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Amalgam Insights Speaking at Informatica’s Virtual Summit on AI-Powered Data Cataloging

On August 27, Amalgam Insights invites you to Informatica’s AI-Powered Data Cataloging Virtual Summit which focuses on the core questions of

Where is all your data?

and

How do you find it, organize it, and accelerate time-to-value with AI?

The virtual summit features the following speakers

Awez Syed, Sr. Vice President for Metadata Intelligence and Enterprise Data Catalog – Informatica
Hyoun Park, CEO and Principal Analyst – Amalgam Insights
Rob Ray, Senior Data Architect – Nissan
Dave Falder, Senior Technical Specialist – Maersk
Anil Bandarupalli, Sr. Software Engineer – Rabobank
Dan Jewett, Vice President of Product Management – Tableau
Joe Brandenburg, Data Steward of the Enterprise Data Governance Office, New York Life

This virtual summit will be held on Tuesday, August 27, 2019 at 9:00 AM PDT | 12:00 PM EDT. We look forward to seeing you there!

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The Second Half of 2019 Has Already Begun! Amalgam Insights Highlights

We’ve reached July 1, 2019. It has been an amazing first half of the year both for Amalgam Insights and the tech world in general! From our perspective, it has been a good half as we’ve written 53 blogs, published 13 reports, and grown bookings 66% over the second half of 2018. Special Thanks to our corporate clients for your financial support that allows us to continue being a voice for changing the future of technology.:

And this gives you an idea of the companies that align with our perspective of technology being more global, usable, efficient, and financially sustainable in the here and now.

It is easy to be overwhelmed by the sheer hype of tech news cycles, but the past few months have been part of a fundamental shift in the world of IT that seems to happen once a decade. However, our audience has shown broad interest in topics across data management, cloud management, the future of finance, the neuroscience of learning, and enterprise-grade data science over the last six months. Heres a quick summary of the topics that Amalgam’s audience found most compelling in the first half of this year.

Amalgam Insights’ Top 10 for the First Half of 2019

  1. The Death of Big Data and the Emergence of the Multi-Cloud Era – Author: Hyoun Park

    Just as we saw the emergence of the Internet as a powerful business enabler around 2000 and saw the rise of Big Data and Analytics in 2010, we now face the emergence of Multi-cloud replacing CapEx as a fundamental basis for tech this year as we enter the 2020s.Based on that, it was no surprise that The Death of Big Data and the Emergence of the Multi-Cloud Era has been the most popular piece on Amalgam Insights in the first half of 2019.

  2. Docker Enterprise 3.0 is the Docker We’ve Been Waiting For – Author: Tom Petrocelli

    DevOps Research Fellow Tom Petrocelli describes in this research piece how Docker has been moving away from the commodity business of container infrastructure and reinventing itself as a developer tools company. It provides context to the DevOps community on why Docker 3.0 addresses one of the largest problems in DevOps, today.

  3. Microsoft “Early Adopts” New ASC 606 Revenue Recognition Standard – Author: Hyoun Park

    This piece continues to provide guidance to companies on how businesses prepared for ASC 606 accounting and has been a starting point for some of you to ask us about the likes of Zuora, Aria, Oracle BRM, SAP Hybris Billing, Sage Intacct, FinancialForce, Flexera Software Monetization Platform, Gemalto On-Demand Subscription Manager, and other subscription business platforms.

  4. Analyst Insight: 7 Key Technology Expense Management Predictions for 2019 – Author: Hyoun Park

    This report, published at the beginning of this year, provides7 predictions to help financially-minded technology managers gain 30% savings on operational cloud, network, and telecom expenses while gaining the visibility and governance needed to responsibly manage digital change and technology as a competitive advantage. This report, which comes with free analyst inquiry time, served as a strategic kickstart for enterprise IT and procurement teams in 2019.

  5. Technical Guide: A Service Mesh Primer – Author: Author: Tom Petrocelli 

    This groundbreaking Amalgam Insights Technical Guide on the Service Mesh provides enterprise architects, CTOs, and developer teams with the guidance they need to understand the microservices architecture, service mesh architecture, and OSI model context necessary to conceptualize Service Mesh.

  6. 2019 Top 6 Trends in Learning & Development and Talent Management – Author: W. Todd Maddox Ph.D. 

    Our resident Neuroscientist of Technology, Todd Maddox, provided Chief Learning Officers and enterprise training organizations with a headstart to 2019 with this overview of the six major trends that Amalgam Insights’ research suggests will dominate the Learning & Development and Talent Management landscape including: the Impact of Psychology and Brain Science, AI and machine learning as innovation drivers, the Emphasis on Empathy, the need for Scenario-enhanced Microlearning, best practices for using immersive and augmented reality, and the Power of Personality.

  7. SmartList Market Guide on Service Mesh and Building Out Microservices Networking – Author: Tom Petrocelli

    This piece, a companion to the Technical Guide for Service Mesh, is a comprehensive guide to the roles that top technology vendors play in the world of microservices and service mesh in 2019 including their roles in Istio vs. Linkerd, modern microservice architecture considerations, and the three segments of the service mesh market: Control Plane, Data Plane, and Value-Add.

  8. Amazon Aurora Serverless vs. Oracle Autonomous Database: A Microcosm for The Future of IT – Author: Hyoun Park

    This research document continues to provide guidance on the fundamental decision that IT departments need to choose in the world of cloud. Ease-of-Use vs. Granular Management continues to be a key IT struggle as the need for business agility creates conflict between the need for speed of implementation and management vs. the demand for individualized and customized business model construction.

  9. Amazon Expands Toolkit of Machine Learning Services at AWS re:Invent – Author: Lynne Baer

    The interest for data science and machine learning analyst Lynne Baer’s piece on Amazon re:Invent was driven by the interest in Amazon Textract, a service that extracts text and data from scanned documents, without requiring manual data entry or custom code. The promise of Textract in providing Role-based Expert Enhancement by automating manual work continues to be of interest for our enterprise IT audience.

  10. The CLAIRE-ion Call at Informatica World 2019: AI Needs Managed Data and Data Management Needs AI – Author: Lynne BaerThis research note reflects the synergy between modern data management strategies and evolution of artificial intelligence and Amalgam Insights’ recommendations for the data managers, executives, and enterprises in the Informatica ecosystem trying to figure out what to do next in preparing for the exponentially expanding challenge of billions of daily interactions, billions of daily searches, billions of devices and sensors – combined with a shortage of over 800,000 data science professionals.

As you prepare for the second half of 2019, please keep a look out for our upcoming research and review any of our top pieces that have been influencing technology decisions for our subscribers and advisory clients over the first part of 2019. If you are seeking guidance on the Finance of Tech, the Neuroscience of Tech, or the current state of ITOps and DevOps, please send us a note at info@amalgaminsights.com to set up a discussion. We look forward to supporting a better future for managing technology with you.

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Data Science and Machine Learning News Roundup, May 2019

On a monthly basis, I will be rounding up key news associated with the Data Science Platforms space for Amalgam Insights. Companies covered will include: Alteryx, Amazon, Anaconda, Cambridge Semantics, Cloudera, Databricks, Dataiku, DataRobot, Datawatch, Domino, Elastic, Google, H2O.ai, IBM, Immuta, Informatica, KNIME, MathWorks, Microsoft, Oracle, Paxata, RapidMiner, SAP, SAS, Tableau, Talend, Teradata, TIBCO, Trifacta, TROVE.

Domino Data Lab Champions Expert Data Scientists While Outpacing Walled-Garden Data Science Platforms

Domino announced key updates to its data science platform at Rev 2, its annual data science leader summit. For data science managers, the new Control Center provides information on what an organization’s data science team members are doing, helping managers address any blocking issues and prioritize projects appropriately. The Experiment Manager’s new Activity Feed supplies data scientists with better organizational and tracking capabilities on their experiments. The Compute Grid and Compute Engine, built on Kubernetes, will make it easier for IT teams to install and administer Domino, even in complex hybrid cloud environments. Finally, the beta Domino Community Forum will allow Domino users to share best practices with each other, as well as submit feature requests and feedback to Domino directly. With governance becoming a top priority across data science practices, Domino’s platform improvements around monitoring and making experiments repeatable will make this important ability easier for its users.

Informatica Unveils AI-Powered Product Innovations and Strengthens Industry Partnerships at Informatica World 2019

At Informatica World, Informatica publicized a number of key partnerships, both new and enhanced. Most of these partnerships involve additional support for cloud services. This includes storage, both data warehouses (Amazon Redshift) and data lakes (Azure, Databricks). Informatica also announced a new Tableau Dashboard Extension that enables Informatica Enterprise Data Catalog from within the Tableau platform. Finally, Informatica and Google Cloud are broadening their existing partnership by making Intelligent Cloud Services available on Google Cloud Platform, and providing increased support for Google BigQuery and Google Cloud Dataproc within Informatica. Amalgam Insights attended Informatica World and provides a deeper assessment of Informatica’s partnerships, as well as CLAIRE-ity on Informatica’s AI initiatives.

Microsoft delivers new advancements in Azure from cloud to edge ahead of Microsoft Build conference

Microsoft announced a number of new Azure Machine Learning and Azure AI capabilities. Azure Machine Learning has been integrated with Azure DevOps to provide “MLOps” capabilities that enable reproducibility, auditability, and automation of the full machine learning lifecycle. This marks a notable increase in making the machine learning model process more governable and compliant with regulatory needs. Azure Machine Learning also has a new visual drag-and-drop interface to facilitate codeless machine learning model creation, making the process of building machine learning models more user-friendly. On the Azure AI side, Azure Cognitive Services launched Personalizer, which provides users with specific recommendations to inform their decision-making process. Personalizer is part of the new “Decisions” category within Azure Cognitive Services; other Decisions services include Content Moderator, an API to assist in moderation and reviewing of text, images, and videos; and Anomaly Detector, an API that ingests time-series data and chooses an appropriate anomaly detection model for that data. Finally, Microsoft added a “cognitive search” capability to Azure Search, which allows customers to apply Cognitive Services algorithms to search results of their structured and unstructured content.

Microsoft and General Assembly launch partnership to close the global AI skills gap

Microsoft also announced a partnership with General Assembly to address the dearth of qualified data workers, with the goal of training 15,000 workers by 2022 for various artificial intelligence and machine learning roles. The two companies will found an AI Standards Board to create standards and credentials for artificial intelligence skills. In addition, Microsoft and General Assembly will develop scalable training solutions for Microsoft customers, and establish an AI Talent network to connect qualified candidates to AI jobs. This continues the trend of major enterprises building internal training programs to bridge the data skills gap.

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The CLAIRE-ion Call at Informatica World 2019: AI Needs Managed Data and Data Management Needs AI

Knowledge of the Past, Uncertainty of the Future

From May 20-23, Amalgam Insights attended Informatica World 2019, Informatica’s end user summit dedicated to the world of data management. Over the years, Informatica has transformed from a data integration and data governance vendor to a broad-based enterprise data management vendor offering what it calls the “Intelligent Data Platform,” consisting of data integration, Big Data Management, Integration Platform as a Service, Data Quality and Governance, Master Data Management,  Enterprise Data Catalog, and Data Security & Privacy. Across all of these areas, Informatica has built market-leading products with the goal of providing high-quality point solutions that can work together to solve broad data challenges.

To support this holistic enterprise data management approach, Informatica has developed an artificial intelligence layer, called CLAIRE, to support metadata management, machine learning, and artificial intelligence services that provide context, automation, and anomaly recognition across Informatica’s varied data offerings. And at Informatica World 2019, CLAIRE was everywhere as AI served as a core theme of the event.

CLAIRE was mentioned in Informatica’s Intelligent Cloud Services, automating redundant, manual data processing steps. It was in Informatica Data Quality, cleansing and standardizing incoming data. It was in Data Integration, speeding up the integration process for non-standard data. It was in the Enterprise Data Catalog, helping users to understand where data was going across their organization. And it was in Data Security, identifying patterns and deviations in user activities that could indicate suspicious behavior, while contextually masking sensitive data for secure use.

What’s meant by CLAIRE? It’s the name tying together all of Informatica’s automated smart contextual improvements across its Intelligent Data Platform, surfacing relevant data, information, and recommendations just-in-time throughout various parts of the data pipeline. By bringing “AI” to data management, Informatica hopes to improve efficiency throughout the whole process, helping companies manage the growing pace of data ingestion.

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Market Milestone: Informatica and Google Cloud Partner to Open Up Data, Metadata, Processes, and Applications as Managed APIs

[This Research Note was co-written by Hyoun Park and Research Fellow Tom Petrocelli]

Key Stakeholders: Chief Information Officers, Chief Technical Officers, Chief Digital Officers, Data Management Managers, Data Integration Managers, Application Development Managers

Why It Matters: his partnership demonstrates how Informatica’s integration Platform as a Service brings Google Cloud Platform’s Apigee products and Informatica’s machine-learning-driven connectivity into a single solution.

Key Takeaway: This joint Informatica-Google API management solution provides customers with a single solution that provides data, process, and application integration as well as API management. As data challenges evolve into workflow and service management challenges, this solution bridges key gaps for data and application managers and demonstrates how Informatica can partner with other vendors as a neutral third-party to open up enterprise data and support next-generation data challenges.

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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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Lynne Baer: Clarifying Data Science Platforms for Business

Word cloud of data science software and terms

My name is Lynne Baer, and I’ll be covering the world of data science software for Amalgam Insights. I’ll investigate data science platforms and apps to solve the puzzle of getting the right tools to the right people and organizations.

“Data science” is on the tip of every executive’s tongue right now. The idea that new business initiatives (and improvements to existing ones) can be found in the data a company is already collecting is compelling. Perhaps your organization has already dipped its toes in the data discovery and analysis waters – your employees may be managing your company’s data in Informatica, or performing statistical analysis in Statistica, or experimenting with Tableau to transform data into visualizations.

But what is a Data Science Platform? Right now, if you’re looking to buy software for your company to do data science-related tasks, it’s difficult to know which applications will actually suit your needs. Do you already have a data workflow you’d like to build on, or are you looking to the structure of an end-to-end platform to set your data science initiative up for success? How do you coordinate a team of data scientists to take better advantages of existing resources they’ve already created? Do you have coders in-house already who can work with a platform designed for people writing in Python, R, Scala, Julia? Are there more user-friendly tools out there your company can use if you don’t? What do you do if some of your data requires tighter security protocols around it? Or if some of your data models themselves are proprietary and/or confidential?

All of these questions are part and parcel of the big one: How can companies tell what makes a good data science platform for their needs before investing time and money? Are traditional enterprise software vendors like IBM, Microsoft, SAP, SAS dependable in this space? What about companies like Alteryx, H2O.ai, KNIME, RapidMiner? Other popular platforms under consideration should also include Anaconda, Angoss (recently acquired by Datawatch), Domino, Databricks, Dataiku, MapR, Mathworks, Teradata, TIBCO. And then there’s new startups like Sentenai, focused on streaming sensor data, and slightly more established companies like Cloudera looking to expand from their existing offerings.

Over the next several months, I’ll be digging deeply to answer these questions, speaking with vendors, users, and investors in the data science market. I would love to speak with you, and I look forward to continuing this discussion. And if you’ll be at Alteryx Inspire in June, I’ll see you there.

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As API Management Problem Grows, Informatica Jumps into the Market

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Tom Petrocelli, Amalgam Insights Contributing Analyst

API management is a necessary but boring practice. As developers make use of a mix of public cloud, purchased or open source libraries, and homegrown services, the number of APIs used by developers quickly renders pouring through documentation impractical.

Microservices, usually accessed via RESTFul APIs, cause API calls to rapidly proliferate. Even modest-sized microservices-based systems experience API overload quickly. Agile development can exacerbate the problem of understanding and using APIs. The rapid pace of Agile, especially Scrum, leaves little time for proper documentation of APIs. Documentation often takes a back seat to continuous deployment.

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28 Hours as an Industry Analyst at Strata Data 2017

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Companies Mentioned: Aberdeen Group, Actian, Alation, Arcadia Data, Attunity, BMC, Cambridge Semantics, Cloudera, Databricks, Dataiku, DataKitchen, Datameer, Datarobot, Domino Data Lab, EMA, HPE, Hurwitz and Associates, IBM, Informatica, Kogentix, LogTrust, Looker, < MesoSphere, Micro Focus, Microstrategy, Ovum, Paxata, Podium Data, Qubole, SAP, Snowflake, Strata Data, Tableau, Tamr, Tellius, Trifacta.

Last week, I attended Strata Data Conference at the Javitz Center in New York City to catch up with a wide variety of data science and machine learning users, enablers, and thought leaders. In the process, I had the opportunity to listen to some fantastic keynotes and to chat with 30+ companies looking for solutions, 30+ vendors presenting at the show, and attend with a number of luminary industry analysts and thought leaders including Ovum’s Tony Baer, EMA’s John Myers, Aberdeen Group’s Mike Lock, and Hurwitz & Associates’ Judith Hurwitz.

From this whirwind tour of executives, I took a lot of takeaways from the keynotes and vendors that I can share and from end users that I unfortunately have to keep confidential. To give you an idea of what an industry analyst notes, following are a short summary of takeaways I took from the keynotes and from each vendor that I spoke to:

Keynotes: The key themes that really got my attention is the idea that AI requires ethics, brought up by Joanna Bryson, and that all data is biased, which danah boyd discussed. This idea that data and machine learning have their own weaknesses that require human intervention, training, and guidance is incredibly important. Over the past decade, technologists have put their trust in Big Data and the idea that data will provide answers, only to find that a naive and “unbiased” analysis of data has its own biases. Context and human perspective are inherent to translating data into value: this does not change just because our analytic and data training tools are increasingly nuanced and intelligent in nature.

Behind the hype of data science, Big Data, analytic modeling, robotic process automation, DevOps, DataOps, and artifical intelligence is this fundamental need to understand that data, algorithms, and technology all have inherent biases as the following tweet shows:

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