data warehousing and data mining

If you are related to an industry that needs some level of technological sophistication and uses the internet, you would be familiar with the role that data plays in its general functioning and growth. If the 18th century was marked by new inventions, the 21st century’s data surpasses all of it. Crude oil and steam engine were some of the crucial discoveries and inventions in the history of mankind, data and data science holds the same standing in contemporary times.
 

The basics of data warehousing and data mining

Data Mining
Data Mining is a process or a method that is used to extract meaningful and usable insights from large piles of datasets that are generally raw in nature. Data mining deals with analysing data patterns from large chunks using a range of software that is available for analysis. 
The present world is all about sourcing valuable data from the tons of available databases, and using that to derive meaningful conclusions. These insights are used by business houses to identify trends and further their business agendas through better customer targeting and acquiring new audiences. The data mining process is applied across multiple industries and segments, mostly in the research and development sectors.
Data Warehousing
Data warehousing, as the name suggests, deals with the storage and retrieval of data. It is the collection and management of large amounts of data that can be used in the future to derive meaningful conclusions. On a more technical note, it can be explained as electronic storage of a big chunk of information by an organization, designed for addressing queries and making general analysis instead of only transaction processing. It is a process that comes before converting data into relevant information and making it available for different users on a timely basis for their specific uses. 

How data warehousing and data mining are linked?

Before sprinting over to how the two processes related to data extraction and storage differ, let’s find out what commonalities they share and how they’re linked to each other.
If you are to use a gun in a fight, the prerequisites require you to carry sufficient ammunition. This analogy will help us understand the relationship between data warehousing and data mining. Instead of being mutually exclusive, data warehousing and data mining work in conjunction with one another. Data mining is all about extracting useful information from piles of data. Now, how do you know what’s useful? Naturally, for anything to be stated as useful you have to have a comparative scale against what is not useful. 
This is where data warehousing plays a crucial role, it stores piles and piles of data from various sources and helps in managing the same. So, it supplements the data mining process by providing it with all the information that is there to assess and derive insights from. Putting it in simpler terms, data mining is more about deriving inferences and forecasting business needs, while data warehousing provides the source for this forecasting and analysis. That sums up the connecting link between data mining and data forecasting through a more pragmatic approach.

Industries using data mining and warehousing & its business applications

This section draws our attention towards the practicality of the processes by identifying the industries using the methods mentioned above and their business applications. Since we have already established the link between both the processes we can see the industries that use the combination of the two to function smoothly.
Healthcare Industry
One of the most prominent industries to use and apply the data warehousing method in their day to day operations is the healthcare sector. It uses data warehousing functions to hold all of their clinical and human resource records, using it to strategise and predict the outcome of a situation, generate patient reports and records. 
Data mining also depicts a huge potential when it comes to improving healthcare systems. It utilizes data & analytics to establish standards that will help to improve facilities and aid in cost optimization. Data scientists who carry out research works generally practice data mining approaches such as data visualization, machine learning, etc. to derive meaningful conclusions related to their hypothesis. In addition to this, data mining can also be used to make predictions about the number of patients in different classes.  
Government sectors 
In the contemporary scenario to run a country smoothly on a growth trajectory, the government needs to be one step ahead of its citizens. This is where the data warehousing has a crucial role. It helps the government in every aspect, from the research database to human resource management for the public sector. It also helps to store health-related data and criminal records that help in proper planning to curb out the anomalies.  
Criminology
Criminology can be defined as a process whose objective is to find out the crime dynamics. The process of crime analysis incorporates discovering and identifying crimes and putting the information obtained in the context of criminal psychology.
Education Sector
The education industry has been growing dynamically due to the rapid changes in technology. The application of data warehousing can be seen in the case of universities that use data warehouses for the extraction of relevant information which is used for the proposal of research grants. In addition to this, the universities also use data warehousing to understand their student demographics, and for human resource management. 
Data mining has had a significant impact in Educational Data Mining. One application of EDM is to predict the students’ future learning curve and for behavioural analysis. It also aids in studying the effects of educational support, in turn progressing scientific information insights about learning. Data mining can be used by an institution to make accurate decisions and to predict student results in the context of their past performance and related projections. 
Finance & Banking
The finance industry has evolved with the advent of data science and general technological advances. Data warehousing is mainly used by banks for effective resource management. Analysis of card holder’s transactions, spending patterns and merchant classification needs a whole lot of data, data warehousing provides a helping hand here as well. Some of the banks utilize the warehousing process for market research and general performance analysis of every financial product available. 
Data mining has a crucial role to play in the evolution of the finance industry, from fraud detection to digital banking. With the advent of digital & mobile banking, the volume of transactions has increased and with it, the data that is generated from the same. Data mining helps resolve problems in the industry by identifying trends and determining relationships among different variables that might be indirectly related but is not very evident to people working in the industry.

Pros & Cons of using Data Mining & Warehousing

There is always a trade-off involved in using different technologies, and data mining is no exception to this. Let’s take a closer look: 
Pros
– Predictive Analysis: Data mining & warehousing techniques help businesses to predict future trends and it also helps to draw inferences from meaningful data sets that project the future course of action. It aids in better customer targeting and desired marketing results.
– Cost optimization & revenue maximization: Data mining & warehousing helps to reduce the occurrence of failed business strategies by the constant flux of data. This aids in almost real-time problem solving and removes the chances of customer dissatisfaction thereby creating goodwill for the brand, this will help to obtain a loyal customer base that will help sustain profits in the long run.
Cons
– Invades Privacy: Every data that is generated is private to the user, most of the information is directly available in public records but with the penetration of data science in every industry, the privacy of people is in jeopardy.
– Security: From the security point of view, the availability of high volumes of data in the public domain can be a threat to organisational security. If it falls in the wrong hands, the potential effects can be devastating. 

Jobs availability in data mining & warehousing segments

Today, one of the most lucrative fields for career progression is in data science. One of the prime reasons there is more demand for the skills needed for data processing roles is that the present workforce supply is not at par with the industry demand.
The job roles in the data mining field range from business analyst to data engineer/ scientist to machine learning engineer, data miner, and many more. You can even opt for more specialized roles that come in the form of data mining specialists. On similar grounds, the job roles available in the data warehousing segments include the likes of data warehousing business analysts, data warehouse architects, data warehousing developer, and more.
Basically, all these job roles deal with finding hidden information from vast databases. They involve putting the information derived in context to understand how it relates to a particular organization.
If you want to learn more about data warehousing and data mining techniques, please visit the data science section of our blog
You can also sign up for Great Learning’s PG program in Data Science and Business Analytics to upskill yourself and build a successful data science career.

3

LEAVE A REPLY

Please enter your comment!
Please enter your name here