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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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Data Science and Machine Learning News Roundup, February 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,…

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Data Science Platforms News Roundup, July 2018

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, Anaconda, Cloudera, Databricks, Dataiku, DataRobot,  Datawatch, Domino, H2O.ai, IBM, Immuta, Informatica, KNIME, MathWorks, Microsoft, Oracle, Paxata, RapidMiner, SAP, SAS, Tableau, Talend, Teradata, TIBCO, Trifacta.

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What Data Science Platform Suits Your Organization’s Needs?

This summer, my Amalgam Insights colleague Hyoun Park and I will be teaming up to address that question. When it comes to data science platforms, there’s no such thing as “one size fits all.” We are writing this landscape because understanding the processes of scaling data science beyond individual experiments and integrating it into your business is difficult. By breaking down the key characteristics of the data science platform market, this landscape will help potential buyers choose the appropriate platform for your organizational needs. We will examine the following questions that serve as key differentiators to determine appropriate data science platform purchasing solutions to figure out which characteristics, functionalities, and policies differentiate platforms supporting introductory data science workflows from those supporting scaled-up enterprise-grade workflows.

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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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Cloudera Analyst Conference Makes The Case for Analytic & AI Insights at Scale

On April 9th and 10th, Amalgam Insights attended the fifth Cloudera’s Industry Analyst and Influencer Conference (which I’ll self-servingly refer to as the Analyst Conference since I attended as an industry analyst) in Santa Monica. Cloudera sought to make the case that it was evolving beyond the market offerings that it is currently best known for as a Hadoop distribution and commercial data lake in becoming a machine learning and analytics platform. In doing so, Cloudera was extremely self-aware of its need to progress beyond the role of multi-petabyte storage at scale to a machine learning solution.
Cloudera’s Challenges in Enterprise Machine Learning 
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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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