15 Leading Data Science & Analytics Platforms for Enterprise- 2017

Posted on Posted in Data Science, Machine Learning

“A resilient Data Science Platform is a necessity to every centralized data science team within a large corporation. It helps them centralize, reuse, and productionize their models at peta scale”.

If You are a small team and building two or three use cases then it is not required for any data science platform but if you’re a central team with many internal customers then it will be helpful for production.

Here is a list of Data Science & Analytics platform providers who are popular in the market:

Note: This is not ranking of the products. It is just a list which include fews good Data Science Platform providers. Please leave a comment below if you have a suggestion.

There are open source platforms like Python and R that play an important role in the Data Science & Analytics market but this list only includes  commercial vendors.


IBM is a leader beacuse of their SPSS Modeler and SPSS Statistics. IBM’s new Data Science Experience(DSx), IBM Watson, Cognos Cognos Analytics platform is also popular in the market. IBM strengths include its vast customer base and continued innovation of its data science and machine learning capabilities.


“Real Data Science, Fast and Simple”

RapidMiner makes data science teams more productive through an open source platform for data prep, machine learning, and model deployment. RapidMiner Studio provides a visual workflow designer for data science teams. RapidMiner Server Share, reuse, and deploy predictive models from RapidMiner Studio. Whereas RapidMiner Radoop Run data science workflows directly inside Hadoop.


SAS is the leader in analytics. Through innovative analytics, business intelligence and data management software and services. SAS is very famous for SAS Enterprise Miner (EM) and the SAS Visual Analytics Suite (VAS).


KNIME offers open-source KNIME Analytics Platform with strong functionality for advanced data scientists. It is strong in several industries, especially in manufacturing and life sciences.


Alteryx is a leading platform for Self-Service Data Analytics that enables deeper inshigts from data, faster than ever before.

“I need to prep and blend data and do advanced analytics”

In their words Alteryx Analytics provides analysts with the unique ability to easily prep, blend and analyze all of their data using a repeatable workflow, then deploy and share analytics at scale for deeper insights in hours, not weeks. Analysts love the Alteryx Analytics platform because they can connect to and cleanse data from data warehouses, cloud applications, spreadsheets and other sources, easily join this data together, then perform analytics – predictive, statistical and spatial – using the same intuitive user interface, without writing any code.

Domino Data Lab

Domino Data Lab provides one place for Data Science Work to develop, deploy, and collaborate — using your existing tools and languages. Their Data Science Workbench provides faster iteration and experimentation. You can deploy Models as APIs and accelerate time from insight to impact.


Dataiku is a collaborative Data Science Platform Prototype, Deploy, & Run at Scale. Dataiku DSS is the collaborative data science software platform for teams of data scientists, data analysts, and engineers to explore, prototype, build, and deliver their own data products more efficiently.


Microsoft provide Azure Machine Learning platform, part of the Microsoft Cortana Intelligence Suite, which offers a strong cloud-based data science platform.


H2O.ai offer open-source data science platform with fast execution of Deep Learning and other advanced Machine Learning methods.


Teradata offers Aster Analytics platform, with 3 layers: analytic engines, prebuilt analytic functions, and the Aster AppCenter for analysis and connectivity to external BI tools.


FICO provides analytics software and tools used across multiple industries to manage risk, fight fraud, build more profitable customer relationships, optimize operations and meet strict government regulations.

Alpine Data

They have created an enterprise-grade platform called Chorus. Chorus makes model deployment easy by providing turn-key deployment options that can have you up and running within minutes, without needed to go through complex configurations of your data sources and production environments.


Yhat was founded in 2013 and is based in New York. Yhat provides an end-to-end data science platform for developing, deploying, and managing real-time decision APIs.

Algorithmia Enterprise

Algorithmia ‘s CODEX is Elastic Infrastructure for AI and available on any public cloud and on-premise. You can instantly deploy machine and deep learning models as production-ready, self-healing, auto-scaling microservices. Mix and match frameworks without losing momentum.

Cloudera Data Science Workbench

“Machine learning is all about the data, but it’s often out of reach for analytics teams working at scale. Cloudera Data Science Workbench enables fast, easy, and secure self-service data science for the enterprise.”

Cloudera Data Science Workbench accelerate data science from exploration to production using R, Python, Spark and more. It  is secure and compliant by default, with support for full Hadoop authentication, authorization, encryption, and governance. Finally, data scientists can easily access Hadoop data and run Spark queries in a safe environment. It also allows data scientists to manage their own analytics pipelines, including built-in scheduling, monitoring, and email alerting. Quickly develop and prototype new machine learning projects before deploying to production.

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  • Thanks, great article.

  • Saurav Datta

    Good article….thanks for sharing.

  • Thank you for this article. Data science platform should be such that it helps data scientists in finding and understanding previous work without starting it from the beginning.

  • A resilient Data Science Platform is a necessity to every centralized data science team within a large corporation. It helps them centralize, reuse, and productionize their models at peta scale.