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

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.


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,, 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


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. Secures $10 Million in Funding, 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.


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 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.

Posted on 2 Comments

June 11: From BI to AI: Special Snowflake Edition (Alteryx, Amazon, Dataiku, DataRobot, Domino Data Lab, Informatica, Talend, and of course, Snowflake)

This week’s “From BI to AI” update is a little different from the usual. Snowflake Summit occurred June 8-10, bringing a slew of announcements related to Snowflake’s new features, and many Snowflake partners timed their own related announcements in sync with the Summit.

Snowpark and Java UDFs

On Tuesday, June 8, Snowflake launched Snowpark, their “developer experience.” Data scientists, data engineers, and developers can build in Java or Scala within Snowpark, and then execute their workloads directly within Snowflake.

Also on the coding side, Snowflake announced support for Java UDFs (user-defined functions) within Snowflake, allowing customers to import their custom code and business logic to Snowflake. Both Snowpark and Java UDFs within Snowflake are currently in private preview, with public preview coming soon.

Snowflake also announced the Snowpark Accelerated Program, where partner vendors can access Snowflake technical experts and be provided with additional exposure to existing Snowflake customers.

Snowflake Partner Announcements

Numerous Snowflake partner vendors followed up with their own announcements on Wednesday, June 9.

Snowflake can now be used as a data source within Amazon SageMaker Data Wrangler. This integration allows data prep for machine learning in SageMaker to occur in Snowflake.

Alteryx announced a deeper integration of Alteryx with Snowflake. Alteryx Designer is now directly available on Snowflake; data prep, data blending, and automated analytics processing are pushed down into Snowflake for better performance and scalability. Joint Alteryx-Snowflake customers can also augment existing data sources with those available on the Snowflake Data Marketplace. Current Snowflake customers have access to a free trial of Alteryx within their Snowflake account.

Dataiku debuted their Snowflake integration with Snowpark and Java UDFs. Dataiku-Snowflake users will be able to push computation down to Snowflake, so that data prep and scoring can happen within Snowflake.

DataRobot’s new Snowflake integration also joins DataRobot with Snowpark, growing the existing DataRobot-Snowflake partnership. Data prep tasks from Zepl (a recent DataRobot acquisition) can be pushed into Snowflake for feature engineering, while providing a preconfigured environment for model development within Snowpark. DataRobot’s Java Scoring Code also pairs with Snowflake Java UDFs to enable DataRobot models to do scoring within Snowflake.

Domino Data Lab inaugurated its Snowflake partnership this week with Snowpark integration as well. Joint Domino-Snowflake customers will be able to build data pipelines within Snowpark, and execute MLOps workflows from Domino within Snowflake.

Building on its 2020 Partner of the Year status, Informatica announced tighter integrations between its Intelligent Data Management Cloud and Snowflake, offering support for Java UDFs for joint Informatica-Snowflake customers and advancing its mass-ingestion ELT capabilities. Users will be able to transform, cleanse, and govern data from a wide variety of enterprise applications automatically, en masse, on its way to ingestion in Snowflake.

Finally, Talend revealed Talend Trust Score for Snowflake. This new capability will allow joint Talend-Snowflake users to verify data quality within Snowflake, using Snowpark and Java UDFs.

(Sidenote: the acquisition of Talend by Thoma Bravo is proceeding apace; Thoma Bravo has begun the tender offer to acquire all outstanding ordinary shares and American Depository Shares of Talend.)

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

Posted on 1 Comment

Alation Raises $110 Million D Round to Help Businesses Better Understand Their Data

On June 3rd, Alation announced a $110 million D round led by Riverwood Capital with participation from new investors Sanabil Investments and Snowflake Ventures. Current investors Costanoa Ventures, Dell Technologies Capital, Icon Ventures, Salesforce Ventures, Sapphire Ventures, and Union Grove Partners also contributed to the round. With this round, Alation has raised a total of $217 million and is valued at $1.2 billion.

One of the first things that stands out with this investor list is how Alation serves as an example of “investipartnering” where business partners also become limited equity partners. With Snowflake, Salesforce, and Dell all on the cap table, Alation stands out as being a strategic partner for some of the biggest cloud players on the planet. 

Here’s why this investment makes sense in today’s data environment.
One of the biggest challenges for data in 2021 is effectively governing & defining data across a wide variety of sources. Alation has been both a pioneer and now a consistent market-leading data catalog both from a revenue and functionality perspective. 

Yet, there is still a massive greenfield opportunity to rationalize taxonomies, naming conventions, integrations, and data-centric decisioning processes within the larger enterprise data ecosystem. These data challenges were already challenging enough for analytics, where businesses have had data warehouse, master data, and data integration tech for decades. But now this data also has to be prepped for machine learning & AI, where these structures are less useful. One of the reasons that a variety of industry estimates state that data scientists spend as much as 80% of their time cleansing data is because data scientists have either eschewed traditional enterprise data structures or are simply unaware of the analytic data ecosystem that has been built in enterprises over the past several decades as they seek to tackle problems.

 In an agile, “Post-Big Data” data world, the true hub of data intelligence is either at the catalog or datalake level, depending on how data is used and organized. In today’s data world, the data warehouse is an important piece of core infrastructure for enterprise data, but is not typically agile enough to support the rapid data selection, augmentation, transformation, and analysis associated with both self-service analytics. and machine learning efforts. In this modern data context, Alation is a vital player in advancing the cause of referencing, contextualizing, and linking datasets together rapidly.

And the investment by Snowflake Ventures reflects that Snowflake knows they need more control over less structured data. Snowflake is under pressure to justify its massive valuation as a cloud data leader and now has to meet the growth expectations of being worth well over $50 billion and having had a peak valuation of over $125 billion in its brief tenure as a public company. Alation will be a vital part of Snowflake’s story in providing a more agile environment for the entirety of enterprise data as Snowflake moves closer to a variety of datalake capabilities that allow for more flexible data transfer.

I’ve covered Alation since its Series A in 2015 led by Costanoa Ventures, which has now established itself as a premier early stage investor in data-driven startups, back when I was the Chief Research Officer at Blue Hill Research. Their focus on data navigation at a time when Hadoop was seen as The Big Data Answer ended up being prescient and Alation’s value is now established with over 250 enterprise clients. 

But there is a larger opportunity. As Alation has expanded from a data catalog solution to a broader data discovery, context, governance, and collaboration solution and as the challenges of data and metadata management move downmarket, Alation’s capabilities are increasingly aligned with fundamental market needs to categorize and share data effectively.

To become a global solution, Alation needs to get into thousands of organizations, a goal that I think is now realistic with this latest round of funding that both boosts sales in the short term and sets a path to ongoing scalable growth.

Recommendation for the Data Management Community

The key takeaway for the data community is that legacy data management tools typically lack the speed and functionality necessary to identify, classify, and organize new data for new analytics, machine learning, and AI use cases. This includes everything from unstructured documents to relevant binary files to time-series, graph, and geographic data. This problem has driven both the commercial and investor interest in Alation and this problem is moving downmarket as more organizations seek to start building scalable and repeatable machine learning, data science, and analytic application development environments. Organizations that are not actively planning to improve their metadata and data collaboration efforts will find themselves fundamentally hampered in trying to make the leap from BI to AI and in keeping up with the new business world of augmented, automated, process mapped, natural language-based, and iterative feedback-driven transformation. Behind all the buzzwords, companies must first understand their existing data and contextualize their new data.

Posted on Leave a comment

June 4, 2021: From BI to AI featuring Alation, Cazena, Cloudera, Datacoral, Dataiku, Interative, and Stemma

This week’s roundup From BI to AI features Alation, Cazena, Cloudera, Datacoral, Dataiku, Interative, and Stemma. If you would like your announcement to be included in Amalgam Insights’ weekly data and analytics roundups, please email


Cloudera Acquires Datacoral and Cazena, is Acquired by Clayton, Dubilier, and Rice and KKR for $5.3 Billion

On June 1, Cloudera announced that it had agreed to be acquired by investment companies Clayton, Dubilier, and Rice, and KKR for a $5.3B sum, transitioning to a private company. Financial results for Q12021 were released at the same time, with subscription revenue up 7% year over year.

Cloudera also acquired two SaaS companies in separate transactions. Datacoral enables data transformations and data integration, while Cazena implements quick cloud data lakes. Both companies provide fully managed services that facilitate data preparation for self-service analytics.


Alation Announces $110 Million Series D to Accelerate Growth

On Thursday, June 3, Alation, an enterprise data intelligence platform announced that it had raised a $110M Series D funding round. Riverwood Capital led this round of funding. Other participants also included existing investors Costanoa Ventures, Dell Technologies Capital, Icon Ventures, Salesforce Ventures, Sapphire Ventures, and Union Grove Partners, along with new investments from Sanabil Investments and Snowflake Ventures. Amalgam Insights’ Hyoun Park wrote about this example of “investipartnering,” and provides recommendations for the data management community.

Stemma Launches, Reports Seed Funding of $4.8 Million

On Thursday, June 3, Stemma announced that it had raised $4.8M in seed funding, led by Sequoia, and subsequently officially launched their data catalog product. Built atop the open-source data catalog Amundsen, Stemma provides enterprise-scale management capabilities and an intelligence layer based on relevant context.

MLOps Company Iterative Raises $20 Million Series A Funding Led by 468 Capital, an MLOps platform, announced Wednesday, June 2 that it had raised a $20M Series A round. 468 Capital and Florian Leibert led the round, which also included prior investors True Ventures and Afore Capital. also debuted its first commercial product, DVC Studio, a visual front-end on its open source projects DVC (Data Version Control) and CML (Continuous Machine Learning) intended to enhance collaboration above and beyond data scientists’ usual Git methods.

Product Launches and Updates

Dataiku Now Available in the Microsoft Azure Marketplace

On June 1, Dataiku announced availability through the Azure Marketplace. Azure customers can now purchase Dataiku with their existing Azure cloud budget and relationship, taking advantage of integrated access to Azure’s cloud storage and compute resources for their data science workflows.

Posted on Leave a comment

Market Alert – Zylo Delivers on a Platform for SaaS-Centric Business Management

Key Stakeholders: Chief Information Officers, Chief Technology Officers, Chief Financial Officers, Finance and Accounts Payable Directors and Managers, Human Resources Officers, Procurement Directors, Software Asset Managers, IT Architects, Vice President/Director/Manager of IT Operations, DevOps Managers, System Architects, Product Managers, IT Sourcing Directors and Managers, IT Procurement Directors and Managers

Why It Matters: SaaS management requires an integrated set of applications to coordinate the business activites driven by SaaS costs, inventory, and usage. With this RESTful API combined with Zylo’s existing application, SaaS optimization capabilities, and integrations, Zylo delivers not only a strong toolkit for companies to support SaaS management, but a platform for businesses to gain a SaaS-centric view of business activities, projects, and goals.

Top Takeaway: With the Zylo API in place, organizations now have a starting point to gain a SaaS-centric view of the business that maps SaaS to the people, processes, projects, technology, and financial drivers of the business.

About the Zylo API

On May 26, 2021, Zylo, a SaaS (Software as a Service) management company, announced the launch of its independent API to provide access to application license and usage data. This API provides companies with the ability to export SaaS subscription data to analytics and asset management tools, use subscription data to push service order workflows to disconnect and optimize subscription usage, and bring new applications into the Zylo platform to support SaaS portfolio optimization.  

In going through the API documentation for the Zylo platform, Amalgam Insights notes that the Zylo API includes access to application-specific subscriptions, data imports, and data exports. Data access includes SaaS categories and subcategories, business units and goals, supplier name, cost center, expense report information, and user license information. Once these fields are mapped to their equivalent categories across applications, Zylo customers will be able to fully synchronize data on an ongoing basis through a RESTful API. 

This API is comparable in some ways to the API provided by Zylo competitor Productiv, but Amalgam Insights notes that the Productiv API both rate-limits and time-limits data batches that are not currently limited in the Zylo API. SaaS Management Bettercloud also has an API for its platform, but Bettercloud’s focus on SaaS operations and its GraphQL and graph analysis makes Bettercloud’s API and platform more aligned to the tactical challenges of workflow and app network analysis rather than finance and accounting.

Business Context for Why This API Matters

Amalgam Insights takes the view that technology expense management should be a core capability for the financially responsible CIO and that SaaS, cloud, and network expense management solutions can be used as the hubs of activity to determine the financial and business activities associated with technology and business transformation. However, one of the biggest challenges to this vision has been the inability for these expense-based platforms to provide their granular data and optimization recommendations to all of the systems that require increasingly detailed technology usage to support business model transformation and agile finance and accounting-based forecasting exercises.

In addition, SaaS is one of the fastest-growing spend categories in the business world. As a market, SaaS is expected to grow over 20% annually over the next five years leading to a $275 billion market in 2025. SaaS currently makes up half of all new software spend and roughly 10% of all new technology spend that will occur in 2021. From a practical perspective, Amalgam Insights estimates that SaaS will save approximately 25 billion employee-hours or 12.5 million employee years of manual work this year. And Amalgam Insights also notes that businesses start supporting 250 SaaS apps on their network as soon as they get to 500 employees, on average. After that point, the SaaS count grows incrementally as employee count increases. These are all different ways of pointing out the importance of SaaS, whether it be in context of software spend, IT spend, or employee productivity.

So, SaaS matters as a business spend category and it can potentially be used to support a variety of business management and analysis tasks. But the technological access to this data has often been limited in the past to flat files that required an intermediate level of data translation or summary data that lacked the granularity to help support individual employees, assets, plans, projects, and products dependent on the optimal usage of SaaS.

This is why the Zylo API fundamentally matters from Amalgam Insights’ perspective. In today’s technology world, every application wants to call itself a “platform” upon inception, which makes the word almost meaningless. But from a practical perspective, a technology platform is one that can help manage multiple applications and to bring together the data, workflows, and outputs of multiple applications in an integrated fashion to improve business outcomes. This is the promise of Zylo’s API in providing a SaaS-centric way of looking at the business usage of SaaS for a variety of applications. 

From Amalgam Insights’ perspective, this was the missing link in seeking an app-based vision for tracking, planning, budgeting, and forecasting business activity from a SaaS-based perspective. If apps and the cloud are as important as everyone in the technology and business worlds claims and if digital transformation is truly important to justify the billions being spent on it, businesses need to find a way to track work not only by department and employee, but by application as well. This next level of visualizing work, employee enablement, project resource dependencies, and digital workflow supply chains is what Amalgam Insights ultimately finds most interesting about the emergence of this API.

Recommendations for the Technology Expense Community

Our key recommendation is this: Work to gain a SaaS-centric view of the business that maps SaaS to the people, processes, projects, technology, and financial drivers of the business. 

To contexualize this recommendation, consider that the technology expense community largely comes from three areas at the moment: telecom expense management, software asset management, and cloud cost management (also known as FinOps). These three categories of workers typically bring together some understanding of technology, sourcing, and accounting to help the business based on the IT Rule of 30, which states that every unmanaged IT subscription spend category averages 30% in waste.  By optimizing these spend categories, the technology expense community has proven its worth as we have saved large enterprises millions of dollars while providing visibility to opaque and complex spend categories.

But we are facing a moment of reckoning when technology has become increasingly important to the foundational operations of a company. The COVID pandemic proved the need to both manage and support employees quickly and regardless of their physical location. As connectivity, data access, and application access became the biggest bottlenecks to accessing expertise and completing work that required multiple employees, the need to understand the cost basis of SaaS, the SaaS licenses and access that each employee required to be fully productive, and the infrastructure and security requirements associated with SaaS all became mandatory to track and visualize on a regular basis. 

In this light, the key recommendation Amalgam Insights provides to the technology expense community based on this announcement is clear: it is no longer enough to only cut costs and businesses require a SaaS-centric business view to go along with finance-centric and people-centric views. You may not be in the position to fully act on the repercussions of what you find, but you are in a perfect position to empower your CIO, CFO, and CEO on this vital view of apps that run the business. 

Amalgam Insights believes that the Zylo API combined with the existing Zylo application, data schema, and integrations provides a strong foundation for visualizing the SaaS-enabled enterprise. Whether companies choose to work with Zylo, work with another SaaS management provider, or build their own solution, Amalgam Insights believes that the future of IT management is dependent not only on cost visibility and savings, but on providing a business lens to link technologies to core business functions.