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    Big Data Strategy Assignment Help

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    Big Data Strategy Assignment Help


    Introduction

    The term big data refers to the very large volume of data both structured and unstructured. The big data consists of very diverse sets of data. The size of the data and the speed at which the data must be processed differentiate the big data from traditional database. The source of data for big data mainly includes the scientific instrumentation and experiments, sensors and RFIDs and the social networking platforms. However, the internet of things is bringing more raw data into the system. Virtually every electronic device can be connected to the internet which generates huge amount of data in daily basis. The variation in the type and the application of the data requires the big data to be equipped with different architecture and software tools which are different from the software tools that are used to handle traditional data(Rouse, 2016)

    The big data analytics is transforming the way the business decision are made. The big data provides a scientific and statistical way to view the business operation and process. Hence, the business can make decision based on the objective information and data. The decision made using big data analytics are likely to be more balanced and risk averse. The fact that the data are generated and analyzed in real time allows business to make action quickly and gain better time value. Though many business have turn to big data many still have to utilize the big data in their decision making process. The business organization must be able to figure out the appropriate tools, technologies and analytics to take advantage of big data in the process of decision making. The traditional tools which are based on relational database are not well suited for the big data. The software also cannot handle the large computational demand present in the big data technology. The NoSQL database and Hadoop and the similar tools provides solution for the modern application using big data. NoSQL database is able to work with the dynamic data which may be structured or unstructured. Some business might uses the Hadoop and the NoSQL database as the preliminary entry point for the data before passing it through data analytics(Rouse, 2017). The process may transform the data into the form which is resemble a relational structure.

    Business Strategy for developing new decison making process

    The business strategy to use big data for the process of making decision must consider its business objective. The framework must be developed on the basis of which the big data strategy and technology can be deployed.

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    Figure 1 Big data Framework

    The framework consist of four business strategy using the big data technology and analytics to enhance the business decision making process.

    1.  1) Performance Management

    This strategy involves the process of understanding the meaning of big data. The business organization are equipped with the business intelligence tools which can answer the queries related to the regular business process. The data generated from the regular business process can be used to answer the questions like the most profitable product or customers and make decision. Different visual analytics software can be used to visualize trends. The real time gathering and the analysis of the structured data can help to make plan and decision in quickly.

    A business organization name BizTech, a technology firm, has used business intelligence to increase its business revenue. The company witnessed the growth in sales to approximately to 14 million USD. The company has used the business intelligence application to improve the business activity and customer’s satisfaction. The business sales department was able to generate new reports which include information extracted from business data analytics. Those reports was also used for developing skills and knowledge of the sales representatives.

    2.  2) Data Exploration

    Data exploration refers to the process of applying statistical tools and techniques to find answers to the business questions. The exploration techniques encompass many modelling techniques which can predict and analyze the business environment, market landscape or customers behavior.In Cluster analysis the customer’s behavior can be analyzed and classified into various groups based on some similar attributes they possess to launch customized marketing plan.

    The data mining can also be used to extract information related to the customer behavior and their buying habit. Target, for example, determined that a customers was going through pregnancy by analyzing the customer buying behavior. Target identified the product such as vitamins and lotions and other 25 items as the product related to the pregnancy. It then assigned a score based on customer’s buying behavior involving these items.

    3.  3) Social Analytics.

    In social analytics most of the data are obtained are generated from the social media platforms. Social analytics can be used by business to align their content with the buyers behavior(Shively, 2015).The business organization can uses the comments and feedback made by the public using social media like Facebook to analyze the trend and determine customers need. Social analytics can measure the three categories: awareness, engagement and reach(Hoffman and Fodor, 2010). Engagement is the measurement of user generated activity on the social media platform related to the business product. The reach measures how much the business content has spread in the public. The success of the marketing campaign can also be measured in terms of the “digital footprint”. The score determine the influence of the individual or the organization in the social media platform. Virgin America, for example, offered free flight to an individual with high digital footprint. The result was they gained 1.4M impressions and 4600 retweets which show their increased social influence.

    4.) Decision Science

    Decision Science is the process of using the data analytics to improve the decision making process(Rao, 2015).The technique tries to avoid any possible human error that may be introduced in the decision making process. The assessment of risk and benefit associated with any process is computationally intensive and time consuming. Hence the computers can perform this task better. Whirlpool, for example, in 2009 wanted to reform their products. They used social media to collect customer’s feedback on their product. They used Attensity360 to monitor people view on their product. The output from the Attensity360 was successfully used to model customer’s behavior predict customer attrition and customers satisfaction. The company was able to increase their sales and customers satisfaction by improving the decision making process.

    Business initiatives and objectives.

    The information obtained from the big data can be used to make decision which can benefit the business financially(Rijmenam, 2014). There are certain initiative and the objectives of the business which must align with the strategy developed by the business for using the big data technology.

    Knowing the customers:

    With the big data technology it is easier to know and classify the customers. The huge amount of information can be used to model and classify customers based on their behavior. Knowing the customer better result in the development of the better product and services. Target which has been discuss earlier the example of the company which has used the big data technology to know about their customers.

    Innovation:

    Innovating new product is the aim of every business organization. Innovation keeps the business organization ahead in the sever market competition. Big data technology facilitates the innovation process because business organization would be clearer about the requirement and specifications of the product. Organizations can determine what customers want and also get their feedback. Hence, deciding on type of product and innovation is far easier. 

    Risk Assessment:

    The business organization must profile every customer, supplier and process in accordance with the risk associated with them. The process of assessing risk associated with people or process is very complex. Insurance company, for example, can use big data to determine the risk associated with the given customer accurately. Various techniques like social analytics, pattern recognition can be used to get a comprehensive view of customer profile and behavior. The process can reduce the number of cases of fraud and lower risk.

    Business opportunities:

    The large data set can be used to simulate market condition. The business organization can make use of the simulation to innovate new products or determine the market for their products. The data can also help business organization to determine their business penetration. An organization called Vestas for example determine the most suitable place to place their wind turbines using big data technology(Christensen, 2014).

    Big data technolgy Stack

    The big data technological stack consists of the following layer(Seale, 2016).

    • Redundant physical infrastructure

    • Security infrastructure
    • Interfaces and feeds to and from applications
    • Operational databases
    • Organizing data services and tools
    • Analytical data warehouses
    • Big data analytics
    • Big data applications 

    The stack captures the general architecture of the big data. The business organization can focus on different layer according to their needs and requirements.

    Redundant physical Layer: A physical layer consists of all the hardware and networking devices. A redundant physical layer provides the network facility for the large amount of data. The network must be able to process large volume of data in high velocity. The network infrastructure must be designed in accordance with the business needs and requirements. The network must be robust and also flexible.

    Security infrastructure:  The business organization must take into account the security concern of the big data. Big data are prone to various type of threats. The large volume of data usually means high vulnerabilities. The business organization must maintain a stringent data access and application access system. The data must be encrypted to protect them from theft and loss.

    Operational Database: Operational database consists of the actual data element of the organization. The database must be solid, secure and flexible. The SQL database is one of the most popular database query language which is used to access database. Operational database are atomic in nature which means either operation is fully successful or fully discarded. Operational database should also be consistent. They must not operate on incomplete or corrupted data. The transaction must also be isolated from each other One transaction should not affect others. Durability is another feature of the operational database. The data written must be permanent.

    Organizing data service and tools:  Since, the volume of data in big data is very high we require to assemble and organize various data service and tools. The data are diverse in nature each technique has to be customized to the type of data and tool. Organizing data services and tools consists of the distributed file system, coordination service and ETL tools.

    Analytical Data Warehouse: Analytical Data warehouse are techniques which are used to analyze and optimize data to facilitate decision making(Lacefield, 2012).  Data warehouse techniques are used to develop reports and visualize large data sets. As the data with the analyzing tools are always available the techniques are updated via batch processing. However, if the velocity is very high it may demand real time techniques for data warehouse.

    Big data analytics: Big data analytics are tools which can be applied to large amount of disparate data sets, which may be structured or unstructured and of different sizes(Monnappa, 2017). The algorithm for the big data must be able handle such variation in data sets. The big data analytics tools can be classified into three categories: Reporting and dashboards, Visualization and Analytics.  Reporting tools presents data in the readable from. Visualization tools presents data in a more visual and interactive manner. The user and get intuition behind the kind of data and monitor changes. Analytics are used for modelling, data mining and predictive analysis.

    Big data application:  The big data application layer consists of the third party application which provides a way to manipulate or distribute data. The application may be a general purpose applicationor an application specifically designed to serve organization need. Big data application developed by different developers are required to meet the rapidly changing dynamics of the big data technology.

     

    Master Data Management

    Master Data Management (MDM) is a method which allows business organization to access all of its critical and important data through a single link called master file. MDM facilitates easier access and sharing of the file and information.

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    Figure 2 Master Data Management.

    The MDM manages the key data of the business organization. For the Business Intelligence (BI), MDM serve as a method to observe the changes in the data and information over time. If MDM consists of the customer data the, it can be used to monitor the customer’s behavior for example customer credit ratings. Those data in conjunction with the business data can produce meaningful reports which could identify the customer’s behavior or changes in the business environment. MDM can also be used to predict BI environment. For example if the organization uses to implement some changes in its sales then it can create a MDM to observe the new sales environment. This data with the old sales data can predict the sales status after the changes take place. MDM can be integrated with BI can keeping it consistent but distribute among multiple systems. The integrated MDM and the transactional data can be imported to a data warehouse. BI can also be used by integrating MDM to single MDS. For the BI processing the operational MDS can be used as the source of data for the data warehouse.

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    Figure 3 Master Data Management

    NoSQL Database.

    NoSQL is the database platform based on the SQL language which supports both relation and non-relational database.NoSQL database is specially designed for the big data applications.NoSQL analytics system can represent data in lossless manner. Hence, the information would not be discarded which is one of main requirement of the efficient business analytics process. The business can extract enough information from the database. This characteristics is not present in other systems. NoSQL database also supports heavily nesting of data. NoSQL database provides the way for slicing and extracting the data from the nested database. The database also provides same support and capabilities for the schemas as for data. Hence, the system can perform analytical operation on the schema. NoSQL database also supports polymorphic queriesi.e. it supports common queries for the structurally different elements. NoSQL also supportsdynamic type identification and discovery(De Goes, 2015).

    The NoSQL database can be classified as follow(Pierson, 2016):

    Column Family: This type of database stores data in a column format. The SQL queries can be used to access the data stored in the database.

    Document: This type of database stores data as a document. They usually use .JSON or .XML format to stores data. CouchDB,for example, is document type database.

    Graph: This type of database stores data in the form of nodes and edges. Hence, it is used for storing network data and information. This type of database are not as flexible and scalable as others. Infinite graph for example is an example of graph database.

    Key-Value: Key-value database uses Key-value pair format.

    The database belonging to column family includes popular databases like Hadoop/Hbase, MapR, Cassandra etc. The column type database, Hadoop, for example, supports map reducing which enhance the database performance for business analytics purpose. The operational database can be transformed to operational database store or OLAP to an enterprises ware house data improve performance and cosumability(Groves, 2013).
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    Figure 4 Hadoop for BI application

    Roles of social media.

    Social media has emerged as a major force in business and economic environment. The organization can communicate with the customers to get hold of their views and ideas. The organization can develop an intimate relationship with their customers to share their plans and collaborate with their customers during decision making.

    The organization likes Nordstrom has used social media for the purpose of decision making and has make huge profit. Nordstrom uses Pinterest pins to collect information on the trending products and promote those product in their stores(Rijmenam, 2013). The store also managed to integrate the online and in-store shopping by the use of the interactive interfaces. The customers can view the product images on the Instagram and posts reviews of the product.  The stores uses big data to figure out the product to be promoted and the medium of promotion. This shows the extent to which social media can benefit the business intelligence. The business can utilize sentiment analysis which classifies the tone and view of public towards the business content. If a business organization detects negative attitude of customers towards some product then can take preventive action. The social media can also be use to analyze topic and trends. The requirements of customers changes rapidly and social meida is a powerful tool to monitor the changes. Tools like Ubermetics can help business to take decision about their content following the trend.

    Big Data value Creation

    The value created by big data is different for business organization with different nature. A telecommunication company can generate data from mobile phone. It can also track a user. The company will benefited from development of product based on phone usage. The company also provide service for public interest e.g. flood warning. The tracking of users makes the communication process more efficient and can increase the profit of the business. An advertising company for example can web analytics to gather information on user behavior. This will increase their advertising profit. The advertising company will also have the added advantage of content promotion and marketing. A ticketing company can collect data through the ticketing system and customers cards. The company can also have information about the customers outside of the public transportation system. The big data analytics can be used by the company to know about customer’s experience. The company can improve or change their service based on the data. The analytics can also change customer’s perception to balance the passengers load across the transportation network. A sales company on the other hand can use big data generated from the social media and determines the customers need and develop product accordingly.

    Reflection and Conclusion

    Many business organization has successfully incorporated big data analytics in their decision making process. Nordstrom, for example, has used the social media and other sensor technology to gather information about the customers’ needs and their behavior. The Nordstrom store collect information about the customers from the social networking sites like Facebook or Instagram to increase their sales. They collect and analyze huge amount of data to provide personalized shopping experience to the customers. AT&T has also employed big data analytics to gather information about the customer’s experience. The AT&T has large number of complex products therefore, their workflow is very complex. AT&T has used big data analytics to analyze the different combinations of the techniques that user experience as a quick solution to a particular situation. The analysis removes the complexity and provide a simple and yet elegant solution. The company has successfully used big data analytics to optimize the network and observe the effect in real time. Hence, they are able to optimize network and increase customer’s experience.

    However, big data analytics can also lead to wrong decision. The data are often noisy and may contain incorrect information. The use of bad analytics tools may lead to bad decision. The data can also be incomplete and out of context. A particular case which illustrate this is the big data project developed to predict U.S unemployment rate. They monitored keywords like “Jobs”, “unemployment”, and “classifieds” in Facebook and Twitter(Armerding, 2013). They used sentiment analysis to see the relation between the use of words and unemployment. During their study they see a sudden increase in use of one of their keywords. However, the increase was not related to the unemployment but the demise of the Steve Jobs. This shows the limitation of the big data analytics in making decision. 

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