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.
Continue reading Data Science Platforms News Roundup, July 2018
The announcement: On July 10, Domino Data Lab announced a partnership with SAS Analytics that will let Domino users run SAS Analytics for Containers in the public cloud on AWS while using Domino’s data science platform as the orchestration layer for the infrastructure provisioning and management. This partnership will allow SAS customers to use Domino as an orchestration layer to access multiple SAS environments for model building, deploy multiple SAS applications on AWS, track each SAS experiment in detail, while having reproducibility of prior work.
What does this mean?
Domino customers with SAS Analytics workloads currently running on-prem will now be able to deploy those workloads to the public cloud on AWS by using SAS Analytics for Containers via the Domino platform. Domino plans to follow up with support for Microsoft Azure and Google Cloud Platform to further enable enterprises to offload containerized SAS workloads in the cloud. By running SAS Analytics for Containers via Domino, Domino users will be able to track, provide feedback on, and reproduce their containerized SAS experiments the same way they do so with other experiments they’ve constructed using Python, R, or other tools within Domino.
Continue reading Domino Deploys SAS Analytics Into a Model-Driven Cloud
Last week at Google Next ‘18, Google announced a new beta capability in their BigQuery cloud data warehouse: BigQuery ML, which lets data analysts apply simple machine learning models to data residing in BigQuery data warehouses.
Data analysts know databases and SQL, but generally don’t have a lot of experience in building machine learning models using Python or R. An additional issue is the expense, time-consumption, and possible regulatory violations of moving data out of storage in order to send it through machine learning models. BigQuery ML aims to address these problems by letting data analysts push data through linear regression models (to predict a numeric value) or binary logistic regression models (to classify a value into one of two categories, such as “high” or “low”), using simple extensions of SQL on Google databases, run in place. Continue reading Google BigQuery ML Extends the Power of (Some) Modeling to Data Analysts
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, Datawatch, Domino, H2O.ai, IBM, Immuta, Informatica, KNIME, MathWorks, Microsoft, Oracle, Paxata, RapidMiner, SAP, SAS, Tableau, Talend, Teradata, TIBCO, Trifacta. Continue reading Data Science Platforms News Roundup, June 2018
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.
Continue reading What Data Science Platform Suits Your Organization’s Needs?
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.
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.
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.
Industry: Data Science Platforms
Key Stakeholders: IT managers, data scientists, data analysts, database administrators, application developers, enterprise statisticians, machine learning directors and managers, current DataScience.com customers, current Oracle customers
Why It Matters: Oracle released a number of AI tools in Q4 2017, but until now, it lacked a data science platform to support complete data science workflows. With this acquisition, Oracle now has an end-to-end platform to manage these workflows and support collaboration among teams of data scientists and business users, and it joins other major enterprise software companies in being able to operationalize data science.
Top Takeaways: Oracle acquired DataScience.com to retain customers with data science needs in-house rather than risk losing their data science-based business to competitors. However, Oracle has not yet not defined a timeline for rolling out the unified data science platform, or its future availability on the Oracle Cloud.
Oracle Acquires DataScience.com
On May 16, 2018, Oracle announced that it had agreed to acquire DataScience.com, an enterprise data science platform that Oracle expects to add to the Oracle Cloud environment. With Oracle’s debut of a number of AI tools last fall, this latest acquisition telegraphs Oracle’s intent to expedite its entrance into the data science platform market by buying its way in.
Oracle is reviewing DataScience.com’s existing product roadmap and will supply guidance in the future, but they mean to provide a single unified data science platform in concert with Oracle Cloud Infrastructure and its existing SaaS and PaaS offerings, empowering customers with a broader suite of machine learning tools and a complete workflow. Continue reading Market Milestone: Oracle Builds Data Science Gravity By Purchasing DataScience.com
This week, everybody is talking about Google Duplex, announced earlier this week at Google I/O. Based on previous interactions with IVRs from calling vendors for customer support, Duplex is an impressive leap forward in natural language AI, and offers future hope at making some clerical tasks easier to complete. Duplex will be tested further by a limited number of users in Google Assistant this summer, refining its ability to complete specific tasks: getting holiday hours for a business, making restaurant reservations, and scheduling appointments specifically at a hair salon.
So what does this mean for most businesses?
Continue reading 4 Key Business & Enterprise Recommendations for Google Duplex
