Discuss about the Application of Big Data In Business.
The advancement of technology and its adoption in running of various businesses has shown great strides made by different organizations and companies. Technology has been identified as an essential item in the running of any business, and various managers and business owner have incorporated its use in the businesses (Bughin, Chui, & Manyika, 2010). The ever advancing technology led to the innovation of Big Data Solutions, which has been accepted by many businesses and organizations in running their businesses.
According to (Paper, 2013), Big Data can be said to be the combination of large volumes of data in terabytes or petabytes from different data sources that are distributed physically. These data are often unstructured, structured, semi-structured and poly-structured. Some of the data are generated at very high speed and sometimes expired at the very same high speed (Chaudhuri, Dayal, & Narasayya, 2011). The White paper says that for a significant value to be realized by the organization, then various aspects are combined in the big data. These aspects include; the large data volumes, the different data types, the varied lines of evidence and the high speed in which they are generated.
The Big data, according to (Chen, Chiang, & Storey, 2012), provides a prospective fortune trove that is not traditional, fewer well thought-out records that can be extracted for helpful data. Examples of these structured data can be from social media platforms, emails, sensors and photographs/ videos. (Chen, Chiang, & Storey, 2012), further illustrates that Big Data is a combination of these three types of data;
Traditional enterprise data that incorporates transactional data, information from customers, general ledger data and web store transactions. Sensor/ machine generated data. It combines all the data from call detail record, manufacturing sensors, web logs, gear logs, elegant meters, and trade system records. Societal records that encompasses social media platforms, micro blogging websites, and customer feedback streams.
Big data require different kinds of technologies majorly software, and according to Oracle (2013), some of these Softwares include;
According to (Fan, Lau, & Zhao, 2015), various technologies exist for significant data based on Forrester analysis. They include;
Predictive analytics that analyzes bug data through deployment of predictive models to mitigate risks and improve the performance of business. Tools for search and knowledge discovery that support self-extraction of information and data from both structured and unstructured sources. Software for stream analysis that can aggregate, filter and scrutinize a large volume of records from numerous different information sources. In memory records fabric; software that enables the dispensation of large quantity of information and enhances the provision of low latency access throughout a computer system. Mongo DB, Amazon Elastic MapReduce (EMR), Apache Pig, Apache Spark Couch Base, and Apache Hive. These are tools for data orchestration/integration across all solutions.
Many organizations and businesses always want to get the best out of all systems they incorporate, and Big Data cannot be left out (Fujitsu, 2012). To select the best Big Data, then the exact unique requirements that will enhance smooth incorporation into the business without any hitches. According to (Gandomi & Haider, 2015), these three major requirements and steps should be considered;
The infrastructure required for data capturing and acquisition should be able to deliver very low predictable expectancy and capture data in executing straightforward and short queries (Katal, Wazid, & Goudar, 2013). These should be able to handle enormous capacities of data frequently in a disseminated environment and support distributed, even, dynamic data.
The infrastructure required for the Integration of data should have the capacity to operate data at the source in both structured and unstructured formats saves both money and time for moving about huge amounts of information (Lohr, 2012). The infrastructure should also be to deal with comprehensive data processes and support their output.
The required infrastructure must be able to sustain deeper analytics in addition to a larger multiplicity data types, deliver quicker response time based on behavioral change, automation of decisions based on models used in analysis and sale to extreme data volumes.
Desired outcomes should incorporate features from Hadoop; a system that is used primarily for an organization of structured data sets for analysis of Big Data while maintaining their footprints as data warehouses. Hadoop combines the use of Not Only SQL (NoSQL), which is used as solutions for developer-centric specialized systems and SQL which is the most trusted and secure type of relational database management systems (Mayer-Schonberger & Cukier, 2013).
Among the superior technologies mentioned for Big Data, let’s talk about Mongo DB for instance as one of the applications used for marketing. According to social media websites, Mongo DB is the software that is designed to enable the storage of data and information in the cloud storage system (DB, 2016). When used, it automatically displays data and allows the balancing of queries across various clusters. The data is managed from redundant servers hence access of data is enabled even when offline and delivered as a service to the end users. According to (DB, 2016), Mongo DB is combined with other software like Hadoop and NoSQL to ensure complex queries are performed while the efficiency and ease of use are maintained.
When these combinations are made, advertising is made easier and assessable to the customers and businesses can capitalize on their Big Data marketing hence improved innovation and successes (Mayer-Schonberger & Cukier, 2013). The advantages that can be found from using Mongo DB include;
It provides both offline and online solutions for a long term analysis. It facilitates informed decision-making processes in the analysis of data and the advertising of products or services. Enhances the combination of both offline and online Big Data technologies. These ensure that data is processed in batches and enhanced joining of multiple documents and maintain operations like the standard deviation (Minelli, Chambers, & Dhiraj, 2012). An example is the combination of Hadoop and Mongo DB.
It provides a dynamic data model that can be used easily thereby the best online data solution for marketing. Some of the other features in Mongo DB that are mentioned on the site apart from the ones already mentioned include;
It enables the creation of ad hoc queries in all the fields involved in the analysis of Big Data hence less costly. Indexing both in primary and secondary indices is enabled for documents used in Mongo DB. Copies of data can be created, written, read o stored/maintained by the use of MongoDB interchangeably in primary and secondary data structures. Balanced distribution of data in a process known as shading over multiple servers and the duplication of data to maintain the system and even keep it running during hardware failure.
Despite the above, some disadvantages can also be seen from Mongo DB and include; the security level of data is not 100% guaranteed, stale reads could be returned on failure to read between two different Mongo DB processes and queries against an index may miss documents that are in the course of being updated since they aren’t atomic.
Evolution of technology has made data analysis very easy for the analysts and replaced conventional methods of storage and analysis of data. According to (Press, 2016), the values found in Big Data include;
It makes the analysis of data easier since it allows al kind of data, structured, semi-structured or unstructured, unlike the traditional method which only used relational databases that did not allow unstructured and semi-structured data (Russom, 2011). It has made data analysis easier since only relevant information can be queried and leaving out the ones that are not relevant. It accommodates large amounts of data which is not structured and further allows the more efficient access to data due to the flexible query language used. It is different with the rigid query system from the relational database from the traditional method.
According to (Zicari, 2012), working with data either offline or online has very different characteristics regarding;
Offline Big Data involves applications that change, transform, and manage data in group perspective and do not generate information while, Online Big Data is ingested, formed, managed and modified /examined to maintain equipped applications and the users. The volume of data generated online is higher and from newer sources compared to that which is created offline. This creates a problem in the discovery of quality relevant data, comprehensiveness and scalability and the course of timely scrutiny and delivery of results hence the need for Big Data solutions (Sharda, Delen, & Turban, 2013).
The management challenges arising from online data since some may be sensitive and need; privacy, security, ethics and proper governance procedures without destroying the reputation of the business (Vera-Baquero, Colomo-Palacios, & Molloy, 2013). On the other hand, files containing offline data that contain sensitive information could only be handled by authorized personnel to preserve their privacy until they are worked on.
Online generated data faces technological obsolescence problems. Since it is still developing, the data and new technology constantly require newer skills that may end up being costly (Chen, Chiang, & Storey, 2012). Offline data, on the other hand, is analyzed by the same methods or newer ones but the system of collection, analysis, and storage may be the same hence fewer inconveniences. Online generated data requires the internet connection to be accessed and worked on while offline data can be accessed and worked on without internet connectivity.
Various businesses have seen positive results from Big Data, and according to (Paper, 2013), some include;
Many organizations can discover facts and insights about their customers who were hidden in the past. Many businesses and organizations can be able to build up a more meticulous perceptive appreciative of their industry that will lead to a robust competitive spot in the market; improved innovation hence enhanced productivity. It provides a perfect measure for digital advertising and in turn leads to retaining of customers at a less expensive method. There is creation of more accurate measures of assessment thereby the optimization of strategies for distribution and production.
The insights and strategies of different suppliers and other competing businesses can be identified from the unstructured data. Discrete information from the markets, market demands, and their operation will be available for the scrutiny and use of the business. Root causes of issues or complaints and comments by customers and the costs of goods and services will be identified from the Big Data hence enables the organizations to adjust. The potential risks that may be facing the organizations and any impending dangers could be identified from the Big Data. Predictions business opportunities and trends that will improve the operational, tactical and strategic decisions can be found from Big Data.
Conclusion
Technology is ever evolving and many businesses and organizations have adopted it for use in efficient running of their activities. The adoption and incorporation of Big Data into the organization has even led to more effective methods of data analysis. The incorporation of technologies like Mongo DB in marketing of organizational products and businesses. More users will be able to access and be served, more insights will be created and used easily and the results will be of more value to the business world. Big data is very important and when correctly managed with the right skills, can produce better results that lead to a stronger competitive position in the market, improved innovation and enhanced productivity.
References
Bughin, J., Chui, M. and Manyika, J., 2010. Clouds, big data, and smart assets: Ten tech-enabled business trends to watch. McKinsey Quarterly, 56(1), pp.75-86.
Chaudhuri, S., Dayal, U. and Narasayya, V., 2011. An overview of business intelligence technology. Communications of the ACM, 54(8), pp.88-98.
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.
DB, M. (2016). Bringing Online Big Data to Business intelligence & analytics. A Mongo DB White Paper .
Fan, S., Lau, R.Y. and Zhao, J.L., 2015. Demystifying big data analytics for business intelligence through the lens of marketing mix. Big Data Research, 2(1), pp.28-32.
Fujitsu. (2012). White Paper: Solutions approaches for Africa.
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.
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.
Lohr, S., 2012. The age of big data. New York Times, 11(2012).
Mayer-Schönberger, V. and Cukier, K., 2013. Big data: A revolution that will transform how we live, work, and think. Houghton Mifflin Harcourt.
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.
Paper, O. W. (2013). Big Data for the Enterprise.
Press, G. (2016). Top 10 Hot Big Data Technologies. Forbe.com .
Russom, P., 2011. Big data analytics. TDWI best practices report, fourth quarter, pp.1-35.
Sharda, R., Delen, D. and Turban, E., 2013. Business Intelligence: A managerial perspective on analytics. Prentice Hall Press.
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.
Zicari, R. (2012). Big Data: Challenges and Opportunity.
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