is not sufficient anymore. Now you need to know more about your buyers’ preferences, places they hang out at, the things they talk about, the issues they are concerned with, the topics that
excites them, etc., to target messages better on social media.
How your customers feel?
It is not just the customers’ life that you should be interested in. In order to make your messages more useful for your brand and your customers you also need to gauge customers’ sentiment
around your brand and your competitors. Big data can help you understand your customers’
sentiment about your brand as well as your competitors.
What they want?
This is one thing every brand tries to get a definite answer of. And I am not saying harnessing big data can give you a definite answer, but it will give you a much clearer picture of your
customers’ needs and wants than you can get from any other source. By tapping into their interactions, things they talk about, things they say, products they buy, and places they visit, etc.,
big data can be help you estimate your customers’ demand.
How do they satiate their information need?
By harnessing big data you can know a great deal about how your customers get the information they need. An understanding of the information sources your customers use to seek information
you need, you can wisely select the vehicle to send your marketing message, and fine tune your message to match the vehicle and mood of your customers.
How do they arrive at the buying decision?
The most important information a marketer wants is the knowledge of the process its customers took right from the 1st stage in the buying cycle; from gathering information to shortlisting
product options to evaluating alternatives to shortlisting items to buy from to finally making the decision, and then its post-purchase interaction with the brand. This is a gold mine of
information, and an access to it will help a marketer fine tune its social media marketing campaign at each stage of customers’ buying cycle.
3. Scope of Study
Interlinking Information Technologies via Linked Data Information Technology connections
Linked data technologies are used as information infrastructure for integration of data from disparate sources. Comparing to mainstream technologies of relational databases, linked data
technologies have advantages in cases where relational databases demonstrate slow performance and inefficacy, namely integrating joining data about a certain object. Another advantage of
linked data technology is its ability to automatically integrate data from RDF database with data
from external sources like web data or sensor data, etc. Linked data could provide structure RDF triples and metadata to unstructured data like textual data or several data streams. At the
same time, most RDF databases have currently slower performance except join operation than most commercial relational database systems. However, there are good commercial RDF
datastores available AllegroGraph, Big OWLIM, Marklogic, etc. and also open source RDF database systems like Virtuoso, 4store, Bigdata etc. Comparing to web services technology that
also enables to create data-integration services, linked data technologies are lighter, more flexible, faster and as such cheaper. Linked data technology is built on existing web standards
and open source RDF stores that enables small and medium size companies to use it for development of data integration solutions. They might not have resources for building data
integration solutions based on web services technology.
Fig: Interlinking Information Technologies via Linked Data
In the following Fig , most important connections of linked data technology with other information technologies considered in this paper are depicted. In addition, the figure also shows
relationships between these other technologies.
Big Data explosion is driven mainly by the following technologies: IoT that provides various sensors and connected devices collecting data about
environment or human activities Social web generating a huge amount of data e.g. audio, video, click-streams, etc. by
different online activities. In addition, social web networks share a huge amount of data of different types.
Linked data technologies play a central role of integrator of other technologies. Link data technology provides tools for the following types of data processing:
Data fusion Structuring of semi- or unstructured data
Providing of semantic metadata Linking of data
Usage of common web standards for representation of data e.g. RDF and metadata or
vocabularies e.g. OWL. Other technologies like Big Data, social media and IoT can benefit from applying linked data
technology tools for processing collected data and developing new innovative applications. In the following subsections, linked data connections with each of analysed technologies are
examined in more detail.
4. Conclusion
