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How to Decode TLV Quickly

In TLV, the format is Tag, Length, and Value. The TLV protocol needs this type of data. Here you will know how to decode TLV data. According to IBM , TLV data is three parts. The tag tells what type of data it is. The length field denotes the length of the value. The Value-field denotes the actual value. Structure of TLV. TLV comprises three field values.  Tag Length Value EMV formulated different tags. They have their meanings. Usually, the Tag and Length together takes 1 to 4 bytes. The Best example for TLV. In the below example, you can find the sample TAG, LENGTH, and VALUE fields. [Tag][Value Length][Value] (ex. " 9F4005F000F0A001 ") where Tag Name =  9F40 Value Length (in bytes) =  05  Value (Hex representation of bytes. Example, "F0" – 1-byte) =  F000F0A001 In the above message, tag 9F40 has some meaning designed by EMV company. Here  you can find a list of EMV Tags. How to read the TLV Tag: 1 or 2 bytes Length: Length of the Value. F0-00-F0-A0-01 ==> 5 By

Social Analytics - How Marketers Will Use

Of all the windows through which a business can peer into an audience, seems most enticing. The breadth of subjects, range of observations, and, above all, the ability to connect and draw inferences make hugely exciting for anyone who is interested in understanding and influencing past, present and potential customers, employees, or even investors.

As individuals leave traces of their activities - personal, social and professional - on the internet, they allow an unprecedented view into their lives, thoughts, influences and preferences. Social analytics attempts to draw useful understanding and inferences, which could be relevant to marketers, sales persons, HR managers, product designers, investors and so on. Thus, as social tools like Facebook, Twitter, LinkedIn, WhatsApp, and many more, host a plethora of social activities of many people, a humongous amount of data is generated about people's preferences, behaviour and sentiments. Like any data, it is amenable to analysis to gain useful insights.

The challenge comes from the sheer volume, velocity, and variety. It is very difficult to ensure that the analysis is relevant and reliable. Besides the daunting technical intricacies of setting up the appropriate analytics, the aspects of choosing information sources, filtering the right data, and its interpretation and aggregation are susceptible to errors and biases. For example, some social activities are relatively easier to access (like activity on Twitter, or public updates on Facebook), many are not. Some types of data (like text, or location) are easy to search and interpret, many (like pictures) are not. So a good analysis model must judiciously compensate for the nature of the sources included, and hence it could be at times very difficult to assess if the analysis is useful or just meaningless mumbo-jumbo.

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