Practice: Accounting Tech

Key Stakeholders: Chief Financial Officers, Chief Accounting Officers, Vice President of Finance, Vice President of Accounting, Corporate Controllers, Financial Close Directors and Managers, Accounting Directors and Managers, Finance Directors and Managers, Accounts Payable Directors and Managers, Financial Analysts, Staff Accountants

Why This Matters: Transaction matching, a key function in the account reconciliation process, is one of the most time-consuming challenges for timely financial close. Amalgam Insights estimates that 80% of mid-market companies currently conduct transaction matching either manually or with only the assistance of ungoverned spreadsheets. Current transaction matching solutions are either limited in transaction scope or extremely challenging for mid-market organizations to implement in a cost-effective and time-efficient manner.

Key Takeaway: With FloQast Matching, mid-sized enterprises and organizations have access to a scalable and usable transaction matching solution that will significantly reduce time-to-close by eliminating painful manual reviews for the vast majority of transactions and reducing matching error rates.
Continue reading Accounting Tech Market Alert: FloQast Provides AI-Powered Transaction Matching to Accelerate the Financial Close

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

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 Accelerates Automatic Machine Learning with New NVIDIA-Powered Data Science Workstations and NVIDIA RAPIDs
• SAS partners with NVIDIA on deep learning and computer vision

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.

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

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

• Part I: The Problem
• Part II: The Brain Science, and
• Part III: xR Hard Skills Applications

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

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).

 

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

I woke up last Tuesday (March 12, 2019) to find an interesting announcement in my inbox. NGINX, the software networking company, well known for its NGINX web server/load balancer, was being acquired by f5. f5 is best known for its network appliances which implement network security, load balancing, etc. in data centers.

The deal was described as creating a way to “bridge NetOps to DevOps.” That’s a good way to characterize the value of this acquisition. Networking has begun to evolve, or perhaps devolve, from the data center into the container cluster. Network services that used to be the domain of centralized network devices, especially appliances, may be found in small footprint software that runs in containers, often in a Kubernetes pod. It’s not that centralized network resources don’t have a place – you wouldn’t be able to manage the infrastructure that container clusters run on without them. Instead, both network appliances and containerized network resources, such as a service mesh, will be present in microservices architectures. By combining both types of network capabilities, f5 will be able to sell a spectrum of network appliances and software tailored toward different types of architectures. This includes the emerging microservices architectures that are quickly becoming mainstream. With NGINX, f5 will be well positioned to meet the network needs of today and of the future.

The one odd thing about this acquisition is that f5 already has an in-house project, Aspen Mesh, to commercialize very similar software. Aspen Mesh sells an Istio/Envoy distribution that extends the base features of the open source software. There is considerable overlap between Aspen Mesh and NGINX, at least in terms of capabilities. Both provide software to enable a service mesh and provide services to virtual networks. ” Sure, NGINX has market share (and brain share) but $670M is a lot of money when you already have something in hand.

NGINX and f5 say that they see the products as complementary and will allow f5 to build a continuum of offerings for different needs and scale. In this regard, I would agree with them. Aspen Mesh and NGINX are addressing the same problems but in different ways. By combining NGINX with the Aspen Mesh, f5 can cover more of the market.

Given the vendor support of Istio/Envoy in the market, it’s hard to imagine f5 just dropping Aspen Mesh. At present, f5 plans to operate NGINX separately but that doesn’t mean they won’t combine NGINX with Aspen Mesh in the future. Some form of coexistence is necessary for f5 to leverage all the investments in both brands.

The open source governance question may be a problem. There is nervousness within the NGINX community about its future. NGINX is based on its own open source project, one not controlled by any other vendors. The worry is that the NGINX community run into the same issues that the Java and MySQL communities did after they were acquired by Oracle which included changes to licensing and issues over what constituted the open source software versus the enterprise, hence proprietary software. f5 will have to reassure the NGINX community or risk a fork of the project or, worse, the community jumping ship to other projects. For Oracle, that led to MariaDB and a new rival to MySQL.

NGINX will give f5 both opportunity and technology to address emerging architectures that their current product lines will not. Aspen Mesh will still need time to grow before it can grab the brain share and revenue that NGINX already has. For a mainstream networking company like f5, this acquisition gets them into the game more quickly, generates revenue immediately, and does so in a manner that is closer to their norm. This makes a lot of sense.

Now that the first acquisition has happened, the big question will be “who are the next sellers and the next buyers?” I would predict that we will see more deals like this one. We will have to wait and see.

Amalgam Insights Brain Science Research Fellow Todd Maddox has released new research on the Rehearsal website focused on the role of collaborative video-based practice and its role in teaching people skills (also known as soft skills). Continue reading Todd Maddox Reveals How Collaborative Video-Based Practice Effectively Trains People Skills: A Brain Science Analysis

At IBM Think in February, IBM made several announcements around the expansion of Watson’s availability and capabilities, framing these announcements as the launch of “Watson Anywhere.” This piece is intended to provide guidance to data analysts, data scientists, and analytic professionals seeking to implement machine learning and artificial intelligence capabilities and evaluating the capabilities of IBM Watson’s AI and machine learning services for their data.

Announcements

IBM declared that Watson is now available “anywhere” – both on-prem and in any cloud configuration, whether private, public, singular, multi-cloud, or a hybrid cloud environment. Data that needs to remain in place for privacy and security reasons can now have Watson microservices act on it where it resides. The obstacle of cloud vendor lock-in can be avoided by simply bringing the code to the data instead of vice versa. This ubiquity is made possible via a connector from IBM Cloud Private for Data that makes these services available via Kubernetes containers. New Watson services that will be available via this connector include Watson Assistant, IBM’s virtual assistant, and Watson OpenScale, an AI operation and automation platform.

Watson OpenScale is an environment for managing AI applications that puts IBM’s Trust and Transparency principles into practice around machine learning models. It builds trust in these models by providing explanations of how said models come to the conclusions that they do, permitting visibility into what’s seen as a “black box” by making their processes auditable and traceable. OpenScale also claims the ability to automatically identify and mitigate bias in models, suggesting new data for model retraining. Finally, OpenScale also provides monitoring capabilities of AI in production, validating ongoing model accuracy and health from a central management console.

Watson Assistant lets organizations build conversational bot interfaces into applications and devices. When interacting with end users, it can perform searches of relevant documentation, ask the user for further clarification, or redirect the user to a person for sufficiently complex queries. Its availability as part of Watson Anywhere permits organizations to implement and run virtual assistants in clouds outside of the IBM Cloud.

These new services join other Watson services currently available via the IBM Cloud Private for Data connector including Watson Studio and Watson Machine Learning, IBM’s programs for creating and deploying machine learning models. Additional Watson services being made available for Watson Anywhere later this year include Watson Knowledge Studio and Watson Natural Language Understanding.

In addition, IBM also announced IBM Business Automation with Watson, a future AI capability that will permit businesses to further automate existing work processes by analyzing patterns in workflows for commonly repeated tasks. Currently, this capability is available via limited early access; general availability is anticipated later in 2019.

Recommendations

Organizations seeking to analyze data “in place” have a new option with Watson services now accessible outside of the IBM Cloud. Data that must remain where it is for security and privacy reasons can now have Watson analytics processes brought to it via a secure container, whether that data resides on-prem or in any cloud, not just the IBM cloud. This opens the possibility of using Watson to enterprises in regulated industries like finance, government, and healthcare, as well as in departments where governance and auditability are core requirements, such as legal and HR.

With the IBM Cloud Private for Data connector enabling Watson Anywhere, companies now have a net-new reason to consider IBM products and services in their data workflow. While Amazon and Azure dominate the cloud market, Watson’s AI and machine learning tools are generally easier to use out of the box. For companies who have made significant commitments to other cloud providers, Watson Anywhere represents an opportunity to bring more user-friendly data services to their data residing in non-IBM clouds.

Companies concerned about the “explainability” of machine learning models, particularly in regulated industries or for governance purposes, should consider using Watson OpenScale to monitor models in production. Because OpenScale can provide visibility into how models behave and make decisions, concerns about “black box models” can be mitigated with the ability to automatically audit a model, trace a given iteration, and explain how the model determined its outcomes. This transparency boosts the ability for line of business and executive users to understand what the model is doing from a business perspective, and justify subsequent actions based on that model’s output. For a company to depend on data-driven models, those models need to prove themselves trustworthy partners to those driving the business, and explainability bridges the gap between the model math and the business initiatives.

Finally, companies planning for long-term model usage need to consider how they plan to support model monitoring and maintenance. Longevity is a concern for machine learning models in production. Model drift reflects changes that your company needs to be aware of. How do companies ensure that model performance and accuracy is maintained over the long haul? What parameters determine when a model requires retraining, or to be taken out of production? Consistent monitoring and maintenance of operationalized models is key to their ongoing dependability.