Showing posts with label Analytics. Show all posts
Showing posts with label Analytics. Show all posts

Monday, November 6, 2017

Industrial Robotics Market Analysis



The industrial robotics market is expected to grow from USD 38.11 Billion in 2016 to USD 71.72 Billion by 2023, at a CAGR of 9.60% during the forecast period. The main objective of the report is to forecast the industrial robotics market size in terms of value and volume for traditional industrial robots and collaborative robots. Further, it includes the detailed information regarding the drivers of the industrial robotics market, such as increase in investments for automation in industries and growing demand from small and medium-scale enterprises in developing countries. It also includes detailed information about restraints, opportunities, and challenges for the industrial robotics market. The study of the value chain of the industrial robotics market is also one of the objectives of the report, which includes information about suppliers and integrators in the value chain of the industrial robotics market.

Years considered for this report:

Base Year: 2016 
Estimated Year: 2017Projected Year: 2023Forecast Period: 2017–2023

Major players in the industrial robotics market ecosystem are identified across regions, and their offerings, distribution channels, and regional presence are understood through in-depth discussions. Also, average revenue generated by these companies, segmented by region, is used to arrive at the overall industrial robotics market size. This overall market size is used in the top-down procedure to estimate the sizes of other individual markets through percentage splits from secondary sources directories, databases (such as Hoovers, Bloomberg Businessweek, Factiva, and OneSource), and primary research. The entire procedure includes the study of annual and financial reports of the top market players and extensive interviews with industry experts such as CEOs, VPs, directors, and marketing executives for key insights.


To know about the assumptions considered for the study, download the pdf brochure


The industrial robotics market ecosystem includes traditional industrial robot and collaborative robot manufacturers such as ABB Ltd. (Switzerland), KUKA AG (Germany), Mitsubishi Electric Corp. (Japan), FANUC Corporation (Japan), Kawasaki Heavy Industries Ltd. (Japan), Yaskawa Electric Corporation (Japan), Seiko Epson Corporation (Japan), Stäubli International AG (Switzerland), NACHI-FUJIKOSHI CORP. (Japan), DENSO CORPORATION (Japan), Comau SpA (Italy), DAIHEN Corporation (Japan), Omron Adept Technologies, Inc. (US), Universal Robots A/S (Denmark), and CMA ROBOTICS SPA (Italy), among others. The ecosystem also includes system integrators such as Dürr AG (Germany) and Artech Automation AS (Norway).

Key Target Audience:

  • Original equipment manufacturers (OEMs)
  • OEM technology solution providers
  • Research institutes
  • Market research and consulting firms
  • Forums, alliances, and associations
  • Technology investors
  • Governments and financial institutions
  • Analysts and strategic business planners
  • End users who want to know more about the technology and the latest technological developments in the industry

The study answers several questions for the stakeholders, primarily which market segments to focus on in the next 2–5 years (depends on the range of forecast period) for prioritizing efforts and investments.


Report Scope:
In this report, the industrial robotics market has been segmented into the following categories:

  • Market, by Type:

    • Traditional Industrial Robots
    • Articulated Robots
    • SCARA Robots
    • Parallel Robots
    • Cartesian Robots
    • Others
    • Collaborative Robots

  • Market, by Industry:

    • Automotive
    • Electrical and Electronics
    • Plastics, Rubber, and Chemicals
    • Metals and Machinery
    • Food and Beverages
    • Precision Engineering and Optics
    • Pharmaceuticals and Cosmetics
    • Others

  • Market, by Geography:

    • North America
      • US
      • Canada
      • Mexico
    • Europe
      • UK
      • Germany
      • France
      • Italy
      • Spain
      • Rest of Europe
    • APAC
      • China
      • Japan
      • Republic of Korea
      • Taiwan
      • Thailand
      • India
      • Rest of APAC
    • RoW
      • Middle East and Africa
      • South America

  • Competitive Landscape
  • Company Profiles: Detailed analysis of the major companies in the industrial robotics market



Available Customizations:

With the given market data, MarketsandMarkets offers customizations according to the company’s specific needs. The following customization options are available for the report:

Product Analysis
  • Product matrix that gives a detailed comparison of product portfolio of each company.
Company Information
  • Detailed analysis and profiling of additional market players (up to 5).

Wednesday, July 5, 2017

Top Most and Hot Big Data Technologies


Forrester’s TechRadar methodology evaluates the potential success of each technology and all 10 above are projected to have “significant success.” In addition, each technology is placed in a specific maturity phase—from creation to decline—based on the level of development of its technology ecosystem. The first 8 technologies above are considered to be in the Growth stage and the last 2 in the Survival stage.

Here is the 10 hottest big data technologies based on Forrester’s analysis:



  1. Predictive analytics: software and/or hardware solutions that allow firms to discover, evaluate, optimize, and deploy predictive models by analyzing big data sources to improve business performance or mitigate risk.
  2. NoSQL databases: key-value, document, and graph databases.
  3. Search and knowledge discovery: tools and technologies to support self-service extraction of information and new insights from large repositories of unstructured and structured data that resides in multiple sources such as file systems, databases, streams, APIs, and other platforms and applications.
  4. Stream analytics: software that can filter, aggregate, enrich, and analyze a high throughput of data from multiple disparate live data sources and in any data format.
  5. In-memory data fabric: provides low-latency access and processing of large quantities of data by distributing data across the dynamic random access memory (DRAM), Flash, or SSD of a distributed computer system.
  6. Distributed file stores: a computer network where data is stored on more than one node, often in a replicated fashion, for redundancy and performance.
  7. Data virtualization: a technology that delivers information from various data sources, including big data sources such as Hadoop and distributed data stores in real-time and near-real time.
  8. Data integration: tools for data orchestration across solutions such as Amazon Elastic MapReduce (EMR), Apache Hive, Apache Pig, Apache Spark, MapReduce, Couchbase, Hadoop, and MongoDB.
  9. Data preparation: software that eases the burden of sourcing, shaping, cleansing, and sharing diverse and messy data sets to accelerate data’s usefulness for analytics.
  10. Data quality: products that conduct data cleansing and enrichment on large, high-velocity data sets, using parallel operations on distributed data stores and databases.


Best Big Data Companies


Tableau

Originally spun out of Stanford University as a research project, Tableau started out by offering visualization techniques for exploring and analyzing relational databases and data cubes and has expanded to include Big Data research. It offers visualization of data from any source, from Hadoop to Excel files, unlike some visualization products that only work with certain sources, and works on everything from a PC to an iPhone.

New Relic

New Relic uses a SaaS model for monitoring Web and mobile applications in real-time that run in the cloud, on-premises, or in a hybrid mix. It uses more than 50 plug-ins from technology partners to connect to its monitoring dashboard. The plug-ins include PaaS/cloud services, caching, database, Web servers and queuing. Its Insights software for analysis works across the entire New Relic product line, and the company offers a product called Insights Data Explorer that is designed to make it easier for everyone on a software team to explore Insights events.

Alation

Alation crawls an enterprise to catalog every bit of information it finds and then centralizes the organization's knowledge of data, automatically capturing information on what the data describes, where the data comes from, who's using it and how it's used. In other words, it turns all your data into metadata, and allows for fast searches using English words and not computer strings. The company's products provide collaborative analytics for faster insight, a unified means of search, provides a more optimized data structure of the company's data, and assists in better data governance.

Teradata

Teradata has built a portfolio of Big Data apps into what it calls its Unified Data Architecture, which includes Teradata QueryGrid, Teradata Listener, Teradata Unity and Teradata Viewpoint. QueryGrid provides a seamless data fabric across new and existing analytic engines, including Hadoop. Listener is the primary ingestion framework for organizations with multiple data streams, Unity is a portfolio of four integrated products for managing data flow throughout the process, and Viewpoint is a custom Web-based dashboard of tools to manage the Teradata environment.

VMware

VMware has incorporated Big Data into its flagship virtualization product, called VMware vSphere Big Data Extensions. BDE is a virtual appliance that enables administrators to deploy and manage the Hadoop clusters under vSphere. It supports a number of Hadoop distributions, including Apache, Cloudera, Hortonworks, MapR and Pivotal.

Splunk

Splunk Enterprise started out as a log analysis tool but has since expanded its focus and now focuses on machine data analytics to make the information useable by anyone. It can monitor online end-to-end transactions, study customer behavior and usage of services in real time, monitor for security threats, and identify spot trends and sentiment analysis on social platforms.

IBM

Besides its mainframe and Power systems, IBM offers cloud services for massive compute scale through its Softlayer subsidiary. On the software side, its DB2, Informix and InfoSphere database software all support Big Data analytics and Cognos and SPSS analytics software specialize in BI and data insight. IBM also offers InfoSphere, the basic platform for building data integration and data warehousing used in a BD scenario.

Striim

Formerly known as WebAction, Striim is a real-time, data streaming analytics software platform that reads in data from multiple sources such as databases, log files, applications and IoT sensors and allows customers to react instantly. Enterprises can filter, transform, aggregate and enrich data as it is coming in, organizing it in-memory before it ever lands on disk.

SAP

SAP's main Big Data tool is its HANA in-memory relational database, which the company says can run analytics on 80 terabytes of data and integrates with Hadoop. Although HANA is a row-and-column database, it can perform advanced analytics, like predictive analytics, spatial data processing, text analytics, text search, streaming analytics, and graph data processing and has ETL (Extract, Transform, and Load) capabilities.
While some companies specialize in one or few sources of data, SAP deals with data from a wide range of sources, including data from sensors, machine logs and other equipment; human generated data – social, point of sale (POS), ERP, emails documents and other things that make up enterprise data.

Alpine Data Labs

A creation of Greenplum employees, Alpine Data Labs puts an easy-to-use advanced analytics interface on Apache Hadoop to provide a collaborative, visual environment for building analytics workflow and predictive models that anyone can use, rather than requiring a high-priced data scientist to program the analytics.

Oracle

Oracle has its Big Data Appliance that combines an Intel server with a number of Oracle software products. They include Oracle NoSQL Database, Apache Hadoop, Oracle Data Integrator with Application Adapter for Hadoop, Oracle Loader for Hadoop, Oracle R Enterprise tool, which uses the R programming language and software environment for statistical computing and publication-quality graphics, Oracle Linux and Oracle Java Hotspot Virtual Machine.

Alteryx

Calling itself the leader in self-service data analytics, Alteryx's software is meant for the business user and not the data scientist. It allows them to blend data from multiple and potentially disparate sources, analyze it and share it so that actions can be taken. Queries can be made from anything from a history of sales transactions to social media activity.

Splice Machine

Splice Machine bills itself as the provider of the only Hadoop relationship database management system (RDBMS). It can act as a general-purpose database that can replace Oracle, MySQL or SQL Server databases for various workloads on Hadoop. The latest version, 2.0, added Spark, which does all analytics in memory instead of on disk. Version 2.0 also added the ability to route work to one of two processing engines either OLTP or OLAP.

Pentaho

Pentaho is a suite of open source-based tools for business analytics that has expanded to cover Big Data. The suite offers data integration, OLAP services, reporting, a dashboard, data mining and ETL capabilities.
Pentaho for Big Data is a data integration tool based specifically designed for executing ETL jobs in and out of Big Data environments such as Apache Hadoop or Hadoop distributions on Amazon, Cloudera, EMC Greenplum, MapR, and Hortonworks. It also supports NoSQL data sources such as MongoDB and HBase. The company was acquired by Hitachi Data Systems in 2015 but continues to operate as a separate subsidiary.

SiSense

SiSense sells its Prism to the largest enterprises and some SMBs alike because of its small ElastiCube product, a high-performance analytical database tuned specifically for real-time analytics. ElastiCubes are super-fast data stores that are specifically designed for extensive querying. They are positioned as a cheaper alternative to HP's Vertica systems.

Thoughtworks

Thoughtworks incorporates Agile software development principals into building Big Data applications through its Agile Analytics product. Agile Analytics helps companies build applications for data warehousing and business intelligence using the fast paced Agile process for quick and continuous delivery of newer applications to extract insight from data.

Tibco Jaspersoft

Tibco's Jaspersoft subsidiary has introduced an hourly offering on Amazon's Cloud where you can buy analytics starting at $0.48 per hour. The company is also big on embedded its analytics – having done so with 130,000 production applications worldwide, used by organizations such as Red Hat, CA, Verizon, Tata, Groupon, British Telecom, Virgin, and the U.S. Navy.

Amazon Web Services

Amazon has a number of enterprise Big Data platforms, including the Hadoop-based Elastic MapReduce, Kinesis Firehose for streaming massive amounts of data into AWS, Kinesis Analytics to analyze the data, DynamoDB big data database, NoSQL and HBase, and the Redshift massively parallel data warehouse. All of these services work within its greater Amazon Web Services offerings.
Most significant, AWS is attempting to woo legacy database customers to its newer offering. Experts disagree on how successful AWS will be in this effort, but it is clearly a highly aggressive competitive move.

Microsoft

Microsoft's Big Data strategy is fairly broad and has grown fast. It has a partnership with Hortonworks and offers the HDInsights tool based for analyzing structured and unstructured data on Hortonworks Data Platform. Microsoft also offers the iTrend platform for dynamic reporting of campaigns, brands and individual products. SQL Server 2016 comes with a connector to Hadoop for Big Data processing, and Microsoft recently acquired Revolution Analytics, which made the only Big Data analytics platform written in R, a programming language for building Big Data apps without requiring the skills of a data scientist.

Google

Google continues to expand on its Big Data analytics offerings, starting with BigQuery, a cloud-based analytics platform for quickly analyzing very large datasets. BigQuery is serverless, so there is no infrastructure to manage and you don't need a database administrator, it uses a pay-as-you-go model.
Google also offers Dataflow, a real time data processing service, Dataproc, a Hadoop/Spark-based service, Pub/Sub to connect your services to Google messaging, and Genomics, which is focused on genomic sciences.

Mu Sigma

Mu Sigma offers an analytics services framework that looks at tables and tables and answers questions for the firm on issues like improved sales and marketing. It cleans up client data to show only relevant data, uses the data to understand it, generates insights from it and gives recommendations to the client. Mu Sigma tries to understand how the business actually works and then identifies where the problem actually is.

HP Enterprise

HP Enterprise has built up a considerable portfolio of Big Data products in a very short time. Its main product is the Vertica Analytics Platform, designed to manage large, fast-growing volumes of structured data and provide very fast query performance on Hadoop and SQL Analytics for petabyte scalability.
HPE IDOL software provides a single environment for structured, semi-structured and unstructured data. It supports hybrid analytics leveraging statistical techniques and Natural Language Processing (NLP).
HPE has a number of hardware products, including HPE Moonshot, the ultra-converged workload servers, the HPE Apollo 4000 purpose-built server for Big Data, analytics and object storage. HPE ConvergedSystem is designed for SAP HANA workloads and HPE 3PAR StoreServ 20000 stores analyzed data, addressing existing workload demands and future growth.

Big Panda

BigPanda offers a data science algorithm-based platform specifically for IT and DevOps staff that is specifically geared toward addressing alert overload. One of the many sources of Big Data is logs, and they can quickly get out of hand with redundant or false alerts. The company noticed that developers were being overwhelmed with alerts from their logs and had no idea which were real and which were false flags. BigPanda filters down that overload to just the meaningful alerts, allowing IT to react quicker to real problems.

Cogito

A highly vertical but important service, Cogito Dialog uses behavioral analytics technology, including analysis of everything from customer emails to social media to analysis of the human voice, to help phone support personnel improve their communications while on the phone with customers and to help organizations better manage agent performance.

Datameer

Datameer claims its end-to-end data analytics solution for Hadoop enables business users to discover insights in any data via wizard-based data integration, iterative point-and-click analytics, and drag-and-drop visualizations, regardless of the data type, size, or source.

Sunday, July 2, 2017

Top and Hot Artificial Intelligence (AI) Technologies

The market for artificial intelligence (AI) technologies is flourishing. Beyond the hype and the heightened media attention, the numerous startups and the internet giants racing to acquire them, there is a significant increase in investment and adoption by enterprises. A Narrative Science survey found last year that 38% of enterprises are already using AI, growing to 62% by 2018. Forrester Research predicted a greater than 300% increase in investment in artificial intelligence in 2017 compared with 2016. IDC estimated that the AI market will grow from $8 billion in 2016 to more than $47 billion in 2020.



Based on Forrester’s analysis, here’s my list of the 10 hottest AI technologies:
  1. Natural Language Generation: Producing text from computer data. Currently used in customer service, report generation, and summarizing business intelligence insights. Sample vendors: Attivio, Automated Insights, Cambridge Semantics, Digital Reasoning, Lucidworks, Narrative Science, SAS, Yseop.
  1. Speech Recognition: Transcribe and transform human speech into format useful for computer applications. Currently used in interactive voice response systems and mobile applications. Sample vendors: NICE, Nuance Communications, OpenText, Verint Systems.
  2. Virtual Agents: “The current darling of the media,” says Forrester (I believe they refer to my evolving relationships with Alexa), from simple chatbots to advanced systems that can network with humans. Currently used in customer service and support and as a smart home manager. Sample vendors: Amazon, Apple, Artificial Solutions, Assist AI, Creative Virtual, Google, IBM, IPsoft, Microsoft, Satisfi.
  3. Machine Learning Platforms: Providing algorithms, APIs, development and training toolkits, data, as well as computing power to design, train, and deploy models into applications, processes, and other machines. Currently used in a wide range of enterprise applications, mostly `involving prediction or classification. Sample vendors: Amazon, Fractal Analytics, Google, H2O.ai, Microsoft, SAS, Skytree.
  4. AI-optimized Hardware: Graphics processing units (GPU) and appliances specifically designed and architected to efficiently run AI-oriented computational jobs. Currently primarily making a difference in deep learning applications. Sample vendors: Alluviate, Cray, Google, IBM, Intel, Nvidia.
  5. Decision Management: Engines that insert rules and logic into AI systems and used for initial setup/training and ongoing maintenance and tuning. A mature technology, it is used in a wide variety of enterprise applications, assisting in or performing automated decision-making. Sample vendors: Advanced Systems Concepts, Informatica, Maana, Pegasystems, UiPath.
  6. Deep Learning Platforms: A special type of machine learning consisting of artificial neural networks with multiple abstraction layers. Currently primarily used in pattern recognition and classification applications supported by very large data sets. Sample vendors: Deep Instinct, Ersatz Labs, Fluid AI, MathWorks, Peltarion, Saffron Technology, Sentient Technologies.
  7. Biometrics: Enable more natural interactions between humans and machines, including but not limited to image and touch recognition, speech, and body language. Currently used primarily in market research. Sample vendors: 3VR, Affectiva, Agnitio, FaceFirst, Sensory, Synqera, Tahzoo.
  8. Robotic Process Automation: Using scripts and other methods to automate human action to support efficient business processes. Currently used where it’s too expensive or inefficient for humans to execute a task or a process. Sample vendors: Advanced Systems Concepts, Automation Anywhere, Blue Prism, UiPath, WorkFusion.
  9. Text Analytics and NLP: Natural language processing (NLP) uses and supports text analytics by facilitating the understanding of sentence structure and meaning, sentiment, and intent through statistical and machine learning methods. Currently used in fraud detection and security, a wide range of automated assistants, and applications for mining unstructured data. Sample vendors: Basis Technology, Coveo, Expert System, Indico, Knime, Lexalytics, Linguamatics, Mindbreeze, Sinequa, Stratifyd, Synapsify.
  10. Text Analytics and NLP: Natural language processing (NLP) uses and supports text analytics by facilitating the understanding of sentence structure and meaning, sentiment, and intent through statistical and machine learning methods. Currently used in fraud detection and security, a wide range of automated assistants, and applications for mining unstructured data. Sample vendors: Basis Technology, Coveo, Expert System, Indico, Knime, Lexalytics, Linguamatics, Mindbreeze, Sinequa, Stratifyd, Synapsify.

Tuesday, June 13, 2017

Web Analytics tools

For deep insights into how users interact with your website or app, look no further than these affordable tools



Analytics tools have evolved considerably since the early days of the internet, when web developers had little more than hit counters to work with. And as the internet continues to evolve, analytics tools will continue to change, giving us greater insight into how our audience uses and interacts with our apps and websites.


  • Google Analytics
  • Kissmetrics
  • Mixpanel
  • Localytics
  • Segment
  • Spring Metrics.
  • Woopra.
  • Clicky.
  • Mint.
  • Chartbeat.
  • UserTesting
  • Crazy Egg
  • Mouseflow

Friday, May 26, 2017

What to expect from the Market of Big Data and Analytics in Telecom and know the Market scenario 2021?

What to expect from the Market of Big Data and Analytics in Telecom and know the Market scenario 2021?
The report provides definite explanation of the systems used across various regions. This report identifies pin-point analysis of changing competition dynamics and keeps you ahead in competition. It offers a forward looking perspective on different factors driving or restraining market growth. It brings a six-year forecast assessed on the basis of how the market is anticipated to grow. It aids in understanding the key product segments and their future. 

It aims at analyzing the market size and future growth potential of this market across different sectors such as products, application, end user and region. The report includes a comprehensive competitive analysis of the important players in the market including their company profiles, SWOT analysis, new developments, and decisive market strategies.

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