Business Intelligence Using Big Data For Business Operations: A Strategy Framework For IBM

Identification, creation and discussion of business strategy for using of big data in IBM

Describe about the Business Intelligence Using Big Data for Business Operations.

Big data is developing continuously as a result it helps in producing a large sum of income from the business operations. It is analyzed that the use case of big data requires some special operations and therefore the structure that is produced with the arrangement of hardware and software provides a technological effect. It is stated by Akerkar (2013 ), that big data analytics are very much useful for outlining new strategies, which helps in managing technology at a faster rate. It also helps in providing precise results from the skills used.

In this report, analysis of big data has been used for forming strategies that would help in supporting the decision-making system of a selected organization. IBM is chosen for implementing big data procedure.  The procedures or strategies of big data have been created for IBM. The report also discusses stack technology that is used for implementing data analytics. The procedure is associated with the recovery, storage and creation analysis of data. There are number of features that are provided by the big data, and those features include diversity, rapidity and volume in the examination of big data. The main objective of this report is to implement the big data framework for the usefulness of different business operations in IBM.

Identification of business strategy framework

The implementation procedure of big data in any business needs a framework for understanding the basic operations (Assuncao et al. 2013). IBM needs to construct and implement a big data structure in the operational structure. The two dimensions on which big data framework is dependent include Business objectives and Data type.

 

Figure1: Strategy Framework of IBM for Big Data

Source (Assunção et al. 2015)

Transactional Data
Methods  used	Business Intelligence: The technique of business intelligence that is used by IBM is user friendly and thus it helps in interactive and multidimensional data analysis (Begoli and Horey 2012). It also provides different features such as rolling up, reporting the capabilities tools and many more. Cluster analysis: It helps in analyzing those objects that have similar attributes and properties. Data Mining: It is used by the organization for extracting as well as processing new patterns. Predictive Models: IBM creates models in order to predict the results from an activity (Buhl et al.2013). SQL: SQL is used for extracting, inserting and managing the values or data in a database.
Vendors	It helps in reporting the services and the analysis from the server with the help of Microsoft SQL (Chaudhuri 2012). It also helps in providing the business objectives from SAS, SAP, and Business intelligence by using Oracle.

Non- Transactional Data
Methods Used	Crowd Sourcing: IBM uses the technique of crowd sourcing for getting the required services, content or ideas by soliciting contributions from a huge mass of people (Chen et al. 2012). Textual Analyzing: The organization uses the method of textual analysis for analyzing the different content of communication rather using the structure of the content. Analysis of Sentiments: The organization uses the process of sentiment analysis for determining the results of analysis. The results can be positive, negative or neutral (Demirkan and Delen 2013). Network Analysis: IBM uses the procedure of network analysis for calculating the relationship between the elements of networks and nodes.
Vendors	Visible technologies, Watson services, Radian6 and many more;

The different business strategy framework of IBM includes:

Performance Management: It is very much easy as well as helpful in accepting the analytics as well as database of big data. Performance management is useful in order to determine the multidimensional queries and related analytics in the organization (Gandomi and Haider 2015). For example, the big data strategy framework is used for analyzing the purchasing activity, expected turnover of the organization. It helps the managers in making short times and long time decision as well as plans. The functionality of different business intelligence tools is very much helpful for improving both the management and the business operations of the organization.

Identification and aligning the business strategy’s initiative, objective and the task of IBM

Data Exploration: The data exploration framework is helpful in using the different procedures of data analytics in order to experiment and answer the questions, which has not been properly thought by the management of IBM (Jagadish et al. 2014). It also helps in implying the different predictive models for managing the user-based behavior in different sections of operation such as management and in transaction department of IBM. Big data helps the organization by supplying information and by designing strategies that would help in retaining the various segments of the users.

Social Analytics: Social analytics framework is very much helpful for the organization as it helps in measuring the huge amount of non-transactional data such as reviews and platform of social media. The big data strategy is categorized by the social analytics (Katal et al. 2013). The three wide divisions of big data strategy include awareness, engagement and reach of the analysis. Engagement is helpful in measuring the level of interaction and involvement among the team members. Awareness helps in checking the exposure of knowledge in very group members. The members of the organization are quantified based on the level of knowledge and about any particular business function.

Decision Science: Decision science helps in analyzing the data that are not related in to the transaction. The big data helps in exploring the rules and regulation in order to focus on the hypothesis and field research (Lazer et al. 2014). It is very much helpful for the IBM for conducting different feedbacks from the community. It helps in fitting both the ideas and it is also used for developing the value of a product.  In order to perform the text analysis of sentiment, it needs listening tools (Liebowitz 2013). IBM uses the tools in order to measure the topics that are related with development and interested products.

Identification of Business strategy	Aligning the formed strategy with objective, Initiatives and task
Integration of multiple strategies of big data	Big data can be implemented for multiple uses and thus the company can levitate for combining the strategies of big data (Lohr 2012). For example, Performance management is useful in gaining better production for forming synchronization with the demands and needs of the customers.
Building capabilities of big data	It is a technology or a process that is required for supporting the initiatives of big data. A plan must be devised by the expertise in order to implement the strategy of big data (Mayer-Schönberger and Cukier 2013).The organization, IBM has to hire skilled managers for guiding the employees who take care of the big data. It is helpful for creating specific group structures in order to focus on the big data analytics and business management.
Proactive creation of big data policy	IBM needs to update itself with the guidelines and policies for using the big data (Minelli et al. 2012). It is helpful in accessing non-transactional and social data for creating and accessing business operations. Therefore, IBM is greatly influenced by the security and privacy of the business operations.

The technology stack of big data analytics of IBM has analyzed some components, which are helpful in forming the analytics. Both external as well as internal data sources are required in the market analysis of IBM, which are shown by the different sources (Moniruzzaman and Hossain 2013). For analyzing data, it creates a lake of data. In order to perform the data analytics procedure, stack technology consists of 3V is which are variety, velocity and volume. Volume consists of various amounts of data that needs to be stored and managed. Variety consists of various types of data that are used in the analytics of big data (Raghupathi and Raghupathi 2014). Variety means the various types of data that are used in the big data analytics. Velocity is defined as a speed in which the data in stack technology are recorded and processed.

Integration of multiple strategies of big data

There are different kinds of stack technologies that are used in order to create the architecture of big data analytics in IBM.

PIG:  A scripting technology is used for processing and analyzing huge quantity of data sets (Sagiroglu and Sinanc 2013).In order to access the engines, apache pig consists of an architectural structure, which also helps in storing clusters of data.

YARN: It is one of the acronyms for resource navigator, which is helpful in large-scale data application for distributing the operating system (Shroff et al. 2013).It is very much helpful in combining both the synchronized as well as central resource managers for reconciliation.

Hive: This stack technology is useful in summarizing, querying and for analyzing the data which will be helpful for the business insights (Vera-Baquero et al. 2013).The tables that are present in hive are organized in the pattern of granular units for creating the taxonomy.

Data analytics: There are three challenges that are required in the management process of big data. The challenges are sorted with the help of the big data analytics. Right data is selected by them in order to handle the operations of data analytics and for using the insights that are gained for transforming the different operations of business (Waller and Fawcett 2013). Big data analytics helps in managing the big data and helps in advancing their analytics. It is very much beneficial in order to deal with the lack of analytical talent that is needed for implementing the big data analytics. It is helpful in creating new roles for job. Big data is acting as revolution in the fields of analytics measurement and administration.

 The big data analytics is helpful in driving data for the process of decision-making in the business operations of IBM. There is a lot of difference between the data driven and information collected in IBM. It is analyzed that the chances of data lose is more when the data are stored for longer period (Wixom et al. 2014). Big data analytics and business analytics helps in analyzing the data that were stored long before as a result they helps in creating effective results by using it. IBM is benefitted by the big data analytics because each data has role, which in turn helps in assisting the process of decision-making.

Master Data management: A method helps in identifying the most important as well as critical data of IBM in order to create a singular source of data for managing the business. It involves different technological solutions to improve the big data processing as well as management, which includes data integration, quality, and management (Wu  et al. 2014).The following characteristic of MDN is helpful in supporting the decision-making system of the organization and its business intelligence.

Building capabilities of big data

Standard Data view: It is helpful in creating single view in order to authorize the critical business management. The MDN process is used by the IBM data analytics in order to resolve the issues such as data disputation, duplication and many more (Begoli and Horey 2012). For example, two people having the same first name will create a trouble in entering the data as a result big data analytics can be used for drawing their last name and addresses in order to distinguish between the two individuals.

Complete overview of the relationship: MDN is a big data analytics that helps in identifying the relationship among the different data entity. It will help the organization in combining one data entity with the other based on the relationship of the coefficient. For example, IBM uses MDN to store the names of the purchaser.

Managing interactions: It is used in order to integrate the occurrence and transaction of social interaction between the clients and the operators of the business (Chaudhuri 2012). It will create a bridge between the customers and data channel partners in order to complete the views of the customers of IBM.

Design features: The factors behind the efficient and proper management of big data analytics include flexibility of the design model, Variability of model operation and scalability functions (Demirkan and Delen 2013). IBM uses all this features in order to use its data analytics. The MDN system does not need coding for its implications therefore and thus it can be easily applied in IBM. The agility of the software process is helpful in creating the focus of the database on the success of the customers.

NoSQL or non-related SQL is helpful in giving various facilities for the big data analytics, which includes scalability, observable alternative different association of strengths, many multinational organizations like Amazon (Gandomi and Haider 2015). Google uses big data NoSQL for working with the operational database.

NoSQL has different characteristics for user-friendly advance, which helps in creating and easing the operations of the business database administration properly (Liebowitz 2013). NoSQL is helpful in empowering most of the organizations.

NoSQL consists of various systems such as payroll systems, reluctance system and data processing system.

NoSQL will be helpful in processing unpredictable as well as unstructured information system in order to provide help to the big data information management of IBM (Lohr 2012).

NoSQL assists in solving different bottleneck errors by processing the unstructured database System (Minelli et al. 2012). Hence, the big data purpose of IBM can be managed by using the system of NoSQL.

Proactive creation of big data policy

NoSQL is not required for knowing the structure beforehand. This is because the system does not lack schema orientation (Raghupathi and Raghupathi 2014). The system is helpful in solving the data, which is arised due to acid property of the data analytics.

Various types of NoSQL databases	Description	Use in Big Data use case of IBM
Key value store	It consists of big hash based table of keys and values Example: Riak used by Amazon	The schema format of this NoSQL database is helpful in forming the database that is value based. This type of key is helpful in creating as well as generating auto type of data base system (Sagiroglu and Sinanc 2013). IBM can use the system for creating auto-generated database in big data analytics.
Document based store	It helps in storing elements that are made up of tagged elements Example: couchDB	The database of NoSQL format uses various types of key and value pair in order to store the values of the data (Shroff et al. 2013). It is very much helpful for IBM for creating structure and encoding for managing the big data analytics
Column based store	Each block of storage consists of data that is formed from one column of the system table Example: Cassandra and HBase	In this type of database schema, the data is stored in row cells instead of column cells. It is helpful for IBM as it provides the organization with the ease of accessing and fast searching (Waller and Fawcett 2013).The big data that is stored in this type off scheme is helpful in aggregating the data on a single column.
Graph based	It is a type of database that uses nodes and edges for storing and representing data over the system table Example: Neo4J	Graph based NoSQL database schema is pictorial representation of database that in based on the structure of flexible data values structure (Assuncao et al. 2013). It is helpful as it provides IBM the ease of transformation of scheme from one model structure to different model structure (Begoli and Horey 2012). The graph consists of edges and nodes therefore it in helpful in creating elation among the nodes of the data.

The social networking plays a crucial function in big data analytics and management of database. It is very much helpful in creating advertisement of the database administration of big data analytics (Buhl et al. 2013). It is helpful in the process of proficient decision-making processes, which became social. The habitual influential cycle of the functions is disrupted with the help of social media and networking. The manager uses the social networking for informing as well as validating the decisions that are related with the big data.

According Demirkan and Delen (2013), the facilities that the social media provides includes:

Helps in searching the feedbacks and responses of the customers or clients

It helps in enhancing the partnership with others.

The reliability of the information is improved (Gandomi and Haider 2015).

Business decisions are researched over the global market

Helps in accessing information or data that are unavailable everywhere

It helps in keeping eye on the co-worker and colleagues (Lazer et al. 2014)

The big data formation process is vast probable in any business. The procedure is very much useful in forming a link between the providers and the customers. The procedure of big data consists of various processes, which includes inventory, manufacturing distribution and marketing (Lohr 2012).The products or services have to go through number of procedures in order to meet the needs and necessities of the customers. It is stated by  Demirkan and Delen (2013), that the steps that are helpful for the company includes:

Manufacture of goods

Creating inventory of products and services

Study of physical resources (Waller and Fawcett 2013).

delivery to retail shops

Mass advertising of goods

It is stated by Moniruzzaman and Hossain (2013), the value creation procedure of IBM   includes:

Increase in the number of clients

Improving the techniques of the market

Optimizing the supply chain (Sagiroglu and Sinanc 2013).

Reducing the price of the stir

Increasing the turnover of the inventory

Enhancing the effectiveness of hiring

Conclusion

It is concluded from the report that big data analytics is used in order to increase the revenue of an organization. Both hardware as well as software technology have affected the operations of the business. The big data analytics is very much useful in meeting the demands of the customers.  It is analyzed that in this assignment IBM is selected for the implementing procedure of big data analytics. The strategies that are used for big data analytics are created using the formation or creation procedure. It is concluded that the big data analytics is very much helpful in creating new technologies and it is extremely helpful in meeting the demands of the customers.

References

Akerkar, R. ed., 2013. Big data computing. CRC Press.

Assuncao, M.D., Calheiros, R.N., Bianchi, S., Netto, M.A. and Buyya, R., 2013. Big Data computing and clouds: challenges, solutions, and future directions. arXiv preprint arXiv:1312.4722.

Assunção, M.D., Calheiros, R.N., Bianchi, S., Netto, M.A. and Buyya, R., 2015. Big Data computing and clouds: Trends and future directions. Journal of Parallel and Distributed Computing, 79, pp.3-15.

Begoli, E. and Horey, J., 2012, August. Design principles for effective knowledge discovery from big data. In Software Architecture (WICSA) and European Conference on Software Architecture (ECSA), 2012 joint working IEEE/IFIP conference on (pp. 215-218). IEEE.

Buhl, H.U., Röglinger, M., Moser, F. and Heidemann, J., 2013. Big data.Business & Information Systems Engineering, 5(2), pp.65-69.

Chaudhuri, S., 2012, May. What next?: a half-dozen data management research goals for big data and the cloud. In Proceedings of the 31st ACM SIGMOD-SIGACT-SIGAI symposium on Principles of Database Systems(pp. 1-4). ACM.

Chen, H., Chiang, R.H. and Storey, V.C., 2012. Business Intelligence and Analytics: From Big Data to Big Impact. MIS quarterly, 36(4), pp.1165-1188.

Demirkan, H. and Delen, D., 2013. Leveraging the capabilities of service-oriented decision support systems: Putting analytics and big data in cloud.Decision Support Systems, 55(1), pp.412-421.

Gandomi, A. and Haider, M., 2015. Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management,35(2), pp.137-144.

Jagadish, H.V., Gehrke, J., Labrinidis, A., Papakonstantinou, Y., Patel, J.M., Ramakrishnan, R. and Shahabi, C., 2014. Big data and its technical challenges. Communications of the ACM, 57(7), pp.86-94.

Katal, A., Wazid, M. and Goudar, R.H., 2013, August. Big data: issues, challenges, tools and good practices. In Contemporary Computing (IC3), 2013 Sixth International Conference on (pp. 404-409). IEEE.

Lazer, D., Kennedy, R., King, G. and Vespignani, A., 2014. The parable of Google flu: traps in big data analysis. Science, 343(6176), pp.1203-1205.

Liebowitz, J. ed., 2013. Big data and business analytics. CRC Press.

Lohr, S., 2012. The age of big data. New York Times, 11.

Mayer-Schönberger, V. and Cukier, K., 2013. Big data: A revolution that will transform how we live, work, and think. Houghton Mifflin Harcourt. Zaman, N., Seliaman, M.E., Hassan, M.F. and Marquez, F.P.G., 2015.Handbook of Research on Trends and Future Directions in Big Data and Web Intelligence. Information Science Reference.

Minelli, M., Chambers, M. and Dhiraj, A., 2012. Big data, big analytics: emerging business intelligence and analytic trends for today’s businesses. John Wiley & Sons.

Moniruzzaman, A.B.M. and Hossain, S.A., 2013. Nosql database: New era of databases for big data analytics-classification, characteristics and comparison. arXiv preprint arXiv:1307.0191.

Raghupathi, W. and Raghupathi, V., 2014. Big data analytics in healthcare: promise and potential. Health Information Science and Systems, 2(1), p.1.

Sagiroglu, S. and Sinanc, D., 2013, May. Big data: A review. In Collaboration Technologies and Systems (CTS), 2013 International Conference on (pp. 42-47). IEEE.

Shroff, G., Dey, L. and Agrawal, P., 2013. Social Business Intelligence Using Big Data. CSI Communications, pp.11-16.

Vera-Baquero, A., Colomo-Palacios, R. and Molloy, O., 2013. Business process analytics using a big data approach. IT Professional, 15(6), pp.29-35.

Waller, M.A. and Fawcett, S.E., 2013. Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. Journal of Business Logistics, 34(2), pp.77-84.

Wixom, B., Ariyachandra, T., Douglas, D., Goul, M., Gupta, B., Iyer, L., Kulkarni, U., Mooney, J.G., Phillips-Wren, G. and Turetken, O., 2014. The current state of business intelligence in academia: The arrival of big data.Communications of the Association for Information Systems, 34(1), p.1.

Wu, X., Zhu, X., Wu, G.Q. and Ding, W., 2014. Data mining with big data.IEEE transactions on knowledge and data engineering, 26(1), pp.97-107.