Abstract Science Construction’s business is in planning, developing and building road projects. The major of its clients are municipalities, city governments, and other public sector entities. While the bankruptcy rates for these clients is very low, when economic downturns happen, their ability to pay in a timely fashion also suffers. This leads to businesses such as Science Construction needing to take on additional debt and to find creative methods in order to stay afloat during times of recession. Methods such as selling accounts receivables at discounted rates and taking larger lines of credit through banks and other lending institutions are some of the ways organizations can remain viable when their cash inflows have turned into a trickle. Science Construction is asking the Turkish Courts to postpone their bankruptcy proceedings for a year while they attempt to restructure. Through this, suggestions such as forcing shareholders to pay their debt to the organization, gaining credi...
In recent years, we have witnessed the success recorded by corporations who have employed the ideas of bid data and analytics to grow revenues and create an unbeatable competitive advantage to remain market leaders. This is made possible through data and its analyses found in the daily operations of businesses. Data are now inter-connected into every function and segment in the world economy, and a lot is dependent on it, just like other factors important production like human capital without which economic activity will collapse (Maggo, 2014). Over time, this data is becoming massive growing at a super-linear rate, if not exponentially, hence, leading to the data explosion, a remarkable success in database management and database technology. Currently, it’s really not easy finding ways on how to collect, process and analyze all that data, leaving us with the up-task on how to ascertain the right set of tools, architectural decisions, facilities and skills to enable different organizations to capitalize on that data (Isaacson, 2018). By implication, raw data should be assigned meaningful interpretation which is as important as its reliability, collection, and management.
Following the super-linear rate of data increase, a large volume of both structured and unstructured data is visible on the global space. For example, the volume of available digital data at the global level went from 150 exabytes in 2005 to 1200 exabytes in 2010 (Maggo, 2014). This data is so large that it is difficult to manage it using traditional database and software techniques, and as it further grows, the problem of managing it becomes harder, which can lead to overload, giving course to the term data explosion (Isaacson, 2018).
The concept also involves the volume of data, the technology, tools, and methods required to handle the large data and storage facilities. Organizations face hitches in being able to create, deploy and manage data explosion especially when dealing with massive datasets. It is also a critical issue in business analytics because of the available tools and procedures are not premeditated to search, collect and analyze large datasets. The factors responsible for this explosion may include; the presence of the internet and the interaction of users in this space, the shift from bricks and mortar stores to online retailing/sales, online advertising, e-commerce (online increased airline ticket bookings, international purchases, online products, etc.), core and specialized social networks (Facebook, Twitter, LinkedIn, Google+, etc.), mobile data, chatter trend analytics applications like Twitter and Facebook chats and many others (Isaacson, 2018).
Presently, organizations are taking numerous methods to gain a competitive advantage with analytics. Some use the process of collecting important data over time about their customers and prospects, while others decide to organize, homogenize and use data that is available to others in an exceptional way that competitors can’t match. We have seen organizations who even develop a proprietary algorithm that leads to better, more insightful analyses upon which to make decisions. And some differentiate themselves by implanting analytics into a distinctive business process. Whatever the case, Thomas Davenport has validated businesses like Netflix, Amazon.com, etc., to have all become market leaders by finding new ways to use the information and the technology driving their strategy is analytics (Davenport, 2006). And competing on analytics is seen as using information resulting from the systematic analysis of data to outexecute the competition. From the firms observed by Davenport, there are forum common characteristics which include, strategic and distinctive capability; enterprise-wide approach; commitment to senior management to analytics; and the will to strategically compete (Davenport, 2006). As time ticks, more companies are likely to discover the possibilities of analytics and its potential to drive competitive advantage, they will be keen to push beyond the limits of analytics in their products and services.
There are a number of firms that have realized competitive advantage through analytics. For instance, in my company, we are now exchanging analytical information with our digital marketing partners. Marketers get analyses involving pricing, sales promotions, and inventory while our customers receive data and analysis that helps them with their own purchase decisions. In researching other organizations, I discovered that Honda uses text mining to identify and head off potential quality problems in its cars. Similar text and web-mining approaches could be used to, for example, better understand customer perceptions of service and brand value. Analytics based organizations make use of statistics and modeling to improve a wide variety of functions. Wal-Mart, for example, insists that suppliers use its Retail Link System (RLS) to monitor product movement by store, to plan promotions and layouts within stores, and to reduce stock-outs or inventories (Davenport, 2006). Meanwhile, companies like Amazon and Yahoo use analytics to improve the quality, efficacy, and safety of products and services.
References
Davenport, T.H. (2006, January). Competing on Analytics. Retrieved on May 6, 2019, from https://hbr.org/2006/01/competing-on-analytics#
Isaacson, C. (2018, August 18). Introduction to Understanding Big Data Scalability. Retrieved on May 5, 2019, from http://www.informit.com/articles/article.aspx?p=2238298&seqNum=3
Maggo, N. (2014, October 2). Data analysis: The Big Data Explosion. Retrieved on May 4, 2019, from https://www.geospatialworld.net/article/data-analysis-the-big-data-explosion/
Following the super-linear rate of data increase, a large volume of both structured and unstructured data is visible on the global space. For example, the volume of available digital data at the global level went from 150 exabytes in 2005 to 1200 exabytes in 2010 (Maggo, 2014). This data is so large that it is difficult to manage it using traditional database and software techniques, and as it further grows, the problem of managing it becomes harder, which can lead to overload, giving course to the term data explosion (Isaacson, 2018).
The concept also involves the volume of data, the technology, tools, and methods required to handle the large data and storage facilities. Organizations face hitches in being able to create, deploy and manage data explosion especially when dealing with massive datasets. It is also a critical issue in business analytics because of the available tools and procedures are not premeditated to search, collect and analyze large datasets. The factors responsible for this explosion may include; the presence of the internet and the interaction of users in this space, the shift from bricks and mortar stores to online retailing/sales, online advertising, e-commerce (online increased airline ticket bookings, international purchases, online products, etc.), core and specialized social networks (Facebook, Twitter, LinkedIn, Google+, etc.), mobile data, chatter trend analytics applications like Twitter and Facebook chats and many others (Isaacson, 2018).
Presently, organizations are taking numerous methods to gain a competitive advantage with analytics. Some use the process of collecting important data over time about their customers and prospects, while others decide to organize, homogenize and use data that is available to others in an exceptional way that competitors can’t match. We have seen organizations who even develop a proprietary algorithm that leads to better, more insightful analyses upon which to make decisions. And some differentiate themselves by implanting analytics into a distinctive business process. Whatever the case, Thomas Davenport has validated businesses like Netflix, Amazon.com, etc., to have all become market leaders by finding new ways to use the information and the technology driving their strategy is analytics (Davenport, 2006). And competing on analytics is seen as using information resulting from the systematic analysis of data to outexecute the competition. From the firms observed by Davenport, there are forum common characteristics which include, strategic and distinctive capability; enterprise-wide approach; commitment to senior management to analytics; and the will to strategically compete (Davenport, 2006). As time ticks, more companies are likely to discover the possibilities of analytics and its potential to drive competitive advantage, they will be keen to push beyond the limits of analytics in their products and services.
There are a number of firms that have realized competitive advantage through analytics. For instance, in my company, we are now exchanging analytical information with our digital marketing partners. Marketers get analyses involving pricing, sales promotions, and inventory while our customers receive data and analysis that helps them with their own purchase decisions. In researching other organizations, I discovered that Honda uses text mining to identify and head off potential quality problems in its cars. Similar text and web-mining approaches could be used to, for example, better understand customer perceptions of service and brand value. Analytics based organizations make use of statistics and modeling to improve a wide variety of functions. Wal-Mart, for example, insists that suppliers use its Retail Link System (RLS) to monitor product movement by store, to plan promotions and layouts within stores, and to reduce stock-outs or inventories (Davenport, 2006). Meanwhile, companies like Amazon and Yahoo use analytics to improve the quality, efficacy, and safety of products and services.
References
Davenport, T.H. (2006, January). Competing on Analytics. Retrieved on May 6, 2019, from https://hbr.org/2006/01/competing-on-analytics#
Isaacson, C. (2018, August 18). Introduction to Understanding Big Data Scalability. Retrieved on May 5, 2019, from http://www.informit.com/articles/article.aspx?p=2238298&seqNum=3
Maggo, N. (2014, October 2). Data analysis: The Big Data Explosion. Retrieved on May 4, 2019, from https://www.geospatialworld.net/article/data-analysis-the-big-data-explosion/
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