Computing systems have become required for a host of activities throughout our lives. For example, we utilize computers for work on a daily basis. Whether it’s typing up a sales report, performing data entry, sending emails to provide your input about a new project, or just keeping employees abreast of what’s going on in the company, computers are a must-have for employment. We carry mini computing systems in our pockets and purses every day as well. Cell phones can be used to browse the internet, and access any number of apps to stream music, watch movies, pay bills, and a host of other uses. Each of these computing systems also has one thing in common: they perform some type of data integration.
To define what data integration is, it helps to look at the function of such a process. At its base level, data integration involves combining data that resides at different sources. This provides users with a unified view of each source of data. This helps to make data more freely available, and easier for consumption by various computing systems and users. No one type of data integration is the same, as there are several different varieties of data integration. When dealing with a particular form of integration of data, it helps to know what its function is. We’ll focus on four different examples of data integration and their various uses.
- Data Consolidation
When you’re seeking out a new home, the first question which will arise is how you’re going to pay for a new house. You’ll find yourself wading through questions about how much you can afford for a down payment, monthly payment for your mortgage, and what home loan amount you’ll need for a home purchase. In addition to this, you’ll have to look into finding the best mortgage rate, including a lower interest rate, and annual percentage rate. At the end of the day, in order to find your best mortgage rate or monthly payment, you’ll have to consolidate your mortgage provider options to find the best home loan amount. You’ll do this until you find a provider which will offer you the best low fixed rate home loans which won’t leave you financially destitute.
The same way you consolidated all of your mortgage loan or home loan provider options to find the one that works for you will be how you will consolidate data. Data consolidation involves physically bringing together data together from a variety of different systems. Once this is done, you have created a version of the consolidated data in one data storage area. For example, you can use such a method when moving data from one Microsoft Excel worksheet to another worksheet. In some instances such as this, you are creating new forms of data.
- Data Propagation
Another one of the other many types of data integration that exists includes data propagation. This takes place when you use an application to copy data from one location to another new location. This might include moving a batch of data from one location to another or moving one single piece of data from one integration platform to another. Most people use this method when populating a database. Also, ensure that the data quality for such data has not been compromised. It can also be used to move data so it can be easily accessed by other users.
- Data Virtualization
One other form of data integration includes data virtualization. This integration approach utilizes an interface to provide a near real-time, and unified view of data from different sources. For example, data can be viewed in one location but doesn’t necessarily have to be confined to just that location. This form of data integration can be used by business users to absorb data through reports, virtual dashboards, and mobile apps.
- Data Warehousing
This data integration approach provides data with a storage repository. This form of data integration also involves the cleansing, reformatting, and storage of data. Data warehousing is one of the most commonly used data integration types. A common use for data warehousing is in the medical industry. Medical providers can make patient data available through a data warehouse, making it easier for providers to be able to use such information when making better clinical and operational decisions.