Ziel des Beitrages ist es insbesondere, die Anwendung des Frameworks in der Praxis zu erleichtern. Dieser Beitrag stellt deshalb ein existierendes Framework vor, mit dessen Hilfe die Ausrichtung der SMA an den Unternehmenszielen gelingen soll. Konkrete Handlungsempfehlungen dafür, wie die Ausrichtung von SMA an den Unternehmenszielen gelingen kann, gibt es bisher nicht.
Dies führt dazu, dass eher auf eine effiziente Datensammlung und -speicherung abgezielt wird, als darauf, welche Daten benötigt und wie diese im Sinne des Geschäftsmodells verwendet werden. Stellenwert wird die Steuerung von SMA jedoch häufig den IT-Abteilungen überlassen, welche die Anwendung von SMA nicht immer ausreichend an den Unternehmenszielen ausrichten, sondern sich zu sehr auf rein technische Fragestellungen der SMA-Implementierung fokussieren. Die meisten Unternehmen haben dies erkannt und wenden verschiedene Ansätze aus dem Bereich der Social Media Analytics (SMA) an, um derartige Daten zu identifizieren und für ihr Geschäftsmodell nutzbar zu machen. Facebook oder Twitter existieren große Mengen an bisher teils ungenutzten, geschäftsrelevanten Daten.
Experiments performed on a Twitter data set of 41.7 million nodes and 417 million posts show that even with a sampling rate of 0.005, the average precision is 0.93 for coordinated trends and 1 for uncoordinated trends. We also propose a sampling technique for structural trend detection and prove that the solution yields in a gain in efficiency and is within an acceptable error bound. Our analyses and experiments show that structural trends are significantly different from traditional trends and provide new insights into the way people share information online. To address this gap, we propose two novel structural trend definitions we call coordinated and uncoordinated trends that use friendship information to identify topics that are discussed among clustered and distributed users respectively. Trend detection methods introduced so far have not used the network topology and has thus not been able to distinguish viral topics from topics that are diffused mostly through the news media. It is also useful for users to stay on top of trends without having to sift through vast amounts of shared information. The identification of popular and important topics discussed in social networks is crucial for a better understanding of societal concerns. This paper is the preliminary analysis of few areas considered from social media like Face book, Twitter etc, where data is gathered and analytics is drawn with the help of connectors through Qlik. The combination of analytics and connectors into social media gives an immense accuracy and popularity throughout different forms of business. Analytics changes everything, not just in the world of business, but also in science, sports, health care and in few field where enormous data are collected. Information has been scrutinize as a powerful weapon, and analytics is the forge that creates it. By implementing the science of numbers, data and analytical discovery to work together, it has been found that what we think or believe produce answers to questions we never thought to ask.
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These computers run on sophisticated software algorithms producing fast insights needed to make fact-based decisions. : In the digital age high-end computers are used for storing an increased amount of data, which might not be possible a decade ago.
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