\nIn all economies, especially in developing and transition economies, there is now a consensus among state policy\u00a0makers, development economists as well as international development partners that small and medium enterprises\u00a0(SMEs) are a potent driving force for their industrial growth and indeed, overall economic development. In recent\u00a0times, the concept of Big Data has been seen as a new solution to help in policy and practice in all sorts of\u00a0application context and domains. The impact of abundance data collected and stored over a number of years by\u00a0various organisations both public and private has led to many innovative data analytics technologies. The thrust of\u00a0this paper therefore is focusing on SME growth, that is, how to assist regional small business growth using Big Data.\u00a0Harnessing big data practice for SME growth has potential to challenge current decision making and policy\u00a0initiatives both at the government level (macro), as well as at the SME level (micro). Thispaper will assess the extent\u00a0to which Big Data can be harnessed for SME growth; and develop a systems based method for making intervention\u00a0based on Big Data practice for SME growth.<\/p>\n
Keywords<\/strong>: SMEs, Big data, Technology in SMEs, Security<\/p>\n I. INTRODUCTION<\/strong> The SMEs in India facing various challenges such as\u00a0the absence of adequate and timely institutional credit\u00a0facilities, limited capital and knowledge, lack of\u00a0access to technology and skilled manpower,\u00a0competition from large enterprises and globalisation.\u00a0These issues need to be addressed to tap the full\u00a0potential of the sector, which brings about social and\u00a0economic development of the country. SMEs are\u00a0facing competition from multinational corporations in\u00a0the domestic market [1]. Small & Medium\u00a0Enterprises Development Act, 2006 the Small and\u00a0Medium Enterprises (SME) are classified in two\u00a0Classes respectively.<\/p>\n 1. Manufacturing Enterprises: The enterprises\u00a0engaged in the manufacture or production of goods\u00a0pertaining to any industry specified in the first\u00a0schedule to the industries (Development and\u00a0regulation) Act, 1951). The Manufacturing Enterprise\u00a0is defined in terms of investment in Plant &\u00a0Machinery. A. Importance of Big Data B. Big data Characteristics In addition to big data challenges induced by\u00a0traditional data generation, consumption, and\u00a0analytics at a much larger scale, newly emerged\u00a0characteristics of big data has shown important trends\u00a0on mobility of data, faster data access and\u00a0consumption, as well as ecosystem capabilities.<\/p>\n In this paper, We studied a system that can scale to\u00a0handle a large number of sites and also be able to\u00a0process large and massive amounts of data. However,\u00a0state of the art systems utilizing HDFS and Map\u00a0Reduce are not quite enough\/sufficient because of the\u00a0fact that they do not provide required security\u00a0measures to protect sensitive data. Moreover, Hadoop\u00a0framework is used to solve problems and manage\u00a0data conveniently by using different\u00a0techniques.<\/p>\n C. Types of Big Data and Sources I. BIG DATA CHALLENGES TO INFORMATION\u00a0SECURITY AND PRIVACY Cloud Secure Alliance (CSA), a non\u00a0organization with a mission to promote the use of\u00a0best practices for providing security assurance within\u00a0Cloud Computing, has created a Big Data Working\u00a0Group that has focused on the major challenges to\u00a0implement secure Big Data services (Cloud Security\u00a0Alliance, 2013). CSA has categorized the different These security and privacy challenges cover the\u00a0entire spectrum of the Big Data lifecycle (Figure 2):\u00a0sources of data production (devices), the data itself,\u00a0data processing, data storage, data transport and data\u00a0usage on different evices.<\/p>\n It is clear that Big Data present interesting\u00a0opportunities for users and businesses; however these\u00a0opportunities are countered by enormous challenges\u00a0in terms of privacy and security (Cloud Security\u00a0Alliance, 2013). Traditional security mechanisms are\u00a0insufficient to provide a capable answer to those\u00a0challenges.<\/p>\n II. BIG DATA PROBLEMS AND CHALLENGES 1. Heterogeneity: 5. System Architecture:<\/p>\n Business data is examined and studied for many\u00a0purposes that might include system log analytics and\u00a0social media analytics for risk measurement,\u00a0customer retention, and brand management etc.\u00a0Typically, such diverse tasks have been handled by\u00a0separate systems, even if each system includes\u00a0common steps. The challenge here is not to build a\u00a0system that is ideally suited for all processing tasks.\u00a0Instead, the need is for the primary system\u00a0architecture to be flexible enough that the\u00a0components built on top of it for showing the various\u00a0kinds of processing tasks can tune it to expeditiously\u00a0run these different workloads. 1. Poor Implementation of Policies: there have been many good policies formulated in the\u00a0past by the government in developing\u00a0countries to improve SMEs, but weak\u00a0implementation has made it impossible to\u00a0realize the goal. 7. Poor Accounting System: the accounting\u00a0system of most small scale business\u00a0enterprises lack standard and does\u00a0 not make\u00a0room for the assessment of their\u00a0performances. That creates opportunity for\u00a0mismanagement, which consequently may\u00a0lead to enterprise failure.<\/p>\n 8. Unstable Policy Environment: Government\u00a0policy instability has not been helpful to\u00a0small scale businesses. That has been\u00a0destabilizing and has indeed sent many\u00a0SMEs to early fold-ups.<\/p>\n REFERENCES<\/strong> [8] Mr. Swapnil A. Kale1.Understanding the big data problems\u00a0and their solutions using hadoop and map-reduce.2014. ME\u00a0(CSE), First Year, Department of CSE, Prof. Ram Meghe\u00a0Institute Of Technology and Research, Badnera,Amravati.\u00a0Sant Gadgebaba Amravati University, Amarvati,\u00a0Maharashtra, India – 444701.<\/p>\n","protected":false},"excerpt":{"rendered":" pp. 31-35 Reena Singh and Ripuranjan Sinha Department of CSE, Suresh Gyan Vihar University, Jaipur *Corresponding Author – reena_mmm@rediff.com Abstract: In all economies, especially in developing and transition economies, there is now a consensus among state policy\u00a0makers, development economists as well as international development partners that small and medium enterprises\u00a0(SMEs) are a potent driving force […]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[18,41],"tags":[],"class_list":["post-924","post","type-post","status-publish","format-standard","hentry","category-journal-of-environment-science-and-technology","category-volume-2-issue-1-2016-journal-of-environment-science-and-technology"],"yoast_head":"\n
\nIn present scenario it is very common thinking that\u00a0Information Systems are useful for the organisations\u00a0in gaining the competitive advantage over the others,\u00a0it helps managers to take decisions according to the\u00a0requirement of the situation and according to the\u00a0available resources. Indian SMEs are considered as\u00a0the backbone of economy contributing to 45% of the\u00a0industrial output, 40% of India\u2019s exports, employing\u00a060 million people, create 1.3 million jobs every year\u00a0and produce more than 8000 quality products for the\u00a0Indian and international markets. With approximately\u00a030 million SMEs in India, 12 million people expected\u00a0to join the workforce in next 3 years and the sector\u00a0growing at a rate of 8% per year, the SMEs are\u00a0deploying information technology to take the\u00a0substantial advantage from it.<\/p>\n
\n2. Service Enterprises: The enterprises engaged in\u00a0providing or rendering of services and are defined in\u00a0terms of investment in equipment.<\/p>\n![\"\"](\"http:\/\/www.gyanvihar.org\/journals\/wp-content\/uploads\/2018\/12\/1-16.jpg\")
<\/p>\n
\nThe government\u2019s emphasis is on how big data\u00a0creates \u201cvalue\u201d \u2013both within and across disciplines\u00a0and domains. Value arises from the ability to analyze\u00a0the data to develop actionable information. There cab\u00a0be five generic ways that big data can support value\u00a0creation for organizations.
\n1. Creating transparency by making big data openly\u00a0available for business and functional analysis\u00a0(quality, lower costs, reduce time to market, etc.)
\n2. Supporting experimental analysis in individual\u00a0locations that can test decisions or approaches, such\u00a0as specific market programs.
\n3. Assisting, based on customer information, in defining\u00a0market segmentation at more narrow levels.
\n4. Supporting Real-time analysis and decisions based on\u00a0sophisticated analytics applied to data sets from\u00a0customers and embedded sensors.
\n5. Facilitating computer-assisted innovation in products\u00a0based on embedded product sensors indicating\u00a0customer responses.<\/p>\n
\nOne view, espoused by Gartner\u2019s Doug Laney\u00a0describes Big Data as having three dimensions:\u00a0volume, variety, and velocity. Thus, IDC\u00a0(International Data Corporation) defined it: Big data\u00a0technologies describe a new generation of\u00a0technologies and architectures designed to\u00a0economically extract value from very large volumes\u00a0of a wide variety of data, by enabling high-velocity\u00a0capture, discovery, and\/or analysis.\u201d Two other\u00a0characteristics seem relevant: value and complexity.
\nWe summarize these characteristics as given below.
\n1. Data Volume:
\nData volume measures the amount of data available\u00a0to an\u00a0organization, which does not necessarily have to own\u00a0all of it as long as it can access it. As data volume\u00a0increases, the value of different data records will\u00a0decrease in proportion to age, type, richness, and\u00a0quantity among other factors.
\n2. Data Velocity:
\nData velocity measures the speed of data creation,\u00a0streaming, and aggregation. Ecommerce has rapidly\u00a0increased the speed and richness of data used for\u00a0different business transactions (for example, web-site\u00a0clicks). Data Variety: Data variety is a measure of the\u00a0richness of the data representation \u2013 text, images\u00a0video, audio, etc.
\n3. Data Value:
\nData value measures the usefulness of data in making\u00a0decisions. It has been noted that \u201cthe purpose of\u00a0computing is insight, not numbers\u201d. Data science is\u00a0exploratory and useful in getting to know the data,\u00a0but \u201canalytic science\u201d encompasses the predictive\u00a0power of big data.
\n4. Complexity:
\nComplexity measures the degree of\u00a0interconnectedness (possibly very large) and\u00a0interdependence in big data structures such that a\u00a0small change (or combination of small changes) in\u00a0one or a few elements can yield very large changes or\u00a0a small change that ripple across or cascade through\u00a0the system and substantially affect its\u00a0behavior, or no change at all.<\/p>\n
\nThere are two types of big data: structured and\u00a0unstructured.
\n1. Structured Data:
\nStructured Data are numbers and words that can be\u00a0easily categorized and analyzed. These data are\u00a0generated by things like network sensors embedded\u00a0in electronic devices, smart phones, and global\u00a0positioning system (GPS) devices. Structured data\u00a0also include things like sales figures, account\u00a0balances, and transaction data.
\n2. Unstructured Data:
\nUnstructured Data include more complex\u00a0information, such as customer reviews from\u00a0commercial websites, photos and other multimedia,\u00a0and comments on social networking sites. These data\u00a0cannot easily be separated into categories or analyzed\u00a0numerically. The explosive growth of the Internet in\u00a0recent years means that the variety and amount of big\u00a0data continue to grow. Much of that growth comes\u00a0from unstructured data.<\/p>\n![\"\"](\"http:\/\/www.gyanvihar.org\/journals\/wp-content\/uploads\/2018\/12\/1-17.jpg\")
<\/p>\n
\nWith the proliferation of devices connected to the\u00a0Internet and connected to each other, the volume of\u00a0data collected, stored, and processed is increasing\u00a0everyday, which also brings new challenges in terms\u00a0of the information security. In fact, the currently used\u00a0security mechanisms such as firewalls and DMZs\u00a0cannot be used in the Big Data infrastructure because\u00a0the security mechanisms should be stretched out of\u00a0the perimeter of the organization’s network to fulfill\u00a0the user\/data mobility requirements and the policies\u00a0of BYOD (Bring Your Own Device). Considering\u00a0these new scenarios, the pertinent question is what\u00a0security and privacy policies and technologies are\u00a0more adequate to fulfill the current top Big Data\u00a0privacy and security demands (Cloud Security\u00a0Alliance, 2013). These challenges may be organized\u00a0into four Big Data aspects such as infrastructure\u00a0security (e.g. secure distributed computa\u00a0MapReduce), data privacy (e.g. data mining that\u00a0preserves privacy\/granular access), data management\u00a0(e.g. secure data provenance and storage) and,\u00a0integrity and reactive security (e.g. real time\u00a0monitoring of anomalies and attacks).\u00a0Considering Big Data there is a set of risk areas that\u00a0need to be considered. These include the information\u00a0lifecycle (provenance, ownership and classification\u00a0of data), the data creation and collection process, and\u00a0the lack of security procedures. Ultimately, the B\u00a0Data security objectives are no different from any\u00a0other data types \u2013 to preserve its confidentiality,\u00a0integrity and availability.\u00a0Being Big Data such an important and complex topic,\u00a0it is almost natural that immense security and privacy\u00a0challenges will arise (Michael & Miller, 2013;\u00a0Tankard, 2012). Big Data has specific characteristics\u00a0that affect information security: variety, volume,\u00a0velocity, value, variability, and veracity (Figure 1).\u00a0These challenges have a direct impact on the design\u00a0of security solutions that are required to tackle all\u00a0these characteristics and requirements (Demchenko,\u00a0Ngo, Laat, Membrey, & Gordijenko, 2014).\u00a0Currently, such out of the box security solution does not exist.<\/p>\n![\"\"](\"http:\/\/www.gyanvihar.org\/journals\/wp-content\/uploads\/2018\/12\/1-18.jpg\")
<\/p>\n
\nsecurity and privacy challenges into four different\u00a0aspects of the Big Data ecosystem. These aspects are\u00a0Infrastructure Security, Data Privacy, Data\u00a0Management and, Integrity and Reactive Security.\u00a0Each of these aspects faces the following security\u00a0challenges, according to CSA:
\n1. Infrastructure Security :
\n1. Secure Distributed Processing of Data
\n2. Security Best Actions for Non\u00a0Data-Bases
\n2. Data Privacy :
\n3. Data Analysis through Data Mining\u00a0Preserving Data Privacy
\n4. Cryptographic Solutions for Data\u00a0Security
\n5. Granular Access Control
\n3. Data Management and Integrity
\n6. Secure Data Storage and Transaction Logs
\n7. Granular Audits
\n8. Data Provenance
\n4. Reactive Security :
\n9. End-to-End Filtering & Validation
\n10. Supervising the Security Level in Real\u00a0Time<\/p>\n![\"\"](\"http:\/\/www.gyanvihar.org\/journals\/wp-content\/uploads\/2018\/12\/1-19.jpg\")
<\/p>\n
\nThe problem comes straight way when the data\u00a0tsunami requires us to make specific decisions, about\u00a0what data to keep and what to reject, and how to store\u00a0what we keep reliably with the right metadata.\u00a0Transforming unstructured content into structured\u00a0format for later analysis is a major challenge. Data\u00a0analysis, organization, retrieval, and modelling are\u00a0other foundational challenges. Since most data is\u00a0directly generated in digital format today, the\u00a0challenge is to influence the creation so as to\u00a0facilitate later linkage and to automatically link\u00a0previously created data.<\/p>\n![\"\"](\"http:\/\/www.gyanvihar.org\/journals\/wp-content\/uploads\/2018\/12\/1-20.jpg\")
<\/p>\n
\nMachine analysis algorithms expect homogeneous\u00a0data, and cannot understand fine distinction.\u00a0Computer systems work most expeditiously if they\u00a0can store multiple items that are identical in size and\u00a0structure. So, data must be carefully structured as a\u00a0first step in (or prior to) data analysis.
\n2. Scale:
\nThe first thing that anyone thinks of in Big data is its\u00a0size. Managing large and rapidly increasing volumes\u00a0of data has
\nbeen an exigent issue for many decades. In the past,\u00a0this challenge was palliated by processors getting faster. But there is a fundamental shift happening\u00a0now: \u2018data volume is scaling faster than computer\u00a0resources\u2019. Unluckily, parallel\u00a0data processing techniques useful in the past for\u00a0processing data across nodes don\u2019t directly apply for\u00a0intra-node parallelism, since the architecture is very different.
\n3. Timeliness:
\nThe larger the data set to be processed, the longer it\u00a0will take to analyze. The design of a system that\u00a0effectively deals with size is likely also to result in a\u00a0system that can process a given size of data set faster.\u00a0However, it is not just this speed that is usually meant\u00a0when one speaks of Velocity in the context of Big\u00a0Data. There are many situations in which the result of\u00a0the analysis is required immediately. Given a large\u00a0data set, it is often essential to find elements in it that\u00a0meet a precise criterion. Scanning the entire data set\u00a0to find suitable elements is obviously unfeasible.
\n4. Privacy and Security:
\nA key value proposition of big data is access to data\u00a0from multiple and diverse domains, security and\u00a0privacy will play a very important role in big data\u00a0research and technology. In domains like social media and health information, more data is gathered\u00a0about individuals, so there is a fear that certain\u00a0organizations will know too much about individuals.\u00a0Developing algorithms that randomize personal data\u00a0among a large data set so as to ensure privacy is a
\nkey research problem.<\/p>\n
\nIII. PROBLEMS OF SMALL AND MEDIUM\u00a0ENTERPRISES
\nBaadom (2004) asserted that the following problems\u00a0militate against the effective operation of small and\u00a0medium enterprises;<\/p>\n
\n2. Lack of Continuity: most small scale\u00a0establishments are sole proprietorship and\u00a0such establishment often ceases to function\u00a0as soon as the owner loses interest or dies.\u00a0This raises the risk of financing such\u00a0business.
\n3. Poor Capital Outlay: inadequate capital outlay has often affected small scale\u00a0business adversely. Financiers often regard\u00a0the sector has high risk area and therefore\u00a0feel skeptical about committing their fund to\u00a0it.
\n4. Poor Management Expertise: Management\u00a0has always been a problem in this sector as\u00a0most small scale businesses do not have the\u00a0required management expertise to carry\u00a0them through once the business start\u00a0growing. The situation gets compounded as\u00a0training is not usually accorded priority in\u00a0such establishments.
\n5. Inadequate Information Base: Small scale\u00a0business enterprises are usually\u00a0characterized by poor record keeping and\u00a0that usually starve of necessary information\u00a0required for planning and management\u00a0purposes. This usually affects the realization\u00a0of the sector.
\n6. Lack of Raw Materials: In some small scale\u00a0business enterprises, raw materials are\u00a0sourced externally, hence the fate of such\u00a0\u00a0enterprises to foreign exchange behavior.\u00a0The fluctuation of foreign exchange may\u00a0therefore make it difficult to plan and that\u00a0\u00a0may precipitate same stock that may\u00a0destabilize the setup.<\/p>\n
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