Big Data Management: Possibilities and Challenges Essay
Big Data Management: Possibilities and ChallengesThe term big data describes the volumes of data generated by an enterprise, including Web-browsing trails, point-of-sale data, ATM records, and other customer information generated within an organization (Levine, 2013). These data sets can be so large and complex that they become difficult to process using traditional database management tools and data processing applications. Big data creates numerous exciting possibilities for organizations, but along with the possibilities, there are challenges. Managers must understand the pitfalls and limitations, as well as the potential of big data (Levine, 2013). The focus of this report is the business potential and implications of big data as well as understanding the challenges and limitations of big data management. The potentials for big data are numerous; however, in this report only five potentials and implications for use are discussed. These include the following: knowledge management, social media, in travel, banking, and marketing and advertising.
Knowledge ManagementOne of the greatest potential for big data is knowledge management. A goal of knowledge management is the ability to integrate information from multiple perspectives to provide the insights required for valid decision-making such as where to invest marketing dollars, how much to invest, or whether to expand into a new geographic market (Lamont, 2012). In terms of knowledge management, three dimensions describe big data: volume, variety, and velocity (Lamont, 2012). Volumes mean breaking up huge amounts of the data, sending out subsets for analysis, and then regrouping the results to produce the output. Variety is a factor because many different types of data may be pertinent to an analysis.
For example, social media feeds such as Twitter are able to combine their analyses to include information from both structured relational databases and content such as videos and Tweets. Velocity is a third factor associated with big data. Not only is there a lot of data, but also it comes in quickly and must be processed quickly. Velocity can vary among users. Take the case of two people clicking through a website.
If data is being collected over time, some users will produce more within a given time period (Essential Guide,2013). Big Data in Social MediaBig data capability is almost mandatory for analyzing social media and there are many ways that social media relates to big data. Text analytics lets users create data from large amounts of unstructured sources and build sentiment scores, which in turn can be related to consumer interests. The benefit is that organizations can use that data to create potentially a much more accurate picture of user behavior than ever before. But the data needs to be properly structured and managed to make that possible (Lamont, 2012). Big Data in TravelA third approach that is working well is using big data techniques to store, process, and retrieve travel information. Expedia.com, the world’s leading online travel site, offers a full range of services, including flight bookings, hotel reservations, and opportunities for special activities at travel destinations.
With more than 75 million annual users of the Expedia sites each year, the amount of data associated with the visits and transactions adds up quickly, placing Expedia squarely in the big data category for its analytical needs (Lamont, 2012). Big Data in BankingBanks can harness big data in the form of transactions, real-time market feeds, customer-service records, correspondence, and social media posts. Successfully harnessing big data can help banks achieve three critical objectives for banking transformation: Create a customer-focused enterprise; Optimize enterprise risk management; and Increase flexibility and streamline operations (Essential Guide, 2013).
Big Data in Marketing and AdvertisingThe advertising industry has reached a consensus that data is extremely powerful and important for virtually every aspect of marketing and advertising. In 2013, advertising firms have become aware of the possibilities that lie inside data and the answers that data holds and how these answers can predict and create new trends. In big data, data analytics informs a successful marketing strategy.
Advertisers in the automobile industry can determine, in milliseconds, whether someone looking for a car is a “luxury” or “used car” buyer, and based on that information, they candetermine whether to even display an ad or not. These types of targeted ads could do very well for the industry Levine, 2013).. The Big Data ChallengesSome organizations believe the big data challenge is about their massive data volumes.
Others think the challenge is in the rapid rate of growth. Still others worry about the challenge handling the new types of data. When people think of big data challenges, they immediately think of the volume and the speed at which it is growing (velocity). The bigger challenge is the variety of information. The world’s “digital universe” will grow to 8 zettabytes (ZB) (1 ZB = 1 billion terabytes) by 2015, up 48 % from 2011.
While these figures are daunting, the bigger challenge is the different data forms within that 8 ZB. By 2015 over 90 % of that data will be unstructured (e.g., images, videos, and other files based on social media). While this data is full of rich information it is hard to understand and analyze (HP, 2012) Bergstrand (2013) of the Drucker Institute noted that the biggest big data challenge now does not stem from the science or technologies, but, rather, from how users struggle to have art and science come together through human beings. A large number of companies are mistakenly convinced that big data experts can add value even when they are talking past or altogether ignoring the managers who must use the data for front-line decisions. To offset these challenges, Bergstrand maintained that organizational managers of big data must be actionable. That is, the managers who must use the data for front-line decisions must do so with a clear strategy, excellent industry and company knowledge, good data quality, and reliance on business and data experts who are committed to learning from one another.
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