BIG DATA and its value to Small Medium Enterprise SME’s Restaurants
Big data can be very difficult to work with and may require to be broken down into simpler parts, so that interpretation is easier. The main challenges that are experienced in managing big data management include; storage, captures, curation, sharing, searching, visualization, analysis and transfer (Zikopoulos 2012). Large data sets emerge from derived additional information that results from the analysis of single and large set that is linked to the data. Therefore, big data is a situation whereby data sets collected are complex and voluminous to an extent that management through the use processing tools as on hand database and other traditional applications for processing of data (Mayer-schönberger & Cukier 2013).
As per IBM, the four V’s of big data include the volume, veracity, variety and the velocity. The volume is mainly meant to show the scale of the given data. The volume can be classified in terms of terabyte and zettabytes to show the scale rate of the big data (Ohlhorst 2013). Variety is used to indicate the different types of data that can be obtained in a big data. A variety of data can either be structured or unstructured. Structured data enables easier management as compared to unstructured. The velocity of big data refers to the rate at which the data can be analyzed in terms of the streaming data while veracity is used to show uncertainty evidence in a big data (Davis & Patterson 2012).
Big data as per historical terms was viewed as ‘information explosion’ in the year 1941. The term was coined from the fact that, American libraries kept growing after every sixteenth year. For example, Fremont Rider a librarian in Wesleyan University forecasted that 2 billion worth of information could fill 6000 miles of shelves in catalogues (Berman 2013). This resulted into new methods coming up that could be used to stored the large data that had been forecasted and also, kept in growing is size. Technology storage of data came into existence in order to control the large data, hence the name big data (Davenport 2014).
In a restaurant, big data can be used in improving the quality of service provided and supplies due to the transparency nature that it contains (Franks 2012). This is because, with big data in a restaurant, prediction, inconsistence performance, availability of specific items required and applicable approach can be determined through the use of big data. Through this, a restaurant can improve its performance in terms of goods and service provision (Schmarzo 2013).
However, the micro processing of big data may give assumptions that have been deduced from mathematical properties with little or no reflection of the real situation at micro processing level. All the data has to be contextualized in terms of political, social and economic settings which may be a misplaced priority in a restaurant setting (Mohanty, Jagadeesh & Srivatsa 2013). Since representative samples are not used in such a condition, there is bound to be biases towards a particular service or provision of goods in a restaurant (Minelli, Chambers & Dhiraj 2013).
The main benefits for restaurants to use big data are attributed to the fact that, through big data, a restaurant can try to outperform one of its main competitors in the industry. This can be achieved from checking the reviews in data form for other related industries (Chen 2014). For most of the restaurants, leveraging of strategies that are data driven is the key to innovation. This provides a ground for competition and capturing of value (Zikopoulos & Melnyk 2013). Adoption of big data by restaurants enables an environment that ensures quality of service and good provision and good customer relations. This in turn aids, in growth of the restaurant due to different approaches that may have been given as suggestions by the clients and customers. The chances for expansion for a restaurant business are also high due to the feedbacks that big data provides in reviews (Shmueli et al. 2011).
The operations required to get and analyze big data of restaurant include having the knowledge of the variety of a data so that it can be classified as unstructured or unstructured. Through this, the most important data can be determined (Steele et al. 2010). This is made possible through figuring the data that is best fit to be incorporated into operations planning and marketing. Predictions can be determined through such operations (Manning 2013).
Big data should be handled in the best way possible. The management department should be classified in terms of the volume of the data, variety, velocity and veracity for easier access and analysis of data (Isson & Harriott 2013). Through considering this, it is possible to obtain the required data in relation to a specific setting as a restaurant (Christian 2011).
Big data is voluminous in nature and, therefore, there should a measure that is considered by most restaurants and small and medium enterprises in business settings. For a business to be successful bid data has to be analyzed via effective and appropriate measures as the determined results are used in forecasting the current views into improving the future conditions. The challenges encountered in big data management can be minimized through classifying the data according to their varieties.
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