dotData | AutoML 2.0 Solutions for Enterprise https://dotdata.com Data Science Automation and Machine Learning Platform | dotData Wed, 04 Dec 2019 19:15:34 -0700 en-US hourly 1 https://wordpress.org/?v=5.3 https://dotdata.com/wp-content/uploads/2019/09/favicon.png dotData | AutoML 2.0 Solutions for Enterprise https://dotdata.com 32 32 Epson Selects dotData to Accelerate Data Science Across Its Organization https://dotdata.com/epson-selects-dotdata-to-accelerate-data-science-across-its-organization/ Wed, 04 Dec 2019 19:15:34 +0000 https://dotdata.com/?p=5776 Global Technology Leader Deploys dotData’s Full-Cycle Data Science Automation Platform to Accelerate Its AI Development.  Read the full article – Epson...

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Global Technology Leader Deploys dotData’s Full-Cycle Data Science Automation Platform to Accelerate Its AI Development.  Read the full article – Epson Selects dotData to Accelerate Data Science Across Its Organization.

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AI’s Impact in 2020: 3 Trends to Watch https://dotdata.com/ai-trends-to-watch-2020/ Mon, 02 Dec 2019 18:50:51 +0000 https://dotdata.com/?p=5757 Our founder and CEO shares his top three trends for data professionals in 2020 in this article: AI’s Impact in...

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Our founder and CEO shares his top three trends for data professionals in 2020 in this article:

AI’s Impact in 2020: 3 Trends to Watch

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10 Hottest Big Data Startups Of 2019 https://dotdata.com/10-hottest-big-data-startups-of-2019/ Mon, 02 Dec 2019 18:29:06 +0000 https://dotdata.com/?p=5751 CRN’s Rick Whiting included Ryohei and dotData in a list of “The 10 Hottest Big Data Startups of 2019.”

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CRN’s Rick Whiting included Ryohei and dotData in a list of “The 10 Hottest Big Data Startups of 2019.”

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Happy Thanksgiving! https://dotdata.com/happy-thanksgiving/ Thu, 21 Nov 2019 12:30:53 +0000 https://dotdata.com/?p=5486 May your table be filled with joy and gratitude this season. From all of us @dotData:

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May your table be filled with joy and gratitude this season. From all of us @dotData:

Thanksgiving 2019

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Mitsui Sumitomo Insurance Selects dotData to Power New “MS1 Brain” AI Platform https://dotdata.com/mitsui-sumitomo-insurance-selects-dotdata-to-power-new-ms1-brain-ai-platform/ Tue, 19 Nov 2019 21:00:55 +0000 https://dotdata.com/?p=5496 Leading Insurance Company to Deploy dotData’s Full-Cycle Data Science Automation Platform to Accelerate its Digital Transformation.  Read the article.

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Leading Insurance Company to Deploy dotData’s Full-Cycle Data Science Automation Platform to Accelerate its Digital Transformation.  Read the article.

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dotData Secures $23 Million in Series A Funding from JAFCO and Goldman Sachs https://dotdata.com/dotdata-secures-23-million-in-series-a-funding-from-jafco-and-goldman-sachs/ Fri, 01 Nov 2019 23:24:38 +0000 https://dotdata.com/?p=5211 The post dotData Secures $23 Million in Series A Funding from JAFCO and Goldman Sachs appeared first on dotData | AutoML 2.0 Solutions for Enterprise.

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SAN MATEO, Calif., Oct. 30, 2019 — dotData, the first and only company focused on delivering full-cycle data science automation and operationalization for the enterprise, today announced that it has raised $23 million in Series A funding, bringing the total amount of funding raised to date to $43 million. The Series A financing round was led by JAFCO with participation by  Goldman Sachs, who both join existing dotData Seed round investors NEC Corporation.

dotData will use the funds to further accelerate the company’s rapid growth by expanding sales and marketing efforts, and enhancing product development innovation of its full-cycle data science automation platform.

The Series A funding comes just 18 months after dotData’s launch and Series Seed round, and builds on an exceptional year for dotData which saw a more than 300 percent increase in revenue growth, multiple product launches and significant recognition as a leader in the rapidly-growing AutoML market, including in The Forrester New Wave: Automation-Focused Machine Learning (AutoML) Solutions, Q2 2019.

dotData offers the most powerful and broad machine learning automation solution as far as we know. We are impressed with their passion to tackle the big challenge of automating the full-cycle data science process from raw data through feature engineering to machine learning in production, to meet the globally-increasing demand for solutions to help enterprises optimize value from their AI and machine learning initiatives,” said Tomotake Kitazawa, Partner of JAFCO. “dotData is well-positioned to lead this growing AutoML market segment with its innovation. We are excited to partner with dotData as they continue to build a leading company in an exciting category.”

“We are pleased with the confidence our investors show in our vision, team, product and ability to execute and expand market share,” said Ryohei Fujimaki, Ph.D., CEO, and founder of dotData. “Our company’s rapid growth over the past 18 months signals a significant market demand for our unique data science automation platform. These funds will enable us to accelerate product development and innovation to continue bringing transformational value to our customers.”

“We are thrilled about dotData’s significant growth since it spun out from NEC, and delighted that it is accelerating its evolution with Series A funds from JAFCO and Goldman Sachs. dotData has proved with our clients that its platform accelerates enterprise data science 10x faster and delivers key business insights via its proprietary AI-powered feature engineering technology,” said Osamu Fujikawa, Senior Vice President of NEC Corporation. “NEC and dotData have already provided solutions to more than 30 companies in Japan and will continue to promote growth and accelerate digital transformation for customers with the support of JAFCO and Goldman Sachs. We are excited about our strengthening partnership with dotData and will continue to support its business expansion and vision to empower all enterprises through its data science technology.”

dotData is one of the only platforms that combines AI-powered feature engineering and AutoML to automate the full life-cycle of the data science process, from source data through feature engineering to implementation of machine learning in production. dotData’s AI-powered feature engineering automatically applies data transformation, cleansing, normalization, aggregation, and combination and transforms hundreds of tables with complex relationships and billions of rows into a single feature table, automating the most manual data science projects.

dotData democratizes data science by enabling existing resources to perform data science tasks, making enterprise data science scalable and sustainable. dotData also operationalizes data science by producing both feature and ML scoring pipelines in production, which IT teams can then immediately integrate with business workflow. This further automates the time-consuming and arduous process of maintaining the deployed pipeline to ensure repeatability as data changes over time. With the dotData GUI, the data science task becomes a five-minute operation, even without significant data science experience nor SQL/Python/R coding.

For more information or a demo of dotData’s AI-powered full-cycle data science automation platform, please visit dotData.com.

About JAFCO

Since establishing the first investment partnership in Japan in 1982, JAFCO has specialized in the private equity investment business. As of March 2019, it has established over 100 investment partnerships with total capital commitments of approximately ¥1 trillion. Its portfolio IPOs have reached 1,005 on a cumulative basis.

In addition to its rich investment experience and management support expertise that it has built over the years, JAFCO will utilize its extensive network with domestic/ overseas venture companies, financial institutions and business firms to carry on investment with a co-founder mindset in growth potential companies.

About The Goldman Sachs Group, Inc.

The Goldman Sachs Group, Inc. is a leading global investment banking, securities and investment management firm that provides a wide range of financial services to a substantial and diversified client base that includes corporations, financial institutions, governments and individuals. Founded in 1869, the firm is headquartered in New York and maintains offices in all major financial centers around the world.

About NEC Corporation

NEC Corporation is a leader in the integration of IT and network technologies that benefit businesses and people around the world. The NEC Group globally provides “Solutions for Society” that promote the safety, security, efficiency and fairness of society. Under the company’s corporate message of “Orchestrating a brighter world,” NEC aims to help solve a wide range of challenging issues and to create new social value for the changing world of tomorrow. For more information, visit NEC at https://www.nec.com

About dotData

dotData is the first and only company focused on full-cycle data science automation. Fortune 500 organizations around the world use dotData to accelerate their ML and AI projects and deliver higher business value. dotData’s automated data science platform speeds time to value by accelerating, democratizing, augmenting and operationalizing the entire data science process, from raw business data through data and feature engineering to machine learning in production. With solutions designed to cater to the needs of both data scientists as well as citizen data scientists, dotData provides unmatched value across the entire organization.

dotData’s unique AI-powered feature engineering delivers actionable business insights from relational, transactional, temporal, geo-locational, and text data. dotData has been recognized as a leader by Forrester in the 2019 New Wave for AutoML platforms. dotData has also been recognized as the “best machine learning platform” for 2019 by the AI breakthrough awards and was named an “emerging vendor to watch” by CRN in the big data space. For more information, visit www.dotdata.com, and join the conversation onTwitter andLinkedIn.

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dotData is Selected by the Microsoft for Startups Program https://dotdata.com/dotdata-microsoft-azure-partner/ Wed, 16 Oct 2019 03:16:51 +0000 https://dotdata.com/?p=3197 In the news, dotData (as a qualified partner) will be able to provide to customers the power of data science...

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In the news, dotData (as a qualified partner) will be able to provide to customers the power of data science automation along with the benefits and capabilities of Microsoft’s highly available, trusted, and scalable Azure cloud platform.  See the full article @Database Trends and Applications: “dotData is Selected by the Microsoft for Startups Program.

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What IS Feature Engineering? https://dotdata.com/what-is-feature-engineering/ Wed, 09 Oct 2019 06:00:46 +0000 https://dotdata.com/?p=3021 What Is Feature Engineering? (And Why Do We Need To Automate it?) The past few years have seen the rapid...

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What Is Feature Engineering?
(And Why Do We Need To Automate it?)
The past few years have seen the rapid rise in the adoption of Artificial Intelligence (AI) and Machine Learning (ML) for a multitude of commercial use-cases. Beyond the “cute” factor of AI that can pick a cat out of a photo array, AI and Machine learning are being deployed to model and predict lending risk, to understand and manage customer churn, provide product recommendations, help with programmatic advertising and much more. The challenge for the business community is that the underlying practice that is at the heart of AI and Machine Learning – data science – is rooted in a complex world of statistical analysis, data manipulation, programming and more. Most businesses don’t have enough data scientists – a fact illustrated by research in 2018 by LinkedIn that showed that there would be a shortfall of over 150,000 people with data science skills in the US alone. The data science process is complex and involves multiple distinct phases, as illustrated below. A typical data science project can take months to complete – with the most complex part being the feature engineering piece.
traditional feature engineering

What IS Feature Engineering?

Surprisingly, even in our daily conversations with clients, we find that there is often some amount of confusion as to what the term “feature engineering” actually means. What exactly is feature engineering? What are the steps of the process and why does it take so long? What can we do to accelerate this process? At a most basic level, feature engineering is comprised of three distinct steps:

  1. Feature ideation 
  2. Feature selection
  3. Feature creation

The first two steps in the process, feature ideation and feature selection, often require a high degree of “domain knowledge.” Domain knowledge refers to knowledge of the underlying business requirements that must be addressed. For example, a bank might employ a team of business analysts and data analysts to work with the data science team to consider “features” that might be useful in predicting if a client is likely to convert on a “zero balance” transfer offer for a new credit card. During this phase, a high degree of analysis of data is required to understand what data sources, tables and columns might be used to create the “features” that will then be tested in the next phase.
Feature creation and testing are the next part of the process. During this phase, data scientists collaborate with business analysts and data engineers to create flat tables that combine data from multiple related tables in one single “feature table.” For example, the same bank in our previous example might take data from their web tracking system, from their customer records, and from other data sources to create a single table that provides data for individual prospective clients that might be used by a machine learning model to predict the likelihood of that consumer accepting an offer. Each feature that is created must then be evaluated against machine learning models to identify which feature/model combinations provide the best possible outcome.

Why Automate Feature Engineering?

Clearly, the process of feature engineering can be lengthy, time-consuming and resource-intensive. Most organizations simply don’t have enough talent or time to effectively evaluate all possible use cases and to evaluate all possible permutations and combinations of tables and columns of data. Automated Feature Engineering can provide a huge benefit to businesses that aim to leverage AI and ML models for their business. The word “automated feature engineering,” however, can often mean different things, depending on which vendor you are evaluating. For most providers of Automated Machine Learning (AutoML) software, “automated feature engineering” describes the process of evaluating which features – built manually using the process described above, will be most beneficial for any given machine learning model. True Automated Feature Engineering, however, leverages Artificial Intelligence (AI) to create and evaluate features automatically. This is why at dotData we talk about discovering the “unknown unknowns” using Automated Feature Engineering. By automating the entire feature building process, you can build and evaluate hundreds of thousands, potentially even millions of features automatically – exposing only the ones that pass a specific threshold – and then providing data scientists with a wealth of additional features that they may have never considered.
To be specific, Automated Feature Engineering is not a replacement for manual feature creation and evaluation but instead can provide two significant benefits: Rapid prototyping and feature augmentation. Automated Feature Engineering can be used by data scientists to accelerate the process of trial and error that is often associated with feature engineering. Feature augmentation, on the other hand, is the process of using Automated Feature Engineering to create additional features that the data scientists, business analysts and data engineerings might have never even considered.

From Months to Days

What are the benefits of Automated Feature Engineering? By far the most valuable benefit is that of accelerated performance. Many dotData clients have leveraged the Automated Feature Engineering features of our dotData Enterprise or dotData Py platforms to accelerate their data science processes, often being able to deliver in days what traditionally took five months or longer to deliver. With the exponential growth in need for AI and ML use-cases and the low availability of data science resources, Automated Feature Engineering – as part of an effective AutoML platform – can help businesses grow exponentially the number of AI and ML projects that are executed and successfully brought into production.
Learn more about our platform and about Automated Feature Engineering by visiting our website.

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dotData Selected for Microsoft for Startups Program https://dotdata.com/dotdata-selected-for-microsoft-for-startups-program/ Tue, 08 Oct 2019 13:30:25 +0000 https://dotdata.com/?p=2896 Prestigious Program Will Help Drive the Continued Development of dotData’s Full-Cycle Data Science Automation Solution and its Adoption on Microsoft...

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Prestigious Program Will Help Drive the Continued Development of dotData’s Full-Cycle Data Science Automation Solution and its Adoption on Microsoft Azure

Microsoft for Startups program

SAN MATEO, Calif., Oct. 8, 2019 – dotData, one of the first and only companies focused on delivering full-cycle data science automation and operationalization for the enterprise, today announced that it has been selected as a qualified partner in Microsoft for Startups, a program designed to support the top startup technology businesses in the world.
Microsoft for Startups selects organizations developing innovative enterprise-ready technology solutions that run on Microsoft Azure. The program will provide dotData access to the full Microsoft ecosystem, including sales, marketing, and technical support, to advance dotData’s expansion and drive continued innovation by helping dotData make its platform available on Azure.
dotData was selected for Microsoft for Startups as its innovative full-cycle data science automation solution is unique in the fast-growing market of AutoML. As a qualified partner, dotData will be able to provide to its customers the power of dotData’s data science automation along with the benefits and capabilities of Microsoft’s highly available, trusted, and scalable Azure cloud platform.
“We are thrilled that Microsoft has recognized dotData’s work and selected us to be among the innovative companies in the Microsoft for Startups program,” said Ryohei Fujimaki, PhD., CEO, and founder of dotData. “By having direct access and insight from Microsoft, we will be able to better support our customers who are on Microsoft Azure. We look forward to working with the Microsoft team to drive the continued rapid growth of our full-cycle data science automation solution on Azure.”  
dotData is one of the only platforms that combines AI-powered feature engineering and AutoML to automate the full life-cycle of the data science process, from source data through feature engineering to implementation of machine learning in production. dotData’s AI-powered feature engineering automatically applies data transformation, cleansing, normalization, aggregation, and combination and transforms hundreds of tables with complex relationships and billions of rows into a single feature table, automating the most manual data science projects.
“Startups are an indisputable innovation engine, and Microsoft is partnering with founders and investors to help propel their growth,” said Shaloo Garg, Managing Director, Microsoft for Startups, Silicon Valley, Southwest. “dotData’s platform offers an innovative solution that supports its customers in their digital transformation. We are pleased to work with dotData as part of our Microsoft for Startups program to help them grow customer and revenue base.”
dotData democratizes data science by enabling existing resources to perform data science tasks, making enterprise data science scalable and sustainable. dotData also operationalizes data science by producing both feature and ML scoring pipelines in production, which IT teams can then immediately integrate with business workflow. This further automates the time-consuming and arduous process of maintaining the deployed pipeline to ensure repeatability as data changes over time.  With the dotData GUI, the data science task becomes a five-minute operation, requiring neither significant data science experience nor SQL/Python/R coding.
For more information or a demo of dotData’s AI-powered full-cycle data science automation platform, please visit dotData.com.
 
About dotData
dotData is the first and only company focused on full-cycle data science automation. Fortune 500 organizations around the world use dotData to accelerate their ML and AI projects and deliver higher business value. dotData’s automated data science platform speeds time to value by accelerating, democratizing, augmenting and operationalizing the entire data science process, from raw business data through data and feature engineering to machine learning in production. With solutions designed to cater to the needs of both data scientists as well as citizen data scientists, dotData provides unmatched value across the entire organization.
dotData’s unique AI-powered feature engineering delivers actionable business insights from relational, transactional, temporal, geo-locational, and text data. dotData has been recognized as a leader by Forrester in the 2019 New Wave for AutoML platforms. dotData has also been recognized as the “best machine learning platform” for 2019 by the AI breakthrough awards and was named an “emerging vendor to watch” by CRN in the big data space. For more information, visit www.dotdata.com, and join the conversation on Twitter and LinkedIn.

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AutoML and Beyond – Part 2 https://dotdata.com/automl-and-beyond-part-2/ Thu, 03 Oct 2019 03:01:06 +0000 https://dotdata.com/?p=2856 The post AutoML and Beyond – Part 2 appeared first on dotData | AutoML 2.0 Solutions for Enterprise.

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Watch Part 2 (the Conclusion) of “AutoML and Beyond.” With AutoML trending in data science, our CEO spoke at #Ai4Finance on data preparation, aggregating tables, feature engineering, the #AutoML process, and AutoML’s missing gaps.  Video: AutoML and Beyond – Part 2

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