A wise guy once said, “An expert always possesses more data than judgment”. The idea of holding data is only beneficial if an organization uses it productively. Data is made up of more than just the name and ids of people. It holds a vast amount of information other than just limited information. How a brand uses this data to strategically plan and execute their plans with the appropriate data completely depends on the business masters. The whole point of having data is too know what your consumers are looking for and what their values are. This ultimately helps in analyzing their needs. It is not just about gathering data, it is about translating it for optimum use.
The term data comes with other analogues. Two of many such analogues is data mining and data integration. Both the terms and processes are highly intermingled. This brings us to a set of questions such as what is data mining. What is Data integration? How does data mining technique and data integration technique work? What is the process? How do we know the difference? Both, data mining and data integration are useful as a method for business growth. So what is the big difference between data mining and data integration?
Data Mining is the art of curating data and analyzing it by all possible ways. It is a process of analyzing hidden patterns of behavior through data according to different perspectives. Later, the same data is categorized into useful information, which is later collected and assembled in common areas where it can be rightfully accessed. Some of these special places are data warehouses (for efficient analysis), and data mining algorithms that facilitates business decision making. All the other information requirements ultimately lead to cost cutting and increase in revenue.
Data mining is also known as data discovery and knowledge discovery and is extremely helpful when it comes to keeping a check on consumer behavior, their preferences and a lot more.
The major steps involved in a data mining process are:
1. Extract, analyze and load data into a data warehouse
2. Store and manage data in an accessible database
3. Provide data business analysts through a software access
4. Present analyzed data in an easy and understandable format, such as graphs or pie charts.
Coming to Data integration, data integration is a process that involves combining data that has been placed in different sources and providing users with a unified view of it. This process becomes significant in a variety of situations; this includes both commercial and non-commercial domains. Even if a company has tons of data, it is always stored in various places and isn’t collectively kept in one place. Data integration is a practice that helps you analyze and combine the data and keep it at a unified place.
There is no set record or universal approach for the process of data integration. However, data integration typically involves a few common elements such as a network of data sources, a master server, and a set of clients who access data from the master server. Speaking of an ideal data integration process, the client sends a request to access the master server of the company’s data. The master server then intakes the data that is needed from internal and external sources. The data is extracted from the sources, and then consolidated into a single, cohesive data set. This is served back to the client for use. This data is then used for various purposes that make up strong revenue for a lot of businesses.
Data mining is the initial step; data integration is the step that follows right after.
While it is popularly believed that data capturing has serious implications on our future privacy, it has its merits that are beneficial too. The core concept is to break the big data down until it reveals its humanity. Data holds the power. It serves as the ground at which many business and marketing companies make growth and development. W. Edwards Deming rightly quotes, “Without data you’re just another person with an opinion” and clearly there are plenty of people with their own set of opinions which makes them completely irrelevant. At the end, statistics is the only truth.