Big Data: Related Technologies, Challenges, and Future Prospects - Report Example

BIG DATA MANAGEMENT By Big Data Management Introduction The term big data is used to describe the availability of very large volumes of data, both unstructured and structured. Such large volumes of data are important to businesses and social life just the same way the internet has become more than a necessity in human and business life. This is because availability of more data and a variety of it may lead to results that are more accurate. The accurate results and analyses means that the business will make confident decisions, and this means the business will be efficient in its operations, reduction of production and operation costs and reduced business risks. Managers should take the advantage of big data to improve services in their organizations. Big data can be used in all management functions from administration, marketing and finance. It acts as an information store in which data is stored over a long period and can be easily retrieved if need be (Baesens, 2014). Big data has five factors that determine is availability and usefulness, the five factors are volume, velocity, variety, variability and flexibility. Big data uses all these aspects for it to be reliable and easily accessible by businesses and individuals. For volume, big data is in very large volumes mostly in terabytes. Increase in data volume is due to many factors including data that is from the daily transactions stored for a long period of time and over the years. Unstructured data from internet platforms like the social media and virtual interaction sites and increased use of sensor machines in most organizations and the machine-to-machine information sharing data that collected over the years. The determination of relevance of the large data volumes, which sometimes may be irrelevant, is an emerging issue with the increasing storage costs. Businesses use some analytics to create usefulness from relevant data and discard the irrelevant data to reduce storage costs (Chamber, Dhiraj & Minelli, 2012). Variety means that big data come in various forms and styles. The variations come by because of the different sources that the big data comes from. This ranges from information originating from the side of business applications, information and data from traditional databases, unstructured data from informal communications like text messages. With such a large variety of source s of data and streaming in on daily basis, management and governance of big data is a technical issue that many organizations are finding it hard to stabilize. Most of the big data is not necessarily useful to the organization and this makes its storage just a waste of resources. Analysis of such large volumes of data to make some usefulness out of it takes a lot of time and requires skilled personnel. This increases operational costs of the organization in training workers or hiring experts (Chen, Leung, Mao & Zhang, 2014). Variability is the seasonability of data flows. Data flow is not consistent in all days. It has peaks seasons and seasons when the flow is low. For instance, when there is something trending in the social media or a new event on the internet that receives many comments, data flow is in large volumes. Such data is mostly unstructured and hence difficult to manage. Velocity of streaming data means the speed in which data flows in. big real time smart meters and sensors can best manage data velocity. Organizations should react quickly enough to be able to deal with big data. Big data originates from different sources and is of various different types making it more complex. Organizations have the task of connecting and correlating information from different linkages to avoid their data to run out of control. This is a hard task and to most organizations, it adds up the operational costs. Variability of big data comes from the fact that the data streams from different sources. Some of it comes from the social media platform, web site searches and the information stored about production patterns and consumer feedbacks (Cukier & Schonberger, 2013). The issue with big data is not that one is getting the data, but how one is harnessing it. Organizations should be able to acquire data from any source, analyze it and get only the important information to help them in smart decision-making. These will help them in the determination of root causes of failures in real time to save the organization many cash. It also helps in detection of malpractices and fraudulent behaviors within the organization and determining ways that maximize income. Increase marketing of real hand commodities and making advertisements handy by sending customers text messages just in time for them to take advantage of available offers hence increasing the business’s revenue (Davenport, 2014). Big Data Opportunities for Business Growth Big data enables opportunities for business growth in many other ways apart from providing enough information to the business. Information does multiply daily in databases and businesses want to tap into this information to get a trigger for their growth in revenue. For instance, configuring marketing campaign should first begin with figuring out the vital prospects from consumers. This is by studying the markets and getting information from data banks and then sending texts or emails and advertisements (Franks, 2012). The following are the growth opportunities that big data offers for business growth. Big data is used to set prices, using big data for market study and formation of marketing strategies. With the availability of big data, companies and organizations have an open opportunity to increase their sales by using it to set prices. Using big data to set prices is a simple tool and saves organizations a lot of money and time that would be spend if this was to be manually. For instance, when a company sets its prices and makes adjustments in time and available to customers, it will gain a lot of customer reliance and hence it will grow bigger (Jagadeesh, Mohanty & Srivatsa, 2013). Big data also offers businesses an opportunity to carry out thorough market research. Organizations get the data and then analyze it to fit in their line of business to help them with identifying market strong points and setbacks. This contributes to business growth. Big data also offers an organization with market and marketing information. This makes the organization to understand consumer trends through analyzing the past consumption history. The organization will then know when and what time to increase scales of production and when to scale down to avoid huge losses. Businesses use this as an open opportunity to grow their business. Open opportunities presented by big data are dependent on the availability of technology like the ideal consumer profile and skilled personnel. When both the two are available, then an organization is ready to venture into digital business operation and cut huge time losses on time wasted to arrange information manually (Kodali & Zadronzy, 2013). Big data also helps the organization to store data in huge amounts in their data banks. This data store is vital to the business’s growth because the organization’s major decisions will depend on history. The organization can also know whether it is growing or stagnating from reviewing the past performance data. Another opportunity for growth that big data offers a business is by providing a platform for communication, either with clients, business partners or just within the organization. Communication between the business and its clients are stored in huge data banks and are often references for business decisions. Such information is vital in ensuring that there is customer satisfaction and hence growing the business. Big data as seen above does provide a business with several opportunities to grow and increase their scale of operation (Lanier, 2014). Big Data Application Initiatives in Various Sectors Big data is an aspect of increasing volumes of data that surrounds every business’s life and more so the marketing sector. Those in the marketing sector look into big data with a lot of anticipation because big data holds the ability to describe potential and target customers with high levels of certainty under good data analysis. Unlike before when it proved so hard for marketers to track consumer’s simple habits like purchasing history and could only track returns on direct mail. These days marketers can have data on consumer habits on anything imaginable, including consumer’s personal preferences based on media postings (Leskovec, Rajarama, & Ullman, 2014). The marketing sector for instance can use big data into their marketing strategies. Predictive integration of analytics into the marketing strategies is one of the ways marketers can use big data initiative to grow their business. Predictive analytics is one of the most creative strategies that those people in the marketing sector can use with big data. Predictive analytic means that marketing managers use the organization’s base data and other third party or partner’s web data that clearly brings out consumer’s future lead behavior. This is through analysis of historical data and information on the past consumer trends. This gives marketing personnel a clear vision of what is to be shed off or added to increase sales and at the same time avoid huge losses during off-peak seasons(Marr, 2015). Another application initiative of big data in the marketing sector is the identification of the specific content that triggers consumers to your products. Marketing personnel can use information on social media that is commonly used by most of people to market his or her products. This can be done through blogs, tweets and mails. Information gathered from consumer feedbacks is used to improve their service delivery. The information can be retrieved from history if there is a good big data management system. If a marketer can identify sites that are liked by most of his customers, mostly social sites, he can be able to increase sales a hundred folds (Marz & Warren, 2015). Another application of big data in business marketing sector is by monitoring Google trends. This is the most appropriate way of utilizing big data. Worldwide marketers can use this application to understand different tests and preferences of people from different nations. This is done through analyzing Google trends about the topics that are often searched in the internet. This is possible because Google has the history of the whole search volumes from all countries. Marketers can know what the consumer’s tastes are through analysis of one search item relative to the whole volume of searched data (Ohlhorst, 2012). Creation of real time personalization to buyers is also another application of big data to business marketing sector. For marketer to retain the consumer engagement to their brand, they should maintain a quick and reliable real hand information exchange with them. Marketers need to keep time and be relevant because these two are the foundation of a better marketing strategy. Big data is a source of timely insights to marketers about who or what the consumers are interested in. personalizing this information to the company’s communication system will make it easier for marketers to answer consumers questions. Marketing information mainly comes from sales persons, consumer feedbacks and the company’s data banks or big data. The most important source of information is from the consumer’s feedbacks, this is from data banks and analysis from big data (Rijmenam, 2014). Markets can make marketing digital by digitizing information to make it clearer about their ideal consumer profiles. Marketers can make more accurate to prove right their intuitions and outline things like what social media profiles consumers use and which buttons they click on the web. This was not as it was before because marketers used their educated guesses to make decisions about things like age, demographics and working profile of their buyers. Big data has made this easier because of accessibility to information and direct engagement to their customers. Adding big data to understand their customer’s profile well and close contact with their wants and consumption trends will definitely increase the marketer’s strategies and control. This will in turn increase the income of the company. An organization can use big data for security reasons. This is through anonymizing consumer’s identity from the consumer feedbacks, this helps to improve the customers relations and avoid criticisms (Schmarzo, 2013). Challenges of Introducing Big Data Practices in Marketing Sector Big data in business marketing sector has it challenges too. The challenges range from management of such huge volumes of data, analyzing it and disseminating it. Big data can be of no use at all if it is available but is not fully into use. There are some of the challenges that introduction of big data posses to the marketing sector. The challenges are mainly technological and they include the volume handling, variety of the data and the big data velocity. This may be because of neglecting the marketing sector from automation and censoring its information from outsiders. With the volume handling, big data is usually in petabytes and terabytes. This is many data such that storage may pose a big challenge to marketers (Townsend, 2013). The marketing sector generally is not considered to have its own data banks and storage facilities and therefore marketers have a challenge using big data. Organizations should handle the data for marketers and only allow them to use it when need arises, this way they will reduce overall costs. If all the organs of the organization had their data banks, the cost of handling big data would increase the overall costs. Marketers too do concentrate much on physical marketing and meeting consumers and so they term using big data as a waste of time. Consumers too need to meet personally with the marketers so that they can have a talk about the goods and to be sure about their transactions. This shows the hardships of digitalizing the marketing sector (Baesens, 2014). Big data has a lot of variety. Variety of big data is a challenge to the marketing sector in several ways. Marketing managers will take a lot of time to analyze and get useful information from big data. This time spend on analysis could be used to improve sales. Most of the marketers are also not educated in the information technology and management sector and therefore they do not welcome the idea of using technology in marketing. The organizations also use many cash in training marketers about big data analysis (Chamber, Dhiraj & Minelli, 2012). Variety of big data and the unstructuredness of some of the data make most of it unuseful to the marketer, these variety wastes more time in analysis stage to get the right out of the rest. This is a big challenge to marketing sector and a setback to marketing. The cost of big data is high and the useful information after analyzing is few and may be irrelevant to the current situation if the data was stored based on history. Another challenge of big data to marketing is the velocity of data. Big data streams in high speed and at every time and so management of such data with high velocity should be fast and of high technologies. Since the marketing sector and marketers do not mostly take advantage of technology, it is hard for them to use big data. In general, the complexity of dealing with data is what makes it a challenge to the marketing sector. Managers can revive the marketing sector go digital and hire skilled marketers to make marketing easy and quick and save the business huge losses (Chen, Leung, Mao & Zhang, 2014). Reference List Baesens, B. (2014). Analytics in a Big Data World: The Essential Guide to Data Science and Its Applications. Hoboken, New Jersey: John Wiley & Sons. Chamber, M., Dhiraj, A., & Minelli, M. (2012). Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Todays Businesses. Hoboken, New Jersey: John Wiley & Sons. Chen, M., Leung, V., Mao, S., & Zhang, Y. (2014). Big Data: Related Technologies, Challenges and Future Prospects. New York: Springer. Cukier, K. & Schonberger, V. (2013). Big Data: A Revolution That Will Transform How We Live, Work, and Think. London: Houghton Mifflin Harcourt. Davenport, T. (2014). Big Data at Work: Dispelling the Myths, Uncovering the Opportunities. Harvard: Harvard Business Review Press. Franks, B. (2012). Taming the Big Data Tidal Wave: Finding Opportunities in Huge Data Streams with Advanced Analytics. Hoboken, New Jersey: Wiley. Jagadeesh, M., Mohanty, S., & Srivatsa, H. (2013). Big Data Imperatives: Enterprise ‘Big Data’ Warehouse, ‘BI’ Implementations and Analytics. Boston: Apress. Kodali, R. & Zadronzy, P. (2013). Big Data Analytics Using Splunk: Deriving Operational Intelligence From Social Media, Machine Data, Existing Data Warehouses, And Other Real-Time Streaming Sources. Boston: Apress. Lanier, J. (2014). Who Owns The Future? California: Simon and Schuster. Leskovec, J., Rajarama, A., & Ullman, J. (2014). Mining Of Massive Datasets. Cambridge: Cambridge University Press. Marr, B. (2015). Big Data: Using SMART Big Data, Analytics and Metrics to Make Better Decisions and Improve Performance. Hoboken, New Jersey: Wiley. Marz, N. & Warren, J. (2015). Big Data: Principles and Best Practices of Scalable Realtime Data Systems. New York: Manning Publications Company. Ohlhorst, F. (2012). Big Data Analytics: Turning Big Data into Big Money. Hoboken, New Jersey: John Wiley & Sons. Rijmenam, M. (2014). Think Bigger: Developing A Successful Big Data Strategy For Your Business. New York: AMACOM. Schmarzo, B. (2013). Big Data: Understanding How Data Powers Big Business. Hoboken, New Jersey: Wiley. Townsend, A. (2013). Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia. New York: W. W. Norton & Company. Read More