Tag: big data

  • Welcome to the fourth installment of our Blog series on Social media and online marketing

    Welcome to the fourth installment of our Blog series on Social media and online marketing

    In this blog we will show how the big data companies like YouTube, Facebook, Instagram, Google and others have grown to a larger market capitalization industry than the oil and gas sector. Much like drilling for oil and gas you have to mine big data and for those of you who don’t know how big data works we will provide this brief example. Let’s say you are in a particular industry for example, you own a micro brewery and you want to sell to consumers who like micro brewed beer.

    Someone with the resources can target market on the internet and use Social Media marketing. For example, Facebook, YouTube, Instagram or Google and target that exact consumer for micro brewed beer. Not only will they see what their competitors are selling their beer for but they can optimize their pricing so that they can get a large part of that particular industry. In short, big data allows a few players to take advantage of entire industries by marketing exactly to those most likely to purchase micro brewed beer.

    But first we should continue our brief look back at the history of online marketing from 2010 to present day.

    2010 – present: Marketers find value in native ads
    Around this time, a new group of media companies began to emerge. Websites like BuzzFeed and Mashable presented advertisers with new opportunities to connect with their audiences through sponsored content and native advertising.

    Advertisers pay to produce articles, videos, and other types of content for news and media sites. The nature of the content itself is promotional, but the format looks less like an ad and more like a regular piece of content on the host’s website. Instead of relying on ads that disrupt their target audience’s online experience, native advertising allows marketers to create promotional content that supplements a user’s online experience. “Marketers interested in targeting ads to specific consumers in an unobtrusive fashion should seriously consider spending some time on native ads.”

    Websites that traditionally generated revenue from display ads began to realize that they could create a better user experience by relying primarily on native ads — rather than traditional display ads — without compromising on ad revenue. However, we are seeing more and more companies today focussing on creating quality original content in order to gain market share and they are shying away from Native Ads.

    The Future of Advertising
    That’s a look back at the history of online advertising — but what about the future?
    According to recent data from HubSpot Research, 91% of respondents say ads are more intrusive today compared to just two to three years ago. It’s clear that the future of digital advertising pivots on developing a targeted ad experience that offers consumers relevant content without feeling nosy or invasive. In order to understand the growth and progression of this type of advertising it is important to examine Big Data.

    “Big data” is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software. “The estimated value for the industry due to digital transformation is about $1.7 trillion. “Data is the new oil. It’s valuable, but if unrefined it cannot really be used. It has to be changed into gas, plastic, chemicals, etc to create a valuable entity that drives profitable activity; so, must data be broken down, analyzed for it to have value.”

    Wikipedia states that Big Data in the analysis of data sets one can find new correlations to “spot business trends, prevent diseases, combat crime and so on.” Scientists, business executives, practitioners of medicine, advertising and governments alike regularly meet difficulties with large data-sets in areas including Internet searches, fintech, urban informatics, and business informatics. Scientists encounter limitations in e-Science work, including meteorology, genomics, connectomics, complex physics simulations, biology and environmental research.

    Data sets grow rapidly, in part because they are increasingly gathered by inexpensive and numerous information-sensing internet-based devices such as mobile devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification (RFID) readers and wireless sensor networks. By 2025, IDC predicts there will be 163 zettabytes of data. One question for large enterprises is determining who should own big-data initiatives that affect the entire organization

    Nexonta Technologies
    On to the Next Level

    Sources:

    Blog.hubspot.com/marketing/history-of-online-advertising

    Towardsdatascience.com/data-is-the-new-oil-even-in-the-oil-and-gas-industry-a3daa58d743d

  • What is Big Data and how is it used?

    26 June 2017

    The term “big data” often refers simply to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. “There is little doubt that the quantities of data now available are indeed large, but that’s not the most relevant characteristic of this new data ecosystem. Analysis of data sets can find new correlations to “spot business trends, prevent diseases, and combat crime and so on.” Scientists, business executives, practitioners of medicine, advertising and governments alike regularly meet difficulties with large data-sets in areas including Internet search, fintech, urban informatics, and business informatics. Scientists encounter limitations in e-Science work, including meteorology, genomics, connectomics, complex physics simulations, biology and environmental research.

    Data sets grow rapidly – in part because they are increasingly gathered by cheap and numerous information-sensing Internet of things devices such as mobile devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification (RFID) readers and wireless sensor networks. The world’s technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s as of 2012, every day 2.5 Exabyte (2.5×1018) of data are generated. One question for large enterprises is determining who should own big-data initiatives that affect the entire organization.

    Relational database management systems and desktop statistics- and visualization-packages often have difficulty handling big data. The work may require “massively parallel software running on tens, hundreds, or even thousands of servers”. What counts as “big data” varies depending on the capabilities of the users and their tools, and expanding capabilities make big data a moving target. “For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider data management options. For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration.

    Data mining is the computing process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. It is an interdisciplinary subfield of computer science. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. Data mining is the analysis step of the “knowledge discovery in databases” process, or KDD.

    The term is a misnomer, because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (mining) of data itself. It also is a buzzword and is frequently applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence, machine learning, and business intelligence.

    Here at Nexonta Technologies Inc we understand that the future belongs to big data and its future applications also organizations should be able to read and interpret big data to forecast future trends.
    Source https://en.wikipedia.org/wiki/Data_mining

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