How to Improve Enterprise Operation Management - Literature review Example

Literature review The traditional enterprises how to grasp the development direction of Big Data to improve on enterprise operation management Literature review Most of the literature on Big Data fails to research on how the traditional enterprises can grasp the development direction of Big Data to improve on enterprise operation management. This is an area that has received low research despite its importance. The gap left by researchers makes it hard to adopt adequate information for the traditional enterprises as they adopt Big Data. This makes it hard for traditional enterprises can embrace Big Data successfully. According to Rajpurohit (2013), it is evident that Big Data can help in shaping operation management decision making. This is due to fact that the main role of enterprise operation management is to make major business decisions. Simon (2013) asserts that there is need to have a clear and concise interpretation for Big Data and how it can be used for enterprise operation management. To gain research on this, there is need to have an in-depth view on Big Data, its role in decision making, challenges of Big Data and how traditional enterprises are grasping it. Chen, Mao and Liu (2014) claim that in the era of internet communication, Big Data solutions continue playing a major role in enterprise operation management. Chen, Chiang and Storey, (2012) asserts that the amount of information transmitted through the internet continues growing each and every day. Such huge amount of data is very beneficial for running of an enterprise. For the traditional enterprises, they have been adopting the development direction of Big Data with an aim of enhancing their operational management. According to Katal, Wazid and Goudar (2013) Big Data is being looked as a definitive source of a competitive advantage. Through combining the Big Data with business analytics, it becomes possible to gain insights into consumer behavior in a better manner compared to traditional approach. Big Data development directions continue constituting a basis for functionality for the enterprises. There have been rise of vendors for Big Data including Cisco Solution Hadoop, Oracle and Terradata (Chen, Mao and Liu, 2014). Chen, Chiang and Storey (2012) assert that through adopting Big Data development direction, it is aimed that it will become possible for traditional enterprises to transform various organizational processes. This includes procurement, product development, manufacturing, distribution, marketing, pricing, merchandising, store operations and human resources. Through Big Data, it becomes possible to enhance transparency in the enterprise management. This is through making of Big Data more accessible to all relevant stakeholders at the right time. It also makes it possible to discover needs, enhance variability and improve on performance. Chen, Mao and Liu (2014) claim that the management is helped through accurate and detailed performance data on all business aspects. Big Data is also associated with the ability to segment population in the right manner and replace human decision making with automated algorithms. Data mining process is a continuous process in the Big Data. Fan and Bifet (2013) assert that in the past few years, the growth of data in size and number is high. There are a thousand of employees working in data mining. The explosion of connected devices high internet access, social media and the increase in user generated content has driven the high level of data. According to Chen, Chiang and Storey (2012) Big Data includes the traditional enterprise data. This includes the customer information gained from the CRM systems and ERP, transactions and ledgers. There is also machine generated data such as call details record and equipment logs. Katal, Wazid and Goudar (2013) assert that social data includes the customer feedback, reviews, and the social media platforms. Based on the current development direction of Big Data, it is expected that there is 40% annual growth of the Big Data. Thus, Big Data is expected to grow 44 times between 2009 and 2020 (Chen, Mao and Liu, 2014). For the traditional enterprises, use of Big Data together with the traditional enterprise data leads to an insightful understanding of business hence helping on management. For most of the traditional enterprises, their critical data from various transactions is stored in relational databases (Fan and Bifet, 2013). This leaves a lot of unstructured data which is generated by the emails, blogs, social media, surveys and scientific experiments. This is data which when mined can be of great help to the business intelligent decisions and management. According to Chen, Chiang and Storey (2012), nontraditional data accounts for about 80% of the enterprise data. This is data that is growing fast over the years. Simon (2013) asserts that enterprises have come up with ways to combine traditional and non-traditional data under the enterprise management strategy. This is what led to the development of the Big Data technologies. The journey to grasp the Big Data by traditional enterprises has started with an aim of helping enterprises in their management. Traditional enterprises have started leveraging on the Big Data. Chen, Chiang and Storey (2012), claim that this is evidenced by the rise of demand for the various online enterprise management systems. Most of the enterprises are already tracking unstructured data online. Some of the enterprises have started investing in development of data warehouses. Chen, Mao and Liu (2014) support that this is the central data system to help in mining, extracting and transforming different types of data both inside and outside the enterprise. Despite this, the increase of data has led to overloading of these systems leading to a major burden. To get rid of the burden, most of the enterprises have started adopting open source tools such as Hadoop to help in offloading the data warehouse (Mithas et al., 2013). Use of open source softwares has proved to be very effective due to their lower costs and high efficiency. Big Data and decision making in enterprises One of the main aspects of Big Data is helping in decision making. Mithas et al. (2013) claim that Big Data has the ability to impact how the decisions are made and who makes them. When there is scarce data which is not in digital form such as in traditional enterprises, it is the role of the top management to make decisions. This is based on the experience that they have built over time and their observations (Fan and Bifet, 2013). They are able to state their opinions on what they think would happen and make plans accordingly. According to Chen, Chiang and Storey (2012), it is important to note that the current development direction of Big Data does not eliminate the need for human vision and insight. Big Data development direction implies that the enterprise must be able to maintain management option when making decisions. From Fan and Bifet (2013), the management in the traditional enterprises is expected to be data driven and ready to override their intuition where it does not agree with Big Data. For the traditional enterprises, there is high reliance on experience and intuition and less on data (Chen, Chiang and Storey, 2012). Those that have adopted Big Data development directions have taken new roles. As the Big Data movement advances, the roles of management continue changing. Management challenges in Big Data Traditional enterprises cannot fully benefit from the Big Data unless they are able to manage in an effective manner. According to Brown, Chui and Manyika (2011), there are five main areas which Big Data have affected management. These are; leadership, talent management, technology, decision making and company culture. Leadership is very important for an enterprise to benefit from the Big Data. Enterprises succeed due to good leadership who can set goals and achieve them rather than having good data only. Fan and Bifet (2013) asserts that having Big Data does not erase the need for strong leadership. There is need for leadership with the ability to identify an opportunity and come up with a creative way forward. According to Brown, Chui and Manyika (2011), successful enterprises are those which are able to adopt Big Data through strong leadership. With the Big Data becoming cheaper, the compliments to Big Data have become more valuable. This includes having professionals acquainted with Big Data analysis such as data scientists and other professionals. In the Big Data ear, an enterprise is able to put information and the right decision in same place. Having an effective leader makes it possible to use the available data and make appropriate decisions. The Big Data has made it possible to break the enterprise culture. This involves moving away from the traditional approach (Brown, Chui and Manyika, 2011). Big Data have helped in transforming decision making. Adoption of Big Data by the traditional enterprises As the traditional enterprises adopt Big Data, there are several challenges that they have faced. The current level of adoption is not optimal. First, there are interoperability challenges. According to Bughin, Chui and Manyika (2010), several traditional enterprises have already invested in the business intelligence solutions. Integrating Big Data with the existing enterprise systems and business intelligence is a difficult task and costs more. There is also lack of adequate flexibility on the existing tools which makes it hard to support all forms of data. In some cases, data ingestion from upstream systems is also a challenge. Despite this, there are developments of tools such as Tableau Adaptor and Cloudera’s Tera Data adapter (Rajpurohit, 2013). The second challenge in the adoption of the Big Data is manageability. For the traditional enterprises, managing Big Data from hundreds of nodes is a major problem. This is especially in the infrastructure of the enterprise. According to Rajpurohit (2013) regular management of the Big Data faces lack of adequate support from the software vendors despite their provision of monitoring and management. The main support has come from the open source offers from the software vendors. Another challenge is Big Data security. Controlling the data within the enterprise context is a major issue. This is due to fact that poor security can lead to compliance issues and unintended data loss (Bughin, Chui and Manyika, 2010). Lack of proper security has raised fears of exposure to non-legitimate users. Despite this, there is rise in the use of proxy to ensure that the server is safe for the regular users (Chen, Mao and Liu, 2014). There are also technology features which are of great importance in Big Data security. This also includes checking on the existing credentials and ability to provide end to end solutions. For the traditional enterprises adopting Big Data, selecting the right vendor is a major challenge. According to Bughin, Chui and Manyika (2010), there are multiple technology providers which make it difficult to settle for the right one. Having the right vendor who is able to comply with the Open Core Model has been the main practice. Mithas et al. (2013) assert that it is important to note that despite the fact that traditional organizations continue adopting Big Data, it is still being used alongside traditional business intelligence. Chen, Mao and Liu (2014) points out that the traditional business intelligence still remains as one of the most cost effective approach in enterprise decision making. This is the main reason that the adoption of Big Data in traditional enterprises has integrated with traditional business intelligence. Big Data and management At the moment, the enterprises which have adopted Big Data are already using it in the recruitment of talent (Mithas et al., 2013). The use of Big Data has made it possible for the enterprise to have a better platform for recruitment compared to the traditional enterprises. Most of the recruitment at the moment is being done online. This is where recruitment is integrated into the social networks where resume information and the applicant information is gathered. This lays groundwork for recruitment based on the Big Data. According to Sagiroglu and Sinanc (2013), most of these enterprises gathers the potential recruits’ data and stores it for future use. Through combing the data from social networking sites with the recruitment data, it becomes possible to gather more candidate information. It becomes possible to have a more vivid image if the candidates applying for the job. The data is also vital in enterprise training. Training is very vital to ensure that there is sustainable development of the enterprise (Simon, 2013). Traditional enterprises have their training organized by the enterprise. According to Bughin, Chui and Manyika, (2010) no matter the method used by the traditional enterprises to train their employees, they end up using a lot of manpower, finances and materials for the training. With the advent of Big Data, the information sharing is very smooth and convenient. It becomes possible for anyone to search for information they need through use of network and can share very easily. It also becomes easy to develop professional training courses through the network. Through use of Big Data, it is possible to come up with training which is more targeted and efficient (Sagiroglu and Sinanc, 2013). The use of software in the enterprises training makes it possible to determine how an individual will improve and keep the staff abreast on new skills. Real-time data analytics is another area that Big Data is moving towards. At the moment, Big Data is easily assessable in real time (Mithas et al., 2013). This is an area that traditional enterprises have been taking advantage of. According to Bughin, Chui and Manyika, (2010) enterprises are able to gain data fast and make decisions based on real-time analytics. The companies are able to benefit from fast time results. The data driven intelligence makes it possible to support the enterprise in real-time. The use of Big Data in real-time makes it possible to have better decisions and explores new opportunities. Sagiroglu and Sinanc (2013) claim that is possible for enterprises using Big Data to know what is happening in the market now and what will happen next. Big Data is being used as an asset to gain a competitive advantage. For the traditional enterprises, grasping development direction for Big Data is going to be a great investment. This is the main way in which the traditional enterprises can survive the current competitive environment. As the enterprises grasp Big Data development direction, there is a misconception that it can solve all the problems. There are many indicators which show that Big Data cannot solve everything (Bughin, Chui and Manyika, 2010). Big Data cannot replace the traditional use of unstructured data in the traditional enterprises. This is due to fact that when compared with the structured data, structured data remains dominant. For several applications, use of structured data is dominant. There are still traditional structured data intensive applications where use of traditional data processing is necessary. According to Fan and Bifet (2013) this makes it not necessary for these applications to embrace Big Data. This also applies to human resource where when a problem can be solved through the traditional data there is no need to use the Big Data technology. This is due to fact that enterprises are expected to focus on the most suitable method. While there is low research on how traditional enterprises are grasping Big Data development direction, it is important to look at Big Data as flowing and updating. According to Mithas et al. (2013), Big Data is the source of human knowledge and is of great value. It is possible for these enterprises to grasp the Big Data and use it to predict direction of their business. Sagiroglu and Sinanc (2013) assert that not all predictions done with Big Data technologies are entirely true. This is due to the increasing amount of data which have the ability to bring the wrong data. Moreover, macro conclusions that can be obtained by the traditional enterprises adopting Big Data may not make sense when used in some of the micro issues (Chen, Mao and Liu, 2014). Thus, as the traditional enterprises grasps development direction of Big Data, it is important to note that not all predictions made can be used in management decisions. In summary, while the traditional enterprises grasp the development direction of the Big Data for their management, it is important to note the positives and drawbacks that are associated with it. They have to take the full advantage of Big Data and avoid associated drawbacks (Simon, 2013). This is an area that has received few studies especially due to fact that the trend is relatively new. There is need for more research on how the traditional enterprises can grasp the development direction of Big Data for their enterprise management. References Brown, B., Chui, M. and Manyika, J., 2011, ‘Are you ready for the era of ‘Big Data’’, McKinsey Quarterly, vol.4, no.1, pp.24-35. Bughin, J., Chui, M. and Manyika, J., 2010, ‘Clouds, Big Data, and smart assets: Ten tech- enabled business trends to watch’, McKinsey Quarterly, vol.56, no.1, pp.75-86. Chen, H., Chiang, R.H. and Storey, V.C., 2012, ‘Business intelligence and analytics: From Big Data to big impact’, MIS quarterly, vol.36, no.4. Chen, M., Mao, S. and Liu, Y., 2014, ‘Big Data: A survey’, Mobile Networks and Applications, vol. 19, no.2, pp.171-209. Chen, M., Mao, S. and Liu, Y., 2014, ‘Big Data: A survey’, Mobile Networks and Applications, vol.19, no.2, pp.171-209. Fan, W. and Bifet, A., 2013, ‘Mining Big Data: current status, and forecast to the future’, ACM sIGKDD Explorations Newsletter, vol.14, no.2, pp.1-5. Katal, A., Wazid, M. and Goudar, R.H., 2013, ‘Big Data: issues, challenges, tools and good practices’, In Contemporary Computing (IC3), 2013 Sixth International Conference on (pp. 404-409). IEEE. Mithas, S., Lee, M.R., Earley, S., Murugesan, S. and Djavanshir, R., 2013, ‘Leveraging Big Data and Business Analytics [Guest editors' introduction]’, IT professional, vol.15, no.6, pp.18-20. Rajpurohit, A., 2013, October. Big Data for business managers-Bridging the gap between potential and value, In Big Data, 2013 IEEE International Conference on (pp. 29-31). IEEE. Sagiroglu, S. and Sinanc, D., 2013, Big Data: A review, In Collaboration Technologies and Systems (CTS), 2013 International Conference on (pp. 42-47). IEEE. Simon, P., 2013, Too big to ignore: The business case for Big Data (Vol. 72), New York, NY: John Wiley & Sons. Read More