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...
Most of the companies think that information is a valuable commodity and its one of the things that deserves storage (the more information we have, the more we can learn, and the changes that drive business success). But, it is important to remember that data collection and storage costs money - data requires storage and electricity to run, and if the information is sensitive, attention must be paid to security and data compatibility (Marr, 2016).
Of course, the problem becomes bigger when we consider the expected growth in data companies that will produce; experts expect an annual productivity increase of 4,300% by 2020. But companies use only a fraction of the data they collect and store. Therefore; if companies and individuals want to avoid drowning in data while thirsting for insights, they have to develop a smart data strategy that focuses on the few things they really need (Marr, 2016).
Data collection and extraction is a crucial step in big data management. If there is no data in the first place, there is no point to any of the further processes. With the seamless combination of IoT and big data, data extraction is now quicker and hassle-less. For businesses that cannot afford a big data infrastructure of their own, cloud-based big data solutions, for example, Amazon Web Services (AWS) gives a ready platform to utilize cloud computing services (Mayekar, 2018).
Companies questing for killer apps generally focus all their firepower on the one area that promises to create the greatest competitive advantage. Organizations are competing on analytics not just because they can but also because they should. Analytics competitors understand that most business functions can be improved with sophisticated quantitative techniques. These organizations don’t gain advantage from one killer app, but rather from multiple applications supporting many parts of the business (Davenport, 2006).
Analytics competitors are more than simple numbers factories. Certainly, they apply technology with a combination of power and ingenuity to many business problems. But they also direct their energies to finding the right focus, building the right culture, and employing the right people to make the best use of the data they are constantly doing. In the end, people and strategy, as much as information technology, give such organizations strength (Davenport, 2006).
A real example of a company that uses big data analytics to drive customer retention is Coca-Cola. In the year 2015, Coca-Cola managed to strengthen its data strategy by building a digital-led loyalty program. Netflix is also a good example of a big brand that uses big data analytics for targeted advertising. With over 100 million subscribers, the company collects huge data, which is the key to achieving the industry status Netflix boosts. Netflix subscribers are familiar to how they send suggestions of the next movie the subscriber should watch. Basically, this is done using the subscriber past search and watch data. This data is used to give them insights on what interests the subscriber most (Kopanakis, 2018).
References:
- Davenport, T. (2006). Competing on Analytics. Retrieved May 18, 2016, from https://hbr.org/2006/01/competing-on-analytics#
- Kopanakis, J. (2018). 5 Real-World Examples of How Brands are Using Big Data Analytics. Retrieved from: https://www.mentionlytics.com/blog/5-real-world-examples-of-how-brands-are-using-big-data-analytics/.
- Marr, B. (2016). Big Data Overload: Why Most Companies Can't Deal With The Data Explosion. Retrieved May 26, 2016, from http://www.forbes.com/sites/bernardmarr/2016/04/28/big-data-overload-most-companies-cant-deal-with-the-data-explosion/#47fc299d3920.
- Mayekar, S. (2018). Dealing with the big data explosion. Retrieved from: https://www.analyticsinsight.net/dealing-big-data-explosion/.
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