Four Steps of Data Integration

September 20, 2022 (Investorideas.com Newswire) As business owners and software developers ought to know, data integration is one of the most fundamental processes that links the theoretical world of computer science to the more immediate and practical world of business management. Data integration is one of a few items that allows a business to hone the data it collects into growth.

Primarily, the purpose of data integration is to take siloed data from discrete sources and use data to create a display that highlights the relationships among that data. Data integration can require moving data from both internal databases, like preexisting data warehouses, and external databases, like public APIs, to some kind of target interface, usually a dashboard. In fact, any third-party tool that data teams use to draw conclusions from data can be considered a "database." While third-party tools may not seem like databases themselves, one cannot draw any conclusions from data without outsourcing some of the data collection process to someone else, whether the outside party is another person or a digital interface.

Modern data integration has needed to adapt to the capriciousness of running a modern business. Not only do businesses have more ways to grow faster, but their circumstances have more exposure to positive and negative change, so data integration interfaces need to compensate for this brutal landscape. Third-party data collection technologies help data teams avoid manual data collection and maintenance in a way that allows data teams and executive teams to focus more on making decisions and advancing business. Data integration distills meaningful decisions from otherwise useless data, and the best tools make sure that this happens as quickly as possible. Employing similar products and systems against hordes of interminable data, data integration is a process that allows for better work-life balance and better communication among departments within any modern company, so without further ado, here are the key steps in any typical data integration process.

1. Ascertain Data Requirements

In other words, what sources need to provide data, and what information is worth storing upon collection from any original sources? Answering these questions involves talking to executives who will be making decisions, outlining how high-qulity the data needs to be, determining the gap between what executives want and what data sources can realistically offer, and modeling storage databases preemptively so that the data can easily be inserted into any warehouses upon collection. An important caveat here to consider is documentation. Documentation must be up-to-date for the data integration process to be of any use. Such documentation must be available to everyone along the data integration pipeline, including the laymen of executive departments. Keep in mind that the best documentations are the ones that have undergone the fewest changes over the years since conception.

2. Prepare The Data

This step includes the actual process of obtaining data once a business's needs are finally defined. Collecting data also means reformatting tables into which that data will go and consolidating duplicative data into more singular units of data. The inconsistencies among sources and technologies are so myriad as to require data teams' attention at every stage of the data integration pipeline. When data preparation is complete, any data warehouses should be filled with data that illustrates relationships and informs decisions among executive members. One might also call this step extraction.

3. Franchise The Data You Have Mined

Essentially, this process is one of honing further the information data suggests into something that makes decision-making processes easier for executive departments. The key difference between this step and data preparation is that there is no data collection involved in this step. Once data is visible across company-wide dashboards, it can be manipulated: summarized, reformatted, renamed, or filtered. During this process, businesspeople have more of a hands-on role in terms of employing data. To handle the data during this step, executives can easily make decisions by using dashboards among other business intelligence tools. Many note this step as transformation.

4. Consider Future Use

As a final step, data should be maintained so that updates will be easier and stale data will be discarded. Data management is usually handled internally by any third-party business management tools. Unless data teams are using housemade technology, this option may not consist of much else. Still, this step is related to the former two, and it may enhance any business's data integration process. The more end users pay attention to the data integration process, the more maintainable and usable any data will be. As more and more users value data integration in business, more IT professionals and business professionals will have better insights to raise revenue over the decades. Also, IT professionals should value data integration as much as business people do because business people depend on IT professionals to make a profit. This step is sometimes called loading.

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