BUSINESS ENTERPRISE AND BIG DATA: EVOLVING DATABASE CHALLENGES AND APPROACHES

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1 Volume 119 No , ISSN: (on-line version) url: ijpam.eu BUSINESS ENTERPRISE AND BIG DATA: EVOLVING DATABASE CHALLENGES AND APPROACHES D.Prabha 1, P.Manivanan 2, K.Devipriya 3,Ms. A.Priya 4 1 Associate Professor, Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Coimbatore. prabha@skcet.ac.in 2 Assistant Professor, Department of Electronics and Communication Engineering, Park College of Technology, Coimbatore. 3,4 Asistant Professor, Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Coimbatore. devipriya@skcet.ac.in ldshree@gmail.com Abstract Presently, Small and Medium Businesses (SMBs) have required challenging trends on big data which is valuable assets that had needs technological approaches in accessing and controlling the database of an Enterprise System (ES) business strategy. However, the aim of this paper is to provide a scope of critical storage of big data into an ES, specifically focus on SMBs. Today, a more centralised single database is still common on an ES, but coming used less so. Because it's required to incorporate modern trends into initiatives the needs for evolving database centre. Therefore, a systematic literature review had conducted in the refereed journal, review, the survey made by leading enterprises. To ensure the data analytic excellence and to pave the way for single centralised storage excellence through MongoDB platform proven NoSQL cluster strategies access current database technology. As a result, critical big data gained much more important compared with the ES that most influence the success of SMBs. Key words: Small and Medium Businesses, Enterprises System, Big Data, Database, MongoDB. 627

2 1 INTRODUCTION Several factors are driving the ES looks beyond traditional technology on the infrastructure silos and transform the way that they need to view their data centre environment into the business processes. But, the data adoption for a business is connecting to storage-in-memory and running as a parallel processing engine. So, many of the business systems uses their internet together with the data that had to collected and storage in the ES of companies database of customers information. However, it may be posing the marketing teams do their physical activities onto the data that nay involved in transferring goods and services to utilise for the benefit of customers. In many situations, certain challenges have maintained the data privacy and keeping data safe. Big data has the significant shift on penetrating the real day-to-day value which has continues to remain a challenge. Always, big data employs volume, velocity, and variety [8]. Obviously, it can also be characterised as volume, velocity, variety, veracity, and value by 5 V's [6] and [7]. The company ES always keep the records of both an empirical and n workload for an opportunity for new data. The graph shown in figure 1 gives the highest priorities to 2 V s that required for SMBs. So, this paper proposes to investigate the changing volume and variety of the data onto characteristic requirement in the database which can build a new platform in SMBs. This study of paper had focused on the Business-to- Customer (B2C) companies that may concern with already existing data to handle the huge big data that is being collected, to store in the database. The new possible and generated MongoDB platform will access the critical impact on regular data search in the multiple of data with varieties then promised B2C records. According to Gartner reports the big data and business analytics software worldwide revenues will grow from nearly $122 billion in 2015 to more than $187 billion in By 2020, it has been predicted that the 40% of enterprises set new investment in big data analytics. Big data may provide insights unstructured data is provided by the company with potential data to compete and improve on areas they had limited visibility into before. For examples, the company's may include Customer Relational Management (CRM) service in SMB companies like healthcare services, banking, media service, retails business, poultry industries, educations, and insurance. 2 LITERATURE REVIEW 2.1 Business-specific big data challenges in SMBs 628

3 Several big data application scenarios of businesses aim for aligning the need for improving data quality [7]. Business organisations had taken the advantage of the MongoDB analytics solutions to adapt the changes in creating competitive differentiation in their database [9]. It doesn't mean that the ES can automate processes; will inform that the big data has been treated for the valued asset by focusing on new platform approach through extracting and developing [1]. It promises to speak to the pressure that improves marginal performance needs simultaneously enhancing responsiveness and delighting customers and business prospects. Figure 1 shows a new Mongo platform approaches to improve the ES database system. Also, it turns out a better technology and faster data-driven decisions on the database system. International Data Corporation (IDC) forecasts that the U.S. markets for big data and business analytics solutions will reach more than $98 billion within five years. The specific B2C market segments of 35 companies are analysed that they emerge from ES as operational databases, analytic databases for data reporting and analytics. Hadoop also used as a searchbased data platforms. Figure 1. ES with new platform approach The data collected from the analytical questionnaire about an that is Use of MongoDB in ES and its advantages. However, 240 respondents replied with this mail were 30% given it s the good solution for improving, 25% was given best their supports and 5% with a certain point of improving thoughts. As a result, figure 2 shows percent system use of the interactive data onto visualisation and accessibility not satisfied in SMBs enterprise. Table.1 shows the risk of the big data onto the view of data volume and variety with the potential of high and low onto big data opportunities in the different B2C including banking sectors, media service sectors, educations, healthcare, insurance, manufacturing industries, and retails 629

4 business. Moreover, good data centre practice, with new technologies can keep the enterprise market and runs their facilities with agility. Figure 2. The interactive data from SMBs Table.1 Risk given with data volume and variety in SMBs High Low Volume of Data Banking, Media service, Retails business, Poultry industries Educations, Healthcare, Insurance Variety of Data Healthcare, Media service,, Retails business, Insurance Banking, Educations, Poultry industries Health care services have needed high impact insights on varieties of data that can only be realised and accomplished onto the datasets that are willing to share their data assets [9]. However, the actionable data analytics requires specialised tools to perform better [6]. Poultry farms industries with more data than ever before available and collected to ES that they may access automatically the collected big data is necessary to ensure good inventory levels. Otherwise, the main challenges reports make running on lack of quality feeds, market and credit [5]. 630

5 Retailer business has their customer interaction with the high impact on ES which requires both data-driven and data analytics new database platforms to assist to provide the successful services. The US-based shops use Hadoop for processing big data (10 million data) from sale forecasts [4]. However, retail data increases exponentially for data variety, volume, value, and velocity to holds potential for profit. Gartner predicts an approximately $232 billion for retailer investment may be done on big data analytics by Design of MongoDB platform MongoDB provides to handle the operational data that are created, stored, retrieved and analyzed by big data applications. Mostly, ES in the business company evolve into data to handle in cloud device and needs to facilitate communications between multiple organisations. MongoDB is one the NoSQL supports database is a big opportunity and more flexible and highly scalable. The ES had the relational database of Hadoop cluster of data analysis and used to deliver built business architecture of traditional enterprises to extract valuable mission critical analytic application. Normally, the large industries use HBase for storing, processing and analysing distributed semantic. The SMBs having B2C marketing strategies need to simplified big data technologies database platform like MongoDB to access at technical and consumption levels using characteristic of data volume, velocity, variety, veracity, and value as in figure 3. These evaluate by giving in five Likert Scale on strongly agreeing to strongly disagreeing and figure 3 shows the plotted in of most marketing enterprises required volume and varieties. MongoDB takeover the shift of NoSQL databases as a leading piece of the ES has the benefits concepts becomes more pronounced. Gartner's Magic Quadrant for operational Database Management Systems (DMS), which the most NoSQL database, including MongoDB, DataStax, Redis Labs, Mark Logic and Amazon Web Services vendors from Oracle, IBM, Microsoft and SAP. The valuable customer data onto ES systems, integrate to move by MongoDB platforms to their database destinations faster than ever used database includes. 631

6 Figure 3 The characteristic of data level of usage Identifying customers: All the business believes that the marketing department was targeting customer than the individual department. As a result, the B2C shifted as better marketing strategies. Identifying customers' data: Makes requirement templates and report requests have a pinpoint that the business intelligence system with governing data visualization which gives greater marketing decisions. Identifying market gaps: Data analytics led us to build a more robust market opportunity to target a new market and makes the business and marketing sales force automation. 2.3 Database on Replication control MongoDB platform led legacy ES software uses into the world of cloud development. The platform has of containerised application of the replica support of the scalability and resiliency. Enterprises system can't deliver the data as quickly they need. Otherwise, want user s knowledge of back-end approach to internal database development. Else, need external users to improve the data onto high varieties. Also, it allows the content of caches with replication on fixed volumes of the cloud system [2]. Midmarket enterprises have the fastest growth rate of big data initiatives. Also, small businesses consider that big data only bring to governing to Business- To-Business (B2B). However, use the huge data that they can improve on day-today activities almost in every carrier growth of the business. Always, big data refers to enormously for large amounts of data, with database storage in few SMBs. If it comes to big data, in practice requires large data sets. As a result, an SMB does not have to be big to provide business and technology insights in areas such as internal operations, customer behavior, campaign effectiveness, and market opportunities; it can be captured to store and use the data onto B2C. Normally, smaller business has to face the unique challenges, however, and must navigate around ES implementation for the data analytics. It's just to make huge data to mine through, especially for an SMB, to avoid data collection efforts do not have queries. Unfortunately, many SMBs are not sure how to the data and looking in sequence to collect for beginning organised database platform. However, ES usually have to capture the transactional data, before consolidating the data into one place of the centralised database. Recently becomes older ones, uncontrolled data translates into exceptions or undocumented differences in the business. 632

7 Data is stored not only for the data perspective but also for the functional perspective this brings together into central database onto analytics base. A survey conducted with technical consultants related to the data and data analyst for controlling the database in ES. MongoDB is a challenging NoSQL that s quick and speedily runs to storage the data onto microseconds, improve the degree of accuracy, and the data storage depends on the business size. The SMBs bring to collect and consolidate all the useful data that is being gathered at a point. However, it can be used for high impact valuable assets that are necessary for the business. 3 RESULTS AND DISCUSSIONS Open sources MongoDB techniques may analyse to serve the customers for value proposition, without affecting the outcomes of the business and performance is analysed and plotted as a graph in figure 4. It s important that the big data needs to drill down into the operating levels and finds the solution by using characteristic 5V s, newly added with variability, and visualization and denoted by 7V s. It ensures making the enterprises more customer-centric; sharpening the focus on key data initiatives that lead to entering into new markets, creating new business models opportunity, and improving operational performance. In B2C company always shares there's valuing is forecasted and tries to organise a data set to accomplish the better outcome. The new MongoDB technologies emerging as the open-source system had been altering to ES that can gather the data for improving the economy, ultimately producing tangible benefits on business companies. AI and Machine learning and data mining investigate big data has huge research potential for future lies [7] and [3]. 633

8 Figure 4. MongoDB performance in SMBs 4. Conclusions Single source of the ES that can allow focusing on big data to take actions in processing all that data collected that had more to value can have the facility of storage in a centralised database. The dataset in the ES needs additional trends of setup to keeping the reports by providing the advanced database. In recent major ES, technology appears to follow the big data pattern through MongoDB brings more advantages than Hadoop clusters. As a result, NoSQL solution that handles the scaling issues, will achieve in product-marketing fit and can make a MongoDB technology decision based on greater understanding of the business needs. The study says that from 6 percent of big data investment, 9 percent of the companies that made presumably benefited from MongoDB. It delivers universal business remedy and provides with understandable needs of a business on the expected results. The featured research makes enable shard in MongoDB architecture to guarantee data transactions with linear and scale outgrowth, without running out of the budget. References 1. A.S. Drigas, P. Leliopoulos, The Use of Big Data in Education, International Journal of Computer Science Issues, 11, No. 5(2014), E.B. Nascimento, E.D. Moreno, D,M, Dyllon Jeronimo, A Programmable Network Architecture for Information Centric Network using Data Replication in Private Clouds, IEEE 26th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises, (2017), F. Radu Babiceanua, R. Seker, Big Data and virtualization for manufacturing cyber-physical systems: A survey of the current status and future outlook, 81(2016), H-K. Lin, C-I. Chen, Big Data Hadoop for Cloud-Based Enterprise Collaboration Systems, International Journal of Economics & Management Sciences, 4, No.1(2015), K. Lokesh, R. Shalini, K. Joshi, Comparative analysis of NoSQL (MongoDB) with MySQL Database, International Journal of Modern Trends in Engineering and Research, 2(2015),

9 6. M. Dayal, S. Nanhay, (2016)Indian Health Care Analysis using Big Data Programming Tool, Twelfth International Multi-Conference on Information Processing, 89(2016), S. Doruk, O. Melike, O. Vayvay, An Overview of Big Data for Growth in SMEs, 12th International Strategic Management Conference, 235(2016), S. Sonka, Big Data Characteristics, International Food and Agribusiness Management Review, 19, No.1(2015), S. Zillner, S. Neururer, Big Data in the Health Sector, New Horizons for a Data-Driven Economy, (2016),

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