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Amazon Company
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Introduction
We ought to get a little perspective of what fortune 1000 means before we get to too
many details, don’t you think? Well, fortune 1000 is a list of a thousand biggest companies in
America that are ranked based on revenue spawned from consolidated subsidiaries, discounted
operations and core operations. Keep in mind this list is produced yearly by the well-known
magazine fortune. Among these one thousand companies, Amazon Company stands out based on
this discussion. That being said, this article involves discussing Amazon Company, its approach
to big data analytics and business intelligence, where they are on the right and wrong track, and
areas they can improve as a company in maintaining and applying business intelligence and big
data analytics.
Discuss the company
Amazon
The history behind Amazon Company goes way back to 1994, the month of July when it
was founded. Jeff Bezos in Seattle founded the Amazon Company. At the time, Seattle was
advancing technically at a higher rate hence influencing Bezos’ intentions to form a company in
Seattle. In addition, it’s not a walk in the park forming a company by yourself, so Jeff Bezos had
a team of talented and well-specialized individuals. These personalities include Mackenzie Scott.
Amazon Company began by selling books online. In the year 1997, the company was gaining
popularity where it started selling videos and music. The following year amazon progressively
advanced to selling more items like software, toys, games, home appliances, consumer
electronics, among others.
As years progressed, technology was also advancing at a high rate. Therefore, this led to
the introduction of the Amazon web service (AWS). The purpose of AWS was to provide data
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on the internet traffic patterns and website popularity, which made it much easier for developers
and marketers to make analyses or statistics (Ozgur, 2017, p. 355-371). The AWS portfolio
immensely grew in 2006 when data storage through the internet was introduced or rather made
accessible. Between the year from 2012 to 2017, Amazon Company focused on managing and
automating its inventory. Currently Jeff Bezos is not the CEO but the Executive Chair of the
company. Andy Jassy is currently the company’s CEO.
Its approach to big data analytics with business intelligence
Well, to understand how Amazon’s approach to big data with business intelligence on a
deeper level, let’s figure out the terms that stand out in this particular topic. These terms include
big data, big data analytics, and business intelligence. Big data is the systematic way of
analyzing and extracting data from data sets that seem too large to handle using outdated data
processing application software.
Big data analytics uses radical techniques to analyze large data (unstructured data,
structured data, and semi-structured data). In addition, the processes and technical infrastructures
involved in gathering, storing, and analyzing data produced by an organization or company are
known as business intelligence. Furthermore, some individuals would view business intelligence
as process analysis, data mining, or in simple terms, benchmarking (Jin, 2018, p. 10).
As discussed earlier in the article, Amazon is one of the leading e-commerce platforms;
therefore, the amount of data it gathers is massive. Managing this data involves using
technologies in big data. Customers being Amazon’s valuable assets, the company strives to
collect information from its customers. Therefore, this will increase the chances of the company
gaining more clientele and even more insights on how to market the company’s products or the
company at large. Amazon uses the recommendation engine to leverage its gathered data. For
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instance, if a client views a particular product in the AWS, the company utilizes this information
to predict the client’s products of interest. In addition, Amazon keeps records of the product
clients purchase and delivery addresses. By doing this, Amazon can convince customers to
purchase a particular product. In easier terms, Amazon uses a personalized recommendations
system that figures out customers’ behavioral patterns in the company’s platforms, such as
AWS.
Additionally, Amazon uses big data to keep track of the company’s inventory hence
influencing manufacturers to ensure orders are delivered fast. Moreover, if you think about it, big
data enables Amazon to reduce delivery expenses by picking the closest warehouse. In simple
terms, this is known as supply chain optimization.
How Amazon uses big data correctly
Amazon has incorporated the "everything under one roof" model of business which has
been successful. Customers feel overwhelmed when faced with a wide variety of options where
customers have poor insight and little or no idea concerning the best purchasing decision.
Amazon uses Big Data analytics to understand its customers and therefore influence the
purchasing decision of its clients (Kauffmann, 2020). It has helped Amazon edge its competitors.
In support of Amazon using Big Data, they collect clients' information at the browse, which
designs and customizes its recommendation engine. Amazon tries as much as possible to know
its customers at a deep level. It helps them predict the purchasing patterns of their clients. Once
the retailer is aware of one's interests, it makes influencing one to buy easier. Amazon does this
by recommending products to purchase depending on what one searches for mostly. Clients are
happy when they find it easy to get what to buy, increasing the chances of purchasing goods via
Amazon.
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Amazon has incorporated technology referred to as collaborative filtering. It aids in
predicting the user's wants by building personalities then advertising products that people of the
same personality have purchased. Amazon collects data of every customer as they navigate the
site. Amazon monitors what one searches, the purchases, and the address to which things are
shipped. Based on where you live, Amazon can predict income levels based on purchasing
power, where you come from, and purchasing history. Amazon also is keen on checking
customer's feedback which allows adjustments of products and services.
The large volume of data Amazon manages utilized to build a "360-degree view of every
client. Amazon also can link other people who are in the same category. It, therefore, facilitates
targeted marketing whereby goods are advertised to a specific niche. Amazon gathers data from
users, such as the browsing time of each page. Amazon also utilizes external datasets like census
data that contain demographic information. The core business of Amazon is operated in the
primary data warehouse, which entails Hewlett Packard servers running Oracle on Linux.
Customers are overwhelmed when there are too many choices with no guidance, which
leads to poor purchasing decisions. Amazon uses a recommendation engine to predict the wants
of a customer by profiling people and matching similar profiles to check what people of that
category purchase easier. The 360-degree view of clients as individuals is the base for customer
service and Big Data marketing.
What is Amazon doing wrong about Big Data, and what can be done to improve
Challenges Amazon is facing with big data are the same of other companies. These
challenges include; incorporating machine learning, data silos, data controllership, data security,
and difficulty evaluating various datasets. Without further ado, let's have a look at these
challenges.
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Breaking down silos
Companies create data lakes to break down data silos. Having data in different areas
foreseen by various groups makes data invalid. In most cases, it happens to companies that grow
fast or expand to new areas. Most operation planning is done by different teams, which leads to
data being stored in different means and places. It is, however, hard for data to make sense at an
organizational level. With teams operating independently, problem-solving becomes less
efficient, which could be solved easily together (Phillips, 2019)
Since not everyone can access data repositories, getting granular details from the data
isn't easy. When data exceeds the capacity of a spreadsheet, challenges often come up in large
companies with Big Data. A data lake is used to solve this problem. It unites all the data in one
central location. Teams can function independently, but it is linked to the lake data for analytics;
hence no more silos.
Analyzing diverse datasets
Data structures and information vary hence a challenge when different approaches and
systems are used. Amazon prime has data fulfillment centers and packed goods, while Amazon
Fresh has grocery stores and food data. Shipping programs are different in different parts of the
world, whereby countries have different packaging styles in terms of shapes and sizes.
Another challenge when evaluating data comes when different systems having the same
information but are labeled differently. In America, the term used is the cost per package,
whereas, in Europe, the term used refers to the cost per unit. The word used has two meanings
which in this case are different. To solve this, I recommend using a link between two labels to
know it refers to different things.
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Data lakes can be used to import data in any format since there is no predefined schema.
Data can also be computed in real-time. Data can be collected from different sources and moved
into the data lake in its original model. Links can be created between information labeled
differently but representing the same thing. Data lakes allow one to store highly designed data,
which is frequently accessed in a warehouse, while maintaining semi-structured and unstructured
information in the lake storage.
Managing data access
Since data are stored in different locations, it is hard to access them and connect them to
external tools for analysis. The finance data for all operations of Amazon are in more than
twenty databases, with regional teams designing their model of datasets. Databases need access
management credentials to change profiles or passwords.
Audits must be done for every
database to avoid improper access (Yesin, 2019).
Data lake makes it easier to find the right data to the correct people at the right time.
Amazon has to worry only about a specific database instead of managing access to different
locations where data is stored. Data lakes come with the significance of users to see, process, or
access assets. Unauthorized users are blocked from doing any task that would impact data
privacy. In data lakes, data is stored in a format that makes work easier when evaluating data.
The format is compatible with even tools that are not developed yet.
Conclusion
Big data analytics is a game-changer for big companies that have incorporated it.
Amazon has been seen to take full advantage of this system where it used to foresee its
operations. Amazon can predict purchasing patterns of users with the help of this system. It has
made clients' work easier when purchasing since it is difficult, especially when there are various
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options to choose from in the market. Besides Amazon benefiting from Big Data Analytics, it
has faced challenges when incorporating it in its organizations. These challenges, however, have
been solved thanks to data experts. Companies that have not incorporated Big Data Analytics
cannot compete with these giant companies. It has given Amazon a competitive advantage
against its competitors. Big Data analytics has contributed majorly to the success of Amazon
globally.
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References
Kauffmann, E., Peral, J., Gil, D., Ferrández, A., Sellers, R., & Mora, H. (2020). A framework for
big data analytics in commercial social networks: A case study on sentiment analysis and fake
review detection for marketing decision-making. Industrial Marketing Management, 90, 523537.
https://www.sciencedirect.com/science/article/pii/S0019850118307612
Jin, D. H., & Kim, H. J. (2018). Integrated understanding of big data, big data analysis, and
business intelligence: a case study of logistics. Sustainability, 10(10), 3778.
https://www.mdpi.com/2071-1050/10/10/3778
Ozgur, C., Colliau, T., Rogers, G., & Hughes, Z. (2017). MatLab vs. Python vs. R. Journal of
Data Science, 15(3), 355-371.
https://www.airitilibrary.com/Publication/alDetailedMesh?DocID=16838602-201707201711160005-201711160005-355-371
Phillips, R., & Tanner, B. (2019). Breaking down silos between business continuity and cyber
security. Journal of business continuity & emergency planning, 12(3), 224-232.
https://www.ingentaconnect.com/content/hsp/jbcep/2019/00000012/00000003/art00004
Yesin, V. I., Karpinski, M., Yesina, M. V., Vilihura, V. V., Veselska, O., & Wieclaw, L. (2019,
September). Approach to Managing Data From Diverse Sources. In 2019 10th IEEE
International Conference on Intelligent Data Acquisition and Advanced Computing Systems:
Technology and Applications (IDAACS) (Vol. 1, pp. 1-6). IEEE.
https://ieeexplore.ieee.org/abstract/document/8924235