Posted on Leave a comment

July 23: From BI to AI (Cube Dev, Dremio, Google Cloud, Julia Computing, Lucata, Palantir, Redpoint, Sisense, Vertica, Zoom)

If you would like your announcement to be included in these data platform-focused roundups, please email lynne@amalgaminsights.com.

Product Launches and Updates

Dremio Launches SQL Lakehouse Service to Accelerate BI and Analytics

On July 21, at Subservice Live, Dremio debuted Dremio Cloud, a cloud-native SQL-based data “lakehouse” service. The service marries various aspects of data lakes and data warehouses into a SQL lakehouse, enabling high-performance SQL workloads in the cloud and expediting the process of getting started. Dremio Cloud is now available in the AWS Marketplace.

Google Cloud Announces Healthcare Data Engine to Enable Interoperability in Healthcare

On July 22, Google Cloud announced Healthcare Data Engine, now in private preview. Healthcare Data Engine integrates healthcare and life sciences data from multiple sources such as medical records, claims, clinical trials, and research data, enabling a more longitudinal view of patient health along with advanced analytics and AI in a secure environment. With the introduction of Amazon HealthLake last week, it’s clear that expanding healthcare and life sciences analytics capabilities continue to be a top priority among data services providers.

Palantir Introduces Foundry for Builders

Dipping a toenail into the waters outside their usual large established organization customer base, Palantir announced the launch of Foundry for Builders, providing access to the Palantir Foundry platform for startups under a fully-managed subscription model. Foundry for Builders is starting off with limited availability; the initial group of startups provided access are all connected to Palantir alumni, with the hope of expanding to other early-stage “hypergrowth” companies down the road.

Redpoint Global Announces In Situ

On July 20, Redpoint announced In Situ, a service that provides data quality and identity resolution. In Situ uses Redpoint’s data management technology to supply identity resolution and data integration services in real time within an organization’s virtual private cloud, without needing to transfer said private data across the internet.

Sisense Announces Sisense Extense Framework

On July 21, Sisense debuted the Sisense Extense Framework, a way to deliver interactive analytics experiences within popular business applications. Initially supported apps include Slack, Salesforce, Google Sheets, Google Slides, and Google Chrome, now available on the Sisense Marketplace. The Sisense Extense Framework will be released more broadly later this year to partners looking to build similar “infusion” apps.

Vertica Announces Vertica 11

On June 20, at Vertica Unify 2021, Vertica announced the Vertica 11 Analytics Platform. Key improvements include broader deployment support, strengthened security, increased analytical performance, and enhanced machine learning capabilities.

Funding

Cube Dev Raises $15.5 Million to Help Companies Build Applications with Cloud Data Warehouses

On July 19, Cube Dev announced that they had raised $15.5M in Series A funding. Decibel led this round, with participation from Bain Capital Ventures, Betaworks and Eniac Ventures. The funding will be used to scale go-to-market activities and accelerate R+D on its first commercial product. Cube Dev also brought aboard Jonathan E. Cowperthwait of npm as Head of Marketing and Jordan Philips of Dashbase as Head of Revenue Operations to support their commercial expansion.

Julia Computing Raises $24M in Series A, Former Snowflake CEO Bob Muglia Joins Board

Julia Computing announced the completion of a $24M Series A funding round on July 19. Dorilton Ventures led the round, with participation from Menlo Ventures, General Catalyst, and HighSage Ventures. Julia Computing will use the funding to further develop JuliaHub, its secure, high-performance cloud platform for scientific and technical modeling, and to grow the Julia ecosystem overall. Bob Muglia, the former CEO of Snowflake, joined the Julia Computing board on the same day.

Lucata Raises $11.9 Million in Series B Funding to Introduce Next-Generation Computing Platform

Lucata, a platform to scale and accelerate graph analytics, AI, and machine learning capabilities, announced July 19 that it had raised $11.9M in Series B funding. Notre Dame, Middleburg Capital Development, Blu Ventures Inc., Hunt Holdings, Maulick Capital, and Varian Capital all participated in the round. The funding will fuel an “aggressive” go-to-market strategy.

Acquisitions

Zoom to Acquire Five9

On July 18, Zoom announced that it had entered into a definitive agreement to acquire Five9, a cloud contact center service provider, for $14.7B in stock. In welcoming Five9 to the Zoom platform, Zoom expects to build a better “customer engagement platform,” complementary with its Zoom Phone offering. Later in the week, Zoom also announced the launch of Zoom Apps and Zoom Events, further enhancing the collaboration capabilities of the primary Zoom video communications suite.

Posted on Leave a comment

Analyst Insight: Raindrop Systems

Executive Summary

Key Stakeholders: Chief Procurement Officer, Procurement Directors and Managers, Controllers, Vice Presidents of Accounting, Accounting Directors and Managers, Legal Directors, Finance Directors and Managers, Sales Operations Directors and Managers, Marketing Operations Directors and Managers

Why Raindrop Matters: Mid-market organizations between $100 million and $1 billion in annual revenue are in a tricky situation where they have the advantage of being relatively small and nimble, but must also face enterprise-grade operational challenges to support global supply chains, customer bases, and vendor ecosystems. Raindrop provides organizations at this size and above with a freemium SaaS product to support organizations starting to support mature enterprise spend practices while maintaining the governance, compliance, and security issues that come up for enterprises

Top Takeaway: Amalgam Insights recommends Raindrop as a business spend management solution to be considered for organizations with over $20 million in revenue that currently have fragmented or non-existent sourcing management, supplier management, and payment management capabilities.

Introduction to Raindrop Systems

Amalgam Insights recently briefed with Raindrop Systems, an emerging startup focused on Enterprise Spend Management. This vendor got our attention because it has come up in inquiries as a potential solution for managing contracts and spend in mid-market organizations between 100 million and 5 billion dollars in annual revenue, though Raindrop does support enterprise-sized clients exceeding 10 billion dollars in annual revenue.

Founded in 2019 and based in Santa Clara, California in the United States, Raindrop is part of the SaaS trend of “Built by X, for X” companies that was built by procurement professionals to solve enterprise procurement lifecycle demands from sourcing to payment. Amalgam Insights notes that this is a trend in the finance, accounting, and sourcing markets where subject matter experts have come in to build SaaS solutions that have quickly become competitive with legacy solutions that were developed by programmers or technicians lacking deep enterprise practitioner experience. Raindrop was founded by Vijay Caveripakkam and Ward Karson, who bring both provisioning practitioner and consulting backgrounds to the company.

Contextualizing Raindrop in the Enterprise Spend Market

In exploring the Raindrop solution, Amalgam Insights notes that Raindrop’s key differentiation points were associated with providing a modern user experience to support collaboration, workflow automation capabilities, analytic tracking of savings, and clear calls to action to support cost savings, and supplier risk management. The last of these is of particular note, as Amalgam Insights has traditionally seen spend management solutions focus on being systems of record, which led to a practical outcome of creating a data repository that was both complete and overwhelming to analyze on a regular basis. In the 2020s, as Big Data has become regular data and every large data source will overwhelm the human ability to manually audit and query data effectively, people need to use a combination of workflow automation, machine learning, natural language parsing and processing, and rapid contextualization of data to effectively keep up with the constant increases in contract text, transactions, and payments in place.

Raindrop has two key current focus areas. The first is with organizations with less than 50 million dollars in business spend, based on the product’s ease of implementation and a freemium approach that allows clients to sign up for a production-ready instance of the service at no cost to see how contractual obligations can be administrated through a formalized solution. Its second key focus is on larger mid-market to enterprise organizations with 100 to 500 million dollars in business spend to support that customer base. 

Raindrop is designed to be a general spend solution to support planning, supplier management, sourcing, contract management, and payables within a single solution. This integrated lifecycle approach is intended to ensure that data capture associated with contract, vendor, and stakeholder management occurs during transactional execution of payments, contract execution and renewal, and supplier evaluation. As part of this process, Raindrop includes a semi-automated contract loading process that currently automates about half of the contract term entry. From a data privacy perspective, Raindrop has placed a focus on GDPR and on not keeping payment information to support privacy and governance issues such as PCI DSS and FedRAMP compliance.

This solution comes to market at a time when the mid-market procurement market faces a bit of a vacuum. When Scout RFP was purchased by Workday for $540 million, it had made inroads into the mid-market to support cloud-based sourcing and supplier management. However, post-acquisition, Scout RFP development has been more focused on the enterprise customers that Workday focuses on. This development is similar to the work that planning and budgeting solution Adaptive Insights saw post-Workday acquisition.

At the same time, mid-market organizations face greater complexity in their sourcing environments as competitive markets, lowered barriers to purchase, and the proliferation of suppliers in a variety of markets has led to the need to manage spend more closely. As just one example, Amalgam Insights estimates that the average 500 employee company with approximately $100 million in revenue is supporting 250 SaaS vendors, but estimates that less than 100 of them are being formally sourced or managed through procurement. This haphazard approach will lead to spend leakage, missed renewal opportunities, and the inability to aggregate and negotiate licenses over time. And although mid-market companies have typically started to invest in spend management, it is typically in a piecemeal fashion where contract management, purchasing, invoices, payments, and budgeting can each be in a separate application or spreadsheet and there is no direct coordination between each silo other than manual employee changes made on an ad-hoc basis.

Because of both the increasingly complexity of mid-market procurement management and the potential for cost savings through competitive and bulk purchasing exercises, Amalgam Insights believes that the need for mature sourcing and spend management capabilities is now necessary for mid-market organizations that are responsible for good stewardship. Amalgam Insights estimates that the total cost of ownership savings for sourcing management typically matches the expense of an employee or full-time-equivalent at about 100 employees or $20 million in annual revenue and that this cost crossover can occur even earlier in a company’s progression if it is tech-heavy in its operations.

Amalgam Insights’ Recommendations

Amalgam Insights believes that Raindrop’s offering provides a low barrier to entry for business spend as a solution that is as-a-Service, free to start, and provides a modern user interface on par with current web-based applications while providing the breadth of services needed to support business spend management and a standard set of security and compliance requirements needed to support data privacy. In light of the increasing importance of managing the source-to-pay lifecycle for mid-sized organizations, Amalgam Insights recommends Raindrop as a business spend management solution to be considered for organizations with over $20 million in revenue that currently have fragmented or non-existent sourcing management, supplier management, and payment management capabilities.

Posted on 1 Comment

July 16: From BI to AI (AWS, CognitiveScale, GoodData, Hazelcast, Informatica, StrongBox Data Solutions, Vertica)

If you would like your announcement to be included in Amalgam Insights’ weekly data and analytics roundups, please email lynne@amalgaminsights.com.

Product Launches and Enhancements

Informatica Announces Unified Data Governance and Catalog As-a-Service in the Cloud

On July 13, Informatica announced the launch of its Cloud Data Governance and Catalog solution as a key part of its Intelligent Data Management Cloud. As part of Informatica’s continuing expansion to the cloud, Informatica customers will now be able to do data cataloging, quality, data and machine learning model governance through the same “pane of glass.” Amalgam Insights has a forthcoming post on Informatica’s integration of data governance and machine learning model governance.

AWS Announces General Availability of Amazon HealthLake

On July 15, Amazon debuted Amazon HealthLake, a data lake for healthcare and life sciences data that falls under HIPAA requirements. HealthLake uses machine learning to extract and appropriately transform unstructured health data to prepare it for use in analytics and AI models. The service is generally available now.

CognitiveScale Announces Launch Of Cortex Fabric Version 6

On July 15, CognitiveScale announced the release of Cortex Fabric Version 6, a low-code AI app development platform. Cortex 6 lets “citizen developers” build AI-based apps with their “Campaigns” visual framework, focusing on business optimization and process automation use cases.

GoodData and Vertica Partner to Accelerate Cloud-Native Self-Service Analytics Adoption in the Enterprise

GoodData and Vertica announced a strategic partnership on July 15. GoodData.CN, GoodData’s cloud native analytics services, will connect to Vertica’s data warehouse to allow non-technical users to perform self-service analytics.

Hazelcast Unveils Real-Time Intelligent Applications Platform

On July 14, Hazelcast announced the Hazelcast platform, which will allow users to merge streaming data with data at rest. Because Hazelcast can handle realtime event streams as well as access to data lakes and data warehouses, it can act as a single point of access for all types of data. Hazelcast is currently in beta; general availability is expected in August 2021. In addition, certain features of Hazelcast will also be available through the Hazelcast Platform for IBM Cloud Paks.

StrongBox Data Solutions Announces StrongLink 3.2

StrongBox Data Solutions announced the availability of StrongLink 3.2, a data management platform. StrongLink automates policy enforcement across a wide variety of data sources and storage types, aiming to eliminate data silos while providing replication to protect said data’s existence. StrongLink 3.2 is available immediately.

Posted on

Market Alert – Zoho Launches its Business Intelligence Platform

On July 13, 2021, Zoho announced its Business Intelligence Platform which combines data preparation, machine learning, analytics, presentation, and application development into a single suite of services. This platform brings Zoho’s new DataPrep capability and combines is with the existing Zoho Analytics product. The combined platform provides a single solution the clean, augment, analyze, and present data as a visual presentation, application, or as an answer to a question asked in standard English. This analysis is based both on briefings and discussions with Zoho customers.

To provide some background on Zoho, Zoho is a private company founded in 1996 to provide business applications in the cloud. As of now, Zoho supports a business suite of 45 applications available as an integrated suite called Zoho One. Zoho’s Analytics offering, called Zoho Analytics, was first launched as Zoho Reports in 2009 to help users understand their data. Zoho Analytics supports over 10,000 customers across both cloud and on-premises environments. Zoho also supports over 300 white-labelled embedded BI customers across telco, supply chain, and other enterprise environments.

Since 2009, Zoho Analytics has evolved to build in-house capabilities to support data management, a data warehouse, data discovery, collaboration, and a breadth of APIs all used to help support a combination of self-service users and embedded analytics use cases.  Zoho’s analytic platform is based on four key components:

  1. Data Preparation through Zoho DataPrep, which is Zoho’s self-service data preparation application to support data quality, enrichment, transformation, and data modeling capabilities. DataPrep uses machine learning to provide guidance on data enrichment and modeling
  2. Visualization: Zoho enhances traditional dashboarding, reporting, and charting capabilities by providing both access and integration to Zoho Sites, Zoho’s website and portal building product, and Zoho Show, which focuses on presentations. By providing the tools to present and publicly share data outputs, Zoho Analytics extends beyond the dashboard to provide standard access to the digital tools that are typically used to share information: presentations, meetings, intranets, and the internet.
  3. Augmented Analytics, where Zoho’s presentation of descriptive and predictive analytics is enhanced both by the Ask Zia conversational AI (which will be described later in this report) as well as by Zia Insights, which provides a conversational or natural language processing interface for asking questions about the data.
  4. Applications, where Zoho provides a low-code platform called Zoho Creator to build analytic applications that can then be shared through Zoho Marketplace to share either with internal or external customers.

As can be seen from this description, Zoho provides a number of applications in its business suite (including a recently released Zoho Contracts product focused on sourcing) that expand beyond analytics in providing a full business suite of applications for small and medium sized businesses.

Zoho Analytics includes native data management capabilities including data preparation, cleansing, integration, transformation, enrichment, and modeling. This combination of capabilities allows Zoho to keep data relevant over time and to ensure that data is aligned to business terminologies and taxonomies.

Zoho also has its own data warehouse used to model data for analytics. This data warehouse is native to Zoho and includes both in-memory and columnar services to accelerate access to data. Although Zoho typically targets the SMB market, this warehouse has been proven to scale to terabyte-sized data with billions of rows for clients that have needed to grow their analytic environments. This warehouse is currently only available as a part of Zoho Analytics and can be mapped to other cloud data warehouses such as Snowflake or Amazon Relational Database Service (RDS) through metadata mapping.

Zoho Analytics works with Zoho’s applications, but also includes over 250 native integrations to other vendors used by its clients including HubSpot, Microsoft Dynamics, Quickbooks, Salesforce, ServiceNow, Shopify, and Xero with the understanding that some Zoho clients may use other applications as well. In creating these integrations, With these integrations, Zoho Analytics rivals many of its standalone providers, which is demonstrated by 60% of Zoho Analytics’ usage being associated with data outside of the Zoho One suite.

Zoho Analytics provides all of these services on its own cloud, Zoho Cloud, which has both GDPR and CCPA compliance. Based on customer demand, Zoho has also made Zoho Analytics available on-premises for specific geographies, markets, and use cases, which is an interesting departure from Zoho’s typical focus on cloud apps. Based on this combination of data management capabilities, Amalgam Insights considers Zoho Analytics to take a full-stack approach that allows companies to build an analytic environment solely within the Zoho ecosystem.

The pricing for a paid Zoho Analytics account starts at $24 per month as an entry point and can increase up to $455 per month to support up to 50 users, 50 million rows of data, and unlimited workspaces. Zoho Analytics can also be purchased as part of the Zoho One Suite, which brings almost all of Zoho’s applications into a single subscription which ranges from $37 to $105 per user per month depending on whether a company chooses to enroll every employee or seeks a more flexible model for bringing a portion of employees onto Zoho.

Zoho Analytics’ Support for modern analytic capabilites

All of Zoho Analytics’ capabilities across data management, integration, and analytic distribution help to scale the core capability of visualization and analysis. Amalgam Insights views these visualization and analytic capabilities to provide reports and dashboards to be on par with the current analytic market, but today’s market requires analytic companies to push forward to support natural language queries, collaboration, and machine learning. In this light, Amalgam Insights looked at Zoho Analytics’ capabilities across six areas: natural language processing, statistical and advanced analytics, collaboration, mobility, APIs, and future-facing roadmap.

Zoho has taken steps towards this next generation of analytic delivery through its Ask Zia capability. Zia serves as Zoho’s AI assistant. Initially launched in February 2017, Zia was originally designed to be a sales assistant for the Zoho CRM product to help provide reminders for contacting prospects and taking relevant actions at the right time. Since then, Zia has evolved into an assistant that understands natural language questions across multiple applications including the Analytics product. The word “questions” is used here rather than “query” because the inputs for Zoho’s Ask Zia are plain language questions and requests such as “show me the sales for last year” rather than a query-based text string such as “Search sum of sales amount where Year equals 2020.” Although the two phrases are semantically similar, the prior phrase is based on a standard use of language while the latter requires an understanding of querying logic that is not natural. With Ask Zia, Zoho Analytics is a conversational analytics solution that is capable of providing insights and results to any employee based on the language they use in their everyday workplace.

Business analytics solutions are also increasingly asked to support more statistical and advanced analytics capabilities to help more advanced data users understand probabilities, forecasting, data relationships, trending and regression analysis, seasonality and time-series relationships, and model choices to support optimal forecasting and root-cause analysis. Zoho Analytics first announced forecasting capabilities in November of 2018 and continues to increase support for relevant models to improve results for regression, exponential smoothing, and seasonality trend Loess decomposition.

From a consumption perspective, Zoho Analytics supports embedded analytics, web-based apps, as well as the ability to share linked data through collaboration platforms such as Slack, Microsoft Teams, or Zoho’s own Zoho Cliq. Zoho Analytics results can be embedded in webpages, shared as a permalink, placed into slideshows, and provided as public resources. Zoho Analytics also supports analytic storytelling through its slideshows, which work similarly to storyboards that exist in other analytics solutions and are used to present Zoho Analytics workspaces.

All of these analytic outputs are available through the Zoho Analytics mobile app, which is designed to emphasize the workspaces, dashboards, and reports created within Zoho Analytics. The app also supports exporting and commenting on analytic views as well.

From a programmatic perspective, Zoho Analytics functionalities are also available through an API which is accessed by a variety of other Zoho applications, such as Zoho CRM, Zoho Creator (a low-code application platform), Zoho Projects (project management), and Zoho Books (accounting) and can also be used to build add-on capabilities for third-party and custom-built applications. The API can be used with a variety of commonly used programming languages including Java, C#, Python, PHP, Go, and Google Applications through prebuilt “Client Libraries” created to support each language and includes a variety of interfaces to access data, modelling, metadata, collaboration, embedded analytics, and Single Sign-On integration. This API includes SQL access to Zoho Services data for those seeking query-based access to data, through what Zoho calls CloudSQL, which supports ANSI, Oracle, Microsoft SQL, IBM DB2, MySQL, PostgreSQL, and Informix-flavored SQL queries.

Amalgam Insights notes that Zoho Analytics has stated that future roadmap items for Zoho Analytics include further improvements in self-service analytics, data catalog, and the analytic formula engine to increase access to computational tools needed for advanced analytics. Self-service analytics has been a key goal for business intelligence over the past decade, driven by solutions such as Tableau and Qlik. Amalgam Insights considers self-service analytics to be an intermediate stage of analytics between the analyst-controlled reports and dashboards that defined the initial generation of analytic solutions and the current era of contextualized insight where analytic outputs are guided by natural language, pre-contextualized for users through role definitions and machine learning and augmented by a combination of human judgment and third-party data sources to guide business-relevant actions.

The data catalog is increasingly important as companies seek to contextualize data for machine learning and data science. By creating a catalog that effectively categorizes and indexes the relationships within relevant business data, businesses can be more prepared for the near-future where they use advanced algorithms and neural nets to support better decision-making and automate high-volume transactional judgment calls.

And Amalgam Insights believes that access to statistical tools, machine learning algorithms, and production-ready machine learning models will provide strategic advantages to the quantitatively savvy organizations that realize that behind the math are opportunities to improve organizational processes, increase business transaction volume and size, and proactively support customer issues.

Amalgam Insights’ Recommendations

Based on the pricing and new functionality of Zoho Analytics, Amalgam Insights recommends Zoho Analytics both as a starting point for using business intelligence and as a tool to bring together data across small and medium businesses that are starting to bring together a variety of applications and have outgrown their initial data organization strategies.

The variety of capabilities Zoho provides across prep, presentation, and application development also makes Zoho a useful and cost-effective augmentation for organizations have already invested in visualization, self-service analytics, and data discovery. In large organizations, where it is not uncommon to invest in multiple analytics and business intelligence products as long as they drive productivity, Amalgam Insights believes Zoho Analytics should also be considered as an end-to-end analytics solution that can quickly bring new data sources from prep to scalable, cloud-based app.

Posted on Leave a comment

July 9: From BI to AI (AnyVision, Google Cloud, IBM, Immuta, Obviously AI, Opaque)

If you would like your announcement to be included in Amalgam Insights’ weekly data and analytics roundups, please email lynne@amalgaminsights.com.

Funding

AnyVision Raises $235M from SoftBank Vision Fund 2 and Eldridge

AnyVision, a facial recognition AI company, has closed a $235M series C funding round led by SoftBank’s Vision Fund 2 and Eldridge. Amit Lubovsky, director of SoftBank Investment Advisors, will join the board as part of the transaction. Funding will be directed towards further development of AnyVision’s Access Point AI software, as well as further innovation of its SDKs for edge computing functionality. AnyVision’s funding announcement comes at an interesting time for facial recognition startups; concerns around data privacy are subjecting companies creating and using facial recognition to growing scrutiny.

Obviously AI Increases Seed Round Funding to $4.7M

Obviously AI, a no-code AutoML startup, has raised an additional $1.1M from the University of Tokyo Edge Capital Partners, as well as Trail Mix Ventures and B-Capital. The funding will go towards extending Obviously AI to serve more use cases, as well as expanding Obviously AI’s presence in Asian markets. The concept of no-code AI model building is the unicorn everyone dipping into data science is seeking, but Obviously AI is currently limited to supervised learning use cases, and broadening their scope to cover unsupervised learning is the next … obvious step.

Opaque Raises $9.5 Million Seed to Unlock Encrypted Data with Machine Learning

Opaque, a secure data analytics platform, announced July 7 that it had raised a seed round of $9.5M led by Intel Capital. Race Capital, The House Fund, and FactoryHQ also participated in this round. Opaque lets companies analyze encrypted cloud-based data without exposing the data to the cloud provider. Funding will go towards Opaque’s open source contributions to the data security community.

Product Launches and Updates

Immuta Becomes First Data Access Control Solution for Snowflake Partner Connect

On July 7, Immuta, a cloud data control access provider, announced its availability in the Snowflake Partner Connect portal. Snowflake users will now be able to use Immuta to configure automated data access control around their data. The Immuta Snowflake integration launches as an Immuta instance preconfigured with a Snowflake user’s connection credentials, minimizing setup complexity and time needed.

Hiring and Departing

Google announces Adaire Fox-Martin as its new EMEA Cloud president

Google Cloud has appointed Adaire Fox-Martin as its new EMEA Cloud president. Fox-Martin moves over from a 14-year tenure at SAP, most recently as an Executive Board Member leading Global Customer Success. Prior to that, Fox-Martin spent nearly two decades at Oracle.

IBM’s Jim Whitehurst Says He’s Leaving to Find a New Chance to Run Something

Over the holiday weekend, IBM announced that Jim Whitehurst would be stepping down as president, though he would remain in an advisory role for the time being. In an interview this week with Barrons, Whitehurst acknowledged that his reasoning is that he wants to be a CEO again, and with the appointment of Arvind Krishna to that spot at IBM, his own chances of holding that position were unlikely. Whitehurst had come over to IBM with the Red Hat acquisition, having held the CEO position there since 2007.

Posted on Leave a comment

July 2: From BI to AI (Anaconda, Facebook, JetBrains, Tableau, TIBCO)

In anticipation of the long holiday weekend for Americans and Canadians, news was fairly light in the data world this week; most announcements were around updates and enhancements to existing products.

If you would like your announcement to be included in Amalgam Insights’ weekly data and analytics roundups, please email lynne@amalgaminsights.com.

Product Launches and Updates

Tableau Extends Augmented Analytics in Tableau 2021.2

On June 29, Tableau announced the release of Tableau 2021.2, with new and enhanced augmented analytics capabilities. “Ask Data,” a capability that allows users to ask business questions of their data using natural language, and “Explain Data,” a function that provides explanations of data points, both have new interfaces that enhance users’ understanding of their data. Other new features in Tableau 2021.2 include the ability to save clean data from Tableau Prep into Google BigQuery, and to implement machine learning models from Amazon SageMaker within Tableau dashboards.

TIBCO Spotfire 11.4 LTS Release

TIBCO announced the release of Spotfire 11.4 LTS on June 30. Key features in this release include the ability for nontechnical users to embed advanced analytics functions into Spotfire apps, and over a dozen new custom visualizations and apps available as “Spotfire Mods” on the TIBCO Exchange.

Anaconda Collaborates with Intel to Improve Speed and Scale for Machine Learning Workflows

Anaconda announced enhancements to its ongoing partnership with Intel, including better access to libraries and packages optimized for Intel hardware to enhance the performance of machine learning models. Of note, the Intel Extension for Scikit-learn is now available in Anaconda’s package repository; Anaconda says models built using the extension run 27-36x faster than models based on the baseline Scikit-learn.

JetBrains: Announcing Datalore Enterprise

On June 29, JetBrains announced the availability of Datalore Enterprise, an on-premises collaborative version of their single-user cloud-based data science platform. Datalore Enterprise will provide JetBrains collaboration tools atop Jupyter Notebooks, along with existing features of Datalore such as PyCharm coding assistance tools.

Facebook AI Announces Habitat 2.0, plus Introducing the Habitat-Matterport 3D research data set

Finally, Facebook AI announced the latest version of their Habitat platform (Habitat 2.0), a simulation platform that lets AI researchers teach machines to navigate and interact with both virtual and physical 3D environments. Improvements include ReplicaCAD, an extension of Facebook’s Replica data set, built to support movement and object manipulation as a digital twin, In collaboration with Matterport, Facebook AI also published HM3D, an open-source licensed data set consisting of over 1,000 indoor 3D scans. (This last year, prospective property buyers couldn’t go to open houses, but they could at least investigate a given property’s digital twin, and Matterport supplied a number of these virtual house tours for property listings.) Future AI-enhanced assistants and robots will need to interact with complex 3D environments; advancing “embodied” AI will be a top priority in order to build such assistants. Suggested scenarios include asking one’s AI-enhanced glasses where your housekeys were last observed, or asking a robot to check your desk for your laptop and if it’s there, to bring it to you.

Posted on 3 Comments

The Emerging Age of Decision Intelligence

Amalgam Insights recently caught up with diwo, a decision intelligence solution that provides companies with a consistent approach for contextualizing recommendations and developing portfolios of strategies, scenarios, and actions. We first spoke with diwo in 2017 at its launch at the Strata Conference.

Based on discussions with the vendor and with enterprises facing challenges with their analytics, machine learning, Big Data, and data management environments, Amalgam Insights believes that decision intelligence will be a foundational evolutionary stage for enterprise analytics environments in the 2020s and that diwo has an opportunity to be a leading player in this market.

To explain why, consider how the value chain from data recognition and contextualization all the way to recommendation has been largely ignored in the enterprise analytics world as analytics products have been overly focused on the process of “self-service” analytics that drives employees into constant and unending cycles of discovery used to identify data that might be of value.

There are four core issues in enterprise data and analytics that decision intelligence solves:

  1. Decision Intelligence makes analytic recommendations more human.
  2. “Analysis paralysis,” where the search for results and self-driven discovery can lead to a never-ending set of analysis.
  3. A third challenge that exists in the analytic market at large is that the solutions that support natural language and search-based interfaces to ask data questions typically lack “memory.”
  4. A fourth challenge is that even if end-users ask the correct questions, it is hard for analytics solutions to provide semantic or contextual sense of whether those fields are relevant. “Correlation is not Causation” is a standard Statistics 101 truism. But in traditional analytics solutions, correlation is often conflated and is presented as causation.

To read the rest of this piece, read our new report diwo and the Emerging Age of Decision Intelligence available at no cost until July 2nd. This report covers key aspects of this emerging era and how Amalgam Insights believes diwo can play a role in this new era.

Posted on

June 25: From BI to AI (including Apache Kafka, Confluent, Dataiku, Datarobot, Domino Data Lab, Firebolt, Incorta, Palantir, Primer, Rasgo, Splunk)

If you would like your announcement to be included in Amalgam Insights’ weekly data and analytics roundups, please email lynne@amalgaminsights.com.

Product Launches and Updates

Domino 4.4 Now Available

On Tuesday, June 21, Domino Data Lab announced the availability of Domino 4.4. New capabilities include Durable Workspaces, allowing data scientists to operate with multiple environments open at once; CodeSync, enhancing Domino’s existing reproducibility capabilities with native integration with common Git repositories; and the abilities to encrypt data in transit and mount NFS volumes directly to Domino. Domino 4.4 is available for existing customers immediately.

Dataiku Launches in AWS Marketplace

On June 21, Dataiku announced its availability in the AWS Marketplace. AWS customers can now use Dataiku’s visual interface to orchestrate their data pipelines and machine learning models applied to their cloud data, and Dataiku projects based on AWS-hosted data can also incorporate AWS Machine Learning Services such as computer vision or text analytics.

Palantir, DataRobot Partner to Bring Speed and Agility to Demand Forecasting Models

DataRobot and Palantir announced a new partnership on Thursday, June 24, around solving demand forecasting problems for retailers. The new Demand Forecasting framework links Palantir Foundry with DataRobot’s Model Development and Model Deployment capabilities. Prepped data is piped directly from Foundry into DataRobot where forecasting models are trained, then brought back into Foundry for operationalization.

Splunk Launches New Security Cloud

On June 22, Splunk debuted the Splunk Security Cloud, a SecOps platform with integrated security analytics and threat intelligence and an open ecosystem to correlate data across all security tools. Splunk also announced a $1B investment from Silver Lake; the funding will go towards further growth of Splunk and its ongoing cloud transformation, as well as managing a newly authorized share repurchase program.

Funding

Firebolt Ignites Growth with a $127M Series B Funding Round

Firebolt, a cloud data warehouse company, raised $127M in Series B funding this week, following up on a $37M Series A round from December 2020. All investors from the A round participated, including Angular Ventures, Bessemer Venture Partners, TLV Partners, and Zeev Ventures, with new investors Dawn Capital and K5 Global joining the B round. Firebolt will use the funding to expand its product, engineering, and go-to-market teams.

Incorta Raises $120M in Series D Funding

Wednesday, June 23, Incorta announced a $120M Series D funding round led by Prysm Capital. Other participants included GV, Kleiner Perkins, M12, Sorenson Capital, Telstra Ventures, Wipro Ventures, and new investor National Grid Ventures. This round of funding will go towards expanding Incorta’s go-to-market operations and meeting demand for Incorta’s data analytics platform.

Primer Raises $110M Series C

Primer, a natural language processing company, raised $110M in a Series C funding round, announced on Tuesday, June 24. Lee Fixel’s Addition led the round, with participation from existing investors Amplify Partners, Avalon Ventures, Bloomberg Beta, DCVC, Lux Capital, and Section 32, as well as new investors Crumpton Ventures, J2 Ventures, Sands Capital, and Steadfast. Primer also announced two partnerships: one with Microsoft to make Primer available within Azure, as well as a partnership with Palantir to make Primer available within the Palantir platform.

Rasgo Raises $20M Series A

Rasgo, a feature store, announced that it had raised an additional $20M in funding as a Series A round. Insight Partners led the round, with participation by existing investor Unusual Ventures. Rasgo will use the funds to expand its team with a focus on engineering talent, accelerate product development, and build its go-to-market.

Confluent IPO

Confluent, a data streaming platform, had its IPO June 24, raising $828M. Even with an initial offering price of $36/share, above its intended range of $29-$33/share, shares of Confluent closed up at over $45/share by the end of the first day of trading to reach a valuation of over $11 billion, indicating the continued importance of streaming analytics in supporting two key challenges: real-time context and real-time response.

Posted on Leave a comment

June 18: From BI to AI (Altair SmartWorks, Crate.io, Dataiku, Dataiku Online, Datarobot, Neo4j, SAS, Transform)

If you would like your announcement to be included in Amalgam Insights’ weekly data and analytics roundups, please email lynne@amalgaminsights.com.

Funding

Neo4j Announces $325 Million Series F Investment, the Largest in Database History

On June 17, Neo4J announced a $325M Series F funding round. Eurazeo led the round, with participation from existing investors Creandum, Greenbridge Partners, and One Peak, as well as new participants DCTP, GV, and Lightrock. Neo4J plans to use this money along three key vectors: buffing up their multi-cloud service offerings, growing capabilities to support enhanced machine learning models in graph-based data science, and expanding their market reach. Amalgam Insights’ Hyoun Park assesses the Neo4J funding more thoroughly, and highlights the importance of graph databases as the next step in enterprise analytics, and the key role they will have in supporting the next generation of machine learning models.

Introducing Transform: a ‘metrics store’ to make data accessible

Transform, a centralized metrics store, has come out of stealth, announcing $24.5M in funding across two rounds. Index Ventures and Redpoint Ventures led the round, with participation from Fathom Capital and Work Life Ventures. Transform is looking to double their headcount with this funding. General availability of Transform is projected for Fall 2021.

Crate.io Secures $10 Million in Funding

Crate.io, the developers of the CrateDB database platform, raised $10M in additional funding, bringing their total funding up to $31M. Draper Esprit and Vito Ventures participated in this round. The funding will be used to expand sales, grow functionality and add more partner integrations, and promote the open source developer community around CrateDB.

Product Launches and Updates

Cloud-native Altair® SmartWorks™ Empowers Enterprises to Make Data-driven Decisions

On June 14, Altair debuted Altair SmartWorks, a cloud-native analytics platform. SmartWorks integrates the data prep capabilities of Altair Monarch and their machine learning and predictive analytics solution Knowledge Studio under one roof, providing access to analytics, machine learning, and IoT no matter one’s comfort level with coding. SmartWorks is available now via Altair Units, their subscription-based licensing model.

Dataiku Announces Fully Managed, Online Analytics Offering

On June 14, Dataiku launched Dataiku Online, providing cloud-based access to their machine learning platform for smaller organizations without the extensive IT departments of their larger counterparts. In particular, seed-stage companies and other young startups are eligible for highly discounted pricing. A 14-day free trial is available now. Via Dataiku Online, customers can access data storage tools from Google BigQuery, Amazon Redshift, and Snowflake, and Snowflake customers can likewise access Dataiku Online through the Snowflake Marketplace.

DataRobot 7.1 Introduces Enhancements to Take AI Projects to the Next Level

On June 15, DataRobot announced its 7.1 platform release. Key new features include MLOps Management Agents, which manage remote machine learning models’ lifecycles; the no-code AI App Builder to turn deployed models into AI-based apps without needing customers to write code; and the feature discovery integration with Snowflake, announced last week at Snowflake Summit. The 7.1 release is available now.

Hiring

SAS Names Jenn Chase as Chief Marketing Officer, Executive Vice President

SAS promoted Jenn Chase, Senior Vice President and Head of Marketing, to the Chief Marketing Officer and Executive Vice President position. Chase’s 20-year career with SAS includes time in both R+D and marketing. As SVP, Chase initiated the relaunch of the SAS brand earlier this year, and led the pandemic-induced online pivot for the two most recent SAS Global Forums.

DataRobot Expands C-Suite with New CPO, CTO, and CMO

DataRobot grew its C-Suite this week, pulling in Elise Leung Cole from Cisco to serve as the new Chief People Officer, and promoting Michael Schmidt and Nick King from within as the new CTO and CMO respectively. Cole previously was the VP & Deputy General Counsel at Cisco, leading the team supporting sales and marketing, and creating compliance, training, and career developments within the organization. Prior to her time at Cisco, Cole served as General Counsel at AppDynamics.

Schmidt came to DataRobot as the founder of Nutonian, which DataRobot acquired in 2017. He helped develop DataRobot’s Automated Time Series product, and led the partnership with the US government to assure speedy and equitable COVID-19 vaccine trials. King joined DataRobot in April as the SVP of Marketing. Prior to that, King held executive positions at Cisco, VMWare, Google, and Microsoft. The expanded CMO role puts King in charge of global marketing and brand strategy.

Posted on 1 Comment

Neo4j Takes on the Battle for Context with a $325 Million F Round

On June 17th, 2021, Neo4j, a graph database company, announced a $325 million investment led by a $100 million investment by Eurazeo and joined by new investors GV (previously named Google Ventures), DTCP, and Lightrock as well as existing investors Creandum, Greenbridge Partners, and One Peak. Eurazeo is private equity company with over 15 billion Euro in Assets Under Management as part of a larger investment portfolio of over 22 billion Euro. With this round, Eurazeo Managing Director Nathalie Kornhoff-Brüls joins the Neo4j Board of Directors.

This monster funding round speaks to the confidence that investors have in the future of Neo4j. But in this particular instance, Amalgam Insights believes that this large funding amount is especially important because of what it means for breaking the status quo of enterprise analytics.

Analytics and data management in the business world have been built around the relational database focused on controlling and governing individual data inputs. This fundamental framework has been very useful in creating an environment that can be configured to present a single shared source of truth. However, it is not especially good at supporting and processing data relationships, which is a challenge in today’s data environment as data grows quickly and data relationships increasingly represent some level of transaction or behavior aligned with a business activity that needs to be tracked or analyzed in near-real-time.

In addition, the hype regarding artificial intelligence and machine learning has finally crossed over into practical reality as the toolkits for operationalizing models have reached mainstream availability. Even as enterprises may not fully understand machine learning, but they can easily purchase access or use open source projects to access the data management, model creation, storage, and compute capabilities needed to support machine learning projects. But for companies to fully execute on the promise of machine learning, they need to create more efficient relationship-based data environments that allow models to be tested and to provide results. Building relationship-centered data is part of what I originally called the Battle for Context when Amalgam Insights was first founded.

And now four years later, Neo4j has a chance to deliver on this challenge for context at a global scale. Neo4j has been a graph data leader for years, especially since it started back in 2007 before the need for graph database management was fully clear to the enterprise market at large. Since then, Neo4j has been a stalwart in its market education of graph data. But it has fundamentally been fighting a status quo where companies have been either unwilling or unable to translate their key transactional data environments into the relationship-based models that will be necessary for broad machine learning. With this round of funding, Neo4j finally has a chance to conduct the volume of marketing and sales needed to educate the data and analytics audience. In contrast to other large rounds of funding announced in the data world, such as Snowflake’s $479 million round in February 2020 or Databricks’ $1 billion round in February 2021, Amalgam Insights believes that Neo4j’s funding round serves a slightly different purpose.

Those previously-mentioned funding rounds were all seen as final rounds of funding before an upcoming IPO with participation by software vendor partners in their ecosystem. In contrast, Neo4j both has a more foundational opportunity and challenge in that graph should be the foundation of enterprise machine learning and relationship-driven data environments, but the ecosystem and platform maturity are still not quite where the data warehouse market is. Amalgam Insights sees this round as being more similar to DataRobot’s $270 million round raised in November 2020 which allowed DataRobot to continue acquiring companies and building out its platform to fit enterprise challenges.

Ultimately, the goals that enterprises should associate with graph data are the combinations of unlocking relationships within data that will take orders of magnitude in time, money, and skillsets to discover in relational data as well as the opportunity to unlock tens to hundreds of millions of dollars in value through ongoing machine learning and artificial intelligence operationalization opportunities that have already been identified but cannot run at high-performance levels without a better data environment. The acquisition and use of graph databases is a technological bottleneck that will prevent enterprises from fully unlocking AI and we are only now reaching a point where the understanding of relationship data, training data, machine learning feedback, and transactional data is sufficient for business managers to understand the value of dedicated graph databases rather than simply placing a graph structure on relational or multimodel data.

Recommendation to the Amalgam Insights database and analytics community

At the very least, start learning about graph data structure as combinations of edges, vertices, and relationships as well as linear algebra to gain an understanding of how graph data differs from the standard high school algebraic logic of relational databases. Yes, learning math and a new set of data relationships is not as easy as downloading a library or learning a new software functionality. But graph relationships are a fundamental change in the way that data will be managed over the next couple of decades and there will be a great deal of work needed both to ETL/ELT relational data into graph databases as well as to manage graph databases for the upcoming world of AI replacing aspects of standard business analytics.

If your organization is looking at relationship analytics or machine learning initiatives beyond a single project, look at Neo4j, which currently has a dominant position as a standalone graph database and is available as open source under GNU General Public License (GPL v3).

And if you have questions about the current state of Neo4j or are trying to bridge gaps from BI to AI in your organization, please contact research@amalgaminsights.com to schedule time to speak with our analysts. We look forward to serving you in our continued role in helping you to understand the future of your data.