business strategy, assess the organization’s maturity and capabilities across its data ecosystem, and determine long-term goals and “quick wins” that will provide measurable progress towards those goals.

Getting Started with Data Science, Machine Learning, and Artificial Intelligence Initiatives

Actually getting started on data science, machine learning, and artificial intelligence initiatives remains a point of confusion for many organizations looking to expand beyond the basic data analytics they’re currently doing. Both Kristin Serafin and Lizzie Westin of FINRA and Vinay Seth Mohta of Manifold led sessions discussing how to turn talk about machine learning and artificial intelligence into action in your organizations, and how to do so in a way that can scale up quickly. Key takeaways: your organization needs to understand its data to understand what questions it wants answered that require a machine learning approach; it needs to understand what tools are necessary to move forward; it needs to understand who already has pertinent data capabilities within the organization, and who is best positioned to improve their skills in the necessary manner; and you need to obtain buy-in from relevant stakeholders.

Data Job Roles

Data job roles were discussed in multiple sessions; I attended one from the perspective of how analytical jobs themselves are evolving, and one from the perspective of analytical career development. Despite the hype, not everyone is a data scientist, even if they may perform some tasks that are part of a data science pipeline! Data engineers are the difference between data scientists’ experiments sitting in silos and getting them into production where they can affect your company. Data analysts aren’t going anywhere – yet. (Though Michael Stonebraker, in his keynote Tuesday morning, stated that he believed data science would eventually replace BI, pending upskilling a sufficient number of data workers.) And data scientists spend 80% of their time doing data prep instead of building machine learning models; they’d like to do more of the latter, and because they’re an expensive asset, the business needs them to be doing less prep and more building as well.

By the same token, there are so many different specialties across the data environment, and the tool landscape is incredibly large. No one will know everything; even relatively low-level people will need to provide leadership in their particular roles to bridge the much-bemoaned gap between IT and Business. So how can data people do that? They’ll need to learn to talk about their initiatives and accomplishments in business terms – increasing revenue, decreasing cost, managing risk. By doing this, data strategy can be tied to business strategy, and this barrier to success can be surmounted.

Data Integration at Scale

Michael Stonebraker’s keynote highlighted the growing need for people with data science capabilities, but the real meat of his talk centered around how to support complex data science initiatives: doing data integration at scale. One example: General Electric’s procurement system problem. Clearly, the ideal number of procurement systems in any company is “one.” Given mergers and acquisitions, over time, GE had accumulated *75* procurement systems. They could save $100M if they could bring together all of these systems, with all of the information on the terms and conditions negotiated with each vendor via each of these systems. But this required a rather complex data integration process. Once that was done, the same process remained for dealing with their supplier databases, and their customer databases, and a whole host of other data. Machine learning can help with this – once there are sufficient people with machine learning skills to address these large problems. But doing data integration at scale will remain a significant challenge for enterprises for now, with machine learning skills being relatively costly and rare, data accumulation continuing to grow exponentially, and bringing in third-party data to supplement existing analyses..

Knowledge Graphs and Semantic AI

A number of sessions discussed knowledge graphs and their importance for supporting both data management and data science tasks. Knowledge graphs provide a “semantic” layer over standard relational databases – they prioritize documenting the relationships between entities, making it easier to understand how different parts of your organization’s data are interrelated. Because having a knowledge graph about your organization’s data provides natural-language context around data relationships, it can make machine learning models based on that data more “explainable” due to the additional human-legible information available for interpretation and understanding. Another example: if you’re trying to perform a search, most results rely on exact matches. Having a knowledge graph makes it simple to pull up “related” results based on the relationships documented in that knowledge graph.

Data Access, Control, and Usage

My big takeaway from Scott Taylor’s Data Architecture session: data should be a shared, centralized asset for your entire organization; it must be 1) accessible by its consumers 2) in the format they require 3) via the method they require 4) if they have permission to access it (security) 5) and they will use it in a way that abides by governance standards and laws. Data scientists care about this because they need data to do their job, and any hurdle in accessing usable data makes it more likely they’ll avoid using official methods to access the data. Nobody has three months to wait for a data requisition from IT’s data warehouses to be turned around anymore; instead, “I’ll just use this data copy on my desktop” – or more likely these days, in a cloud-hosted data silo. Making centralized access easy to use makes data users much more likely to comply with data usage and access policies, which helps secure data properly, govern its use appropriately, and prevent data silos from forming.

Digging a bit more into the security and governance aspects mentioned above, it’s surprisingly easy to identify individuals in a set of anonymized data. In separate presentations, Matt Vogt of Immuta demonstrated this with a dataset consisting of anonymized NYC taxi data, even as more and more information was redacted from it. Jeff Jonas of Senzing’s keynote took this further – as context accumulates around data, it gets easier to make inferences, even when your data is far from clean. With GDPR on the table, and CCPA coming into effect in nine months, how data workers can use data, ethically and legally, will shift, significantly affecting data workflows. Both the use of data and the results provided by black-box machine learning models will be challenged.


Data scientists and machine learning practitioners should familiarize themselves with the broader data management ecosystem. Said practitioners understand why dirty data is problematic, given that they spend most of their work hours cleaning that data so they can do the actual machine learning model-building, but there are numerous tools available to help with this process, and possibly obviate the need for a particular cleaning job that’s already been done once. As enterprise data catalogs become more common, this will prevent data scientists from spending hours on duplicative work when someone else has already cleaned the set they were planning to use and made it available for the organization’s use.

Data scientists and data science managers should also learn how to communicate the business value of their data initiatives when speaking to business stakeholders. From a technical point of view, making a model more accurate is an achievement in and of itself. But knowing what it means from a business standpoint builds understanding of what that improved accuracy or speed means for the business as a whole. Maybe your 1% improvement in model accuracy means you save your company tens of thousands of dollars by more accurately targeting potential customers who are ready to buy your product – that’s what will get the attention of your line-of-business partners.

Data science directors and Chief Data or Chief Analytics Officers should approach building their organization’s data strategy and culture with the long-term view in mind. Aligning your data strategy with the organization’s business strategy is crucial to your organization’s success. Rather than having both departments tugging on opposite ends of the rope going in different directions, develop an understanding of each others’ needs and capabilities and apply that knowledge to keep everyone focused on the same goal.

Chief Data Officers and Chief Analytics Officers should understand their organization’s capabilities by conducting an assessment both of their data capabilities and capacity available by individual, and to assess the general maturity in each data practice area (such as Master Data Management, Data Integration, Data Architecture, etc.). Knowing the availability of both technical and people-based resources is necessary to develop a scalable set of data processes for your organization with consistent results no matter who the data scientist or analyst is in charge of executing on the process for any given project.

As part of developing their organization’s data strategy, Chief Data Officers and Chief Analytics Officers must work with their legal department to develop rules and processes for accumulating, storing, accessing, and using data appropriately. As laws like GDPR and the California Privacy Act start being enforced, data access and usage will be much more scrutinized; companies not adhering to the letters of those laws will find themselves fined heavily. Data scientists and data science managers who are working on projects that involve sensitive or personal data should talk to their general counsel to ensure they remain on the right side of the law.

Google Goes Corporate at Google Next

There’s no doubt that Google exists to make money. They make money by getting companies to buy their services. When it comes to selling ads on search engines, Google is number one. When it comes to their cloud business, Google is… well, number three.

I’m guessing that irks them a bit especially since they sit behind a company whose main business is selling whatever stuff people want to sell and a company that made its name in the first wave of PCs. Basically, a department store and a dinosaur are beating them at what should be their game.
Continue reading “Google Goes Corporate at Google Next”

Todd Maddox Explains Why Extended Reality (xR) Technologies Will Disrupt Corporate L&D

Research Fellow Todd Maddox, Ph.D. has just published a new Analyst Insight: Leveraging Learning Science: Why Extended Reality (xR) is Poised to Disrupt Corporate Learning and Development.

In this Analyst Insight, Todd Maddox, Ph.D. provides guidance on why Augmented and Virtual Reality are set to disrupt corporate learning. This report focuses on a learning science evaluation of the potential for extended reality (xR) technologies to disrupt corporate L&D and show how xR technologies have the potential to improve the quality and quantity of training, to accelerate learning and enhance retention in all aspects of corporate learning to provide the following benefits:

  • Faster learning and stronger retention
  • Reduced training time
  • Time-effective, cost-effective and scalable training across a variety of hard skills, soft skills and situational awareness
  • Ability to train expensive, dangerous and rare situations in total safety and to expertise.
  • Improved understanding of the learning process through subjective, objective, attention, and engagement metrics built from xR Big Data.
  • More effective, efficient, and valuable learning environments.

To learn more about the key aspects of augmented and virtual reality that will change coporate learning environments, download this Analyst Insight, which is available at no cost through April 10th: Leveraging Learning Science: Why Extended Reality (xR) is Poised to Disrupt Corporate Learning and Development.

Data Science and Machine Learning News Roundup, March 2019

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.

Dataiku Releases Version 5.1 in Anticipation of AI’s Surge in the Enterprise

Dataiku released version 5.1 of their software platform. This includes a GDPR framework for governance and control, as well as user-experience upgrades such as the ability to copy and reuse analytic workflows in new projects, coders being able to use their preferred development environment from within Dataiku, and easier navigation of complex analytics projects where data sources may number in the hundreds.

Being able to document when sensitive data is being used and prevent inappropriate use of such data is key for companies trying to work within GDPR and similar laws and not lose significant funds to violations of these laws. Dataiku’s inclusion of a governance component within its data science platform distinguishes it from its competitors, many of whom lack such a component natively, and enhances Dataiku’s attractiveness as a data science platform.

Domino Data Lab Platform Enhancements Improve Productivity of Data Science Teams Across the Entire Model Lifecycle

Domino announced three new capabilities for their data science platform. Datasets is a high-performance data store that will make it easier for data scientists to find, share, and reuse large data resources across multiple projects, saving time in the search process. Experiment Manager gives data science teams a system of record for ongoing experiments, making it easier to avoid unnecessary duplicate work. Activity Feed provides this type of information for data science leads to understand changes in any given project when they may be tracking multiple projects at once. Together, these three collaboration capabilities enhance Domino users’ ability to do data science in a documented, repeatable, and mature fashion.

SAS Announces $1 Billion Investment in Artificial Intelligence (AI)

SAS announced a $1B investment in AI across three key areas: Research and Development, education initiatives, and a Center of Excellence. The goal is to to enable SAS users to use AI to some degree even without a significant baseline of AI skills, to help SAS users improve their baseline AI skills through training, and to help organizations using SAS to bring AI projects into production more quickly with the help of AI experts as consultants. A significant percent of SAS users aren’t currently using SAS to perform complex machine learning and artificial intelligence tasks; helping these users to  get actual SAS-based AI projects into production enhances SAS’ ability to sell its AI software.

NVIDIA-Related Announcements

H2O.ai and SAS both announced partnerships with NVIDIA this month. H2O.ai’s Driverless AI and H2O4GPU are now optimized for NVIDIA’s Data Science Workstations, and NVIDIA RAPIDS will be integrated into H2O as well. SAS disclosed future plans to expand NVIDIA GPU support across SAS Viya, and plan to use these GPUs and the CUDA-X AI acceleration library to support SAS’ AI software. Both H2O.ai and SAS are using NVIDIA’s GPUs and CUDA-X to make certain types of machine learning algorithms operate more quickly and efficiently.

These follow prior announcements about NVIDIA partnerships with IBM, Oracle, Anaconda, and MathWorks, reflecting NVIDIA’s importance in machine learning. With NVIDIA GPUs making up an estimated 70% of the world market share, data science and machine learning software programs and platforms need to be able to work well on the de facto default GPU.

Tom Petrocelli Releases Groundbreaking Technical Guide on Service Mesh

On April 2, 2019, Amalgam Insights Research Fellow Tom Petrocelli published Technical Guide: A Service Mesh Primer, which serves as a vital starting point for technical architects and developer teams to understand the current trends in microservices and service mesh. This report provides enterprise architects, CTOs, and developer teams with the guidance they need to understand the microservices architecture, service mesh architecture, and OSI model context necessary to conceptualize service mesh technologies.

In this report, Amalgam Insights provides context in the following areas: Continue reading “Tom Petrocelli Releases Groundbreaking Technical Guide on Service Mesh”

Why Extended Reality (xR) is Poised to Disrupt Corporate Learning and Development – Part IV: xR Behavioral Skills Applications, and Recommendations

Note: If you missed Parts I, II, and III of this blog series, catch up and read

This is part of a four-blog series exploring the psychology and brain science behind the potential for extended reality tools to disrupt corporate Learning & Development.

xR and Behavioral Skills Learning: Whereas hard skills learning involves knowing what to do, behavioral skills learning involve knowing how to do it. People (aka soft) skills, such as the ability to communicate, collaborate, and lead effectively, or to show empathy and to embrace diversity, are behavioral skills. Similarly, technical skills, such as the ability to learning how to use new software, to upskill to a new software release, or to use and maintain a piece of hardware or equipment, are behavioral skills.

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Coming Attractions: Groundbreaking Service Mesh Research

In early January, I started researching the service mesh market. To oversimplify, a service mesh is a way of providing for the kind of network services necessary for enterprise applications deployed using a microservices architecture. Since most microservices architectures are being deployed within containers and, most often, managed and orchestrated using Kubernetes, service mesh technology will have a major impact on the adoption of these markets.

As I began writing the original paper, I quickly realized that an explanation of service mesh technology was necessary to understand the dynamic of the service mesh market. Creating a primer on service mesh and a market guide turned out to be too much for one paper. It was unbearably long. Subsequently, the paper was split into two papers, a Technical Guide and a Market Guide.

The Technical Guide is a quick primer on service mesh technology and how it is used to enhance microservices architectures, especially within the context of containers and Kubernetes. The Market Guide outlines the structure of the market for service mesh products and open source projects, discusses many of the major players, and talks to the current Istio versus Linkerd controversy. The latter is actually a non-issue that has taken on more importance than it should given the nascence of the market.

The Technical Guide will be released next week, just prior to Cloud Foundry Summit. Even though service mesh companies seem to be focused on Kubernetes, anytime there is a microservices architecture, there will be a service mesh. This is true for microservices implemented using Cloud Foundry containers.

The Market Guide will be published roughly a month later, before Red Hat Summit and KubeCon+CloudNative Summit Europe, which I will be attending. Most of the vendors discussed in the Market Guide will be in attendance at one or the other conference. Read the report before going so that you know who to talk to if you are attending these conferences.

A service mesh is a necessary part of emerging microservices architectures. These papers will hopefully get you started on your journey to deploying one.

Note: Vendors interested in leveraging this research for commercial usage are invited to contact Lisa Lincoln (lisa@amalgaminghts.com).


Why Extended Reality (xR) is Poised to Disrupt Corporate Learning and Development – Part III: xR Hard Skills Applications

Note: If you missed Parts I and II of this blog series, catch up and read Part I: The Problem, and Part II: The Brain Science. This is part of a four-blog series exploring the psychology and brain science behind the potential for extended reality tools to disrupt corporate Learning & Development.

xR Applications in Corporate L&D

The key ingredient of xR technology in corporate L&D is the experiential and immersive nature of the technology that provides rich, coordinated contextual cues that lead to a sense of “presence”. You are either in a real-world experience augmented with information (Augmented Reality or AR), or you are transported into a new virtual world (Virtual Reality or VR). In both cases, experiential learning systems are engaged in synchrony with cognitive, behavioral, and emotional learning systems in the brain. I elaborate below.

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