Using Big Data to Gain Market Advantage - Essay Example

Big Data of the of the Background of Big Data Big Data has emerged from the territory of science based projectsof Web companies for supporting giant enterprises, like, telecommunication. It is useful for them to recognize customers who are discontented with their services, causes for the dissatisfaction and also, predict those who are going to transform carriers. To get hold of this information, millions of loosely-structured bytes of information in various locations are required to be processed, until the needle is found in the haystack. The analysis enables to fix out-of-order processes, help the customers at risk and retain them. The real trade impact lies in the fact that big data technologies can perform this within weeks or months, much faster than conventional data warehousing methods. The Information technologies and tools to carry out big data processing are fresh, new and more importantly, exciting. Big Data is too large to execute using conventional methods. It came into existence when Web search organisations started facing problems of querying largely distributed, loosely-structured information. Google created MapReduce to bear dispersed computing on large information sets on mainframe or computer clusters. Summary Big Data can be referred to as the ability to acquire an enormous quantity of data, analyse them instantly and draw amazing conclusions from them. This approach can translate the myriad phenomena as one that provides huge details for the purpose of searching, ranging from the text of billions of books to prices of train and airlines tickets. It contains data sets of all sizes, which cannot be managed by normal software tools with such ease and within a considerable specified time. In a single set of data, sizes, which range from a few dozens to many pet bytes, are present. Big Data has immense amount of applicability in business. For enterprises with billions of customers, it is difficult to recognise each customer. Big Data is a solution to all these problems, which is greatly faced by organisations. It helps them to recognise each of their valuable customers more easily. Currently, Big Data set includes formless data and permits extraction of results from multiple data types, such as, log files, emails, media, business transactions and a series of others. It increases efficiency of the stores too, besides facilitating growth, sustainability and progress of enterprises. Source, collection and storage of Big Data Big Data strategies help to recognize procedure for administrating and getting value from the rising incidence of numerous data that enterprises have to deal with. The volumes of data from different sources are increasing with the help of Big Data; therefore, it would not slow down any time soon. As per market analyst, IDC, who in the study of Digital University have predicted that volumes of data would distend 50-fold by 2020, as the number of required servers has increased by an exact factor of 10. E-mail messages, computer files, videos and other such unstructured data will, as a result, account for 90 percent of all information created in the subsequent decade, as per IDC. The data can be found in both structured as well as unstructured forms, constituting a range of document files, e-mail messages, text messages, tweets, audio as well as video. It will be produced from a variety of sources, such as, document management feeds, social media feeds and predominantly, government sensors. The data will be received at increasing speed. In case of few agencies, like, the intelligence community and Defence Department, data will be transferred within a millisecond from various sensors installed. The data can be stored, distributed and collected at levels, which would quickly overpower traditional management techniques. Management of Big Data and turning data into knowledge Big Data is difficult to manage, regardless of using relational information or database management system, desktop statistics as well as visualization package. Instead, it requires a number of parallel running software on tens or thousands of servers. It also depends on management’s capability to run the software as well as on applications that are conventionally used to analyse and process the set of data in its domain (Callahan, 2013). Organisations, handling thousands of gigabytes of information for the very first time, need to reassess their data management options. Hadoop software can be used by organisations to examine and transform unstructured as well as structured data. Oracle is the software used by government agencies. Challenges of Big Data Big Data presents several challenges, which are rather complicated and complex (McDonnell, 2011). The first challenge can be the usage of Big Data, when it comes in an unstructured form, such as, video or text. The second challenge that it faces is the way to capture essential data as it occurs and deliver the same to the target in real-time. The third major challenge is the way to store information after analysing and understanding it, given its enormous size and within user’s computational ability. It is difficult to select the desired information from a huge set of data and make use of it. There are many other challenges faced from the point of security and privacy to access data. A critical review of the market dynamics The Big Data market and market forecast In 2013, the market for Big Data reached a profitable position of $18.6 billion. This indicates a growth rate of 58 percent from 2012. The total revenue related to services of Big Data counts up to 40% in total, with profit being 22% and 38% for software and hardware, respectively. As per the forecasts, growth of this market will slow down to 53% ($28.5 billion) for the year 2014 (Hannon, 2013). The current pace suggests that the compounded annual growth rate will be 38% ($50 billion) in 2017, over the period of six years from 2001 (Hribar, 2000). With the maturity of the market, Big Data and cloud based applications will attain more vital role and will be adapted by users, regardless of being polished or unpolished. Key players, market driver and major suppliers The key players are Hadoop, MapReduce, EMCs Greenplum appliance, HPs Vertica platform, IBMs DB2-based SMA (Smart Analytic System), Microsoft’s Parallel Data Warehouse and Netezza. List of smaller database players includes Kognitio, Infobright and ParAccel. In 2011, Teradata was dominating and picking off defectors from industry’s top, Oracle. SAPs Sybase unit is still evolving Sybase IQ, the original column-store database. In short, these are the key market players, who are evolving and growing each day. There were several important market and growth drivers of Big Data in 2013, such as: 1) The pure-play and IT-vendors of Big Data market took steps in order to articulate their products roadmap and outsized visions for Big Data in the organisation, thereby creating more confidence among buyers of the enterprise (Hazan & Banfi, 2013). 2) The products in this market continued to mature, in terms of features, in 2013. These modifications embrace the arrival of YARN, extensively laying the base for Hadoop as a multi-application framework as well as the continuation and evolution of cloud-based services of Big Data for large-scale application development and analytics. 3) This technology also took steps to create greater enterprise-grade capabilities that are necessary for mass acceptance of enterprise (Hannon, 2013). Therefore, it provided better security, privacy, governance capabilities, improved recovery as well as backup of security and particularly, high-availability for Hadoop. 4) Partnerships also have an important role to play in maturing of the Big Data landscape. More importantly, there were a couple of technical partnerships and reseller agreements between non-Big Data and Big Data vendors in 2013. This resulted in easier ways of adapting and integrating Big Data technologies (Kelly, 2014). The major suppliers of Big Data are Google, Cloudera, VMware, HortonworksNet, Spunk, MapR, and 10Gen, Sap and Microsoft, among others. Supplier strategy for Big Data market The supplier strategy for Big Data market will be to optimise cost and enhance efficiency, which could be done through advancements in technology and business (Ltamore, n.d.). Lucrative acquisitions and strategic alliances will help in advancement of business among the big and small vendors (Spakes, n. d.). On the other hand, technological advancement should include inventing new and strong products as well as solutions to influence company’s ROI (return on investment) scenario. Market segments and product requirement Big Data has the requirement in all market segments, ranging from big business houses to the Government sector (Donnelly & Simmons, 2014). It has immense amount of requirement for handling huge databases and getting rid of traditional database management systems. Google’s Hadoop provides a base to handle vast databases. As a result, users can get right information among diverse options. The enterprises also gain in the form of understanding and retaining the important customers. It provides government sector with equal amount of information. Rightfully, it is the change that everybody is adapting to for making their work easier and acquiring appropriate information in least time (McDonnell, 2011). Trends The prime objective of vendors in the near future would be harnessing the potential of this market. So, they are bringing in innovative and new technologies in this market. Some latest trends in technology in this sector are cloud based storage and analytics, combined appliances, NoSQL databases, Hadoop and other security intelligence products (Chouffani, 2012) These technologies will grow in the coming years, provided there are skilled and trained professionals in the marketplace so as to understand and implement them. The integration of professional services is an area of major growth in future (Kelly, 2014). Work flows The adaptation of Big Data will enhance the flow of work. It will make information availability smoother and easier to handle (Donnelly & Simmons, 2014). The information sector, government sector, vendors and customers will enjoy enormous amount of benefit from the adaptation of Big Data, thereby making work effortless and lucid. Objectives for using Big Data The objective of using Big Data is basically to make information more transparent and readily available, improve decision making process through proper analytical information, minimise risks as well as to develop the next generation technologies (Ltamore, n. d.). The sole objective is, thus, to gather, store and dissipate more information with sufficient amount of flexibility. Channel of distribution The channel of distribution will be online as well as offline. The Big Data will be distributed through cloud based storage system. The opportunities for new companies Big Data was useful for many companies. The most significant of them is the company named Fair Isaac Corporation. The company is one of the top decision management and predictive analytics Software Company. In 2013, it announced the partnership it made with Big Data start-up ERN. ERN became the marketing solution for FICO. It had incorporated Looop, which is a platform for Big Data analytics. It helped to provide the ability to the marketers related to the retail and financial services to carry out merchant-funded marketing strategies which are highly personalized and based on detailed analysis of each and every consumers needs and preferences. The initial focus of the partnership was on the financial markets in Europe, UK, and Asia. Loop, which is a stable and secure Big Data analytics successfully, helped merchants, as well as the banks to analyse the customer behaviour on a micro as well as macro scale. Ficco managers got significant help for using the predictive optimization and analytics capabilities in delivering relevant offers and personalised services to its valuable customers at the right time when they can act on them. This helped to maximise the potential of the transactional data provided by Loop. The analytics also balanced thousands of decision data mathematically through the process of optimization so as to inform about the offers. This helped the FICO Analytics Offer Manager to target offer as and when they found the customers are more likely to react to them. Therefore, all these helped to strengthen and advance the strategic objectives of FICO’s business and maximised the relevance to the customers. The Big Data start-up ERN helped FICO a lot to advance its activities. It benefitted and had competitive advantages over other companies as it was able to forecast and judge several activities of the customers and act accordingly. Example of a company which might not benefit from the Big Data is the web based company like Google. By using Big Data there might be information overload as a result they might not present the appropriate solution to a problem from the huge bulk of the data it has. Big data can be troublesome to find out the best solution or data out of a series of past as well as present data to the users for these types of companies. Though Big Data has more positive points than negative therefore, many companies in the future will turn to the usage of Big Data. This market will have opportunities for growth, insights into products that will be well-designed and be able to deliver them efficiently (PR Newswire Association LLC., 2014). It will help them to be more dynamic, handle customers efficiently, have more accurate information and increase work pace. The opportunities for new companies adapting to the Big Data market will, thus, be diverse and results will be quite profitable (Data, 2013). References Callahan, S. (2013). How to Use Big Data to Gain Market Advantage. Retrieved from http://www.insurancenetworking.com/blogs/big-data-market-advantage-32836-1.html Chouffani, R. (2012). Big Data Analytics a Big Benefit for Marketing Departments. Retrieved from http://www.cio.com/article/716778/Big_Data_Analytics_a_Big_Benefit_for_Marketing_Departments Data, C. (2013). The benefits of Big Data solutions for enterprises. Retrieved from http://www.thehindu.com/sci-tech/technology/the-benefits-of-big-data-solutions-for-enterprises/article5340022.ece Donnelly, C., & Simmons, C. (2014). Is There Hope for Small Firms, the Have-Nots in the World of Big Data? Retrieved from http://blogs.hbr.org/2013/12/is-there-hope-for-small-firms-the-have-nots-in-the-world-of-big-data/ Hannon, T. (2013). Big Data in action in strategic sourcing. Retrieved from http://blog.supplymanagement.com/2013/08/big-data-in-action-in-strategic-sourcing/ Hazan, E., & Banfi, F. (2013). Leveraging big data to optimize digital marketing. Retrieved from http://www.mckinsey.com/client_service/marketing_and_sales/latest_thinking/leveraging_big_data_to_optimize_digital_marketing Hribar, P. (2000). The market pricing of components of accruals. New York: Cornell University. Ltamore, B. (no date). Big Data market profitability and forecast. Retrieved from http://siliconangle.com/blog/2014/02/10/big-data-market-reaches-18-6-b-heading-for-50-b-in 2017/ McDonnell, S. (2011). Big Data challenges and opportunities. Retrieved from http://spotfire.tibco.com/blog/?p=6793 PR Newswire Association LLC. (2014). Global Big Data market report 2013. Retrieved from http://www.prnewswire.com/news-releases/global-big-data-market-report-2013---scenario-trends-industry-analysis-size-share-and-forecast-to-2018-239284331.html Spakes, G. (n. d.). Four ways big data can benefit your business. Retrieved from http://www.sas.com/news/feature/big-data-benefits.html Kelly, J. (2014). Big Data: Hadoop, Business Analytics and Beyond. Retrieved from http://wikibon.org/wiki/v/Big_Data:_Hadoop,_Business_Analytics_and_Beyond Read More