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What is Data Integration?

SwoopTalent
November 14, 2018

Data integration is the collection of data from different sources and the combination of this data into a unified set that can easily be used for a predetermined business function.

In basic terms, a typical data integration involves a network of data sources, a master server pulling data collected from these sources and clients accessing data on the master server, which is now in a unified and usable format.

One of the most common business applications of data integration is a data warehouse, which is a centralized location for the storage of company data. Using a data warehouse, related data sets from different systems can be analyzed side by side and better insights can be drawn from this information more quickly and easily. 


Primary Benefits of Data integration


Data integration is becoming more and more common, as numerous apps and companies race to meet consumer demand to have all of their data collected in one place and in a useful format.

From SMBs that want to spend less employee time creating reports, to large corporations taking a deep dive into an array of user statistics, data integration is indispensable for businesses that want to compete in today’s economy.    

These are the primary benefits offered to businesses through data integration:

Inter-System Cooperation Through Data Integration

 When systems containing valuable data are integrated across an organization, then the data gathered by one app or department can benefit the company as a whole, not just a team or individual.

Intersystem cooperation ensures that your company is free from information silos that benefit the few, and allow data gathered across the organization to be viewed and used by anyone who needs access to it.

Intersystem corporation gives you the ability to take deep dives into business processes. For example, integrating data from multiple online stores can give you a more complete understanding of customer behavior and payment processing preferences.  

Eliminating Errors Through Data Integration

Eliminating errors in data sets used for business intelligence and decision making is one of the primary advantages of data integration.

When data is gathered manually, then every tool, database and user account must be properly accounted for and set up prior to collecting this data, meaning that any data source overlooked or added last minute results in an incomplete data set as the end result. This also means that reporting protocols must be re-worked whenever a new data source is added.

When data systems are integrated properly, however, errors from overlooked sources do not occur and reports can be run and accessed in real time.


Saving Time Through Data Integration

When the data that’s needed to answer a business question is spread across multiple systems, teams or apps, it can be very time consuming to gather all of the required data from these disparate sources.

If the business question you are trying to answer is time sensitive, then manually gathering and integrating data can mean that the data is no longer relevant or accurate to your question by the time it is collected.

When systems are properly integrated, collecting data and converting it into its final, usable format becomes a quick, easy task instead of a treasure hunt across your company’s data assets.


Businesses Operations and Data Integration

Businesses are using data integration to make better choices based on deeper understanding of their business data.

Here are some of the most common uses of data integration in business operations.

Data Warehouses

One of the most common business uses of data integration is the creation of data warehouses, which integrate different sources of data into a master relational database.

Creating a data warehouse allows you to run queries across integrated data sources, compile reports drawing from all integrated data sources, and analyze and collect data in a uniform, usable format from across all integrated data sources.

ETL and Business Operations

ETL (Extract, Transform, Load) is the process of sending data from source systems an organization possesses to the data warehouse where this information is viewed and used.

ETL is the process driving the functionality of data warehouses: collecting data from various sources to become centralized and stored in the correct format for its intended purpose.

Note: With SwoopTalent, you can eliminate the need for ETL because it leverages an extensible database. Rendering ETL unnecessary. 

Centralized Business Intelligence

Through providing a cohesive and centralized look at the entirety of an organization’s information, data integration streamlines the process of gaining business intelligence insights.

When all of a company’s critical data is neatly collected and easily available, it’s much easier to assess micro and macro processes, assess client/customer behavior/preferences, assess operations and make strategic decisions based on this business intelligence.

Data Lakes

Data lakes are the bread and butter of big data companies like Google, and consist of the trillions of incoming data points that are aggregated every day by these companies.

The data integration processes used to collect, reformat, store and repurpose the information in data lakes are advanced and often experimental, and are at the forefront of big data technology.

How Companies Integrate Data

There are many ways to create an integrated data set to support any number of business objectives, but not every method of data integration will be practical.

The most effective data integration method for a given outcome should always be chosen by assessing the amount of data, the number of data sources, the completeness of related data sets and other relevant characteristics of the data sources you are integrating.

These are common data integration methods being used by companies, and each method has its own unique advantages and drawbacks.

Application Based Data Integration

This method of data integration is performed through a software application extracting, transforming and loading data from various sources to its final destination.

These applications can simplify the data integration processes by being an out-of-the-box data integration solution for companies.   

Middleware Data Integration

Traditional: Middleware integration is similar to application based data integration which acts as a middle-man between source and warehouse, standardizing data sets and bringing them to their destination.

This form of integration is commonly used when a data integration system is unable to access a particular sub-system or data source.

Modern: Applications such as SwoopTalent, combine middleware integrations with a data lake so you have a hub and spoke model rather than a whole bunch of individual integrations. Creating a more simple, streamlined, and manageable process. 

Manual Data Integration

Manual data integration is the process of someone manually collecting data from various sources, formatting this data to be consistent and transferring the data to a warehouse.

This method of data integration is very time consuming and is not recommended for companies with a high volume of data.  

Bespoke Data Integration

Bespoke data integrations are custom data integration systems created for specific companies to integrate their data sources.

By using internal talent or partnering with an outside developer, companies choose bespoke data integration options to maintain control over the data integration process and create a custom solution for their organization.

Common Storage Integration

Common storage integration is when copies of data drawn from their original sources are kept in a warehouse and transformed for a standardized visualization of this data.

This is the most common method of storing integrated data and is the fundamental process driving classic data warehousing models.

Uniform Access Integration

Uniform access integration allows you to view data from multiple sources in a consistent format, without extracting this data from its original location.

Through uniform access integration, object oriented database management systems can visualize data in a standardized format from two databases with disparate data formats. 

Keep in mind, this is also something a data lake does as well.

Final Thoughts: HR Data Integration

Data integration is widely used across all applications and systems. However, when integrating different HR systems such as Workday, Taleo, or an ATS, you can simplify the process by implementing a tool like SwoopTalent. SwoopTalent makes integrating different HR system data as simple as flipping a switch. You can schedule a demo here to see how you can automatically connect the data across all of your HR systems to drive a higher ROI on your systems, and lower costs from third-party consultants. 

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