IT PARK
    Most Popular

    Is the enterprise ready to protect its cloud computing?

    Mar 18, 2023

    Has the development of big data come to an end?

    Mar 13, 2023

    Three ways of Internet of Things changing e-commerce

    Mar 17, 2023

    IT PARK IT PARK

    • Home
    • Encyclopedia

      Differences between SSDs and HDDs

      Mar 22, 2023

      What is a discrete graphics card

      Mar 21, 2023

      What is Qualcomm three carrier aggregation

      Mar 20, 2023

      What is resolution? What does resolution mean?

      Mar 19, 2023

      How to solve the problem of computer blue screen? What about the blue screen of the computer?

      Mar 18, 2023
    • AI

      What is the neural network of artificial intelligence?

      Mar 22, 2023

      What is the core issue of AI technology?

      Mar 21, 2023

      What is AI?

      Mar 20, 2023

      Will the latest AI "kill" programming

      Mar 19, 2023

      Neural AI, the next frontier of artificial intelligence

      Mar 18, 2023
    • Big Data

      What is the maximum value of big data

      Mar 22, 2023

      How does big data start? From small data to big data

      Mar 21, 2023

      What is big data? What can big data do?

      Mar 20, 2023

      Benefits of big data analysis and how to analyze big data

      Mar 19, 2023

      Six benefits of big data for enterprises

      Mar 18, 2023
    • CLO

      SaaS sprawl: meaning, hazard, status quo and mitigation plan

      Mar 22, 2023

      What is the difference between cloud computing and virtualization?

      Mar 21, 2023

      What is cloud computing?

      Mar 20, 2023

      Four advantages are highlighted, and cloud computing is the trend

      Mar 19, 2023

      Is the enterprise ready to protect its cloud computing?

      Mar 18, 2023
    • IoT

      How does the Internet of Things affect business?

      Mar 22, 2023

      Five effective business models of Internet of Things

      Mar 21, 2023

      Use the Internet of Things to find new business models

      Mar 20, 2023

      Six ways for the Internet of Things to change the business model

      Mar 19, 2023

      6 Ways to Make Money for IoT Products

      Mar 18, 2023
    • Blockchain

      Blockchain Foundation - What is Blockchain Technology

      Mar 22, 2023

      After the collision between quantum computing and blockchain - quantum blockchain

      Mar 21, 2023

      What is blockchain? Simply understand blockchain

      Mar 20, 2023

      How does the Internet of Things affect the working world?

      Mar 19, 2023

      What is Bitcoin?

      Mar 18, 2023
    IT PARK
    Home » Big Data » 10 Misunderstandings of Big Data Application
    Big Data

    10 Misunderstandings of Big Data Application

    The British science and technology news media V3 recently listed 10 relevant misconceptions about big data applications.
    Updated: Mar 16, 2023
    10 Misunderstandings of Big Data Application

    The British science and technology news media V3 recently listed 10 relevant misconceptions about big data applications.

    1. Big data is a new technology

    Big data is a new term, but its concept of massive data analysis is not new. Many people, including Stephen Brobst, chief technology officer of Teradata, believe that big data is misleading for users who are just beginning to realize the value of data. Brobst said: "Big data is a long-term project, not a 12-month period, but a 24-36 month period."

    1. Big data is a commodity

    When you first contact the concept of big data, you will think it is a special form of data, independent of other low-end data formats. But this is not the case.

    "Can you buy a database data?" said Donald Feinberg, an outstanding analyst at Gartner. "Yes, you can buy 100 servers, but can you buy big data? Therefore, this is not a market. It is only part of the IT market. It is worth 1 billion dollars? Yes, but it is not a market. It is not even a commodity, and it is not new."

    1. Big data is a problem

    This is an open argument with the nature of nearly half a dozen, but based on its basic form, big data has great potential, even if it is not used correctly or even not used at all.

    Therefore, as long as data exists and can be processed with an effective method in the future, there should be opportunities. It is also an expensive opportunity, maybe, but there is still an opportunity.

    The problem of data is how to transform it into clear and practical content through analysis, which is a huge challenge for enterprises.

    1. Your data is only useful to you

    According to Gartner, 30% of enterprises will find a way to cash in their data in the next few years. Selling user data to the highest bidder will cause fear and panic, but in nine cases out of ten it will be guaranteed or threatened.

    1. People don't care how you use their data

    Many people do not like targeted or relevant advertising, but it is a fact that big data driven marketing is the next focus. But when you enter a store and your mobile phone starts to vibrate, telling you that you can buy the same product at a lower price in a competitor's store, you will think of the service provider you signed.

    Even the harmless attempt to take advantage of people's behavior that has been criticized is of certain value. The WiFi Smartbin in London is a typical example. It keeps track of people's smart phone MAC addresses and displays targeted advertisements in the advertising bellows. Soon after the London City Management Company realized that this was happening, it banned this behavior, but it also reminded us of the $20 million class action lawsuit Facebook was facing.

    1. Big data won't land in prison

    At this point, we are dealing with a controversial topic. But Feinberg of Gartner is sure that there will be considerable data collection in this field.

    "How many CIOs will go to jail? If I think I'm joking, I'll make another bold assumption: I think the president of Facebook will go to jail before he leaves Facebook. I don't know when, but it will happen." Feinberg said, whether exaggerated or not, it is worth thinking about.

    1. The government is not interested in your social media data

    Many people like to abuse politicians on Twitter - they don't see it anyway, do they? Maybe, but it has certain reference value for understanding the intentions of voters, Feinberg said.

    "Obama cares because he was elected. If you look at how he was elected, his team uses social data and emotional analysis to find out who he can't win. I'm not saying that's the only reason why he was elected, but for government departments, social data and data have become very important." Feinberg said.

    1. You need new data for analysis

    After you have a business goal and the data warehouse is filled with 0 and 1, you can analyze and use your data. Research shows that most enterprises have started to use big data to obtain information. Once they think of a problem, they try to solve it through big data analysis.

    As DHL, the global logistics company, explained earlier to the reporter of V3, although there was tracking at every stage of parcel delivery, there was no way to use these data before the analysis system was established.

    1. Many people use big data

    Wrong. This is a worldwide problem.

    Gartner statistics show that there is such a shortage of skilled data analysis scientists that more than 75% of big data analysis positions are vacant in the company. The competition is fierce. In other words, it's a great career.

    That said, it also depends on how you define a data analysis scientist. Duncan Apthorp of Tesco, a big data analyst, said that his company does not require famous universities, which means ordinary graduates also have opportunities.

    1. Big companies know what they're doing

    Obviously not. According to Gartner's research on hundreds of enterprise cases: "In 2016, 85% of Fortune 500 enterprises will not be able to use big data to gain competitive advantage."

    Tasso Argyros, senior vice president of Teradata, said: Traditional business intelligence starts with a well-defined problem. For big data discovery, you have a starting point, but it is not a business problem, it is a business goal. The problem is that you don't know what questions to ask or what data to use, just say, 'Look at these data, let's start. It's usually easy to fail.

    Therefore, the answer to the question is "no". Not everyone knows what they are doing, and it is difficult to formulate strategies for efficient use of big data.

    big data big data application misunderstanding
    Previous Article What is the neural network of artificial intelligence?
    Next Article What is the core issue of AI technology?

    Related Articles

    Big Data

    Uncover 10 big data myths

    Mar 15, 2023
    Big Data

    Six benefits of big data for enterprises

    Mar 18, 2023
    Big Data

    Has the development of big data come to an end?

    Mar 13, 2023
    Most Popular

    Is the enterprise ready to protect its cloud computing?

    Mar 18, 2023

    Has the development of big data come to an end?

    Mar 13, 2023

    Three ways of Internet of Things changing e-commerce

    Mar 17, 2023
    Copyright © 2023 itheroe.com. All rights reserved. | Privacy Policy

    Type above and press Enter to search. Press Esc to cancel.