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Big Data Market by Leading Companies, Solutions, Use Cases, Infrastructure, Data Integration, IoT Support, Deployment Model and Services in Industry Verticals 2021 – 2026

Big Data Market by Leading Companies, Solutions, Use Cases, Infrastructure, Data Integration, IoT Support, Deployment Model and Services in Industry Verticals 2021 – 2026

  • January 2021
  • 314 pages
  • ID: 6004353
  • Format: PDF
  • Mind Commerce Publishing

Summary

Table of Contents

Overview:
This report provides an in-depth assessment of the global big data market, including business case issues/analysis, application use cases, vendor landscape, value chain analysis, and a quantitative assessment of the industry with forecasting from 2021 to 2026. This report also evaluates the components of big data infrastructure and security framework.

This report also provides analysis of leading big data solutions with key metrics such as streaming IoT data analytics revenue for leading providers such as Teradata, IBM, Oracle, SAS and Datameter. The report evaluates, compares and contrasts vendors, and provides a vendor ranking matrix. Analysis takes into consideration solutions integrating both structured and unstructured data.

Select Report Findings:
• Big data in SCM will exceed $6.6B globally by 2026
• Data Integration and Quality Tools $9.9B globally by 2026
• Enterprise performance analytics will reach $27.8B globally by 2026
• Big data in business intelligence applications will reach $50.4B by 2026
• Combination of AI and IoT (AIoT) will rely upon advanced big data analytics software
• Real-time data will be a key value proposition for all use cases, segments, and solutions
• Market leading companies are rapidly integrated big data technologies with IoT infrastructure

Big data solutions are relied upon to gain insights from data files/sets so large and complex that it becomes difficult to process using traditional database management tools and data processing applications. Mind Commerce sees key solution areas for big data as commerce, geospatial, finance, healthcare, transportation, and smart grids. Key technology integration includes AI, IoT, cloud and high performance computing.

AI facilitates the efficient and effective supply of information to enterprises for optimized business decision-making. Some of the biggest opportunity areas are commercial applications, search in the big data environment, and mobility control for generation of actionable business intelligence.

In terms of big data integration with cloud-based infrastructure, cloud solutions allow companies that previously required large investments into hardware to store data to do the same through the cloud at a lower cost. Companies save not only money, but physical space where this hardware was previously stored. The trend to migrate to big data technologies is driven by the need for additional information derivable from analysis of all of the electronic data available to a business.

To realize the true potential to transform intelligence information from the huge amount of unstructured data, government agencies cannot leverage traditional data management technologies and DB techniques in terms of processing data. To understand patterns that exist in unstructured data, government agencies apply statistical models to large quantities of unstructured data.

Industry verticals of various types have challenges in capturing, organizing, storing, searching, sharing, transferring, analyzing and using data to improve business. Big data is making a big impact in certain industries such as the healthcare, industrial, and retail sectors. Every large corporation collects and maintains a huge amount of data associated with its customers including their preferences, purchases, habits, travels, and other personal information. In addition to the large volume, much of this data is unstructured, making it hard to manage.

Big data technology will help financial institutions maximize the value of data and gain competitive advantage, minimize costs, convert challenges to opportunities, and minimize risk in real-time. As an example in the transportation industry, real-time applications can match loads to a vehicle’s capacity using data analytics. Big data provides shipping and delivery companies with real-time notifications and updates to increase efficiency and accuracy.

Big data technologies provide financial services firms with the capability to capture and analyze data, build predictive models, back-test and simulate scenarios. Through iteration, firms will determine the most important variables and also key predictive models. Financial firms are increasingly migrating their data and analytics to the cloud, leading to reduced cost, better data management, and better customer service. Data and insights can also be transferred far quicker than before, allowing representatives to provide customers with real-time data backed insights.

Healthcare services can be applied more accurately with big data. Decisions based on real-time data and assistance from AI/ML solutions. Private health insurance providers can gain access to previously inaccessible information and databases through big data. Healthcare customer service processes can also be streamlined while providing personalized more personalized medical care to individuals.

Big data analytics allows retail companies to examine and interact with their audience online in new ways. Predictive analytics can analyze a consumer’s activity and recommend suggested items to them. Once a consumer has purchased from a company, big data can help retain that customer by better understanding what a person wants. For example, Amazon collects all its customers’ data to provide a personalized experience, earning up to 35% of its revenue from its customers’ data.

Customer Relationship Management (CRM) is a model of managing relationship and interaction between company and customer. This includes using technology for organizing, automating, and synchronizing all customer-related information like sales, marketing, services, support and more. Big data represents a big business opportunity and it is poised to do more than just improve CRM.

Data analytics is useful for Supply Chain Management (SCM) because it can analyze a variety of variables across a business’ operations. SCM service providers use advanced analytics to analyze materials, products in inventory and imports/exports to better understand needs. This helps a business to manage its assets better, saving time and money. Data analytics can predict future risks based on history and a large set of data.

Target Audience:
• IoT companies
• Network service providers
• Systems integration companies
• Big Data and Analytics companies
• Advertising and media companies
• Enterprise across all industry verticals
• Cloud and IoT product and service providers

Companies in Report:
• 1010Data
• Accenture
• Actian Corporation
• AdvancedMD
• Alation
• Allscripts Healthcare Solutions
• Alpine Data Labs
• Alteryx
• Amazon
• Anova Data
• Apache Software Foundation
• Apple Inc.
• APTEAN
• AthenaHealth Inc.
• Attunity
• BGI
• Big Panda
• Bina Technologies Inc.
• Booz Allen Hamilton
• Bosch
• Capgemini
• Cerner Corporation
• Cisco Systems
• CLC Bio
• Cloudera
• Cogito Ltd.
• Computer Science Corporation
• Compuverde
• CRAY Inc.
• Crux Informatics
• Ctrl Shift
• Cvidya
• Cybatar
• Data Inc.
• Data Stax
• Databricks
• DataDirect Network
• Dataiku
• Datameer
• Definiens
• Dell EMC
• Deloitte
• Domo
• eClinicalWorks
• Epic Systems Corporation
• Facebook
• Fluentd
• Flytxt
• Fujitsu
• Genalice
• General Electric
• GenomOncology
• GoodData Corporation
• Google
• Greenplum
• Grid Gain Systems
• Groundhog Technologies
• Guavus
• Hack/reduce
• Hitachi Data Systems
• Hortonworks
• HP Enterprise
• HPCC Systems
• IBM
• Illumina Inc
• Imply Corporation
• Industry Connectivity Consortium
• Informatica
• Intel
• Inter Systems Corporation
• Jasper (Cisco Jasper)
• Juniper Networks
• Knome,Inc.
• Leica Biosystems (Danaher)
• Longview
• MapR
• Marklogic
• Mayo Medical Laboratories
• McKesson Corporation
• Medical Information Technology Inc.
• Medio
• Medopad
• Microsoft
• Microstrategy
• MongoDB (Formerly 10Gen)
• MU Sigma
• Netapp
• N-of-One
• NTT Data
• Open Text (Actuate Corporation)
• Opera Solutions
• Oracle
• Palantir Technologies Inc.
• Pathway Genomics Corporation
• Pentaho (Hitachi)
• Perkin Elmer
• Platfora
• Qlik Tech
• Quality Systems Inc.
• Quantum
• Quertle
• Quest Diagnostics Inc.
• Rackspace
• Red Hat
• Revolution Analytics
• Roche Diagnostics
• Rocket Fuel Inc.
• Salesforce
• SAP
• SAS Institute
• Selventa Inc.
• Sense Networks
• Shanghai Data Exchange
• Sisense
• Social Cops
• Software AG/Terracotta
• Sojern
• Splice Machine
• Splunk
• Sqrrl
• Sumo Logic
• Sunquest Information Systems
• Supermicro
• Tableau
• Tableau Software
• Tata Consultancy Services
• Teradata
• ThetaRay
• Think Big Analytics
• Thoughtworks
• TIBCO
• Tube Mogul
• Verint Systems
• VMware
• VolMetrix
• Wipro
• Workday (Platfora)
• WuXi NextCode Genomics
• Zoomdata

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