What is e-commerce?

What is meant by E-Commerce?

Electronic commerce, widely written as e-commerce or eCommerce basically refers to the routine of trading, buying and selling of products and services electronically, that is over the internet. In the past few years, the eCommerce sector has expanded rapidly with more and more firms making their presence felt online. With the current trend, eCommerce is expected to grow exponentially in the coming years and completely changing the way we shop.

E-commerce is a sector that has grown steadily and made its way into our day to day life. We are all a part of the eCommerce ecosystem in ways we don't even notice like those endless hours we spend scrolling through Amazon and Snapdeal looking for books, clothes, shoes, electronic gadgets and what not. One of the main reasons why eCommerce took off so quickly was the ease it provided to the consumers as well as to businesses.

Consumers can view everything from availability of hotel rooms to grocery items from the comfort of their homes with just a tap of the finger. Sellers or traders are[ able to cater to thousands of customers online without having to worry about being short staffed or overcrowded. The most evident advantage of participating in eCommerce is its 24 hour operational facility. Especially as a consumer, weekdays can become grocery shopping days after work and weekends can be spared for fun only activities. Another advantage of eCommerce is the sheer variety and options that online stores provide as opposed to the physical counterparts. Nowadays, with the option of ordering gourmet online, basil from the farms in Thailand and authentic Parmesan cheese from Italy can be flown right at your door step.

Like any other business model, eCommerce too has its disadvantages. The most prominent ones being the intangibility of the products we wish to buy and the delay in getting our hands on on our purchase. When time is a crucial factor in making any sort of a purchase, eCommerce may not be the best option. After purchasing, the supplier may take long to ship it to the consumer desired destination and hence on such occasions online shopping can be done without.

The numerous eCommerce transactions that are carried out every minute all over the world give rise to massive amount of dynamic data. eCommerce is one of the major contributing sectors to the Big Data Explosion seen in recent years. This data generated due to eCommerce websites and platforms, when analysed, mined for information and represented in appropriate formats can be extremely powerful to decision makers. The trends and patterns observed from these information sets can prove to be important tools in business expansion and strategy design,

REFERENCES AND FURTHER READINGS
1)http://www.investopedia.com/terms/e/ecommerce.asp
2)http://www.networksolutions.com/education/what-is-ecommerce/
3)http://www.websitebuilderexpert.com/e-commerce-examples-case-studies/

What are the different types of data analysis?

Types of Data Analysis

Data Analysis refers to carrying out specific processes on sets of data with the target of procuring maximum amount of information from the same. These processes include scrutiny, segregation, modification, modeling, etc. that aid in extracting conclusive reports that aid in decision making.

In the previous blog, I spoke about data mining, which is a specific application of data analysis that focuses on predictive type of data analysis more than the deterministic type. Another application of data analysis is business intelligence (BI), the focal point for which is purely data crucial for business.

Data Analysis Types are a result of a number of classifications based on either its function, type of data being operated on or even areas of application. Below mentioned are a few ways of classifying data analysis procedures and the types arising out of them.

>>Based on its functions, the science of statistical data analysis can be divided broadly into two categories.

1)Exploratory Data Analysis

2)Confirmatory Data Analysis

Exploratory Data Analysis (EDA)

It characterizes data analysis processes that lead to generation of information or conclusions from a given work space consisting of data. As the name suggests, its sole purpose is to comb through volumes of data to explore new trends, patterns, etc. and provide useful insights as a result. It is referred to as descriptive statistics and follows the inductive approach

Confirmatory Data Analysis (CDA)

It encompasses the set of processes carried out on data sets that produce information that further helps to examine and prove existing hypotheses and claims to either be true or false. Hence, since it 'confirms' the true or false status of an already existing notion, such processes are known as confirmatory data analysis. It comes under inferential statistics and follows the deductive approach.

>>Based on the type of data under scrutiny, data analysis may also be classified as

1)Qualitative Data Analysis 

2)Quantitative Data Analysis

Qualitative Data Analysis 

Analysis of non-numeric data is called qualitative data analysis. The basic goal of this type of analysis is to make inferences from data consisting of a string of characters by identifying existing patterns and abstract trends to determine a general feel of the given non-numerical data. It is important to note that this type of analysis comes into picture when the given data set is such that classical statistical analysis cannot be applied. The most common example where qualitative data analysis is performed is in the fields of social sciences, psychology, etc. where large amounts of data comprising of letters and symbols is generated as a result of research. 

Quantitative Data Analysis 

As the name suggests, quantitative data analysis refers to the set of processes carried out on numerical type of data to make logical inferences. It is also referred to as statistical data analysis as unlike qualitative data analysis, this type of data analysis relies heavily on statistical operations and classifications. This type of analysis is used widely in the fields of finance, banking, e-commerce, marketing, sales, etc. where extremely large amount of numeric data is generated and is required to be analysed for decision making purposes. 

References and recommended readings

1) http://searchdatamanagement.techtarget.com/definition/data-analytics

2) https://en.wikipedia.org/wiki/Data_analysis

3) http://study.com/academy/lesson/the-difference-between-qualitative-quantitative-measurement.html

4) http://www.geog.ucsb.edu/~joel/g210_w07/lecture_notes/lect01/oh07_01_2.html

What is Data Mining?

DATA MINING

What is Data Mining?

Data mining is the process of analyzing data to mine from it useful information which is further used to aid decision making through inference. To be more specific, data mining leads to identifying patterns or trends in given sets of data to make useful conclusions. Data mining tools and softwares makes use of elaborate algorithms to sift through large data sets to automatically segregate data and evaluate the probability of future events. Hence, data mining is aptly referred to as Knowledge Discovery in Data (KDD).

Where is it used? What are the most common applications?

Nowadays, all kinds of businesses are using data mining tools to identify trends and patterns conducive to the growth of their corporation. 

For example, large supermarket chains like WalMart and Cosco rely on knowledge discovery techniques to identify numerous consumer factors that affect management processes. Factors such as identifying the products that have higher demands, effects of seasonal changes in purchasing patterns, effect of the location of the store, etc. are just a few of the many inferences that can be made by analyzing the massive amount of data being generated at these stores on an everyday basis.

Another very important application of data mining is that in Univerisities and educational institutions. Data mining in education plays a huge role by making information available for devising new lesson plans, improved teaching methodology and drawing strategies to improve the overall performance of students. Data mining operations on class quizzes, surveys, assignments and other evaluation work reveals surprising amount of information that helps educationalist rethink the approach to quality education.

Data mining is also important when it comes to industries dealing with supply chain management and production, service enterprises and basically any type of industry dealing with consumer goods. Using data mining and analytics, industries and policy makers are able to better understand consumer behavior and predict future purchasing trends. By altering production and other processes to suit the needs of the future, industries are able to up their game and eliminate competition.

Another area of the spectrum where data mining plays an important role is that of the banking and finance sector. By examining customer data, data generated by the interactions between the company and consumers and other data generated within the company, a bank or insurance company is able to identify a demographic which when targeted with the suitable approach can maximize the prospects of increasing profits. This may be in terms of getting more and more people to open accounts with their bank or issue a policy from their firm, etc. 

Thus, data mining has a wide range of applications and can find respectable place in almost all industries. Can you think of any other applications of data mining? Let us know in the comments below.

REFERENCES

1. http://www.anderson.ucla.edu/faculty/jason.frand/teacher/technologies/palace/datamining.htm

2. https://docs.oracle.com/cd/B28359_01/datamine.111/b28129/process.htm

3. http://www.thearling.com/text/dmwhite/dmwhite.htm