I was watching the documentary on Netflix, “The Great Hack” where, Brittany Kaiser, former Director of Business Development for Cambridge Analytica, rightly stated that data is now more valuable than oil and also data mining. The pace at which the data is being collected and companies are using it to derive further benefits and strategies are commendable.
What is Data Mining?
Data mining is the process of analyzing data from a large range of sources and collating this information into useful business intelligence. The gathered data is studied to uncover interesting patterns, major market trends, anticipate future rich opportunities, which wasn’t initially visible.
The term is fairly new and was coined in 1990, but this technique dates back to the year 1700, where Bayes Theorem was used to identify patterns. With the evolution of the data and the technologies to store and examine data, it has become crucial for investigators to dig deeper into the chunks of data and derive a piece of meaningful reliable information from it.
Functioning of Data Mining
Now that we know what is Data Mining, let’s find out how Data Mining actually works.
The collected raw data from various sources is stored on the servers. The data is further compiled and using various data mining techniques is segregated and analyzed to derive meaningful information for assisting in critical decision making.
Data Mining is implied all over the industry segment ranging from Supply Chain to Healthcare, Travel, Retail, Manufacturing, Shipment, Entertainment, Advertising, and Marketing.
For example;
Doctors use the family history to predict if there is any hereditary disease that a newborn child may develop and the precautions parents must take.
A shipment company provides the estimated time to deliver the package based on the historic data, current events, and various possible calamities.
An advertising industry to publish specific ads to customers based on their preferences on social media.
5 simple steps of Data Mining
Any Data can be turned into reliable meaningful information, in 5 simple steps:
- Collection – The most important part of Data Mining is the collection of the Data in the warehouse for specific business profiles. The data is either managed and stored on-premises server or on cloud storage.
- Understanding – On the basis of the business problem statement, the Business Analysts and Data Scientists, examine the data for further processing. They explore the data via various querying, reporting methods.
- Preparation – The data over confirmation is further cleaned, constructed, and formatted in the desired form. The data is explored deeply to uncover the most insightful information.
- Modeling – A data model is created to obtain the interconnection between the various data sets present in the structured data. This data model’s validity and quality are validated. On successful assessment and discussion with the involved stakeholders the model is finalized.
- Evaluation and Deployment – Finally the model results are evaluated and checked concerning business and new arising requirements as per the new arising patterns. The go or no-go decision must be made in this step to move to the deployment phase. The deployment phase involves creating a final report for assisting in decision making or planning the implementation for future support.
- Techniques of Data Mining
Data Mining is a complex process. Adopting the below techniques makes it more refined and reliable for data miners to achieve the desired results.
Using the right technique will surely surprise you with the quality of information your Data may reveal
4 Essential Data Mining Languages
In order to become a data miner, one should have a hold on these essential programming languages: Python, R, SQL, and SAS.
Python – As one of the most adaptable programming languages, Python can handle everything from data mining to website construction to running embedded systems, all in one unified language.
R – R is an integrated suite of software facilities for data manipulation, calculation, and graphical display. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. Polls, surveys, and studies of scholarly literature databases show substantial increases in popularity.
SQL – SQL is a domain-specific programming language designed for managing and querying data held in a relational database management system. You can use SQL to read and retrieve data from a database or update/insert new data.
SAS – SAS is a software suite that can mine, alter, manage and retrieve data from a variety of sources. And perform statistical analysis on it. SAS provides a graphical point-and-click user interface for non-technical users and more through the SAS language.
Top 5 future trends
The raw data may come in both analog and digital formats. And is inherently based on the source of the data. In the next ten years, it will become more ubiquitous and change the world forever. Here are the top 5 future trends.
- Multimedia Data Mining
The latest method is catching up due to the involvement of data extraction from various sources such as audio, text, hypertext, video, images, etc., And converting it into a numerical representation in different formats.
- Ubiquitous Data Mining
This method involves the mining of data from mobile devices to get information about individuals.
- Distributed Data Mining
As its name suggests, this type of data mining involves gathering massive amounts of data found in different company locations or across multiple organizations. That can be done only by using super-advanced algorithms. That can extract data from multiple sources simultaneously. And analyze it to the smallest of details to gain valuable insights and generate reports.
- Spatial and Geographic Data Mining
This is a new trending type of data mining that includes extracting information from environmental, astronomical, and geographical data. Which also includes images taken from outer space. The extracted data can be used to improve maps, get an accurate reading on distances and topology, etc.
- Time Series and Sequence Data Mining
The primary application of this type of data mining is the study of cyclical and seasonal trends. The extracted data can make it easier to analyze all events. Even those we didn’t plan that happen in a regular series of events.
This data can then be used by retail companies. That will give them a better understanding of what their buyers want, how they behave, and what they buy, and why.
Conclusion:
These methods tend to focus on the discovery of specific patterns of data. So, it’s important that data is extracted with the context in mind. That’s why top brands partner with experts in data mining and data entry.
Data Mining experts don’t just scrap information from random sources and paste it into a spreadsheet. They use complex software to create sophisticated algorithms that provide value to a company.
To save cost and get reliable information out of the data, connecting with contextual experts is the smartest move.
Call to Action: Take the next step and come closer to success, contact us or email us @ info@orientalsolutions.com or call at +91 95000 47196
For the latest updates, follow us @ https://www.linkedin.com/company/oriental-solutions-private-limited/
