Discuss about the Business Intelligence Using Big Data.
The enterprise world is changing rapidly with various innovative demands with it. This change is also introducing several applications and technological advancements. Business intelligence is one of the most effective and innovative concept that changing the entire set up of information system within business sector.
In this report, the impact of big data is analyzed in forming new strategies for the decision-making perspective or the chosen organization. In contrast with the scenario of this report the chosen organization is Mercedes (Mercedes-benz.co.in 2016). Mercedes is chosen for the analyzing the impact of big data on the platform of business analytics. In addition to this, the report is elaborating the strategies for Mercedes. The technology stack required for the decision-making perspectives of the concerned organization is also discussed in this report (Alexandrov et al. 2014). Other than these aspects about the usages of big data within Mercedes, the variety of tasks performed by big data and their velocity of performance are elaborated with proper examples.
The application of the big data within any business sector or concerned organization requires a particular framework to be implemented. Mercedes needs to know the use of big data to implement it within their organizational structure (Analytics 2013). The following framework has to be followed by the Mercedes in order to incorporate this system within their organization. The data management model is appropriate to explain the framework required for implementing big data within Mercedes.
Figure 1: Business strategy for big data
(Source: Riggins and Wamba 2015, pp- 1531)
|
Data Management Model |
|
|
Step involved within DMM |
Detailed Techniques |
|
Data Management Strategy |
Data management strategies involves the followings: ü Importance of official data ü Implementation of oversights used for communication and coordination ü Business IT alignment ü Official data cases |
|
Data Quality |
Data quality involves the following aspects: ü Meta data oversight ü Official data transaction over big data platform ü Profiling results that share service architecture ü Funding to the data |
|
Data operations |
Data operations involves the followings : ü Crowd sourcing ü Textual analysis ü Network analysis ü SQL ü Predictive Models |
|
Supporting Processes |
Following are the supporting process that are being used for the Mercedes for data management techniques: ü Measurement and Analysis ü Process Management ü Risk Management ü Configuration Management |
|
Platform and Architecture |
Platform and Architecture that is needed for the data management framework for Mercedes: ü Architectural Approach ü Standard of Architecture ü Integration of Data |
|
Data Governance |
Following aspects can be followed by Mercedes for managing the customer data: ü Management of Governance ü Important Business aspects ü Metadata management |
Data management Strategy: There are mainly four strategies in order to manage the customer data with full accuracy and safety (Bennett et al. 2013). In contrast with this discussion, the strategies are end to end data governance, end to end data quality, end to end data integration and accuracy of the data platform. These four models or perspectives are used in order to maintain potential data management within any group of network (Buyya et al. 2015). Mercedes uses their cloud base Azure to incorporate these aspects to manage the customer data.
Figure 2: Data Managemenet Strategy
(Source: Gandomi and Haider 2015, pp- 137)
Data Quality Management: Data quality management is one of the most important aspects that the business professionals before their dealings verify must (Cevher et al. 2014). This data quality management includes the following aspects to be followed: designing, development and validation of the data (Chen et al. 2012). Validation of data involves analyzing the data, standardization of the data, check of the duplicity in data combinations and enrichment of the data with important information.
Figure 3: Data Quality Management
(Source: Slavakis et al. 2014, pp- 18)
Data governance: Data governance protocol is nothin ggbut the validation of the meta data trnsfered within the information system. This includes the the data quality and data intigration within the ocncerned platform (D’Anca et al. 2016). These two aspeect checks the validity of the dta anadd then it is transferred to the destination as per the requirements.
Figure 4: Data Governance
(Source: Talia 2013, pp- 63)
Technological advancements are needed in order to support the initiative related to the big data. According to the experienced and organizational heads decided to incorporate a new strategy for implementing big data within Mercedes for their developmental perspectives (Fisher et al. 2012). This strategy is “skilled statistician”. This strategy allows the manager to guide their employees about the reason behind selecting big data. This aspect will help Mercedes in analyzing the need for using big data and business intelligence within their own platform (Ghazal et al. 2013). The organization can easily achieve their business growth with respect to the application of big data.
Mercedes needs to maintain their policies and regulating measures that will protect their data or transferred data. This aspect will make them capable of accessing data that are being transferred to the external business resources. In addition to this for following above stated measures, Mercedes should have to follow “compliance, security and privacy” related business operation (Hu et al. 2014). This will help Mercedes in maintaining their business operations that they are following.
Big data have several forms that can be implemented within Mercedes. Mercedes can easily increase these strategies of big data in order to have a good growth of revenues. Such as data governance and data quality management can be used in order to have accurate data for an essential purpose (Kambatla et al. 2014). This aspect helps to synchronize the data with a proper field. The important tasks of Mercedes can be easily done with the help of big data analytics and business intelligence perspectives.
The technology stack of Mercedes for big data analytics are essential aspect that is being used for improving the performance of the organization from various perspectives. Various external and internal data resources are used in order meet the needs for the data resources. These data are used for data analysis (Kwon et al. 2014). Three V’s are popular in order to emphasize the data analytics; these are variety, volume and velocity. These three aspects explain about the different type, velocity and amount of data transferred. In addition to this the business strategy of the organization is “Driving safety performance” (Lazer et al. 2014). In accordance with this perspective the organization needs to utilize “SAP HANA” technology stack that will help the organization in meeting their goals and demands.
SAP HANA: This platform is used for maintaining the remote data sync options in accordance with the driving safety performing measures for Mercedes (Moniruzzaman and Hossain 2013). This technological stack provides capability of the remote data syncing to the databases of Mercedes.
Figure 5: SAP HANA Platform
(Source: Tsai et al. 2016, pp- 15)
Structure of SAP HANA: This platform used in the big data analytics is combination of few subparts that have to be incorporated within the platform in order to control the entire framework (Rahm 2016). These parts are Application service, database service, integration service. In addition to this, all of these factors specifically introduce some functionality within the system architecture.
Figure 6: SAP HANA Architecture
(Source: Talia 2013, pp- 98)
Multitier storage option: This platform provides the huge amount of data storage facility that is offered to the system architecture. Multitier storage option activates all the layers of the database to perform various options at a time.
Data streaming option: The data streaming option avails the information to be transmitted to the destination of the data resources (Riggins and Wamba 2015). In this process the data packets are transferred in order to maintain the data flow.
Data management option: The data management options are based on the management of data. This is related to the execution, architecture and practices involved in the data management process. This aspect needs to manage the information life cycle.
Real-time replication option: Synchronous data offers replicating measures to the application platform (Shang et al. 2013). This type of application of data helps to monitor the data management process.
Benefits of SAP HANA
Data Analytics: Data analytics is used among various organizations in order to take right decisions with respect to the right choice of perspectives. Mercedes also uses data analytics in order to find the measures that will benefit their goals and objectives related to maintain the driving safety (Gandomi and Haider 2015). Three essential aspects are considered as the most effective challenges in managing the data. Selection of the right data, managing the operations while considering the data analytics and transformation of the data into the concerned type. This provides the chance s to apply the data for particular usages in the field of driving safety. The decision making of Mercedes is entirely supported by the data analytics tools. The organization can easily choose the options for the raw materials and other technical support for betterment of their system architecture of manufacturing the cars. Robotization can be improved with the help fo the data analytics.
Master Data Management: Master data management is related to the method that identifies the most effective and innovative data. Mercedes can use a singular source of data that helps in managing the business process with respect to the data types. Master data management helps the organization in improving their technological development in the sector of data integrity and quality management (Slavakis et al. 2014). These above mentioned aspects are considered for only the data related to the raw materials and other technologies that Mercedes has been chosen to incorporate within their system. AHP data management tool can help Mercedes in managing the master data within their organization.
Standard Data view: Standard view of the data structures and other data bases can be done through the single view of standard view. The disputes, duplications and other conflicting situation within Mercedes can be resolved with the help of the Standard data view (Sun and Reddy 2013). This approach helps the organization in finding the right environment and perspectives, which will guide them in taking the right decisions. Oracle database can be used by Mercedes to get the standard view of the data used by them.
Complete Overview of relationships: The data analytics of MDM can help Mercedes to identify the relationship among all the data entities that are used by several aspects required in the organization. In addition to this, this aspect also helps in establishing relationship with other entities within the organization.
Managing Interactions: This aspect can be used for making the interaction between the social entities used by the Mercedes (Talia 2013). This aspect is used for the improvement of the database management. All the business operations are operated with the help of the efficient use of the management of interactions within database.
Design Features: Flexibility and control of database operations are done with the help of the designing features of the big data analytics. The big data analytics and designing features of the big data can benefit Mercedes (Tsai et al. 2016). Mercedes can use oracle data management scheme. The software bases helps to identify the database formed for helping the customers. The data analytics can be improved with the help of the designing features of the big data.
A Non- relational SQL database helps in identifying the facilities for big data analytics. These uses are scalability, alternatives, and time testing equipments. Several organizations use these applications. Examples of these organizations are Amazon, LinkedIn etc. there are several advantages of using Non- SQL databases in big data (Alexandrov et al. 2014). These advantages are explained as follows:
|
Types of NoSQL databases |
Description |
Use in big data use case of Mercedes |
|
Key value store |
This contains the large amount of hash table data with their concerned key values and information. |
This is a type of schema that follows NoSQL format of database. This schema deals with the key value information stored in the hash tables. Therefore, this schema can be used in Mercedes to maintain their auto generated databases. |
|
Document bases storing system |
This database application of the SQL server helps to identify the tagged elements concerned with the non-relational databases (Cevher et al. 2014). |
In this type of NoSQL database, the key values and data storage features are used for checking the values. This feature will help Mercedes in storing the data with the data storage system concerned with the encoding structure of the NoSQL database. |
|
Column based storing system |
In this database management application, the No-SQL tagged elements are used. In this type of application, the column based system mainly focused in order to capture and block the storages. |
This type of database schemas uses the storage of the data related to the column cells. This aspect can helps to arrange the databases of Mercedes for managing the big data storage. This aspect will be helpful in managing aggregation of data on a single column basis. |
|
Graph based system |
This is a network-based database that uses the nodes and edges, which is represented and stored within the system table (Chen et al. 2012). |
This is a graph based schema that is used for the pictorial representation of database based on flexible and available data values. This feature can provide the transformation of schemas through one structure to another. In contrast with the structure of the database the nodes and links are helpful in forming a relation within the databases and other structures. |
Social media plays a great role in big data analytics in order to manage the data within the several organizations. The relational and No-SQL databases related to the big data management helps to make decisions within the concerned organization. In this report, the concerned organization is Mercedes (D’Anca et al. 2016). It is helpful for managing the databases within data analytics platform. This application of big data helps in decision-making perspective. The business objectives and goals become more oriented towards social life and perspectives. In case of the previous applications of SQL databases, it is clear that at that time the social media disrupted traditional operations (Fisher et al. 2012). The traditional influences cycle related to the NoSQL database the social media is helpful in promoting the features of big data. The managers of Mercedes are helpful in using and promoting the big data analytics with the help of the social media.
The social media to the big data analytics provide several facilities. These facilities are described as follows:
The big data value creation process is very much essential for the betterment of the organization. In contrast with the company chosen in this report for evaluating the impact of big data on Business intelligence are elaborated in this report. The applications of big data combines all the process related to the data management process (Kambatla et al. 2014). All the applications that are involved within the data management system used to meet the customer requirements. All of these processes mentioned within the big data management system meets the requirements of the customers. Several aspects must be involved within the value creation process of the big data. These aspects are explained as follows:
Conclusion
Big data is being used in organizations as well as in the social media and other data sources. Integration of the advanced analytics for using the big data is an essential step for achieving the good returns from the investments on information system. In this report, the importance of big data has been discussed with respect to the objectives and goals of Mercedes. All the important strategies mandatory for the customer data management is discussed here. In addition to this, the technology stack important for this organization has been discussed. In contrast with the concerned scenario of Mercedes, implementation of big data is analyzed. NoSQL for big data analytics are discussed with respect to the objectives of Mercedes that they have to deliver driving safety to their customers.
References
Alexandrov, A., Bergmann, R., Ewen, S., Freytag, J.C., Hueske, F., Heise, A., Kao, O., Leich, M., Leser, U., Markl, V. and Naumann, F., 2014. The Stratosphere platform for big data analytics. The VLDB Journal, 23(6), pp.939-964.
Analytics, B.D., 2013. Big data analytics for security.
Bennett, P., Giles, L., Halevy, A., Han, J., Hearst, M. and Leskovec, J., 2013, October. Channeling the deluge: research challenges for big data and information systems. In Proceedings of the 22nd ACM international conference on Conference on information & knowledge management (pp. 2537-2538). ACM.
Buyya, R., Ramamohanarao, K., Leckie, C., Calheiros, R.N., Dastjerdi, A.V. and Versteeg, S., 2015, December. Big Data Analytics-Enhanced Cloud Computing: Challenges, Architectural Elements, and Future Directions. InParallel and Distributed Systems (ICPADS), 2015 IEEE 21st International Conference on (pp. 75-84). IEEE.
Cevher, V., Becker, S. and Schmidt, M., 2014. Convex optimization for big data: Scalable, randomized, and parallel algorithms for big data analytics.IEEE Signal Processing Magazine, 31(5), pp.32-43.
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.
D’Anca, A., Conte, L., Palazzo, C., Fiore, S. and Aloisio, G., 2016, April. A big data approach for climate change indicators processing in the CLIP-C project. In EGU General Assembly Conference Abstracts (Vol. 18, p. 18285).
Fisher, D., DeLine, R., Czerwinski, M. and Drucker, S., 2012. Interactions with big data analytics. interactions, 19(3), pp.50-59.
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.
Ghazal, A., Rabl, T., Hu, M., Raab, F., Poess, M., Crolotte, A. and Jacobsen, H.A., 2013, June. BigBench: towards an industry standard benchmark for big data analytics. In Proceedings of the 2013 ACM SIGMOD international conference on Management of data (pp. 1197-1208). ACM.
Hu, H., Wen, Y., Chua, T.S. and Li, X., 2014. Toward scalable systems for big data analytics: A technology tutorial. IEEE Access, 2, pp.652-687.
Kambatla, K., Kollias, G., Kumar, V. and Grama, A., 2014. Trends in big data analytics. Journal of Parallel and Distributed Computing, 74(7), pp.2561-2573.
Kwon, O., Lee, N. and Shin, B., 2014. Data quality management, data usage experience and acquisition intention of big data analytics. International Journal of Information Management, 34(3), pp.387-394.
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.
Mercedes-benz.co.in. 2016. Welcome to the official website of Mercedes Benz India. Explore the information on the range of vehicles.. [online] Available at: https://www.mercedes-benz.co.in/content/india/mpc/mpc_india_website/enng/home_mpc/passengercars.html [Accessed 29 Sep. 2016].
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.
Rahm, E., 2016. Big Data Analytics. it-Information Technology.
Riggins, F.J. and Wamba, S.F., 2015, January. Research directions on the adoption, usage, and impact of the internet of things through the use of big data analytics. In System Sciences (HICSS), 2015 48th Hawaii International Conference on (pp. 1531-1540). IEEE.
Shang, W., Jiang, Z.M., Hemmati, H., Adams, B., Hassan, A.E. and Martin, P., 2013, May. Assisting developers of big data analytics applications when deploying on hadoop clouds. In Proceedings of the 2013 International Conference on Software Engineering (pp. 402-411). IEEE Press.
Slavakis, K., Giannakis, G.B. and Mateos, G., 2014. Modeling and optimization for big data analytics:(statistical) learning tools for our era of data deluge. IEEE Signal Processing Magazine, 31(5), pp.18-31.
Sun, J. and Reddy, C.K., 2013, August. Big data analytics for healthcare. InProceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 1525-1525). ACM.
Talia, D., 2013. Toward cloud-based big-data analytics. IEEE Computer Science, pp.98-101.
Tan, W., Blake, M.B., Saleh, I. and Dustdar, S., 2013. Social-Network-Sourced Big Data Analytics. IEEE Internet Computing, 17(5), pp.62-69.
Tsai, C.W., Lai, C.F., Chao, H.C. and Vasilakos, A.V., 2016. Big Data Analytics. In Big Data Technologies and Applications (pp. 13-52). Springer International Publishing.
Discuss about the Business Intelligence Using Big Data.
The enterprise world is changing rapidly with various innovative demands with it. This change is also introducing several applications and technological advancements. Business intelligence is one of the most effective and innovative concept that changing the entire set up of information system within business sector.
In this report, the impact of big data is analyzed in forming new strategies for the decision-making perspective or the chosen organization. In contrast with the scenario of this report the chosen organization is Mercedes (Mercedes-benz.co.in 2016). Mercedes is chosen for the analyzing the impact of big data on the platform of business analytics. In addition to this, the report is elaborating the strategies for Mercedes. The technology stack required for the decision-making perspectives of the concerned organization is also discussed in this report (Alexandrov et al. 2014). Other than these aspects about the usages of big data within Mercedes, the variety of tasks performed by big data and their velocity of performance are elaborated with proper examples.
The application of the big data within any business sector or concerned organization requires a particular framework to be implemented. Mercedes needs to know the use of big data to implement it within their organizational structure (Analytics 2013). The following framework has to be followed by the Mercedes in order to incorporate this system within their organization. The data management model is appropriate to explain the framework required for implementing big data within Mercedes.
Figure 1: Business strategy for big data
(Source: Riggins and Wamba 2015, pp- 1531)
|
Data Management Model |
|
|
Step involved within DMM |
Detailed Techniques |
|
Data Management Strategy |
Data management strategies involves the followings: ü Importance of official data ü Implementation of oversights used for communication and coordination ü Business IT alignment ü Official data cases |
|
Data Quality |
Data quality involves the following aspects: ü Meta data oversight ü Official data transaction over big data platform ü Profiling results that share service architecture ü Funding to the data |
|
Data operations |
Data operations involves the followings : ü Crowd sourcing ü Textual analysis ü Network analysis ü SQL ü Predictive Models |
|
Supporting Processes |
Following are the supporting process that are being used for the Mercedes for data management techniques: ü Measurement and Analysis ü Process Management ü Risk Management ü Configuration Management |
|
Platform and Architecture |
Platform and Architecture that is needed for the data management framework for Mercedes: ü Architectural Approach ü Standard of Architecture ü Integration of Data |
|
Data Governance |
Following aspects can be followed by Mercedes for managing the customer data: ü Management of Governance ü Important Business aspects ü Metadata management |
Data management Strategy: There are mainly four strategies in order to manage the customer data with full accuracy and safety (Bennett et al. 2013). In contrast with this discussion, the strategies are end to end data governance, end to end data quality, end to end data integration and accuracy of the data platform. These four models or perspectives are used in order to maintain potential data management within any group of network (Buyya et al. 2015). Mercedes uses their cloud base Azure to incorporate these aspects to manage the customer data.
Figure 2: Data Managemenet Strategy
(Source: Gandomi and Haider 2015, pp- 137)
Data Quality Management: Data quality management is one of the most important aspects that the business professionals before their dealings verify must (Cevher et al. 2014). This data quality management includes the following aspects to be followed: designing, development and validation of the data (Chen et al. 2012). Validation of data involves analyzing the data, standardization of the data, check of the duplicity in data combinations and enrichment of the data with important information.
Figure 3: Data Quality Management
(Source: Slavakis et al. 2014, pp- 18)
Data governance: Data governance protocol is nothin ggbut the validation of the meta data trnsfered within the information system. This includes the the data quality and data intigration within the ocncerned platform (D’Anca et al. 2016). These two aspeect checks the validity of the dta anadd then it is transferred to the destination as per the requirements.
Figure 4: Data Governance
(Source: Talia 2013, pp- 63)
Technological advancements are needed in order to support the initiative related to the big data. According to the experienced and organizational heads decided to incorporate a new strategy for implementing big data within Mercedes for their developmental perspectives (Fisher et al. 2012). This strategy is “skilled statistician”. This strategy allows the manager to guide their employees about the reason behind selecting big data. This aspect will help Mercedes in analyzing the need for using big data and business intelligence within their own platform (Ghazal et al. 2013). The organization can easily achieve their business growth with respect to the application of big data.
Mercedes needs to maintain their policies and regulating measures that will protect their data or transferred data. This aspect will make them capable of accessing data that are being transferred to the external business resources. In addition to this for following above stated measures, Mercedes should have to follow “compliance, security and privacy” related business operation (Hu et al. 2014). This will help Mercedes in maintaining their business operations that they are following.
Big data have several forms that can be implemented within Mercedes. Mercedes can easily increase these strategies of big data in order to have a good growth of revenues. Such as data governance and data quality management can be used in order to have accurate data for an essential purpose (Kambatla et al. 2014). This aspect helps to synchronize the data with a proper field. The important tasks of Mercedes can be easily done with the help of big data analytics and business intelligence perspectives.
The technology stack of Mercedes for big data analytics are essential aspect that is being used for improving the performance of the organization from various perspectives. Various external and internal data resources are used in order meet the needs for the data resources. These data are used for data analysis (Kwon et al. 2014). Three V’s are popular in order to emphasize the data analytics; these are variety, volume and velocity. These three aspects explain about the different type, velocity and amount of data transferred. In addition to this the business strategy of the organization is “Driving safety performance” (Lazer et al. 2014). In accordance with this perspective the organization needs to utilize “SAP HANA” technology stack that will help the organization in meeting their goals and demands.
SAP HANA: This platform is used for maintaining the remote data sync options in accordance with the driving safety performing measures for Mercedes (Moniruzzaman and Hossain 2013). This technological stack provides capability of the remote data syncing to the databases of Mercedes.
Figure 5: SAP HANA Platform
(Source: Tsai et al. 2016, pp- 15)
Structure of SAP HANA: This platform used in the big data analytics is combination of few subparts that have to be incorporated within the platform in order to control the entire framework (Rahm 2016). These parts are Application service, database service, integration service. In addition to this, all of these factors specifically introduce some functionality within the system architecture.
Figure 6: SAP HANA Architecture
(Source: Talia 2013, pp- 98)
Multitier storage option: This platform provides the huge amount of data storage facility that is offered to the system architecture. Multitier storage option activates all the layers of the database to perform various options at a time.
Data streaming option: The data streaming option avails the information to be transmitted to the destination of the data resources (Riggins and Wamba 2015). In this process the data packets are transferred in order to maintain the data flow.
Data management option: The data management options are based on the management of data. This is related to the execution, architecture and practices involved in the data management process. This aspect needs to manage the information life cycle.
Real-time replication option: Synchronous data offers replicating measures to the application platform (Shang et al. 2013). This type of application of data helps to monitor the data management process.
Benefits of SAP HANA
Data Analytics: Data analytics is used among various organizations in order to take right decisions with respect to the right choice of perspectives. Mercedes also uses data analytics in order to find the measures that will benefit their goals and objectives related to maintain the driving safety (Gandomi and Haider 2015). Three essential aspects are considered as the most effective challenges in managing the data. Selection of the right data, managing the operations while considering the data analytics and transformation of the data into the concerned type. This provides the chance s to apply the data for particular usages in the field of driving safety. The decision making of Mercedes is entirely supported by the data analytics tools. The organization can easily choose the options for the raw materials and other technical support for betterment of their system architecture of manufacturing the cars. Robotization can be improved with the help fo the data analytics.
Master Data Management: Master data management is related to the method that identifies the most effective and innovative data. Mercedes can use a singular source of data that helps in managing the business process with respect to the data types. Master data management helps the organization in improving their technological development in the sector of data integrity and quality management (Slavakis et al. 2014). These above mentioned aspects are considered for only the data related to the raw materials and other technologies that Mercedes has been chosen to incorporate within their system. AHP data management tool can help Mercedes in managing the master data within their organization.
Standard Data view: Standard view of the data structures and other data bases can be done through the single view of standard view. The disputes, duplications and other conflicting situation within Mercedes can be resolved with the help of the Standard data view (Sun and Reddy 2013). This approach helps the organization in finding the right environment and perspectives, which will guide them in taking the right decisions. Oracle database can be used by Mercedes to get the standard view of the data used by them.
Complete Overview of relationships: The data analytics of MDM can help Mercedes to identify the relationship among all the data entities that are used by several aspects required in the organization. In addition to this, this aspect also helps in establishing relationship with other entities within the organization.
Managing Interactions: This aspect can be used for making the interaction between the social entities used by the Mercedes (Talia 2013). This aspect is used for the improvement of the database management. All the business operations are operated with the help of the efficient use of the management of interactions within database.
Design Features: Flexibility and control of database operations are done with the help of the designing features of the big data analytics. The big data analytics and designing features of the big data can benefit Mercedes (Tsai et al. 2016). Mercedes can use oracle data management scheme. The software bases helps to identify the database formed for helping the customers. The data analytics can be improved with the help of the designing features of the big data.
A Non- relational SQL database helps in identifying the facilities for big data analytics. These uses are scalability, alternatives, and time testing equipments. Several organizations use these applications. Examples of these organizations are Amazon, LinkedIn etc. there are several advantages of using Non- SQL databases in big data (Alexandrov et al. 2014). These advantages are explained as follows:
|
Types of NoSQL databases |
Description |
Use in big data use case of Mercedes |
|
Key value store |
This contains the large amount of hash table data with their concerned key values and information. |
This is a type of schema that follows NoSQL format of database. This schema deals with the key value information stored in the hash tables. Therefore, this schema can be used in Mercedes to maintain their auto generated databases. |
|
Document bases storing system |
This database application of the SQL server helps to identify the tagged elements concerned with the non-relational databases (Cevher et al. 2014). |
In this type of NoSQL database, the key values and data storage features are used for checking the values. This feature will help Mercedes in storing the data with the data storage system concerned with the encoding structure of the NoSQL database. |
|
Column based storing system |
In this database management application, the No-SQL tagged elements are used. In this type of application, the column based system mainly focused in order to capture and block the storages. |
This type of database schemas uses the storage of the data related to the column cells. This aspect can helps to arrange the databases of Mercedes for managing the big data storage. This aspect will be helpful in managing aggregation of data on a single column basis. |
|
Graph based system |
This is a network-based database that uses the nodes and edges, which is represented and stored within the system table (Chen et al. 2012). |
This is a graph based schema that is used for the pictorial representation of database based on flexible and available data values. This feature can provide the transformation of schemas through one structure to another. In contrast with the structure of the database the nodes and links are helpful in forming a relation within the databases and other structures. |
Social media plays a great role in big data analytics in order to manage the data within the several organizations. The relational and No-SQL databases related to the big data management helps to make decisions within the concerned organization. In this report, the concerned organization is Mercedes (D’Anca et al. 2016). It is helpful for managing the databases within data analytics platform. This application of big data helps in decision-making perspective. The business objectives and goals become more oriented towards social life and perspectives. In case of the previous applications of SQL databases, it is clear that at that time the social media disrupted traditional operations (Fisher et al. 2012). The traditional influences cycle related to the NoSQL database the social media is helpful in promoting the features of big data. The managers of Mercedes are helpful in using and promoting the big data analytics with the help of the social media.
The social media to the big data analytics provide several facilities. These facilities are described as follows:
The big data value creation process is very much essential for the betterment of the organization. In contrast with the company chosen in this report for evaluating the impact of big data on Business intelligence are elaborated in this report. The applications of big data combines all the process related to the data management process (Kambatla et al. 2014). All the applications that are involved within the data management system used to meet the customer requirements. All of these processes mentioned within the big data management system meets the requirements of the customers. Several aspects must be involved within the value creation process of the big data. These aspects are explained as follows:
Conclusion
Big data is being used in organizations as well as in the social media and other data sources. Integration of the advanced analytics for using the big data is an essential step for achieving the good returns from the investments on information system. In this report, the importance of big data has been discussed with respect to the objectives and goals of Mercedes. All the important strategies mandatory for the customer data management is discussed here. In addition to this, the technology stack important for this organization has been discussed. In contrast with the concerned scenario of Mercedes, implementation of big data is analyzed. NoSQL for big data analytics are discussed with respect to the objectives of Mercedes that they have to deliver driving safety to their customers.
References
Alexandrov, A., Bergmann, R., Ewen, S., Freytag, J.C., Hueske, F., Heise, A., Kao, O., Leich, M., Leser, U., Markl, V. and Naumann, F., 2014. The Stratosphere platform for big data analytics. The VLDB Journal, 23(6), pp.939-964.
Analytics, B.D., 2013. Big data analytics for security.
Bennett, P., Giles, L., Halevy, A., Han, J., Hearst, M. and Leskovec, J., 2013, October. Channeling the deluge: research challenges for big data and information systems. In Proceedings of the 22nd ACM international conference on Conference on information & knowledge management (pp. 2537-2538). ACM.
Buyya, R., Ramamohanarao, K., Leckie, C., Calheiros, R.N., Dastjerdi, A.V. and Versteeg, S., 2015, December. Big Data Analytics-Enhanced Cloud Computing: Challenges, Architectural Elements, and Future Directions. InParallel and Distributed Systems (ICPADS), 2015 IEEE 21st International Conference on (pp. 75-84). IEEE.
Cevher, V., Becker, S. and Schmidt, M., 2014. Convex optimization for big data: Scalable, randomized, and parallel algorithms for big data analytics.IEEE Signal Processing Magazine, 31(5), pp.32-43.
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.
D’Anca, A., Conte, L., Palazzo, C., Fiore, S. and Aloisio, G., 2016, April. A big data approach for climate change indicators processing in the CLIP-C project. In EGU General Assembly Conference Abstracts (Vol. 18, p. 18285).
Fisher, D., DeLine, R., Czerwinski, M. and Drucker, S., 2012. Interactions with big data analytics. interactions, 19(3), pp.50-59.
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.
Ghazal, A., Rabl, T., Hu, M., Raab, F., Poess, M., Crolotte, A. and Jacobsen, H.A., 2013, June. BigBench: towards an industry standard benchmark for big data analytics. In Proceedings of the 2013 ACM SIGMOD international conference on Management of data (pp. 1197-1208). ACM.
Hu, H., Wen, Y., Chua, T.S. and Li, X., 2014. Toward scalable systems for big data analytics: A technology tutorial. IEEE Access, 2, pp.652-687.
Kambatla, K., Kollias, G., Kumar, V. and Grama, A., 2014. Trends in big data analytics. Journal of Parallel and Distributed Computing, 74(7), pp.2561-2573.
Kwon, O., Lee, N. and Shin, B., 2014. Data quality management, data usage experience and acquisition intention of big data analytics. International Journal of Information Management, 34(3), pp.387-394.
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.
Mercedes-benz.co.in. 2016. Welcome to the official website of Mercedes Benz India. Explore the information on the range of vehicles.. [online] Available at: https://www.mercedes-benz.co.in/content/india/mpc/mpc_india_website/enng/home_mpc/passengercars.html [Accessed 29 Sep. 2016].
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.
Rahm, E., 2016. Big Data Analytics. it-Information Technology.
Riggins, F.J. and Wamba, S.F., 2015, January. Research directions on the adoption, usage, and impact of the internet of things through the use of big data analytics. In System Sciences (HICSS), 2015 48th Hawaii International Conference on (pp. 1531-1540). IEEE.
Shang, W., Jiang, Z.M., Hemmati, H., Adams, B., Hassan, A.E. and Martin, P., 2013, May. Assisting developers of big data analytics applications when deploying on hadoop clouds. In Proceedings of the 2013 International Conference on Software Engineering (pp. 402-411). IEEE Press.
Slavakis, K., Giannakis, G.B. and Mateos, G., 2014. Modeling and optimization for big data analytics:(statistical) learning tools for our era of data deluge. IEEE Signal Processing Magazine, 31(5), pp.18-31.
Sun, J. and Reddy, C.K., 2013, August. Big data analytics for healthcare. InProceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 1525-1525). ACM.
Talia, D., 2013. Toward cloud-based big-data analytics. IEEE Computer Science, pp.98-101.
Tan, W., Blake, M.B., Saleh, I. and Dustdar, S., 2013. Social-Network-Sourced Big Data Analytics. IEEE Internet Computing, 17(5), pp.62-69.
Tsai, C.W., Lai, C.F., Chao, H.C. and Vasilakos, A.V., 2016. Big Data Analytics. In Big Data Technologies and Applications (pp. 13-52). Springer International Publishing.
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