NORTHEASTERN UNIVERSITY 1604365102462400 Integrated Experiential Learn

NORTHEASTERN UNIVERSITY
1604365102462400
Integrated Experiential Learn
(ALY6080)
(October 14, 2018)
Submitted by:
Shruti Thakkar
Submitted to:
Agnieszka M. Chomicz-Grabowska  
Big Data for Decision-Making
In my opinion, there will be a huge business transition in organizations and the way business functions with the more and more generation of data every day. There has been massive increase in the way data is being used and is getting difficult to manage data. It is very important to get proper insights as it harnesses the power of big data analytics to inform key strategic decisions. The big data provides competitive advantage to the businesses in terms of decision taking process. Big data analytics helps in taking important decisions as they make it more agile and responsive.

With increasing digitization in the world, businesses now have too much data generated every day. With this increasing data it is very difficult to manage this data and get proper insights from this data. Simply bigger the data, harder the analytical process becomes. As big data doesn’t really mean only the collection of data or just having the information, it also includes all the processes and tools that help in the analysis of this big data and the results derived from it.

The biggest advantage if big data is that it can be applied to time fraud detection, complex competitive analysis, call center optimization, consumer sentiment analysis, intelligent traffic management, and to manage smart power grids. Big data is characterized by three primary factors:
Volume (quantity over quality);
Velocity (too much generation of data every day);
Variety (range and different type of data).
Following are the four types of Big Data BI that affects a business:
Prescriptive Analytics refers to the rules and recommendations for the following steps to be taken.

Predictive Analytics help in analyzing the data and help in knowing what might happen and derive what steps can be taken.

Diagnostic Analytics is usually to analyze the past data and what happened, why it happened. This is to understand how the analysis will help in making the dashboard easier to understand.

Descriptive Analytics will in knowing what is happening now based on incoming data. It uses the real time data and dashboard in order to carry out analysis.
With the big data, along with a lot of benefits, the decision-making process faces a lot of challenges. According to me below are the pros and cons of using big data in the decision-making process.

Advantages of Big Data in Decision-Making
It helps in gaining the market advantage
It helps in building trust with clients
As the speed of collection is often too fast, the decisions can be taken at a faster pace
Disadvantages of Big Data in Decision-Making
There is a higher chance of analyzing and taking decision for the inaccurate data.

Time and money will be wasted if the decisions are taken for the inaccurate data.

Decision making with Big Data requires a lot of talented people to work it in our favor. Mistake in one step would lead to iterating of entire process making it time consuming.

In addition to that, there are always cybersecurity risk which can manipulate the decisions when it comes to data.

Taking example of the Amazon, it has been rightly using big data in all its benefits and has become the biggest ecommerce giant in the world. These are the perks of using big data analytics in the right way. Amazon is the perfect example to understand how big data can work in your favor and help you achieve the market that you are targeting.

Therefore, taking the example of Amazon, it entered the Chinese market without any analysis of the data or without any knowledge of the market. It failed miserably in the market and had to undergo losses. Few years later, it decided to enter Indian market and be its biggest ecommerce retailer. Now, before entering the Indian market, Amazon decided to analyze its mistakes that it made while entering the Chinese market.

Amazon used the diagnostic analysis of the big data to understand its failure in the Chinese market and was able to create the dashboard describing the factors that led to failure. Those factors were
did not get enough support from the customers
could not stand the local competition of Alibaba (local Chinese e-commerce)
did not get enough support from the Chinese government
Amazon did not only analyze the Chinese market, but it also undergoes descriptive, prescriptive and predictive analysis for the Indian market before entering it and created a well-versed dashboard.
Thus, these three factors play an important for any company entering a foreign country. Here that is what Amazon did and I also as a decision maker would have done the same thing as analyzing these three things which made Amazon fail in Chinese market, can actually help it succeed in Indian market. Moreover, in addition to that, there are so many startups in India which needs a boost and needs investment unlike the case in China. So, Indian market has availability of low-cost technical efficiency which helps in avoiding huge costs of outsourcing. Not only that, Indian consumers are always fascinated by these new changes and are always welcoming to more convenient options if available. So, choosing startups rather than outsourcing and making convenience of customer first priority would help in Amazon succeeding the Indian market.

Thus, from the example of Amazon, organizations that employ swift decision-making processes are able to realize better value from real-time data analytics and build a strong competitive advantage. Organizations that streamline their decision-making processes to become agile and responsive can best survive in the face of constant digital disruption. Only by minimizing the time gaps between insight generation, decision-making and implementation can the real value of big data be realized.

References
Bharti Wadhwa and Anubha Vashisht, Davinder Kaur (2017); BUSINESS MODEL OF AMAZON INDIA A CASE STUDY. Int. J. of Adv. Res. 5 (8). 1426-1433 (ISSN 2320-5407)
The pros and cons of implementing big data in your business. (2018, May 04). Retrieved from https://www.churchillfrank.com/blog/big-data-analytics-for-business/Declues, J. (n.d.). Four Types of Big Data Analytics and Examples of Their Use. Retrieved from http://www.ingrammicroadvisor.com/data-center/four-types-of-big-data-analytics-and-examples-of-their-useHow is big data analytics transforming corporate decision-making? (2017, December 14). Retrieved from https://consulting.ey.com/how-is-big-data-analytics-transforming-corporate-decision-making/