Everyone is talking about Big Data. Facebook, Google, Amazon they all have their Big Data strategies and are employing the best engineers in algorithm jobs and machine learning jobs and with this increase in interest is a huge increase in opportunities as many more positions will need to be filled as the demand increases.
The moving average is a commonly used technique for smoothing noisy data. Unlike for example, Kalman filtering, it is easy to understand and easy to implement. The case where data points arrive one by one is easily transformed to a map reduce program. Working proof of concept code has been presented to show that Map-Reduce and Hadoop can be used outside the big data domain.
In the past, there were issues when it came to harnessing and solving the large volumes of data that came in. This is because there were plenty of inaccuracies, traditional collection methods were of no use, and managing it all was a chore. In addition to that, companies never knew how to use the given data, especially for managerial purposes. With this hampering the status of the company’s mission on profit making, big data thus was churned to help with transactional, structured and unstructured data. It helped in a number of studies like consumer behavior along with the services and products status in the market.
Any traditional bank has to cope with the pressures of overcoming the three major risks of banking. Capital risk, market risk, and operational risk. Data Science and big data analytics can immensely help in overcoming these risks by carefully analyzing the inconsistencies in the data used to support various data management processes. “Minimizing risk is mandatory in today’s competitive scenario and it has been proven several times that even simple models when coupled with manager’s intuition can bring in tremendous value.” – Dr. Murthy, renowned Data Scientist
Big Data has proven itself to be a driving force in the way merchants are building their campaigns to boost revenues and reach out to more relevant customers online. One area where we are seeing the direct influence of collected data is in dynamic pricing. This practice of constantly updating prices has been kicked into overdrive as more shoppers turn to the Internet to seek out better deals in the cyber marketplace. While once viewed as a controversial tactic, it is quickly changing the way that we buy and sell online.
How it worked back then In the days gone by, SMEs didn’t bank on big data systems or technological platforms, since they didn’t feel that large volumes of info would be of any help. However, as time passed by, companies were faced with the challenge of realizing the true needs of a customer or consumer, and hence started investing in expert advice as well as technologies related to large data to achieve the same. In the beginning, when such investments were done, it was expensive; however, with newer as well as innovative ways to play safe, technological developments have made it cheaper to use big data platforms or services.
With big data in place, SMEs now understand how important it is to identity in hand what the company needs when hiring such systems or platforms. Companies can use data frameworks as multiple options, for service support reasons especially. However, companies should do their homework to know if big data would help the company make more profits in future or not. The versatility of the system and platform too should be checked, since in most cases there are no complexities and it isn’t bulky.
With the rising cut throat competition in gaining market share, a challenge for the insurance companies today is customer retention and understanding the net profit attributed to the entire future relationship with a customer. With the help of data science and big data analysis, predicting the lifetime value of a customer is no more a mind boggling task.
Companies need real time information, information which can be good enough for them to come up with customized services for their customers, or else they lose appeal in the market. It is thus important for businesses to gather as well as analyze, even manage information related to customers, both internal and external. This would also include knowing about the suppliers as well as the partners too, or else how would a company expand in the large ocean of the business world? Monitoring of the company’s performance too is important on a periodical basis, which brings in real time feedback on the services or products doled out. This would mean finding means to bring down costs for the company and here is where big data adds a lot of value to small and medium enterprises. Information thus gathered would be easily accessible and can be used frequently as well.
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