#dotdata

[neccorporation]

How a data science automation platform benefits retail businesses

Leveraging artificial intelligence and machine learning models is becoming essential for retailers to drive sales, understand customers, and optimize operations. However, developing these predictive analytics applications is an enormously complex and time-consuming process.

NEC India's dotData platform is designed to simplify and accelerate AI & ML development through intelligent data science automation. This end-to-end solution streamlines workflows and empowers businesses to rapidly build and deploy advanced predictive models, without requiring specialized data expertise.

Addressing the major bottleneck

One of the biggest bottlenecks in the data science lifecycle is the painstaking process of preparing data for AI and machine learning models. This manual feature engineering involves stitching together data from multiple sources into datasets through repetitive cycles of making hypotheses, testing, and reworking variables.

Even with skilled data scientists on staff, this process consumes the bulk of project resources before any actual modeling can begin. Leveraging NEC India’s dotData automates the labor-intensive process of getting data ready for analysis, saving a lot of time and effort.

AI & ML Development

Traditional approaches to developing AI and machine learning solutions have required highly specialized knowledge and niche skillsets. This creates barriers for many organizations looking to leverage predictive analytics. A key advantage of dotData is how it democratizes AI/ML capabilities through an intuitive platform. The automated processes empower existing business intelligence teams to rapidly build and deploy predictive models themselves.

Unlocking long-term success for retail industry

For retailers, this means unlocking a wealth of transformative use cases like demand forecasting, inventory optimization, personalized marketing, customer churn prevention, supply chain efficiencies, and much more.

Ultimately, the ability of organizations to cost-effectively develop, deploy, and scale AI solutions will determine which companies are best positioned for long-term success in our data-driven future.

Therefore, in the hyper-competitive retail sector, where leveraging data is existential, dotData represents a democratizing force by automating the most time and resource-intensive aspects of data science workflows.

By eliminating those barriers, dotData sparks a tipping point where AI & machine learning transition from a luxury into a baseline necessity for any retailer aiming to remain profitable and competitive.

Source - https://www.nec.com/en/global/solutions/dotdata/

[nec-india]

dotData is an enterprise-grade platform designed to automate the entire workflow of advanced analytics. It democratizes AI for organizations, simplifying data utilization, and improving ML models with transparent pipelines. No-Code Automated Feature Engineering accelerates insight discovery, automating the process of building Predictive Analytics models in days.

[taka8aru]

PoCを実施したことで、AIを使いこなすためのデータの扱い方を理解できたと市原氏は振り返る。 　「POSデータは日別単位で管理しています。経理や財務の用途なら問題ないのですが、売り上げ予測に活用する場合、日別データよりも時間別データの方が分析結果の精度が高かった。今後dotDataによる分析基盤を構築するなら、トランザクションレベルまで落とし込んだ管理が必要だと分かりました。今回のPoCで得た知見の一つです」（市原氏） POSデータの粒度は時間別が良いと分かった一方で、どの数字も「とにかく詳しければいい」というわけでもなかった。おおまかにカテゴライズした数字を投入したほうが、アウトプットの精度が良くなる項目もあった。例えば、店舗の座席数だ。最初は店舗の座席総数を学習させたが、思うような結果が出なかった。そこで座席数を「大、中、小」の3クラスに分類したところ、実績との乖離が小さくなった。 　天候のデータについても、降水量をそのまま投入するよりも「雨、曇、晴」といった粒度でラベリングする方が結果は良好だった。こうしたデータの扱い方に関する知見を得られたことも、PoCの大きな成果だ。

そば和食「家族亭」がAIで売り上げ予測　店長の経験 vs. データ分析の結果に見る、役立つ“経営判断の材料”の作り方 - ITmedia ビジネスオンライン

[ericvanderburg]

dotData Insight Transforms Raw Data Into Actionable Hypotheses

http://securitytc.com/T0BHSQ

[jcnnewswire]

dotDataとNEC、クラウド上でのAIと機械学習の自動化を加速させる「dotData Cloud」を強化したプランを提供開始

http://dlvr.it/Sm6RNB

[tenichitsukimi]

(AutoMLとは？自動化された機械学習の可能性と代表的なツールから)

代表的なAutoMLツール

AutoML Table（Google）

GoogleのAutoML Tableは、AutoMLの中で最も知名度が高いものでしょう。データの自動処理を主な機能として持ち、具体的には数値や文字列などのデータを自動的に整形し、問題があれば抽出することを自動化できます。

今のところは主にエンジニア向けのツールですが、他のAutoMLツールと比較すると、アップデートが頻繁に行われる分最新の技術が反映されやすい、情報が多い分扱いやすい、といった特徴があります。

Automated ML（Microsoft）

MicrosoftのAutomated MLも機械学習モデルを簡単に構築できるツールですが、平たく言えばデータの整形を自動化するものです。GoogleのAutoML Tableの次に知名度があり、その分ネット上にも情報が多いです。

AutoAI（IBM）

IBMのAutoAIも同様にデータの整形、抽出を便利にする、といった機能を持ちます。

H2O Driverless AI（H2O.ai）

シリコンバレーで2012年に創設されたAIを専門とする企業、H2O.aiが提供するAutoMLです。日本国内では2019年から導入が始まっており、Dell Technologiesと提携しているため、デルのハードウェア上で大きなパフォーマンスを発揮するという特徴があります。

DataRobot

DataRobotは100社以上の企業で導入されていて、アコム、ANA、Calbee、といった有名企業にも導入されています。最近では、自動特徴量探索機能が進化していたり、データの異常を察知して想定外のイベントの根本原因を把握することができるようになっ��います。

Prediction One（Sony）

SonyのPrediction Oneは、GUI（Graphical User Interface）で直感的に操作しやすいという点が最大の特徴です。autoMLの根幹にあるのはAIによるデータ活用を簡単に自動化するということなので、その点でPrediction Oneは理にかなっています。

MatrixFlow

MatrixFlowは1,000社以上に導入されている使いやすいautoMLです。機能性はシンプルで、自社データからの予測、自動抽出、などを得意としています。結果的に、正確な売上予測、不良品判別、社員の能力分析などを実現します。

Nanonets

Nanonetsは最小限の労力でautoMLを扱える仕様になっています。導入事例としては、画像認証、オブジェクト分類といった図形を認識する機能に定評があります。

dotData

dotDataも機械学習プロセスを自動化するAutoMLツールです。データ収集・加工からモデル設計、可視化、運用までのプロセスを数ヶ月から数日に短縮できます。

[marketsnmarkets39]

Business Intelligence Market Size, Growth, Statistics & Forecast Research Report 2021-2025

The report "Business Intelligence Market by Component (Solutions and Services), Solution (Dashboards and Scorecards, Data Integration and ETL), Business Function (Finance, Operation), Industry Vertical (BFSI, Telecom and IT), and Region - Global Forecast to 2025", size to grow from USD 23.1 billion in 2020 to USD 33.3 billion by 2025, at a Compound Annual Growth Rate (CAGR) of 7.6% during the forecast period. Various factors such as the growing focus on digital transformation, rising investments in analytics, rising demand for dashboards for data visualization, increase in adoption of cloud, and increase in data generation are expected to drive the growth of the business intelligence market.

Download PDF Brochure @ https://www.marketsandmarkets.com/pdfdownloadNew.asp?id=1048

Business Intelligence solutions and services providers are witnessing a slowdown in their growth owing to the lockdowns imposed worldwide. Healthcare and life sciences and BFSI verticals have been least impacted by the COVID-19 and are continuing the adoption of Business Intelligence solutions. The competition among key Business Intelligence solutions providers is expected to intensify as most upcoming projects have been put on hold owing to the pandemic. Businesses have already started making efforts to return to the normal and are facing multiple challenges at customer and operational levels. Meeting customer expectations in terms of optimization of processes, as well as an increase in security concerns for connected networks, rise in connectivity issues, and decline in industrial and manufacturing operations, are some of the key challenges faced by businesses. New practices, such as work-from-home and social distancing, have led to the requirement of remote health monitoring of patients and assets and smart payment technologies, as well as the development of digital infrastructures for large-scale technology deployments. Moreover, the imposition of lockdowns has led to an increased focus on cloud-based solutions. With an increased focus on health, there has been a rise in demand for health-related wearable devices.

The fianace business function to hold a larger market size during the forecast period

Finance business function segment is expected to hold a larger market share during the forecast period. The growth is attributed to the increased need of financial organizations to analyze vast amounts of customer data to gain insights about the customers regarding banking, which can be used to improve products and services. HR business segment is projected to register a higher CAGR during the forecast period due to the growing need of workforce management.

The BFSI vertical to hold the largest market size during the forecast period

The business intelligence market is segmented on the basis of vertical. The verticals include retail, manufacturing, government and public services, media and entertainment, transportation and logistics, BFSI, telecom and IT, healthcare and life sciences, tourism and hospitality, and others (real estate, education, and energy and utilities). The BFSI vertical is expected to hold the largest market size during the forecast period owing to the sensitivity of financial data and needs to coordinate with numerous other sectors (stock exchanges, tax authorities, central banks, securities controlling authorities, revenue department, etc.). Improving marketing strategies & customer retention policies, developing new investment strategies, and reducing risks are some of the factors responsible for the adoption of business intelligence by the BFSI vertical. Healthcare and life sciences vertical is expected to register the highest CAGR during the forecast period.

Among services, the support and maintenance segment to grow at a higher CAGR during the forecast period

The support and maintenance service segment is projected to grow at higher during the forecast period. The growth of the support and maintenance services segment can be attributed to the complexities of business intelligence solutions and existing skill gaps, resulting in the need for continuous support post deployment.

North America to hold the largest market size during the forecast period

North America is expected to hold the largest market size in 2020 owing to its technical advancement in business management analytics for their sales, production, and innovation. Most of the major players involved in the advancement of business intelligence are headquartered in North America. APAC is projected to grow at the highest CAGR during the forecast period due to the commercialization of IoT technology and the increasing adoption of advanced technologies in countries such as China, Japan, and India, which are fueling the demand for business intelligence solutions and services. Also, the increasing demand and market for IOT and big data have also provided a more accurate and reliable advantage to the business intelligence market.

Major vendors in the global business intelligence market include IBM (US), Oracle (US), Microsoft (US), SAP (Germany), SAS (US),  Google (US), AWS (US),  Salesforce (US), MicroStrategy (US), Teradata (US), DOMO (US), TIBCO (US), Information Builders (US), Sisense (US), Yellofin (Australia), Qlik (US), Board International (Switzerland), Infor (US), Dundas (Canada), Targit (Denmark), Zoho (India), Vphrase (India), dotdata (US), Amlgo Labs (India), Pentation Analytics (India), Hitachi Vantara (US), Outlier (US), ConverSight AI (US), Element Data(US), Alteryx (US), and ThpughtSpot (US).

Browse Complete Report @ https://www.marketsandmarkets.com/Market-Reports/social-business-intelligence-bi-market-1048.html

[craigbrownphd-blog-blog]

#ICYDK: DotData boasts automated feature engineering for Databricks https://www.zdnet.com/article/dotdata-boasts-automated-feature-engineering-for-databricks/?utm_source=dlvr.it&utm_medium=tumblr#ftag=RSSbaffb68

[nec-india]

dotData is an enterprise-grade platform designed to automate the entire workflow of advanced analytics. It democratizes AI for organizations, simplifying data utilization, and improving ML models with transparent pipelines. No-Code Automated Feature Engineering accelerates insight discovery, automating the process of building Predictive Analytics models in days.

[gamenewsworld]

A data scientist’s new best friend?

A data scientist’s new best friend?

[ad_1]

Founder and CEO of DotData, Ryohei Fujimaki, explains how automation can help the data science industry become more efficient.

Of the many technologies that will shape how we work in the future, automation is one of the most hotly debated. Some look forward to the new avenues it will open up while others fear their skills will become redundant. Dr Ryohei Fujimaki, founder and CEO of…

[ericvanderburg]

dotData Feature Factory Offers Powerful, Data-Centric Data and Feature Discovery

http://i.securitythinkingcap.com/Sph2SG

[sandlerresearch]

Business Intelligence Market by Component (Solutions and Services), Solution (Dashboards and Scorecards, Data Integration and ETL), Business Function (Finance, Operation), Industry Vertical (BFSI, Telecom and IT), and Region - Global Forecast to 2025 published on

https://www.sandlerresearch.org/business-intelligence-market-by-component-solutions-and-services-solution-dashboards-and-scorecards-data-integration-and-etl-business-function-finance-operation-industry-vertical-bfsi-tel.html

Business Intelligence Market by Component (Solutions and Services), Solution (Dashboards and Scorecards, Data Integration and ETL), Business Function (Finance, Operation), Industry Vertical (BFSI, Telecom and IT), and Region - Global Forecast to 2025

“The rising need to create valuable insights from unused data and embedded business intelligence solutions are expected to shape the future of the business intelligence market.”

The global Business Intelligence market size to grow from USD 23.1 billion in 2020 to USD 33.3 billion by 2025, at a Compound Annual Growth Rate (CAGR) of 7.6% during the forecast period. Various factors such as the growing focus on digital transformation, rising investments in analytics, rising demand for dashboards for data visualization, increase in adoption of cloud, and increase in data generation are expected to drive the growth of the business intelligence market.The objective of the report is to define, describe, and forecast the business intelligence market size based on component, organization size, deployment mode, business function, vertical, and region.

The COVID-19 outbreak has affected all markets, as well as the behaviors of customers. It has a substantial impact on economies and societies. With offices, educational institutions, and manufacturing facilities being shut down for an indefinite period, major sports and events being postponed, and work-from-home and social distancing policies being implemented globally, businesses are increasingly making efforts to deploy technologies that assist them through this difficult time. Analytics professionals, BI professionals, and advanced analytics experts have been called to help executives make business decisions to respond to new challenges posed by the COVID-19 spread.

The support and maintenance service segment to grow at a higher CAGR during the forecast period

The support and maintenance service segment is projected to grow at higher during the forecast period. The growth of the support and maintenance services segment can be attributed to the complexities of business intelligence solutions and existing skill gaps, resulting in the need for continuous support post deployment.

The finance business function segment to grow at the highest CAGR during the forecast period

The business intelligence market by business function has been segmented into finance, operations, sales and marketing, and human resources.Finance business function segment is estimated to hold a larger market share during the forecast period. The growth is attributed to the increased need of financial organizations to analyze vast amounts of customer data to gain insights about the customers regarding banking, which can be used to improve products and services. HR business segment is projected to register a higher CAGR during the forecast period due to the growing need of workforce management.

The healthcare and life sciences segment to grow at the highest CAGR during the forecast period

The business intelligence market by industry vertical has been segmented into retail, manufacturing, government and public services, media and entertainment, transportation and logistics, BFSI, telecom and IT, healthcare and life sciences, tourism and hospitality, and others (real estate, education, and energy and utilities). Healthcare and life sciences vertical is expected to register the highest CAGR during the forecast period as the industry vertical generates significant volumes of data comprising clinical, administrative, and financial data on a regular basis. This has increased the need for data insights, improved data quality, and accurate data consolidated in a single document.

Among regions, Asia Pacific (APAC) to grow at the highest CAGR during the forecast period

APAC is projected to grow at the highest CAGR during the forecast period due to the commercialization of IoT technology and the increasing adoption of advanced technologies in countries such as China, Japan, and India, which are fueling the demand for business intelligence solutions and services. Also, the increasing demand and market for IOT and big data have also provided a more accurate and reliable advantage to the business intelligence market.

Breakdown of primaries

In-depth interviews were conducted with Chief Executive Officers (CEOs), innovation and technology directors, system integrators, and executives from various key organizations operating in business intelligence market.

By Company: Tier I: 19%, Tier II: 35%, and Tier III: 46%

By Designation: C-Level Executives: 33%, Directors: 26%, and Others: 41%

By Region: North America: 38%, APAC: 35%, Europe: 11%, Rest of the World: 16%

The report includes the study of key players offering business intelligence solutions and services. It profiles major vendors in the global business intelligence market. The major vendors in the global business intelligence market are IBM (US), Oracle (US), Microsoft (US), SAP (Germany), SAS (US),  Google (US), AWS (US),  Salesforce (US), MicroStrategy (US), Teradata (US), DOMO (US), TIBCO (US), Information Builders (US), Sisense (US), Yellofin (Australia), Qlik (US), Board International (Switzerland), Infor (US), Dundas (Canada), Targit (Denmark), Zoho (India), Vphrase (India), dotdata (US), Amlgo Labs (India), Pentation Analytics (India), Hitachi Vantara (US), Outlier (US), Conver Sight AI (US), Element Data(US), Alteryx (US), and Thpught Spot (US).

Research Coverage

The market study covers the business intelligence market across segments. It aims at estimating the market size and the growth potential of this market across different segments, such as components, deployment modes, organization size, business function, industry vertical, and regions. It includes an in-depth competitive analysis of the key players in the market, along with their company profiles, key observations related to product and business offerings, recent developments, and key market strategies.

Key Benefits of Buying the Report

The report would provide the market leaders/new entrants in this market with information on the closest approximations of the revenue numbers for the overall business intelligence market and its sub segments. It would help stakeholders understand the competitive landscape and gain more insights to better position their business and plan suitable go-to-market strategies. It also helps stakeholders understand the pulse of the market and provides them with information on key market drivers, restraints, challenges, and opportunities.

[marketsnmarkets39]

Business Intelligence Market 2021 Business Strategies, Revenue and Growth Rate Upto 2025

The report "Business Intelligence Market by Component (Solutions and Services), Solution (Dashboards and Scorecards, Data Integration and ETL), Business Function (Finance, Operation), Industry Vertical (BFSI, Telecom and IT), and Region - Global Forecast to 2025", size to grow from USD 23.1 billion in 2020 to USD 33.3 billion by 2025, at a Compound Annual Growth Rate (CAGR) of 7.6% during the forecast period. Various factors such as the growing focus on digital transformation, rising investments in analytics, rising demand for dashboards for data visualization, increase in adoption of cloud, and increase in data generation are expected to drive the growth of the business intelligence market.

Download PDF Brochure @ https://www.marketsandmarkets.com/pdfdownloadNew.asp?id=1048

Business Intelligence solutions and services providers are witnessing a slowdown in their growth owing to the lockdowns imposed worldwide. Healthcare and life sciences and BFSI verticals have been least impacted by the COVID-19 and are continuing the adoption of Business Intelligence solutions. The competition among key Business Intelligence solutions providers is expected to intensify as most upcoming projects have been put on hold owing to the pandemic. Businesses have already started making efforts to return to the normal and are facing multiple challenges at customer and operational levels. Meeting customer expectations in terms of optimization of processes, as well as an increase in security concerns for connected networks, rise in connectivity issues, and decline in industrial and manufacturing operations, are some of the key challenges faced by businesses. New practices, such as work-from-home and social distancing, have led to the requirement of remote health monitoring of patients and assets and smart payment technologies, as well as the development of digital infrastructures for large-scale technology deployments. Moreover, the imposition of lockdowns has led to an increased focus on cloud-based solutions. With an increased focus on health, there has been a rise in demand for health-related wearable devices.

The fianace business function to hold a larger market size during the forecast period

Finance business function segment is expected to hold a larger market share during the forecast period. The growth is attributed to the increased need of financial organizations to analyze vast amounts of customer data to gain insights about the customers regarding banking, which can be used to improve products and services. HR business segment is projected to register a higher CAGR during the forecast period due to the growing need of workforce management.

The BFSI vertical to hold the largest market size during the forecast period

The business intelligence market is segmented on the basis of vertical. The verticals include retail, manufacturing, government and public services, media and entertainment, transportation and logistics, BFSI, telecom and IT, healthcare and life sciences, tourism and hospitality, and others (real estate, education, and energy and utilities). The BFSI vertical is expected to hold the largest market size during the forecast period owing to the sensitivity of financial data and needs to coordinate with numerous other sectors (stock exchanges, tax authorities, central banks, securities controlling authorities, revenue department, etc.). Improving marketing strategies & customer retention policies, developing new investment strategies, and reducing risks are some of the factors responsible for the adoption of business intelligence by the BFSI vertical. Healthcare and life sciences vertical is expected to register the highest CAGR during the forecast period.

Among services, the support and maintenance segment to grow at a higher CAGR during the forecast period

The support and maintenance service segment is projected to grow at higher during the forecast period. The growth of the support and maintenance services segment can be attributed to the complexities of business intelligence solutions and existing skill gaps, resulting in the need for continuous support post deployment.

North America to hold the largest market size during the forecast period

North America is expected to hold the largest market size in 2020 owing to its technical advancement in business management analytics for their sales, production, and innovation. Most of the major players involved in the advancement of business intelligence are headquartered in North America. APAC is projected to grow at the highest CAGR during the forecast period due to the commercialization of IoT technology and the increasing adoption of advanced technologies in countries such as China, Japan, and India, which are fueling the demand for business intelligence solutions and services. Also, the increasing demand and market for IOT and big data have also provided a more accurate and reliable advantage to the business intelligence market.

Major vendors in the global business intelligence market include IBM (US), Oracle (US), Microsoft (US), SAP (Germany), SAS (US),  Google (US), AWS (US),  Salesforce (US), MicroStrategy (US), Teradata (US), DOMO (US), TIBCO (US), Information Builders (US), Sisense (US), Yellofin (Australia), Qlik (US), Board International (Switzerland), Infor (US), Dundas (Canada), Targit (Denmark), Zoho (India), Vphrase (India), dotdata (US), Amlgo Labs (India), Pentation Analytics (India), Hitachi Vantara (US), Outlier (US), ConverSight AI (US), Element Data(US), Alteryx (US), and ThpughtSpot (US).

Browse Complete Report @ https://www.marketsandmarkets.com/Market-Reports/social-business-intelligence-bi-market-1048.html

[blockgeni]

Top AutoML Platforms for 2020

Machine Learning has been serving several industries for the past many years. It has enabled businesses to work easily with data. Moreover, the acceleration in the adoption of ML tools has evolved with time making it even easier to use today. Using AutoML tools, the act of gathering data and turning it into actionable insights has become much convenient. People with even less knowledge of data science and machine learning can work with these automated tools. DataRobot In 2013, DataRobot invented automated machine learning — and an entirely new category of software as a result. Unlike other tools that provide limited automation for the complex journey from raw data to return on investment, the company’s Automated Machine Learning product supports all of the steps needed to prepare, build, deploy, monitor, and maintain powerful AI applications at enterprise scale. DataRobot’s AutoML product accelerates the productivity of your data science team while increasing your capacity for AI by empowering existing analysts to become citizen data scientists. This enables your organization to open the floodgates to innovation and start your intelligence revolution today. Google Cloud AutoML Cloud AutoML is a suite of machine learning products that enables developers with limited machine learning expertise to train high-quality models specific to their business needs. It relies on Google’s state-of-the-art transfer learning and neural architecture search technology. Cloud AutoML leverages more than 10 years of proprietary Google Research technology to help your machine learning models achieve faster performance and more accurate predictions. dotData dotData was born out of the radical idea, unique among machine learning companies, that the data science process could be made simple enough for just about anyone to benefit from it. Led by Dr. Ryohei Fujimaki, a world-renowned data scientist, and the youngest research fellow ever appointed in the 119-year history of NEC, dotData was created to accomplish this mission. The company values its clients and works hard to provide the highest value possible in Automated Machine Learning (AutoML). dotData was first among machine learning companies to deliver full-cycle data science automation for the enterprise. Its data science automation platform speeds time to value by accelerating, democratizing, and operationalizing the entire data science process through automation. Splunk Splunk’s original version started off as a tool for searching through the voluminous log files created by modern web applications. Since then it has grown to analyze all forms of data, especially time-series and others produced in sequence. The latest newest versions of Splunk includes apps that integrate the data sources with machine learning tools like TensorFlow and some of the best Python open-source tools. Such modern tools offer quick solutions for detecting outliers, flagging anomalies, and generating predictions for future values. H2O H2O has made it easy for non-experts to experiment with machine learning. In order for machine learning software to truly be accessible to non-experts, the company has designed an easy-to-use interface that automates the process of training a large selection of candidate models. H2O’s AutoML can also be a helpful tool for the advanced user, by providing a simple wrapper function that performs a large number of modeling-related tasks that would typically require many lines of code, and by freeing up their time to focus on other aspects of the data science pipeline tasks such as data-pre-processing, feature engineering and model deployment. It can be employed for automating the machine learning workflow, which includes automatic training and tuning of many models within a user-specified time-limit. This article has been published froma wire agency feed without modifications to the text. Only the headline has been changed. #AutoMLtools#MachineLearning#DataRobot#GoogleCloudAutoML#dotData#Splunk#H2O#news#blockgeni Source link Read the full article