Domino Debuts Data Science Framework

Domino Model Management

On May 22, Domino held its first Analyst Seminar in advance of its Rev conference for data science leaders. Domino provides an open data science platform to coordinate data science initiatives across enterprises, integrating data scientists, IT, and line of business.

At the Analyst Seminar, Domino introduced its Model Management framework: five pillars supporting a core belief that data science best practices involve data science not just being a siloed department or team, but that its resulting models should drive the business. For this to be possible,  all relevant stakeholders across the enterprise will need to buy into data science initiatives, as this will involve changes to existing business process in order to take advantage of the knowledge gained from data science projects.

Domino finds themselves doing a fair amount of primary education surrounding the need for data science platforms in general. Among Domino’s customers, few are doing so at scale – only 10% of the 250-plus organizations surveyed have more than 50 models in production. So presenting a framework to help data science teams, IT, and line of business get on the same page has the potential to accelerate adoption by making the data science process tangible and  understandable.

Fleshing Out Model Management

Though “model management” is commonly used among data science platforms to refer to the general practice of tracking models while they are in production, Domino’s definition expands “model management” beyond this specific practice to encompass a broader scope of building, delivering, validating, and monitoring models at scale to create a competitive advantage – an end-to-end model creation methodology. From Domino’s perspective, the five pillars that contribute to a good Model Management practice include:

  • Model Technology: You can’t “do data science” without computers that can handle the massive compute loads imposed by analyzing large data sets with complex models. Likewise, you need software tools that your data scientists are familiar with in order to do their work – and above a certain team size, you’ll be looking at a data science platform to permit collaboration on projects.
  • Model Development: The value-added work of data science practitioners is to create and modify models; in Domino’s model, this is home base for data scientists.
  • Model Production: Here, data science work gets operationalized. It’s where  models are deployed either into business-relevant reports, or into models or APIs that can be turned into a service or plugged into a larger software application.
  • Model Governance: This is where Domino envisions data science leaders  spending most of their time, monitoring their data scientists’ projects.
  • Model Context: Domino’s Model Context should be considered a library or catalog of all of the products of the data science: models, reports, APIs.

Domino Model Management

Source: Domino

Domino goes into more depth on these concepts in their whitepaper Introducing Model Management.

If Domino wants the market to think of data science in terms of this framework, then how does this framework map onto the Domino data science platform?

  • Data scientists begin their research within their company’s Model Context in Domino’s Knowledge Center.
  • To augment that, they then pull the appropriate tools into Domino’s Lab, where Model Development occurs.
  • When a model is finished, data scientists move it into Model Production to turn it into a report, an app, or a similar deliverable that line of business users can then reference in Domino’s Launchpad.
  • Organization leaders can monitor their team’s data science initiatives in Domino’s Control Center as Model Governance.
  • Model Technology is the available toolkit for the entire endeavor, encompassing Domino as a whole.

From a macro view, Domino’s data science platform is on par with other data science platforms, and its Model Management framework reflects the current high-level typical enterprise data science workflow. The difference between that and the workflow for smaller data science teams or individual contributors is in the level of collaboration necessary. An individual contributor data scientist typically does a subset of three things: preparing data for use in their model, actual model development, then preparing the model to be operationalized by a software developer. Enterprise-grade data science adds a level of collaboration by centralizing digital resources (such as data, models, reports, APIs) for all relevant stakeholders to access, as well as a level of governance from a higher-level view concerned with relevant business and regulatory requirements.

The need for adding these capabilities when moving from individual contributor-scale data science to enterprise-grade data science isn’t always obvious, which both drives the need for educating enterprises on best practices in conducting collaborative data science and providing a platform that will accept the work that individual data science contributors have created.


Amalgam believes that Domino’s Model Management framework will be most useful for organizations fitting one of the following profiles.

1. If your enterprise already has a dedicated data science department in place, and is seeking to address core business issues where data science can help, the Model Management framework provides a guide for looking at scaling data science initiatives.

2. If your organization has active data science initiatives within a particular department, and is looking to expand data science activities throughout the company, the Model Management framework provides a map of the location you are trying to get to. To be fully prepared, though, you need an executive champion leading the process of getting all relevant stakeholders on the same page when it comes to using data science to drive business for your company: data scientists and data science leads, IT, and line of business. Building the culture of data science in your organization will be key to being able to do enterprise-grade data science.

3. If data science activities form the core of your business, the Model Management framework provides a guide to doing collaborative enterprise-grade data science.

For Domino: Domino recognizes that they are in an emerging market with their data science platform. By providing the Model Management framework, they are conducting an important primary education task for the data science platforms market: teaching enterprises how to effectively operationalize data science. But the vast majority of enterprises haven’t formalized the practice of data science in their organizations to the same extent that they have for data management, business intelligence, and analytics. They want to do data science, but don’t know how to get there.

Right now, trying to find useful, relevant information on implementing enterprise-grade data science initiatives is like trying to find the proverbial needle in a haystack. For their message to stand out, Domino will need to double down on educating the market, likely in concert with partners and key allies to help spread the word. In this context of prioritizing education, Domino’s guide to managing data science at scale is just as important as their Model Management framework for the level of training and education they need to provide.

By virtue of my observations on the difficulty of navigating the data science platforms market, I am working on a Data Science Platforms Vendor Landscape, scheduled for publication in Fall 2018. In this Vendor Landscape, I will be evaluating relevant Data Science Platforms in the context of key market trends and challenges.

Optimizing Leadership Training and Development by Leveraging Learning Science: A Brief Primer

Key Stakeholders: Chief Learning Officers, Chief Human Resource Officers, Learning and Development Directors and Managers, Corporate Trainers, Content and Learning Product Managers, Chief Executive Officer, C-suite, Managers

Top Takeaways:

  • If you want high-quality leadership at all levels of your organization then you need to provide employees with effective broad-based leadership training and development solutions.
  • Optimized leadership training and development are multi-faceted and involve multiple distinct learning systems in the brain that each have different processing characteristics. Thus, a tool that is effective for one aspect of leadership training, may be sub-optimal for another aspect. One-size-does-NOT-fit-all.
  • Many vendors offer a broad suite of tools, with little guidance on what to use when.
  • Learning science serves as a guide for optimally mapping tools onto training problems.
  • Tools optimized for leadership training and development must be grounded in learning science – the marriage of psychology and brain science. In this report, I briefly outline the learning science behind leadership profile, people skills and situational awareness training and development.

Do you want leaders with a deep understanding of the “Leadership Profile” that I define with the following traits?

  • Knowing the definitions and implications of unconscious bias, harassment and diversity
  • Having a strong grasp of their organization’s vision and structure
  • Leading with strong communication and people skills that show empathy and respect for others in every interaction, including those that are challenging (e.g., performance evaluations or conflict resolution)
  • Leading with situational awareness an ability to “read” any situation, “think on one’s feet” and adjust, as well as instill confidence in others
  • Leading individuals as well as their team through adversity, all in a calm and collected manner

If the answer to these questions is “yes”, then your organization wants leadership training and development tools that are grounded in learning science – the marriage of psychology and brain science. You want to find the vendors who have leveraged the $100’s of millions of dollars in psychological and brain science research (over $10 million of which was awarded to the author) by building a scientifically-grounded, optimized platform for leadership training and development. But how can you identify these vendors?

In this era of digital transformation, where organizations rely increasingly on cross-functional and deeply collaborative teams, leadership is becoming more distributed and employees are taking on leadership roles much earlier in their careers. Combine this with some of the recent corporate crises (#metoo, unconscious bias, discrimination) and effective leadership training becomes even more important. The work of thought leaders such as Jim Collins who identify world-class leaders who are humble, clear, and fair show the business value of leaders who understand people.

In this report, I briefly review the psychology and brain science of learning, then map this learning science onto three critical aspects of optimized leadership training and development:

  • Hard Skills of Leadership – The hard skills of leadership training and development provide the leader with all of the knowledge and facts associated with strong leadership. This includes learning the rules, regulations, and compliance requirements, but also includes learning the “hard” skills” of people skills such as the ability to identify unconscious biases, sexual harassment, and discriminatory behavior. Knowledge of verbal and non-verbal communication skills and team dynamics are also critical.
  • “People” Skills Training – The goal is to provide the leader with the people skills necessary to communicate effectively with verbal and non-verbal cues. This includes eliminating any action from the leader’s behavioral repertoire that expresses bias, harassment or discrimination. These people skills must be trained effectively, and across a broad range of typical and atypical situations (e.g., during conflict resolution, performance evaluation, or under time or social pressure).
  • “Situational Awareness” – The best leader can “read” any individual, group or situation, can “think on their feet” and can adjust their strategy and behavior effectively. This involves a rich suite of cognitive, behavioral, but most importantly emotional skills. One must develop the ability to “walk a mile in someone else’s shoes” to understand and “read” another’s state of mind, as well as to understand how one’s own behavior is interpreted by others. This requires training across a broad range of situations. High situational awareness is key to knowing “what to do when”.

Distinct Learning Systems in the Brain (the “What”, the “How”, the “Feel”)

As I have elaborated in detail in other research reports, there are distinct learning systems in the brain. Each system is “optimally” tuned to specific types of learning, and critically, the training tools that most effectively recruit each learning system are different.

The figure below provides an overview of the three main learning systems in the brain, along with the relevant psychological processes, and a schematic of the relevant brain regions.


Cognitive Skills Learning (The “What”): The cognitive skills learning system has evolved to store information and learn facts. This system mediates hard skills learning and I refer to this as the “what” system. Cognitive skill learning relies heavily on working memory and attention and is mediated by the prefrontal cortex in the brain. Processing in this system is optimized when information comes in brief, coherent chunks (often referred to as microlearning), is delivered spaced over time, and is tested periodically to ensure storage of the information in long-term memory that resides in the hippocampus and medial temporal lobe structures. Mental repetitions are key to long-term memory storage. I refer to these procedures as those that “Train for Retention”.

Behavioral Skills Learning (The “How”): The behavioral skills learning system has evolved to learn behaviors. This system mediates people (aka soft) skills learning, and I refer to this as the “how” system. Behavioral skill learning does not rely on working memory and attention, in fact, I have shown that “overthinking it” hinders behavioral skills learning. Behaviors are learned through gradual, incremental, dopamine-mediated reward/punishment learning in the basal ganglia of the brain. Processing in this system is optimized when behavior is interactive and is followed in real-time (literally within milliseconds) by corrective feedback. If a behavior is elicited that is rewarded, dopamine will be released into the basal ganglia, the neural connections that drove that behavior will be strengthened, and the likelihood that behavior will be elicited again will increase. If a behavior is elicited that is punished, dopamine will not be released, the neural connections that drive that behavior will be weakened, and the likelihood that behavior will be elicited again will decrease. Physical repetitions are key to long-term behavior change.

Emotional Learning (The “Feel”): The emotional learning system has evolved to facilitate the development of empathy and understanding of our and others’ behaviors, and to “read” nuance in each situation. This system is critical to situational awareness, affects processing in both the cognitive and behavioral skills learning systems in the brain, and is referred to as the “feel” system. Emotional learning affects how one processes and links hard skills information and facts to specific situations, and what people skills are engaged in specific situations. Emotional learning can be instilled by “walking a mile in someone else’s shoes” and learning to “read” individual and group personality. Emotional learning recruits the amygdala and other limbic structures.

Optimized Leadership Training and Development

Hard Skills of Leadership: The goal of executive training is to provide the leader-in-training with as much leadership-relevant information as possible. This includes information about the rules and regulations that govern the organization to ensure compliance. It also includes fact-based training on important psychological factors such as the definition of unconscious bias, harassment and discrimination. Information on how to identify and avoid inappropriate behaviors is also important. The amount of information is substantial, and the learning science is clear on how to impart this information effectively. Because this type of learning is mediated by the cognitive skills learning system in the brain (the “what”), which has substantive working memory and attentional constraints, brief bursts of compelling content should be utilized. Training should be spaced over time, and retention testing should be incorporated. Training content should be available 24/7 on any device. A number of vendors provide excellent tools for training the leadership profile and hard skills in general.

“People” Skills: The best leader is one who leads by example, says and does the right things in an ever-changing setting, and meets all of these requirements simultaneously. At its core, people skills are about behavior. They are about what we “do”, “how” we do it, and our “intent”. People skills are challenging, nuanced and difficult to master. In leadership, their importance is amplified because the goal of a leader is to maximize productivity and the ROI obtained from employees while simultaneously keeping employees positively engaged, satisfied with their workplace environment, and disinterested in leaving for another organization. Behavior change involves gradual, incremental, dopamine-mediated reward/punishment learning in the basal ganglia of the brain (the “how”) and extensive physical repetitions. People skills training requires in person or virtual role play with real-time interaction and corrective feedback. Ideally, and especially for leadership training and development, the role play should occur under a broad range of environmental settings with different ethnic and gender mixes, typical and atypical settings, and under extreme conditions. Unfortunately, many of the tools that optimize hard skills learning (e.g., the Leadership profile) are suboptimal for people skills training. These include spaced training, microlearning and knowledge testing.

For example, short, focused training on a single situation (microlearning) is ineffective for people skills training because behavioral skills are best learned with longer training sessions and broad variability in scenarios. From a learning science perspective, this is the area most in need of additional corporate offerings. No currently available corporate training platforms include a broad-based, real-time interactive offering that directly engages the behavioral skills learning system in the brain. That said, I fully expect immersive technologies, such as virtual reality, combined with high-end computer graphics and AI to drive the interactions will solve this problem in the near future.

“Situational Awareness”: The best leader is the one who adapts quickly and effectively to any situation—commonly referred to as situational awareness. This involves a deep cognitive (the “what”), behavioral (the “how”) and emotional (the “feel”) understanding. One who is strong in the trait of situational awareness can accurately read any situation, knows what to do in each situation, and has the behavioral repertoire to engage each situation with the appropriate set of behaviors. This involves a keen understanding of individual and group motivation and personality dynamics, empathy, and an ability to “walk a mile in one’s own or another’s shoes” to see all views of a situation. The optimal method for training situational awareness is to combine the cognitive skills associated with Leadership Profile training with the behavioral skills associated with broad-based people skills training through the lens of emotion, motivation and personality. As suggested above this may ultimately be solved by virtual reality technology and high-end AI to drive interactions. Some vendors address specific critical aspects of situational awareness directly (e.g., measuring and leveraging personality) whereas others rely on efforts such as the use of diverse scenario-based training and testing.

Conclusion and Call to Action

As this very brief primer suggests, leadership training and development optimized for brain functioning is critical to an organization’s success. Employees are taking leadership roles earlier in their careers than ever before, and the need for effective corporate leadership is on the rise. When leaders are effective, profits rise, employees are engaged and satisfied, and turnover is low. When leaders are ineffective, profits diminish, the workplace sours, employees leave and organizations can lose billions of dollars overnight (e.g., Facebook, Uber, etc). The best leadership training and development solutions are aligned with the learning science – the marriage of psychology and brain science – and optimally engage the “what”, “how”, and “feel” systems in the brain. Organizations can obtain a competitive advantage by leveraging broad-based leadership training and development solutions that empower employees with the leadership knowledge that they need, the people skills necessary to lead by example, and the awareness to read any situation and adjust effectively when needed. Learning and Training professions should work with the C-suite and management at all levels to evaluate specific leadership needs and gaps, then develop solutions and policies that address these challenges. This approach takes full advantage of learning science to build better leaders.

If you would like to speak with the author of this piece, W. Todd Maddox, Ph.D. to learn more about his 25+ years research in brain science supported by over $10 million in external funding and cited over 10,000 times by his peers, please contact us at to set up a time to chat with Todd.

Alter(yx)ing Everything at Inspire 2018

Alter(yx)ing Everything at Inspire 2018

In early June, Amalgam Insights attended Alteryx Inspire ‘18, where Alteryx Chairman and CEO Dean Stoecker led an energetic keynote to inspire their users to “Alter(yx) Everything.” Based on conversations I had with Alteryx executives, partners, and end-users, I came away with the strong impression that Alteryx wants to make advanced analytics and data science tasks as easy and quick as possible for a broad audience that may not know code – and they want to expand that community and its capabilities as quickly as possible. Data scientists and analytics-knowledgeable employees are in high demand, and the shortage is projected to worsen as the demand for these capabilities grows; data is growing faster than the existing data analyst and data scientist community can keep up with it.

Alteryx has built an enthusiastic community around the Alteryx Analytics platform, and it can credit its ease of use as key to that. Besides appreciating the product itself for the attention it pays to the end user experience, it was clear just how much Alteryx users actually love Alteryx. What other enterprise software company can fill an arena with a couple of thousand people, eagerly watching three of their most competent end-users using that software to solve three business problems inside a 45 minute sprint? Alteryx does it with their annual “Grand Prix” competition at Inspire. The people sitting behind me at the Grand Prix argued cheerfully the entire time over which analytics expert they thought would win, based on their performance in past Alteryx challenges, and whether they thought each was constructing their workflows with the right tools. (Congratulations to Nicole Johnson of T-Mobile for a photo finish of a win!) Alteryx made watching a competitive code sprint a compelling – and understandable! – event, even for first-time attendees discussing it in the hallway afterwards.

At Inspire, Alteryx announced the release of Alteryx Analytics 2018.2, highlighting features that emphasize making advanced analytics and data science capabilities accessible to all and straightforward to use:

  • Analytic templates that cover functional analytic tasks as well as departmental- and industry-specific analytic tasks; these function as a shortcut to learn from or to construct your own analytic workflows.
  • Global community search across Connect, Designer, and Promote will help users find the right data and answers more quickly, and reduce duplication of existing analytic assets and information.
  • Support for Databricks via in-database connections as well as through the Apache Spark Code tool allows users to leverage the power of Spark in a Databricks cluster.
  • Being able to leverage third-party Python libraries and Python code for model development – and even being able to drag and drop it into your workflow via a Python SDK.
  • A more streamlined and personalized onboarding process (“analytic shepherding”) to enable end-users to more quickly learn how to conduct self-service data preparation and advanced analytics.

Alteryx already has an interface its users find easy to use – the addition of these templates and the enhancements to metadata collection and the search process will make building on existing analytics workflows even more simple. Being able to start from a template in a number of cases will help organizations standardize their analytics workflow creation process, make it easier to learn how to create workflows quickly for new users, and speed up the process for those already familiar with workflow construction.

What Alteryx Should Do Next

Now that Alteryx has expanded from a point solution to a more-complete suite, it’s all the “Alteryx Analytics” platform. It’s a suite of products, and making each product’s purpose clear is part of getting users to understand the platform as a whole, and the features they may not yet understand how to leverage. Their descriptions for Connect (“Discover and Collaborate”) and Promote (“Deploy and Manage”), the newer products, are fairly clear; their descriptions for the older Designer (“Prep + Analyze/Model”) and Server (“Share + Scale/Govern”) are still a bit vague for new users to grasp easily without seeing a task being performed, and slash together tasks that don’t overlap so well. From a marketing perspective, Alteryx should clarify and distill these descriptions.

The addition of Connect as a resource enhances end-users’ ability to quickly build and perform analytics tasks on their data, but it marks a shift in the natural starting point for longer-term Alteryx users. If a user starts to construct a workflow in Designer that looks similar to an existing template, whether Alteryx-provided or a custom template for their organization, a gentle nudge could help users take better advantage of the pre-built resources they already have on hand.

Finally, Alteryx identified the majority of its users as “citizen data scientists,” as distinguished from “data scientists.” The term is fairly common as a way to differentiate “coders” from “clickers” in a less-disparaging way, but “citizen” as a qualifier for “data scientist” doesn’t make it clear what either group does or can do. If Alteryx defines “data scientists” without the “citizen” qualifier as the deep specialists in coding complex machine learning models, then let’s “alter” the “citizen data scientist” term to one that better reflects the work they do. Earlier this year, they suggested that Alteryx users would “become the orchestrator directing disparate data, making it flow together and make sense.” I appreciate the level of complex coordination that “just works” implied in the “orchestrator” title reflected in the data tasks users are trying to accomplish when using Alteryx.

But overall, Alteryx holds two big advantages: their easy-to-use platform, and their community of enthusiastic and knowledgeable users that help Alteryx use spread quickly in the organization. Minor quibbles over clarifying terminology aside, the combination is a potent one to help Alteryx go “viral,” and their swiftly-expanding customer base demonstrates they understand the big picture – getting advanced analytic and data science work done quickly and easily.

Workday Surprises the IPO Market and Acquires Adaptive Insights

Key Stakeholders: Chief Information Officers, Chief Financial Officers, Chief Operating Officers, Chief Digital Officers, Chief Technology Officer, Accounting Directors and Managers, Sales Operations Directors and Managers, Controllers, Finance Directors and Managers, Corporate Planning Directors and Managers

Why It Matters: Workday snatched Adaptive Insights away from the public markets only days before IPO, acquiring a proven enterprise planning company, a trained enterprise sales team, and a team deeply skilled in ERP and enterprise application integration.

Top Takeaway:  Amalgam believes Workday’s acquisition of Adaptive Insights provides a net-win for current Adaptive Insights customers, Workday customers seeking more resources dedicated both to financial planning and workforce planning, and Adaptive Insights partners looking for enterprise product enhancements.

On June 11, 2018, Workday announced signing a definitive agreement to purchase Adaptive Insights, a cloud-based business planning platform. Workday will pay about $1.55 billion to fully acquire all shares of Adaptive Insights. This acquisition occurs only three days before Adaptive Insights was scheduled for a $115 million IPO on Thursday, June 14th, which was estimated to value the company at $705 million. This IPO was expected to be successful based on Adaptive Insights’ strong subscription revenue & revenue renewal metrics described in the S-1. With this acquisition, Tom Bogan will continue to lead the Adaptive Insights business unit and report to Workday CEO Aneel Bhusri and the Adaptive Insights team is expected to remain relatively intact.

Amalgam provides further details in the Market Milestone Workday Acquires Adaptive Insights and Gets a Leg Up On Oracle NetSuite where we explore:

  • Adaptive Insights as a pure-play Cloud EPM player
  • Adaptive Insights’ relationship with NetSuite
  • What Workday gains by purchasing Adaptive Insights
  • What Adaptive Insights’ customers and partners should expect
  • Who wins and loses from this acquisition

SaaS Vendor and Expense Management on Display at Oktane 18

Key Stakeholders: CIO, CFO, Chief Digital Officer, Chief Technology Officer, Chief Mobility Officer, IT Asset Directors and Managers, Procurement Directors and Managers, Accounting Directors and Managers Why It Matters: Okta is a key enabler for the discovery and management of SaaS, which is a necessary enabler for establishing the SaaS inventory and user identities needed…

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Why Oktane Has Become The Most Important SaaS Event of the Year

Key Stakeholders: Chief Information Officers, Chief Digital Officers, Chief Information Security Officers, Security Directors and Managers , Security Operations Directors, IT Architects, IT Strategists, Identity and Access Directors and Managers, Software Engineers, Cloud Strategists

Why This Matters: Cloud and digital strategists who are not attending Oktane risk missing out on key SaaS and IT management strategies emerging in an end-user centric model of IT.

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Optimizing Sales Enablement and Sales Training by Leveraging Learning Science: A Brief Primer

Key Stakeholders: Chief Sales Officer, Sales Directors and Managers, Sales Operations Directors and Managers, Training Officers, Learning and Development Professionals

Top Takeaways: If you want an efficient sales team you need to provide them with the right tools and train them effectively. If you want sales enablement and training tools that are highly effective they need to be grounded in learning science – the marriage of psychology and brain science. Many sales teams and sales focused vendors have access to these tools. What is needed is an effective way to leverage these tools to maximum advantage. This is where learning science comes in. In this report, I briefly outline the learning science behind sales enablement, people skills training and situational awareness.

(Note: This report represents the first step in an ongoing research initiative focused on critically evaluating the effectiveness of sales enablement and sales training solutions on the market. My goal in this research is to critically evaluate and accurately reflect the current state of the sales enablement and sales training sector from the perspective of learning science.)
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Cloudera Improves Enterprise Rigor and Reuse by Putting the “Science” in Data Science Workbench

Key Stakeholders: IT managers, data scientists, data analysts, database administrators, application developers, enterprise statisticians, machine learning directors and managers, existing enterprise Cloudera customers

Why It Matters: As Cloudera continues its pivot towards becoming a full-service machine learning and analytics platform, its latest updates enhance its ability to retain existing customers of its commercial data lake and Hadoop distribution looking to expand into data science workflows, and attract net-new data science customers.

Top Takeaway: Cloudera’s additions to its Data Science Workbench provide a more rigorous, scientific approach to data science than prior versions, and allow for speedier implementation of results into enterprise software applications.

Cloudera’s announcements at Strata London in late May reflect the next steps in its transformation from a Hadoop distribution and commercial data lake into a full-service machine learning and analytics platform. Key to this transformation are two new capabilities in Cloudera Data Science Workbench: Experiments, which introduces versioning to DSW, and Models, which streamlines and standardizes the model deployment process. Both of these capabilities add rigor and reproducibility to the data science process.

To read the full report, please download it from our research library.

Microsoft Azure Plus Informatica Equals Cloud Convenience

Tom Petrocelli, Amalgam Insights Research Fellow

Two weeks ago (May 21, 2018), at Informatica World 2018, Informatica announced a new phase in its partnership with Microsoft. Slated for release in the second half of 2018, the two companies announced that Informatica’s Integration Platform as a Service, or IPaaS, would be available on Microsoft Azure as a native service. This is a different arrangement than Informatica has with other cloud vendors such as Google or Amazon AWS. In those cases, Informatica is more of an engineering partner, developing connectors for their on-premises and cloud offerings. Instead, Informatica IPaaS will be available from the Azure Portal and integrated with other Azure services, especially Azure SQL Server, Microsoft’s cloud database and Azure SQL Data Warehouse.

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Informatica Prepares Enterprise Data for the Era of Machine Learning and the Internet of Things

From May 21st to May 24th, Amalgam Insights attended Informatica World. Both my colleague Tom Petrocelli and I were able to attend and gain insights on present and future of Informatica. Based on discussions with Informatica executives, customers, and partners, I gathered the following takeaways.

Informatica made a number of announcements that fit well into the new era of Machine Learning that is driving enterprise IT in 2018. Tactically, Informatica’s announcement of providing its Intelligent Cloud Services, its Integration Platform as a Service offering, natively on Azure represents a deeper partnership with Microsoft. Informatica’s data integration, synchronization, and migration services go hand-in-hand with Microsoft’s strategic goal of getting more data into the Azure cloud and shifting the data gravity of the cloud. Amalgam believes that this integration will also help increase the value of Azure Machine Learning Studio, which now will have more access to enterprise data.

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