What Data Mining Exactly Is?

What Data Mining Exactly Is?

Insight of Data Mining

The information that we discover is data mining. Like mining coal, it is also an extraction of data. As everything that we do is motivated by reason, data mining, also, carries its motto. And the vital motive behind it is analysis of the information that we have extracted under certain criteria. It acts as tool for analysis. From this, useful summary is drawn out. Now, a weapon of information is ready to utilize it for generating hefty revenues and cutting cost.

This is what most of the enterprises wish to have for projecting expansion, growth and supernormal profit. Subsequently, that summary is thoroughly looked at from different dimensions or angles to set up correlations in different patterns.

Innovations acquiring accuracy:

Data mining is not a new term for computer. Almost all companies are relying on it, why? It is just because sifting of data is in its function itself. It has been mapping heights in scanning and analyzing report of market research since day first of its inception. Day by day, innovation in this wonder machine is enhancing disk potential, power of processing and analyzing software. Unbelievably, its cost-effective trait hooks us.

Let’s illustrate it in an example

A whole meal food chain owner hired a market research company for knowing the market trends and customers’ behavior. The report discovered that adults and adolescent prefer to have lunch on weekdays while families and friends hit restaurant at weekend. But the teenagers and school goers come with the demand of fast food every day in the evening. So, the company opened special stalls in the evening to offer fast food only. This market research provided them suggestion worth millions for earning handsome profit.

How data produces knowledge worth million dollars?

Data: The set of facts, figures or text is termed as data. The modern formats prevalent today are:

  • Operational/Transactional data: It includes transactions relating to sales, payrolls and inventory etc.
  • Non-operational data: It consists of sales, macro-economic and anticipated data
  • Meta Data: It includes data based on logic.

Information
Information is what we derive from patterns, correlations and association of data.

Knowledge
Knowledge stands for the valuable conclusion that can be attended for usage. It influences future sale, progress and growth rate of the company as indicated by the determinants of market and marketing.

Data Warehouses
Data warehouses contain captured data for database creation. Thereafter, it is processed, converted and stored for retrieval.

Data warehousing keeps the scattered data at centralized location after doing database management. It would be inconvenient for users to take out data from various places. Therefore, this process strengthens a platform for analysis. Technologies and software are making dramatic improvement every day. This way, data mining opens gateway to users for accessing it and doing extensive market analysis.

Exponential role of Data-mining

The organizations that are creating exhaustive demand for data mining belong to:
• Retail sector
• Financial sector
• Communication sector
• Marketing sector

Data-mining enables determining relationship among all elements of internal factors, such as:
• Price
• Product positioning
• Staff skills

It helps in establishing co-relations in between external factors:
• Economic indicators
• Competition
• Customer demographics
• Impact on sales
• Customer satisfaction
• Corporate profits

Cycle of data-mining functions:
The link between the transactional data and analytical system can easily be established through data mining. The groups, associations and patterns should be separated under appropriate banner to bring out relevant information for analysis. The commonest data patterns are below mentioned:

Classes: It classifies data into predetermined groups provided that the data must be inter-related.

Clusters: In these, data is classified as per logical relationship.

Associations: These are grouped to identify association among varied forms of data.

Sequential patterns: It is quite tough to categorise future behavior of customers and future trends as well. But these patterns help in anticipating those.

9 Basic Laws of Data Mining