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The insideBIGDATA IMPACT 50 List for Q2 2020

The team here at insideBIGDATA is deeply entrenched in following the big data ecosystem of companies from around the globe. We’re in close contact with most of the firms making waves in the technology areas of big data, data science, machine learning, AI and deep learning. Our in-box is filled each day with new announcements, commentaries, and insights about what’s driving the success of our industry so we’re in a unique position to publish our quarterly IMPACT 50 List of the most important movers and shakers in our industry. These companies have proven their relevance by the way they’re impacting the enterprise through leading edge products and services. We’re happy to publish this evolving list of the industry’s most impactful companies!

The selected companies come from our massive data set of vendors and industry metrics. Yes, we use machine learning to analyze the industry in a detailed manner to determine a ranking for this list. We’re using a custom RankBoost algorithm adapted specifically for the big data community along with a plethora of proprietary data sources. The rankings include an indicator for upward movement in the list and also new companies.

If you’re part of a company that you feel is “impactful” in some critical way please contact us immediately to be added to our database in order to be considered for this list. Companies on the list exhibit technology leadership, strength of offering, proven innovation, positivity of message, quality perception in the enterprise, intensity and frequency of social media buzz, high profile of members of the C-suite, and in the case of public companies: positive financial indicators and stock price, and so much more!

IMPACT 50 LIST for Q2 2020 (in order of the most impactful)

#1 NVIDIA – Inventor of the GPU for AI workloads [NASD: NVDA] [+3]

#2 H2O.ai – Open Source Data Science and Machine Learning Platform [+1]

#3 Domino Data Lab – Data Science Platform

#4 Google AI [NASDAQ: GOOGL] [+1]

#5 Intel AI – Harnessing silicon designed specifically for AI [NASDAQ: INTC]

#6 Kinetica – GPU Database

#7 DataRobot – Automated Machine Learning [+2]

#8 Microsoft [NASDAQ: MSFT] [+2]

#9 Snowflake – Cloud Enterprise Data Warehouse

#10 Dell EMC [NYSE: DELL] [+1]

#11 Pure Storage [NYSE: PSTG] [+3]

#12 Teradata [NYSE: TDC]

#13 DataDirect Networks – AI and Deep Learning Storage

#14 HPE [NYSE: HPE]

#15 SAS – Analytics, BI, and data management [+2]

#16 AtScale – Adaptive analytics fabric

#17 TigerGraph – Graph database and analytics platform [+2]

#18 Anaconda – Python Data Science Platform

#19 Alegion – Human in the loop AI training data platform [+2]

#20 OmniSci– GPU database [+12]

#21 Dremio – Data-as-a-Service platform [+3]

#22 StreamSets – Where DevOps meets data integration

#23 Loop AI Labs – Cognitive computing [+2]

#24 Ople – Engineered Intelligence [+4]

#25 TIBCO – Integration, analytics and event-processing software

#26 Binah – Automatic data science engine [+3]

#27 Cazena – Big data platform as a service

#28 Databricks – Unified analytics platform

#29 Brytlyt – GPU Database

#30 Salesforce Einstein AI – Smart CRM assistant [+7]

#31 SkyMind – Enterprise deep learning

#32 Qlik – Data Analytics and Data Integration

#33 MongoDB – Cross-platform document-oriented NoSQL database [NASDAQ: MDB]

#34 Guavus – Real-time big data analytics

#35 SigOpt– Model tuning automation

#36 Neo4j – Graph database [+7]

#37 deepsense.ai – Big data science

#38 Kyndi – Explainable AI platform [+2]

#39 Striim – Real-time data integration [+5]

#40 Cloudera– Enterprise scale analytics platform [NYSE: CLDR]

#41 Rulex – Explainable AI platform

#42 DarwinAI – “AI Building AI” technology

#43 Trifacta – Data wrangling tools

#44 R2.ai – AutoML [+3]

#45 Run:AI – Virtualization layer for deep learning training

#46 Kaskada – End-to-end platform for feature engineering and feature serving NEW

#47 Fiddler Labs – Explainable AI NEW

#48 Qeexo – AutoML at the Edge NEW

#49 Spell – ML and DL platform NEW

#50 Hailo – Specialized deep learning processor for edge devices NEW

HONORABLE MENTION (newly expanded list in alphabetical order):

Allegro AI – DL/ML open source platform NEW

Alluxio – Open source data orchestration for the cloud

AnotherBrain – Pioneers of organic AI

Beyond Limits – Explainable AI NEW

cnvrg.io – Operating system for ML and AI NEW

Comet– Self-hosted and cloud-based meta machine learning platform NEW

Confluent – Stream processing with Apache Kafka

Deepgram – Speech recognition NEW

Digitate – AIOps

Exasol– In-memory analytic database

Gigaspaces – In-memory computing platform

GridGain – In-memory computing platform NEW

HVR – Enterprise data integration software NEW

Incorta – Analyze complex business data NEW

Intuition Machines – Deep learning and visual domain machine learning at scale NEW

Logical Clocks AB – Hopsworks, an enterprise platform for AI

NetApp – Accelerated AI data pipelines

Obviously AI – Data science with out code NEW

OctoML – Deploy machine learning models

OpenAI – Ensures AGI benefits all of humanity

Peltarion – Deep learning cloud platform

PredictHQ – Demand intelligence NEW

Recogni – Real-time object recognition NEW

Contributed by Daniel D. Gutierrez, Managing Editor and Resident Data Scientist of insideBIGDATA. In addition to being a tech journalist, Daniel also is a practicing data scientist, author, educator and sits on a number of advisory boards for various start-up companies. 

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Comments

  1. Erik Windischman says:

    With no criteria given for how you arrive at this ranking for “Big Data Impact”, this list is completely useless. While I can kind of understand Nvidia, why are they they number one? Right now you might as well have posted a dart board and said our darts hit whatever, and that is how we ranked it. “The selected companies come from our massive data set of vendors and industry metrics. Yes, we use machine learning to analyze the industry in a detailed manner to determine a ranking for this list.” What are your industry metrics, and what logic and algorithms are in your ML engine?

    • Thank you for your comment. Yes, I realize that “AI explainability” is a very important topic these days and is also a fertile area of research. This effect of machine learning algorithms making decisions as what many view as a “black box” is of concern in many industries, especially those that have grown to depend on the predictions of ML algorithms without a firm understanding for why the predictions were made. Yet, many industries like radiology for example, still rely on what the technology brings to the table regardless of the black-box nature of the technology. Here at insideBIGDATA, we’re walking a fine line in offering a purely data-driven approach for ranking members of the big data ecosystem without open-sourcing the technology. But we are working to expose more of what’s going on. For now, I can point you to a seminal paper on the RankBoost algorithm that I worked to improve for my graduate research project. This was the genesis of what’s in place on insideBIGDATA. Our long-term goal is come up with something similar to the “Forrester Wave Methodology Guide.” Until then, I hope you can find some value for our IMPACT 50 without a deep dive into the interpretability of our algorithm.

  2. Jay Urbain says:

    No AWS?

    • Hi Jay, YES, we’d love to cover AWS, alas they prefer to not have any contact with us for reasons unknown. We’ll keep pushing to include them in a future IMPACT 50 list. — Daniel

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