Business organizations have been looking for ways to stay competitive in the market which cannot be achieved without vast information of the business and market behavior Singhal et al (2013). Big data has been acquired by both big and small business organizations to help them excavate to the last hidden information and not leaving any information to chance. Big data analytics experts are incorporated in business to help in extracting information from big data as they are collected to help in betterment of the business organization. Though challenges have been experienced with most of the businesses working with big data, the experts have been on the rise to improve and provide correct information as they really are where later the information is passed to the organizations’ administration and decision makers to come up with the right decision that suits the desires of the customers and maintain or widen the market coverage of the offered products Chen et al (2012). This report is aimed at developing the business strategies using big data and how they are useful to fulfil the business objectives. Some of the strategies that have been identified for the big data users are performance management, data exploration, social analytics and decision science.
7-Eleven is American-Japanese international that operates the chain of convenience stores that is having branches in almost 18 countries (e.g. Indonesia, Malaysia, Hong Kong, China, Japan, Singapore etc.) with its headquarter in Irving, Texas in the United States. Until 1946 it was known as Tote’m Stores where it was later renamed to the current name 7-Eleven. In the countries like the United States, 7-Eleven supply Slurpee drinks (i.e. soft drinks that are partially frozen), fresh fruit, salads etc. 7-Eleven corporate is renowned for supplying big drink sizes such as big gulp that is about 946ml. due to the need of continuous supply of fresh food on daily basis to the stores all over their various locations, the management had incorporated technology to keep the pace. The corporate has the mission of being close to their customers and convenient at all times. This mission was set to ensure that the services offered by the corporate business meet the requirements of the customers, fast, convenient and friendly. The vision on the other hand is to ensure that they become the best retailer of convenience. In line with the vision and missions set by the 7-eleven management team, there are some of the strengths, weaknesses, opportunities and threats that the business face.
Some of the strengths as highlighted by the business management are; regarding franchising and licensing, the company had earned a name to be the largest convenience store. It operates in over 39000 locations tha makes it ahead of its close competitor McDonalds. The company has a large number of well trained employees of about 45,000 and the company as well has active services and consistent performance which makes it part of franchise 500.
Some of the weaknesses a company has are as follows; the company lack touch and communication with the customers due to least developed web based facilities and the internet, the company as well lack that togetherness as a result of the diverse demographic locations since the branches are spread to operate in different governmental policies and lastly, it has been having ill management which can be proved by the turnover of employees more often.
Opportunities; the company signed a contract of 20 years with CITGO which has added an advantage to it by placing it in the sustainable path. The purchasers’ trend is being shifted towards privately labeled products. And lastly the threats that are experienced by the company are as follows; the company faces stiff competition from the discounted stores like Wall Mart and others that are quickly adopting technology and enhancing their growth. Unavailability of some of the products at the time of need by the customers as a result of the product being out of stock is another major threat that would deteriorate the company operations.
To match the competitors in the market, 7-Eleven had to acquire and incorporate technology in their daily operation. Their competitors like the Wall Mart have shown fast and rapid growth with the increased use of technology like the websites and internet to reach their customers. Social media provide the platforms for interaction between the company management and the customers. In return, the company is able to collect data including the messages and chats regarding the products supplied by the company and storing them in their databases. The amount of data in the company is seen to be increasing and doubling for every two years due to numerous branches and wide coverage the company has. Significant baggage are brought by big data, since the problems business has do not end with having data, but use of data to extract information in the daily operation of the business matters a lot. The decision making of the business are based on the collected data as the data will be telling them what people are desiring from them and how the market situation is at any moment and time. A process has to be engaged in order to leverage and benefit from big data. The need for big data was then developed to help the business know their customers who were digital and mobile.
Due to volume, speed and structure of big data, big data has become impossible to handle using traditional tools. As a result therefore, business organizations that make use of big data like the 7-eleven Company there has been need to come up with big data strategies. Teradata were collected by the company to create CRM systems with customer segmentation in the integrated transnational data. The first strategic plan applied by 7-eleven comapany when using big data was to have the performance management of big data. This strategy brought the sense of understanding collected big data as they stream into the business databases Bharadwaj et al (2013). Big data type that are used in this strategy are transactional data. They are analyzed to answer some of the business questions and the information obtained from the analysis can be used to make short term business decisions and also develop long term business plans from them.
Collecting Teradata and not exploring them would seem waste of time and company resources. Big data are then collected and thorough data exploration done to them. Data exploration strategy helps in applying statistical knowledge in the business experiment to help answer some of the difficult questions that might not have been thought of by the manager or the decision makers in the company. In this case, predictive model techniques are applied to forecast the behavior of the business basing on the past business transactions Woerner and Wixom (2015). To fully leverage the data and extract all the useful information, cluster analysis is applied where customers are categorized into groups basing on their attributes and fully screening them.
Large non-transactional data are measured using social analytics. These types of data are acquired by the business organization from the social media platforms i.e. twitter, facebook, Instagram etc. Various categories such as awareness, engagement and word of mouth are all measured using social analytics McAfee et al (2012). All these categories are focused in measuring the extent to which customers are reached through the social media platform and their reactions towards the products offered by the business organization.
Lastly, decision science is applied to analyze non-transactional data to help the company draw informed decisions from the available data from social media platform Woerner and Wixom (2015). Each question is treated as a hypothesis where they are then tested by leveraging social big data. As well, the questions from the company can be transmitted to the consumers about their products through crowdsourcing. NoSQL is the widely known set of technology that is used to handle big data and various NoSQL databases like the Hadoop adopted for the implementation of the big data.
Data analytics is the process involving qualitative and quantitative techniques used to improve the productivity of the business and its gains. Basing on the organizational requirements, data are extracted from different categories to identify and analyze patterns, techniques and behavioral data. Categorizing data into different groups by the time of carrying out data analysis results to understanding information extracted from the data then improving the ideas whenever making decisions. Information is always seen as a critical resource by the managers and also to exploit competitive advantage, they require and incorporate systems for information exploitation. Multidimensional data and online analytical processing give better way of using organizational data. Information from company databases are summarized and presented by OLAP and MDDBs. Multidimensional analysis use the multidimensional structure that enable the managers of the companies devise views concerning the company performance by fully exhausting and drilling data to spotting the troubles Chevalier et al (2015). The only big challenge is to come up with the most suitable system for online transaction processing (OLTP). Decision science strategy is then applied on the data to interpret the data to derive meaning from the data that can then be used by the managers in supporting the decision making process.
Dominance of big data since its emergence in the past few years and the inability of the traditional tools to handle such data has led to the development of other technologies that help in leveraging big data. Big data come in different forms i.e. they can be structured, unstructured or even semi-structured. Traditional tools that are only structured into rows and columns find it impossible to handle big data especially when they are semi-structured and unstructured. Big-data analytics and decisions (B-DAD) comprised of big data analytics tools and methods that need to be applied in the process of decision making are used Assunção et al (2015). Moreover, big data as received, they as well need to be stored. In this case, the collected big data cannot be stored using relational databases since they only deal with structured data. Being that unstructured and semi-structured data are mostly part of the collected data in big data, Extract Transform Load (ETL) is used to upload data from operational data stores to the storage.
Various technologies have been associated with storing, processing and analyzing big data in 7-Eleven company. Apache Hadoop is one of the technologies that use java based free software framework that is capable of storing vast clustered data effectively O’Driscoll et al (2013). It has its storage system called Hadoop Distributed File System (HDFS) that is responsible in splitting big data into clusters. Distribution of data into clusters gives way for high availability of data.
This is set of another technology that provide solution of big data from Microsoft and it is powered by Hadoop available and located in the cloud. The default file system used by HDInsight is called Azure Blob storage that uses windows. This is seen important due to the fact that it provides high availability data at a relatively low cost, this is according to Pokorny (2013). This technology is associated in big data handling by 7-Eleven company because of its pocket friendliness in the data analytics process.
This, apart from the traditional SQL that could handle large volume of structured data effectively, the Not only SQL (NoSQL) is used to handle unstructured data Leavitt (2010). Since all social media data obtained by 7-Eleven company are unstructured data of no particular plan, they are stored in the NoSQL databases. Massive data performance enhancement is experienced better with NoSQL. Many of the NoSQL databases are available in analysis of such big collected data.
Most of the analyst seem composed and comfortable with analyzing data using excel. Excel 2013 has had an extra feature that is capable of connecting data in Hadoop. Power view feature in excel can be used to obtain easy summary of big data stored in the Hadoop platform using Excel 2013.
Business intelligent system always has master data management represented as dimensions. Data analytics is the process that is applied by the big data analysts to leverage big data. Master data management system (MDM) are not used in business intelligence to support business transaction but rather to create positive impact in BI systems Ellis et al (2012). MDM ensure that the names and the definitions of data used in giving the description of master data entities are standard names and definitions for a business enterprise. Shared business vocabulary (SBV) are contained in the master data definitions. Same data definitions can be reused across all available dimensional model in BI due to consistency drive across dimensional data. Adopting the use of master data SBV is important in that it brings the sense of the in depth understanding of the presented data in BI system report, scorecards, dashboards and OLAP analyses. BI can only be trusted if it has the compliance with master data SBV. Data integration in a BI system can be impacted on the arrival of an MDM system in the enterprise. Lack of MDM system will make your BI system to depend on classical warehouse architecture where master data is divided into multiple data stores and business operational system be on different lines. BI systems with MDM makes the data easy to analyze using data analytics tools. Decision making process in the company is supported by the analytical data through the analysis and identification of churn, profitability and marketing groups Ding et al (2010).
Problem in the storage of big data collected in business led to development of non-relational databases where NoSQL is one of them. These databases are useful in managing unstructured data as they focused in data model flexibility, large scaling and makes the deployment and development of the applications simple Barlow (2013). Data storage and management are separated by NoSQL databases since they are highly focused on the performance scalable data storage that adds further advantage by allowing the data management tasks not to have been written in database specific language but to have them written in the application layer, this makes big data analytics more easy. One of the most popular NoSQL database used is Apache Cassandra where many other are well available and used in analyzing unstructured data that are stored in the cloud on multiple servers.
These are databases that were developed to help in handling non-relational large amount of data. These databases are important in business enterprises especially when vast volumes of data are involved and need to be analyzed. NoSQL databases are classified into four categories.
Data are stored in alphanumeric identifiers in these database management systems (DMS) where values associated are transferred to hash tables Moniruzzaman and Hossain (2013). The referred values in these DMS might be in form of simple texts or a much more complicated list and set. These key value stores are simple and suited for lightning fast and are useful in highly scalable retrieval of the values that might be needed by tasks carried out by the application such as managing user profiles and also retrieving the names of the products. Some of the existing examples of key value stores are LinkedIn, Redis etc.
These are the databases that were designed basically for managing and storing documents. The value columns as found in the documents databases are more powerful as they handle semi-structured data that are in pair of names where one column can handle hundreds of the same attributes as the variation in number can be seen from row to row Moniruzzaman and Hossain (2013). They are used in business in the management and storage of big data or collection of literal documents. Example of referred documents are email messages and XML documents that might be received from the customers in interaction with the products. CouchDB (JSON), MongoDB etc. are database examples schema free and document oriented Abramova and Bernardino (2013).
This category of NoSQL databases are responsible and capable of holding multiple attributes per key. Cassandra is one of the examples of the databases in this category that is suitable in handling distributed data storage particularly the versioned data due to WC/CF time stamping functions. As well, it is used to conduct exploratory and predictive analysis hence resulting to informed decision making by the business management.
They are the types of NoSQL that are concerned with relations and as a result therefore, they are used in replacing relational tables using structured relational graphs thus making them human friendly than other discussed categories. From the data collected in business, these graph databases are important in examining the relationships that exist between data and itself.
People (customers) are nowadays in the constant use of the social media for communication and many more. As a result, they are capable of sharing what they have including their experience about products produced by a certain business organization (i.e. 7-Eleven). Decision makers in 7-Eleven have to stay keen because once the customer gives a negative comment about the product and manages to share their worst experience of the product with friends on social media, the business will risk losing many customers. As a result therefore, 7-Eleven have created websites and joined the social media such as Facebook, twitter etc. where they collect opinions of the consumers of their products where by applying social media analysis are able to come up with business decisions as per the findings from social media Aral et al (2013). Businesses are then keen not to take for granted any information they obtained from the social media pages since they count in making decisions. Social media provide a platform where producers are able to meet with their customers and have a chat over the quality and the preference of the customers to the already existing products in the market Zeng and Gerritsen (2014). When this is conducted effectively, business organization can decide to innovate or bring a totally new product.
Acquiring and storing big data has always been one story while making good use of big data and turning it into something profitable for the business organization is another story altogether. Big data incorporated in value creation should be concerned with improving the experience of the customers Lavalle et al (2011). This will help in comprehending all the issues the customers raise about the business organization and acting upon them to let stay in touch with the business. Big data opens access to leveraging both internal and external sources of data across structured and unstructured data concerning the customers towards building firmly on their journey and rich experience of the business. Another value of big data in the value creation is to improve and raise the precision of internal decision making of the 7-Eleven company. Insights are provided in the process of decision making to ensure that quality decision are made. Decisions such as changing the marketing plans, complain decisions or reducing or increasing some of the assignments in business are precisely enhanced when big data are fully exhausted.
Conclusion and recommendations
In conclusion, big data are useful in the business organization in various aspects. When properly analyzed, even the unimaginable information can be extracted that could help in the performance of the business. Organizations’ decision makers are supposed to take into account all the collected information as revealed from big data analytics and social media analytics. It is therefore recommended that any of the information irrespective of where it is collected concerning the business should be treated with seriousness and in decision making.
References
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